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DOMAIN FEATURE ENGINEERING ATLAS

The formula may be simple. The meaning belongs to the domain.

A reusable feature is more than a number: it declares what was observed, when it became available, how it was calculated and what it must not be interpreted to mean. Compare twelve domains using the same learning sequence.

01Domain
02Features
03Data example
04Calculation
05Time & cost
01

vital signs · laboratory · encounter

Healthcare & Clinical Analytics

สุขภาพและการวิเคราะห์ทางคลินิก

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Heart-rate variability

SDNN, RMSSD จากระยะ R–Rสะท้อนความแปรปรวนของจังหวะหัวใจ ไม่ใช่คำวินิจฉัย
WHAT IT MEASURES AND WHY

สะท้อนความแปรปรวนของจังหวะหัวใจ ไม่ใช่คำวินิจฉัย

SOURCE INPUT
vital signs · laboratory · encounter
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
patient_id, event_time, HR=96, HR_prev=82, SpO2=93, temp=38.1Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. SDNN, RMSSD จากระยะ R–R
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
HR_delta = 96−82 = 14 bpm; fever = 1; hours_since_medication = 6A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
heart-rate_variability__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
O(n) ต่อผู้ป่วย; rolling 30 วันบน 1M events ≈ วินาที–นาทีใน columnar engineActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ต้องยึด event time, หน่วยวัด และช่วงที่ข้อมูลพร้อมใช้จริง ห้ามนำผลตรวจที่ออกภายหลังมาใช้ย้อนหลัง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

NEWS-like trend

แนวโน้มชีพจร ความดัน อุณหภูมิ SpO₂ใช้คัดกรองการเปลี่ยนแปลงของผู้ป่วย
WHAT IT MEASURES AND WHY

ใช้คัดกรองการเปลี่ยนแปลงของผู้ป่วย

SOURCE INPUT
vital signs · laboratory · encounter
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
patient_id, event_time, HR=96, HR_prev=82, SpO2=93, temp=38.1Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. แนวโน้มชีพจร ความดัน อุณหภูมิ SpO₂
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
HR_delta = 96−82 = 14 bpm; fever = 1; hours_since_medication = 6A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
news-like_trend__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
O(n) ต่อผู้ป่วย; rolling 30 วันบน 1M events ≈ วินาที–นาทีใน columnar engineActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ต้องยึด event time, หน่วยวัด และช่วงที่ข้อมูลพร้อมใช้จริง ห้ามนำผลตรวจที่ออกภายหลังมาใช้ย้อนหลัง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Lab delta

ค่าปัจจุบัน − ค่าก่อนหน้าเห็นทิศทางที่ค่าเดี่ยวอาจซ่อนอยู่
WHAT IT MEASURES AND WHY

เห็นทิศทางที่ค่าเดี่ยวอาจซ่อนอยู่

SOURCE INPUT
vital signs · laboratory · encounter
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
patient_id, event_time, HR=96, HR_prev=82, SpO2=93, temp=38.1Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ค่าปัจจุบัน − ค่าก่อนหน้า
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
HR_delta = 96−82 = 14 bpm; fever = 1; hours_since_medication = 6A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
lab_delta__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
O(n) ต่อผู้ป่วย; rolling 30 วันบน 1M events ≈ วินาที–นาทีใน columnar engineActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ต้องยึด event time, หน่วยวัด และช่วงที่ข้อมูลพร้อมใช้จริง ห้ามนำผลตรวจที่ออกภายหลังมาใช้ย้อนหลัง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Comorbidity count

จำนวนโรคร่วมในช่วงเวลาที่กำหนดสรุปความซับซ้อนของผู้ป่วย
WHAT IT MEASURES AND WHY

สรุปความซับซ้อนของผู้ป่วย

SOURCE INPUT
vital signs · laboratory · encounter
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
patient_id, event_time, HR=96, HR_prev=82, SpO2=93, temp=38.1Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. จำนวนโรคร่วมในช่วงเวลาที่กำหนด
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
HR_delta = 96−82 = 14 bpm; fever = 1; hours_since_medication = 6A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
comorbidity_count__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
O(n) ต่อผู้ป่วย; rolling 30 วันบน 1M events ≈ วินาที–นาทีใน columnar engineActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ต้องยึด event time, หน่วยวัด และช่วงที่ข้อมูลพร้อมใช้จริง ห้ามนำผลตรวจที่ออกภายหลังมาใช้ย้อนหลัง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Medication exposure

dose × duration และเวลาหลังได้รับยาเชื่อมผลลัพธ์กับการได้รับยาอย่างมีเวลา
WHAT IT MEASURES AND WHY

เชื่อมผลลัพธ์กับการได้รับยาอย่างมีเวลา

SOURCE INPUT
vital signs · laboratory · encounter
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
patient_id, event_time, HR=96, HR_prev=82, SpO2=93, temp=38.1Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. dose × duration และเวลาหลังได้รับยา
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
HR_delta = 96−82 = 14 bpm; fever = 1; hours_since_medication = 6A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
medication_exposure__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
O(n) ต่อผู้ป่วย; rolling 30 วันบน 1M events ≈ วินาที–นาทีใน columnar engineActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ต้องยึด event time, หน่วยวัด และช่วงที่ข้อมูลพร้อมใช้จริง ห้ามนำผลตรวจที่ออกภายหลังมาใช้ย้อนหลัง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

patient_id, event_time, HR=96, HR_prev=82, SpO2=93, temp=38.1
CALCULATION

From data to feature

HR_delta = 96−82 = 14 bpm; fever = 1; hours_since_medication = 6
COMPUTE TIME

How cost grows

O(n) ต่อผู้ป่วย; rolling 30 วันบน 1M events ≈ วินาที–นาทีใน columnar engine

02

DNA · RNA · protein · omics

Bioinformatics

ชีวสารสนเทศ

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

GC content

(G+C)/sequence lengthบอกองค์ประกอบลำดับและ bias เบื้องต้น
WHAT IT MEASURES AND WHY

บอกองค์ประกอบลำดับและ bias เบื้องต้น

SOURCE INPUT
DNA · RNA · protein · omics
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
sequence=ACGCGTATGC; expression=120; cohort_mean=80; sd=20Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. (G+C)/sequence length
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
GC=6/10=0.60; expression_z=(120−80)/20=2.0A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
gc_content__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
composition O(n); k-mer O(n); alignment โดยทั่วไป O(nm); RNA folding แบบคลาสสิก O(n³)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ระบุ genome build, strand, transcript version, normalization และ tool version ทุกครั้ง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

k-mer frequency

จำนวนคำยาว k เช่น 3-mer/6-merแทนรูปแบบเฉพาะที่โดยไม่ต้อง alignment
WHAT IT MEASURES AND WHY

แทนรูปแบบเฉพาะที่โดยไม่ต้อง alignment

SOURCE INPUT
DNA · RNA · protein · omics
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
sequence=ACGCGTATGC; expression=120; cohort_mean=80; sd=20Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. จำนวนคำยาว k เช่น 3-mer/6-mer
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
GC=6/10=0.60; expression_z=(120−80)/20=2.0A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
k-mer_frequency__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
composition O(n); k-mer O(n); alignment โดยทั่วไป O(nm); RNA folding แบบคลาสสิก O(n³)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ระบุ genome build, strand, transcript version, normalization และ tool version ทุกครั้ง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

ORF length

ความยาวช่วงเปิดการอ่านใช้บอกศักยภาพการเข้ารหัสโปรตีน
WHAT IT MEASURES AND WHY

ใช้บอกศักยภาพการเข้ารหัสโปรตีน

SOURCE INPUT
DNA · RNA · protein · omics
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
sequence=ACGCGTATGC; expression=120; cohort_mean=80; sd=20Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ความยาวช่วงเปิดการอ่าน
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
GC=6/10=0.60; expression_z=(120−80)/20=2.0A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
orf_length__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
composition O(n); k-mer O(n); alignment โดยทั่วไป O(nm); RNA folding แบบคลาสสิก O(n³)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ระบุ genome build, strand, transcript version, normalization และ tool version ทุกครั้ง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Secondary structure

MFE, paired fraction, stem/loopแทนเสถียรภาพและรูปทรงที่คาดการณ์
WHAT IT MEASURES AND WHY

แทนเสถียรภาพและรูปทรงที่คาดการณ์

SOURCE INPUT
DNA · RNA · protein · omics
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
sequence=ACGCGTATGC; expression=120; cohort_mean=80; sd=20Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. MFE, paired fraction, stem/loop
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
GC=6/10=0.60; expression_z=(120−80)/20=2.0A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
secondary_structure__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
composition O(n); k-mer O(n); alignment โดยทั่วไป O(nm); RNA folding แบบคลาสสิก O(n³)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ระบุ genome build, strand, transcript version, normalization และ tool version ทุกครั้ง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Expression z-score

expression เทียบกลุ่มอ้างอิงทำให้ยีนต่างสเกลเปรียบเทียบกันได้
WHAT IT MEASURES AND WHY

ทำให้ยีนต่างสเกลเปรียบเทียบกันได้

SOURCE INPUT
DNA · RNA · protein · omics
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
sequence=ACGCGTATGC; expression=120; cohort_mean=80; sd=20Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. expression เทียบกลุ่มอ้างอิง
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
GC=6/10=0.60; expression_z=(120−80)/20=2.0A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
expression_z-score__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
composition O(n); k-mer O(n); alignment โดยทั่วไป O(nm); RNA folding แบบคลาสสิก O(n³)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ระบุ genome build, strand, transcript version, normalization และ tool version ทุกครั้ง
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

sequence=ACGCGTATGC; expression=120; cohort_mean=80; sd=20
CALCULATION

From data to feature

GC=6/10=0.60; expression_z=(120−80)/20=2.0
COMPUTE TIME

How cost grows

composition O(n); k-mer O(n); alignment โดยทั่วไป O(nm); RNA folding แบบคลาสสิก O(n³)

03

price · volume · quote · fundamentals

Finance & Capital Markets

หุ้นและการเงิน

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Log return

ln(Pt/Pt−1)ผลตอบแทนที่บวกสะสมตามเวลาได้
WHAT IT MEASURES AND WHY

ผลตอบแทนที่บวกสะสมตามเวลาได้

SOURCE INPUT
price · volume · quote · fundamentals
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
close_prev=100, close=103, bid=102.8, ask=103.2, debt=60M, equity=40MUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ln(Pt/Pt−1)
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
return=ln(103/100)=2.956%; spread=0.4/103=0.388%; D/E=1.5A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
log_return__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling feature O(T); 5,000 assets × 10 years daily ≈ วินาที–นาทีแบบ vectorizedActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ปรับ split/dividend, calendar และ survivorship bias; Indicator ไม่ใช่คำสั่งซื้อขาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Realized volatility

SD ของ return ใน windowวัดความผันผวนในอดีต ไม่ใช่ความเสี่ยงทั้งหมด
WHAT IT MEASURES AND WHY

วัดความผันผวนในอดีต ไม่ใช่ความเสี่ยงทั้งหมด

SOURCE INPUT
price · volume · quote · fundamentals
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
close_prev=100, close=103, bid=102.8, ask=103.2, debt=60M, equity=40MUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. SD ของ return ใน window
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
return=ln(103/100)=2.956%; spread=0.4/103=0.388%; D/E=1.5A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
realized_volatility__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling feature O(T); 5,000 assets × 10 years daily ≈ วินาที–นาทีแบบ vectorizedActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ปรับ split/dividend, calendar และ survivorship bias; Indicator ไม่ใช่คำสั่งซื้อขาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

RSI

อัตราส่วน average gain/lossบอกตำแหน่ง momentum ในกรอบที่เลือก
WHAT IT MEASURES AND WHY

บอกตำแหน่ง momentum ในกรอบที่เลือก

SOURCE INPUT
price · volume · quote · fundamentals
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
close_prev=100, close=103, bid=102.8, ask=103.2, debt=60M, equity=40MUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. อัตราส่วน average gain/loss
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
return=ln(103/100)=2.956%; spread=0.4/103=0.388%; D/E=1.5A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
rsi__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling feature O(T); 5,000 assets × 10 years daily ≈ วินาที–นาทีแบบ vectorizedActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ปรับ split/dividend, calendar และ survivorship bias; Indicator ไม่ใช่คำสั่งซื้อขาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Relative spread

(ask−bid)/midประมาณ quoted liquidity ณ เวลาหนึ่ง
WHAT IT MEASURES AND WHY

ประมาณ quoted liquidity ณ เวลาหนึ่ง

SOURCE INPUT
price · volume · quote · fundamentals
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
close_prev=100, close=103, bid=102.8, ask=103.2, debt=60M, equity=40MUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. (ask−bid)/mid
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
return=ln(103/100)=2.956%; spread=0.4/103=0.388%; D/E=1.5A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
relative_spread__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling feature O(T); 5,000 assets × 10 years daily ≈ วินาที–นาทีแบบ vectorizedActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ปรับ split/dividend, calendar และ survivorship bias; Indicator ไม่ใช่คำสั่งซื้อขาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Debt-to-equity

total debt/equityอ่านโครงสร้างเงินทุนของกิจการ
WHAT IT MEASURES AND WHY

อ่านโครงสร้างเงินทุนของกิจการ

SOURCE INPUT
price · volume · quote · fundamentals
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
close_prev=100, close=103, bid=102.8, ask=103.2, debt=60M, equity=40MUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. total debt/equity
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
return=ln(103/100)=2.956%; spread=0.4/103=0.388%; D/E=1.5A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
debt-to-equity__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling feature O(T); 5,000 assets × 10 years daily ≈ วินาที–นาทีแบบ vectorizedActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ปรับ split/dividend, calendar และ survivorship bias; Indicator ไม่ใช่คำสั่งซื้อขาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

close_prev=100, close=103, bid=102.8, ask=103.2, debt=60M, equity=40M
CALCULATION

From data to feature

return=ln(103/100)=2.956%; spread=0.4/103=0.388%; D/E=1.5
COMPUTE TIME

How cost grows

rolling feature O(T); 5,000 assets × 10 years daily ≈ วินาที–นาทีแบบ vectorized

04

sensor · machine state · quality

Manufacturing & Predictive Maintenance

การผลิตและการบำรุงรักษาเชิงคาดการณ์

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

RMS vibration

sqrt(mean(x²))พลังงานสั่นสะเทือนของเครื่อง
WHAT IT MEASURES AND WHY

พลังงานสั่นสะเทือนของเครื่อง

SOURCE INPUT
sensor · machine state · quality
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
vibration=[1,−2,2,−1]; temp 70→82°C in 20 min; 3 defects/500 unitsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. sqrt(mean(x²))
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
RMS=sqrt(10/4)=1.58; slope=12/20=0.6°C/min; defect_rate=0.6%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
rms_vibration__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
window W ต่อ sensor: O(N); FFT feature O(N log N); streaming latency มัก ms–sActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
แยก machine mode และ maintenance period มิฉะนั้น model จะสับสนระหว่างงานหนักกับความเสียหาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Kurtosis

ความหนาหางของสัญญาณช่วยเห็น impulsive fault
WHAT IT MEASURES AND WHY

ช่วยเห็น impulsive fault

SOURCE INPUT
sensor · machine state · quality
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
vibration=[1,−2,2,−1]; temp 70→82°C in 20 min; 3 defects/500 unitsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ความหนาหางของสัญญาณ
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
RMS=sqrt(10/4)=1.58; slope=12/20=0.6°C/min; defect_rate=0.6%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
kurtosis__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
window W ต่อ sensor: O(N); FFT feature O(N log N); streaming latency มัก ms–sActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
แยก machine mode และ maintenance period มิฉะนั้น model จะสับสนระหว่างงานหนักกับความเสียหาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Temperature slope

Δtemperature/Δtimeจับภาวะร้อนขึ้นต่อเนื่อง
WHAT IT MEASURES AND WHY

จับภาวะร้อนขึ้นต่อเนื่อง

SOURCE INPUT
sensor · machine state · quality
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
vibration=[1,−2,2,−1]; temp 70→82°C in 20 min; 3 defects/500 unitsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. Δtemperature/Δtime
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
RMS=sqrt(10/4)=1.58; slope=12/20=0.6°C/min; defect_rate=0.6%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
temperature_slope__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
window W ต่อ sensor: O(N); FFT feature O(N log N); streaming latency มัก ms–sActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
แยก machine mode และ maintenance period มิฉะนั้น model จะสับสนระหว่างงานหนักกับความเสียหาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Cycle-time deviation

cycle−baselineเห็นคอขวดหรือเสื่อมสมรรถนะ
WHAT IT MEASURES AND WHY

เห็นคอขวดหรือเสื่อมสมรรถนะ

SOURCE INPUT
sensor · machine state · quality
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
vibration=[1,−2,2,−1]; temp 70→82°C in 20 min; 3 defects/500 unitsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. cycle−baseline
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
RMS=sqrt(10/4)=1.58; slope=12/20=0.6°C/min; defect_rate=0.6%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
cycle-time_deviation__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
window W ต่อ sensor: O(N); FFT feature O(N log N); streaming latency มัก ms–sActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
แยก machine mode และ maintenance period มิฉะนั้น model จะสับสนระหว่างงานหนักกับความเสียหาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Defect rate

defects/unitsคุณภาพผลผลิตตาม line/shift
WHAT IT MEASURES AND WHY

คุณภาพผลผลิตตาม line/shift

SOURCE INPUT
sensor · machine state · quality
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
vibration=[1,−2,2,−1]; temp 70→82°C in 20 min; 3 defects/500 unitsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. defects/units
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
RMS=sqrt(10/4)=1.58; slope=12/20=0.6°C/min; defect_rate=0.6%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
defect_rate__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
window W ต่อ sensor: O(N); FFT feature O(N log N); streaming latency มัก ms–sActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
แยก machine mode และ maintenance period มิฉะนั้น model จะสับสนระหว่างงานหนักกับความเสียหาย
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

vibration=[1,−2,2,−1]; temp 70→82°C in 20 min; 3 defects/500 units
CALCULATION

From data to feature

RMS=sqrt(10/4)=1.58; slope=12/20=0.6°C/min; defect_rate=0.6%
COMPUTE TIME

How cost grows

window W ต่อ sensor: O(N); FFT feature O(N log N); streaming latency มัก ms–s

05

transaction · customer · product

Retail & Customer Analytics

ค้าปลีกและการวิเคราะห์ลูกค้า

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

RFM

recency, frequency, monetaryสรุปความสัมพันธ์กับลูกค้าแบบตีความง่าย
WHAT IT MEASURES AND WHY

สรุปความสัมพันธ์กับลูกค้าแบบตีความง่าย

SOURCE INPUT
transaction · customer · product
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
last_purchase=10 days; 4 orders/90d; spend=3200; baseline=100; promo_sales=135Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. recency, frequency, monetary
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
R=10, F=4, M=3200; apparent_lift=(135−100)/100=35%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
rfm__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
group-by O(N); exact basket pairs O(B·k²); 100M lines ≈ นาทีใน distributed warehouseActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
Promotion lift ที่ไม่มี control ไม่ใช่ causal effect; คืนสินค้าและ identity resolution ต้องถูกรวม
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Basket size

จำนวน/มูลค่าต่อใบเสร็จอ่านพฤติกรรมการซื้อแต่ละครั้ง
WHAT IT MEASURES AND WHY

อ่านพฤติกรรมการซื้อแต่ละครั้ง

SOURCE INPUT
transaction · customer · product
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
last_purchase=10 days; 4 orders/90d; spend=3200; baseline=100; promo_sales=135Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. จำนวน/มูลค่าต่อใบเสร็จ
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
R=10, F=4, M=3200; apparent_lift=(135−100)/100=35%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
basket_size__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
group-by O(N); exact basket pairs O(B·k²); 100M lines ≈ นาทีใน distributed warehouseActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
Promotion lift ที่ไม่มี control ไม่ใช่ causal effect; คืนสินค้าและ identity resolution ต้องถูกรวม
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Category affinity

สัดส่วนยอดซื้อรายหมวดใช้ personalization และ assortment
WHAT IT MEASURES AND WHY

ใช้ personalization และ assortment

SOURCE INPUT
transaction · customer · product
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
last_purchase=10 days; 4 orders/90d; spend=3200; baseline=100; promo_sales=135Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. สัดส่วนยอดซื้อรายหมวด
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
R=10, F=4, M=3200; apparent_lift=(135−100)/100=35%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
category_affinity__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
group-by O(N); exact basket pairs O(B·k²); 100M lines ≈ นาทีใน distributed warehouseActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
Promotion lift ที่ไม่มี control ไม่ใช่ causal effect; คืนสินค้าและ identity resolution ต้องถูกรวม
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Promotion lift

ยอดช่วงโปร − baselineประมาณผลต่างที่สัมพันธ์กับแคมเปญ
WHAT IT MEASURES AND WHY

ประมาณผลต่างที่สัมพันธ์กับแคมเปญ

SOURCE INPUT
transaction · customer · product
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
last_purchase=10 days; 4 orders/90d; spend=3200; baseline=100; promo_sales=135Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ยอดช่วงโปร − baseline
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
R=10, F=4, M=3200; apparent_lift=(135−100)/100=35%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
promotion_lift__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
group-by O(N); exact basket pairs O(B·k²); 100M lines ≈ นาทีใน distributed warehouseActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
Promotion lift ที่ไม่มี control ไม่ใช่ causal effect; คืนสินค้าและ identity resolution ต้องถูกรวม
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Stockout exposure

เวลาที่สินค้าขาด/เวลาขายแยก demand ต่ำออกจากของไม่มีขาย
WHAT IT MEASURES AND WHY

แยก demand ต่ำออกจากของไม่มีขาย

SOURCE INPUT
transaction · customer · product
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
last_purchase=10 days; 4 orders/90d; spend=3200; baseline=100; promo_sales=135Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. เวลาที่สินค้าขาด/เวลาขาย
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
R=10, F=4, M=3200; apparent_lift=(135−100)/100=35%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
stockout_exposure__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
group-by O(N); exact basket pairs O(B·k²); 100M lines ≈ นาทีใน distributed warehouseActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
Promotion lift ที่ไม่มี control ไม่ใช่ causal effect; คืนสินค้าและ identity resolution ต้องถูกรวม
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

last_purchase=10 days; 4 orders/90d; spend=3200; baseline=100; promo_sales=135
CALCULATION

From data to feature

R=10, F=4, M=3200; apparent_lift=(135−100)/100=35%
COMPUTE TIME

How cost grows

group-by O(N); exact basket pairs O(B·k²); 100M lines ≈ นาทีใน distributed warehouse

06

flow · log · identity · endpoint

Cybersecurity & Network Analytics

ความมั่นคงปลอดภัยไซเบอร์และเครือข่าย

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Flow duration

last packet−first packetแยก session สั้น/ยาว
WHAT IT MEASURES AND WHY

แยก session สั้น/ยาว

SOURCE INPUT
flow · log · identity · endpoint
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
flow: 20 packets, 12,000 bytes, 4 sec; login failures=12/5minUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. last packet−first packet
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
600 bytes/packet; 5 packets/s; failure_velocity=2.4/minA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
flow_duration__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
flow aggregation O(P); entropy O(k); streaming ต่อ event ≈ sub-ms–ms หาก state อยู่ใน memoryActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
รักษา source clock, NAT และ asset role; rarity ไม่เท่ากับ malicious และ alert ต้องมีบริบท
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Bytes/packet

bytes/packet countลักษณะ payload โดยไม่อ่านข้อความ
WHAT IT MEASURES AND WHY

ลักษณะ payload โดยไม่อ่านข้อความ

SOURCE INPUT
flow · log · identity · endpoint
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
flow: 20 packets, 12,000 bytes, 4 sec; login failures=12/5minUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. bytes/packet count
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
600 bytes/packet; 5 packets/s; failure_velocity=2.4/minA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
bytes_packet__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
flow aggregation O(P); entropy O(k); streaming ต่อ event ≈ sub-ms–ms หาก state อยู่ใน memoryActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
รักษา source clock, NAT และ asset role; rarity ไม่เท่ากับ malicious และ alert ต้องมีบริบท
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Failed-login velocity

failure ต่อ user/IP/windowเห็น burst ของการพยายามเข้าใช้
WHAT IT MEASURES AND WHY

เห็น burst ของการพยายามเข้าใช้

SOURCE INPUT
flow · log · identity · endpoint
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
flow: 20 packets, 12,000 bytes, 4 sec; login failures=12/5minUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. failure ต่อ user/IP/window
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
600 bytes/packet; 5 packets/s; failure_velocity=2.4/minA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
failed-login_velocity__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
flow aggregation O(P); entropy O(k); streaming ต่อ event ≈ sub-ms–ms หาก state อยู่ใน memoryActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
รักษา source clock, NAT และ asset role; rarity ไม่เท่ากับ malicious และ alert ต้องมีบริบท
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Destination entropy

entropy ของปลายทางบอกความกระจายของ scan/beacon
WHAT IT MEASURES AND WHY

บอกความกระจายของ scan/beacon

SOURCE INPUT
flow · log · identity · endpoint
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
flow: 20 packets, 12,000 bytes, 4 sec; login failures=12/5minUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. entropy ของปลายทาง
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
600 bytes/packet; 5 packets/s; failure_velocity=2.4/minA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
destination_entropy__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
flow aggregation O(P); entropy O(k); streaming ต่อ event ≈ sub-ms–ms หาก state อยู่ใน memoryActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
รักษา source clock, NAT และ asset role; rarity ไม่เท่ากับ malicious และ alert ต้องมีบริบท
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Rare process score

−log frequency(process)เน้นโปรเซสที่ไม่ปกติในบริบทเดียวกัน
WHAT IT MEASURES AND WHY

เน้นโปรเซสที่ไม่ปกติในบริบทเดียวกัน

SOURCE INPUT
flow · log · identity · endpoint
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
flow: 20 packets, 12,000 bytes, 4 sec; login failures=12/5minUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. −log frequency(process)
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
600 bytes/packet; 5 packets/s; failure_velocity=2.4/minA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
rare_process_score__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
flow aggregation O(P); entropy O(k); streaming ต่อ event ≈ sub-ms–ms หาก state อยู่ใน memoryActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
รักษา source clock, NAT และ asset role; rarity ไม่เท่ากับ malicious และ alert ต้องมีบริบท
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

flow: 20 packets, 12,000 bytes, 4 sec; login failures=12/5min
CALCULATION

From data to feature

600 bytes/packet; 5 packets/s; failure_velocity=2.4/min
COMPUTE TIME

How cost grows

flow aggregation O(P); entropy O(k); streaming ต่อ event ≈ sub-ms–ms หาก state อยู่ใน memory

07

document · token · embedding

Natural Language Processing

การประมวลผลภาษาธรรมชาติ

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

TF–IDF

term frequency × inverse document frequencyให้น้ำหนักคำที่จำเพาะต่อเอกสาร
WHAT IT MEASURES AND WHY

ให้น้ำหนักคำที่จำเพาะต่อเอกสาร

SOURCE INPUT
document · token · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
docs=100; “pipeline” appears in 4 docs; term frequency in doc=3Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. term frequency × inverse document frequency
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
IDF=ln(100/4)=3.219; TF–IDF=3×3.219=9.657A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
tf_idf__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
TF–IDF O(tokens); transformer โดยคร่าว O(L²d) ต่อ layer; batch GPU ≈ ms–s/documentActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ภาษา การตัดคำ tokenizer และ model version เปลี่ยน feature; sentiment ข้าม domain อาจตีความผิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Length/readability

token, sentence, syllable statisticsบอกโครงสร้าง ไม่ใช่คุณภาพเนื้อหา
WHAT IT MEASURES AND WHY

บอกโครงสร้าง ไม่ใช่คุณภาพเนื้อหา

SOURCE INPUT
document · token · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
docs=100; “pipeline” appears in 4 docs; term frequency in doc=3Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. token, sentence, syllable statistics
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
IDF=ln(100/4)=3.219; TF–IDF=3×3.219=9.657A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
length_readability__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
TF–IDF O(tokens); transformer โดยคร่าว O(L²d) ต่อ layer; batch GPU ≈ ms–s/documentActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ภาษา การตัดคำ tokenizer และ model version เปลี่ยน feature; sentiment ข้าม domain อาจตีความผิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Sentiment score

polarity/confidenceสรุปท่าทีตาม model/domain
WHAT IT MEASURES AND WHY

สรุปท่าทีตาม model/domain

SOURCE INPUT
document · token · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
docs=100; “pipeline” appears in 4 docs; term frequency in doc=3Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. polarity/confidence
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
IDF=ln(100/4)=3.219; TF–IDF=3×3.219=9.657A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
sentiment_score__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
TF–IDF O(tokens); transformer โดยคร่าว O(L²d) ต่อ layer; batch GPU ≈ ms–s/documentActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ภาษา การตัดคำ tokenizer และ model version เปลี่ยน feature; sentiment ข้าม domain อาจตีความผิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Named entities

จำนวนและชนิดบุคคล องค์กร สถานที่สร้างสัญญาณเชิงความหมาย
WHAT IT MEASURES AND WHY

สร้างสัญญาณเชิงความหมาย

SOURCE INPUT
document · token · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
docs=100; “pipeline” appears in 4 docs; term frequency in doc=3Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. จำนวนและชนิดบุคคล องค์กร สถานที่
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
IDF=ln(100/4)=3.219; TF–IDF=3×3.219=9.657A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
named_entities__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
TF–IDF O(tokens); transformer โดยคร่าว O(L²d) ต่อ layer; batch GPU ≈ ms–s/documentActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ภาษา การตัดคำ tokenizer และ model version เปลี่ยน feature; sentiment ข้าม domain อาจตีความผิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Embedding

dense semantic vectorเทียบความคล้ายเชิงบริบท
WHAT IT MEASURES AND WHY

เทียบความคล้ายเชิงบริบท

SOURCE INPUT
document · token · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
docs=100; “pipeline” appears in 4 docs; term frequency in doc=3Use only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. dense semantic vector
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
IDF=ln(100/4)=3.219; TF–IDF=3×3.219=9.657A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
embedding__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
TF–IDF O(tokens); transformer โดยคร่าว O(L²d) ต่อ layer; batch GPU ≈ ms–s/documentActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
ภาษา การตัดคำ tokenizer และ model version เปลี่ยน feature; sentiment ข้าม domain อาจตีความผิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

docs=100; “pipeline” appears in 4 docs; term frequency in doc=3
CALCULATION

From data to feature

IDF=ln(100/4)=3.219; TF–IDF=3×3.219=9.657
COMPUTE TIME

How cost grows

TF–IDF O(tokens); transformer โดยคร่าว O(L²d) ต่อ layer; batch GPU ≈ ms–s/document

08

image · region · pixel · embedding

Computer Vision

คอมพิวเตอร์วิทัศน์

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Color histogram

การกระจายค่าสีต่อ channelสรุปสีโดยไม่รักษาตำแหน่ง
WHAT IT MEASURES AND WHY

สรุปสีโดยไม่รักษาตำแหน่ง

SOURCE INPUT
image · region · pixel · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
object area=400 px²; perimeter=100 px; edges=2,000/40,000 pixelsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. การกระจายค่าสีต่อ channel
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
edge_density=5%; compactness=4πA/P²=0.503A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
color_histogram__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
histogram O(HW); convolution ≈ O(HWk²CinCout); GPU inference ≈ ms–100ms/imageActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resize, crop, lighting และ camera domain เป็นส่วนหนึ่งของนิยาม Feature ไม่ใช่ preprocessing ที่มองข้ามได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Edge density

edge pixels/all pixelsแทนความซับซ้อนของเส้นขอบ
WHAT IT MEASURES AND WHY

แทนความซับซ้อนของเส้นขอบ

SOURCE INPUT
image · region · pixel · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
object area=400 px²; perimeter=100 px; edges=2,000/40,000 pixelsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. edge pixels/all pixels
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
edge_density=5%; compactness=4πA/P²=0.503A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
edge_density__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
histogram O(HW); convolution ≈ O(HWk²CinCout); GPU inference ≈ ms–100ms/imageActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resize, crop, lighting และ camera domain เป็นส่วนหนึ่งของนิยาม Feature ไม่ใช่ preprocessing ที่มองข้ามได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Texture

LBP/GLCM contrast, homogeneityอ่านพื้นผิวและรูปแบบซ้ำ
WHAT IT MEASURES AND WHY

อ่านพื้นผิวและรูปแบบซ้ำ

SOURCE INPUT
image · region · pixel · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
object area=400 px²; perimeter=100 px; edges=2,000/40,000 pixelsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. LBP/GLCM contrast, homogeneity
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
edge_density=5%; compactness=4πA/P²=0.503A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
texture__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
histogram O(HW); convolution ≈ O(HWk²CinCout); GPU inference ≈ ms–100ms/imageActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resize, crop, lighting และ camera domain เป็นส่วนหนึ่งของนิยาม Feature ไม่ใช่ preprocessing ที่มองข้ามได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Shape descriptors

area, perimeter, compactnessตีความรูปทรงของ region
WHAT IT MEASURES AND WHY

ตีความรูปทรงของ region

SOURCE INPUT
image · region · pixel · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
object area=400 px²; perimeter=100 px; edges=2,000/40,000 pixelsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. area, perimeter, compactness
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
edge_density=5%; compactness=4πA/P²=0.503A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
shape_descriptors__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
histogram O(HW); convolution ≈ O(HWk²CinCout); GPU inference ≈ ms–100ms/imageActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resize, crop, lighting และ camera domain เป็นส่วนหนึ่งของนิยาม Feature ไม่ใช่ preprocessing ที่มองข้ามได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

CNN embedding

vector จากชั้นลึกแทนลักษณะภาพระดับสูง
WHAT IT MEASURES AND WHY

แทนลักษณะภาพระดับสูง

SOURCE INPUT
image · region · pixel · embedding
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
object area=400 px²; perimeter=100 px; edges=2,000/40,000 pixelsUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. vector จากชั้นลึก
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
edge_density=5%; compactness=4πA/P²=0.503A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
cnn_embedding__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
histogram O(HW); convolution ≈ O(HWk²CinCout); GPU inference ≈ ms–100ms/imageActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resize, crop, lighting และ camera domain เป็นส่วนหนึ่งของนิยาม Feature ไม่ใช่ preprocessing ที่มองข้ามได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

object area=400 px²; perimeter=100 px; edges=2,000/40,000 pixels
CALCULATION

From data to feature

edge_density=5%; compactness=4πA/P²=0.503
COMPUTE TIME

How cost grows

histogram O(HW); convolution ≈ O(HWk²CinCout); GPU inference ≈ ms–100ms/image

09

telemetry · event stream · device

IoT & Time-series Systems

IoT และระบบอนุกรมเวลา

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Rolling mean/std

ระดับและความผันผวนใน windowลด noise และเห็น regime
WHAT IT MEASURES AND WHY

ลด noise และเห็น regime

SOURCE INPUT
telemetry · event stream · device
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
temperature=[20,22,23,25]; timestamps every minute; one 5-min gapUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ระดับและความผันผวนใน window
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mean=22.5; slope=(25−20)/3=1.67°C/min; gap_flag=1A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
rolling_mean_std__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling incremental O(1)/event; FFT O(N log N); edge latency µs–ms/featureActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resampling ต้องระบุ timezone, late event และ gap policy; interpolate อาจสร้างเหตุการณ์ที่ไม่เคยเกิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Lag features

x(t−1), x(t−k)เก็บความจำของระบบ
WHAT IT MEASURES AND WHY

เก็บความจำของระบบ

SOURCE INPUT
telemetry · event stream · device
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
temperature=[20,22,23,25]; timestamps every minute; one 5-min gapUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. x(t−1), x(t−k)
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mean=22.5; slope=(25−20)/3=1.67°C/min; gap_flag=1A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
lag_features__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling incremental O(1)/event; FFT O(N log N); edge latency µs–ms/featureActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resampling ต้องระบุ timezone, late event และ gap policy; interpolate อาจสร้างเหตุการณ์ที่ไม่เคยเกิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Rate of change

Δx/Δtเห็นการเปลี่ยนเร็วผิดปกติ
WHAT IT MEASURES AND WHY

เห็นการเปลี่ยนเร็วผิดปกติ

SOURCE INPUT
telemetry · event stream · device
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
temperature=[20,22,23,25]; timestamps every minute; one 5-min gapUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. Δx/Δt
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mean=22.5; slope=(25−20)/3=1.67°C/min; gap_flag=1A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
rate_of_change__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling incremental O(1)/event; FFT O(N log N); edge latency µs–ms/featureActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resampling ต้องระบุ timezone, late event และ gap policy; interpolate อาจสร้างเหตุการณ์ที่ไม่เคยเกิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Missingness

gap length, missing ratioคุณภาพ sensor เป็นสัญญาณหนึ่ง
WHAT IT MEASURES AND WHY

คุณภาพ sensor เป็นสัญญาณหนึ่ง

SOURCE INPUT
telemetry · event stream · device
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
temperature=[20,22,23,25]; timestamps every minute; one 5-min gapUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. gap length, missing ratio
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mean=22.5; slope=(25−20)/3=1.67°C/min; gap_flag=1A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
missingness__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling incremental O(1)/event; FFT O(N log N); edge latency µs–ms/featureActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resampling ต้องระบุ timezone, late event และ gap policy; interpolate อาจสร้างเหตุการณ์ที่ไม่เคยเกิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Spectral energy

พลังงานตามย่านความถี่จับวงจรที่ time domain มองยาก
WHAT IT MEASURES AND WHY

จับวงจรที่ time domain มองยาก

SOURCE INPUT
telemetry · event stream · device
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
temperature=[20,22,23,25]; timestamps every minute; one 5-min gapUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. พลังงานตามย่านความถี่
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mean=22.5; slope=(25−20)/3=1.67°C/min; gap_flag=1A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
spectral_energy__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
rolling incremental O(1)/event; FFT O(N log N); edge latency µs–ms/featureActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
resampling ต้องระบุ timezone, late event และ gap policy; interpolate อาจสร้างเหตุการณ์ที่ไม่เคยเกิด
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

temperature=[20,22,23,25]; timestamps every minute; one 5-min gap
CALCULATION

From data to feature

mean=22.5; slope=(25−20)/3=1.67°C/min; gap_flag=1
COMPUTE TIME

How cost grows

rolling incremental O(1)/event; FFT O(N log N); edge latency µs–ms/feature

10

coordinate · trajectory · region

Geospatial & Mobility

ภูมิสารสนเทศและการเคลื่อนที่

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Haversine distance

ระยะบนทรงกลมระหว่างพิกัดวัดการเคลื่อนที่ทางภูมิศาสตร์
WHAT IT MEASURES AND WHY

วัดการเคลื่อนที่ทางภูมิศาสตร์

SOURCE INPUT
coordinate · trajectory · region
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
A=(13.7563,100.5018), B≈1 km east, elapsed=120 secUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ระยะบนทรงกลมระหว่างพิกัด
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
distance≈1,000m; speed≈8.33m/s≈30km/hA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
haversine_distance__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
distance O(N); spatial join index ≈ O(N log M); naive pairwise O(NM)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
GPS accuracy, map matching และ privacy มีผลต่อการตีความ; cell boundary ทำให้จุดใกล้กันอยู่คนละกลุ่มได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Speed/acceleration

distance/time และ Δspeed/timeอ่านรูปแบบการเดินทาง
WHAT IT MEASURES AND WHY

อ่านรูปแบบการเดินทาง

SOURCE INPUT
coordinate · trajectory · region
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
A=(13.7563,100.5018), B≈1 km east, elapsed=120 secUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. distance/time และ Δspeed/time
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
distance≈1,000m; speed≈8.33m/s≈30km/hA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
speed_acceleration__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
distance O(N); spatial join index ≈ O(N log M); naive pairwise O(NM)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
GPS accuracy, map matching และ privacy มีผลต่อการตีความ; cell boundary ทำให้จุดใกล้กันอยู่คนละกลุ่มได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Geohash/H3 cell

รหัสพื้นที่หลาย resolutionรวมข้อมูลเชิงพื้นที่อย่างมีสเกล
WHAT IT MEASURES AND WHY

รวมข้อมูลเชิงพื้นที่อย่างมีสเกล

SOURCE INPUT
coordinate · trajectory · region
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
A=(13.7563,100.5018), B≈1 km east, elapsed=120 secUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. รหัสพื้นที่หลาย resolution
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
distance≈1,000m; speed≈8.33m/s≈30km/hA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
geohash_h3_cell__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
distance O(N); spatial join index ≈ O(N log M); naive pairwise O(NM)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
GPS accuracy, map matching และ privacy มีผลต่อการตีความ; cell boundary ทำให้จุดใกล้กันอยู่คนละกลุ่มได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

POI density

จำนวนจุดสนใจใน radiusแทนบริบทของพื้นที่
WHAT IT MEASURES AND WHY

แทนบริบทของพื้นที่

SOURCE INPUT
coordinate · trajectory · region
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
A=(13.7563,100.5018), B≈1 km east, elapsed=120 secUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. จำนวนจุดสนใจใน radius
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
distance≈1,000m; speed≈8.33m/s≈30km/hA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
poi_density__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
distance O(N); spatial join index ≈ O(N log M); naive pairwise O(NM)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
GPS accuracy, map matching และ privacy มีผลต่อการตีความ; cell boundary ทำให้จุดใกล้กันอยู่คนละกลุ่มได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Dwell time

เวลาที่อยู่ในพื้นที่แยกผ่านทางออกจากการหยุด
WHAT IT MEASURES AND WHY

แยกผ่านทางออกจากการหยุด

SOURCE INPUT
coordinate · trajectory · region
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
A=(13.7563,100.5018), B≈1 km east, elapsed=120 secUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. เวลาที่อยู่ในพื้นที่
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
distance≈1,000m; speed≈8.33m/s≈30km/hA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
dwell_time__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
distance O(N); spatial join index ≈ O(N log M); naive pairwise O(NM)Actual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
GPS accuracy, map matching และ privacy มีผลต่อการตีความ; cell boundary ทำให้จุดใกล้กันอยู่คนละกลุ่มได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

A=(13.7563,100.5018), B≈1 km east, elapsed=120 sec
CALCULATION

From data to feature

distance≈1,000m; speed≈8.33m/s≈30km/h
COMPUTE TIME

How cost grows

distance O(N); spatial join index ≈ O(N log M); naive pairwise O(NM)

11

activity · assessment · sequence

Learning Analytics

การวิเคราะห์การเรียนรู้

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Mastery estimate

correct/attempts แยกทักษะบอกหลักฐานความเข้าใจราย concept
WHAT IT MEASURES AND WHY

บอกหลักฐานความเข้าใจราย concept

SOURCE INPUT
activity · assessment · sequence
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
answers=[0,1,1,1]; times=[50,42,35,31] sec; concept=OSI routingUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. correct/attempts แยกทักษะ
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mastery=3/4=75%; time slope=(31−50)/3=−6.33 sec/attemptA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
mastery_estimate__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(events); sequence model O(L)–O(L²); dashboard มัก sub-second หลัง aggregateActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
คะแนนคือหลักฐานบางส่วน ไม่ใช่ตัวตนผู้เรียน; ปกป้องข้อมูลและแยก “ยังไม่เรียน” จาก “ทำไม่ได้”
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Response time

เวลาอ่านและตอบแยกความคล่องจากการเดาได้บางส่วน
WHAT IT MEASURES AND WHY

แยกความคล่องจากการเดาได้บางส่วน

SOURCE INPUT
activity · assessment · sequence
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
answers=[0,1,1,1]; times=[50,42,35,31] sec; concept=OSI routingUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. เวลาอ่านและตอบ
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mastery=3/4=75%; time slope=(31−50)/3=−6.33 sec/attemptA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
response_time__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(events); sequence model O(L)–O(L²); dashboard มัก sub-second หลัง aggregateActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
คะแนนคือหลักฐานบางส่วน ไม่ใช่ตัวตนผู้เรียน; ปกป้องข้อมูลและแยก “ยังไม่เรียน” จาก “ทำไม่ได้”
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Attempt trajectory

คะแนน/ความผิดพลาดตามลำดับเห็นการพัฒนา ไม่ใช่เพียงคะแนนสุดท้าย
WHAT IT MEASURES AND WHY

เห็นการพัฒนา ไม่ใช่เพียงคะแนนสุดท้าย

SOURCE INPUT
activity · assessment · sequence
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
answers=[0,1,1,1]; times=[50,42,35,31] sec; concept=OSI routingUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. คะแนน/ความผิดพลาดตามลำดับ
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mastery=3/4=75%; time slope=(31−50)/3=−6.33 sec/attemptA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
attempt_trajectory__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(events); sequence model O(L)–O(L²); dashboard มัก sub-second หลัง aggregateActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
คะแนนคือหลักฐานบางส่วน ไม่ใช่ตัวตนผู้เรียน; ปกป้องข้อมูลและแยก “ยังไม่เรียน” จาก “ทำไม่ได้”
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Spacing

ช่วงห่างระหว่างการทบทวนอธิบายรูปแบบการฝึก
WHAT IT MEASURES AND WHY

อธิบายรูปแบบการฝึก

SOURCE INPUT
activity · assessment · sequence
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
answers=[0,1,1,1]; times=[50,42,35,31] sec; concept=OSI routingUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ช่วงห่างระหว่างการทบทวน
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mastery=3/4=75%; time slope=(31−50)/3=−6.33 sec/attemptA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
spacing__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(events); sequence model O(L)–O(L²); dashboard มัก sub-second หลัง aggregateActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
คะแนนคือหลักฐานบางส่วน ไม่ใช่ตัวตนผู้เรียน; ปกป้องข้อมูลและแยก “ยังไม่เรียน” จาก “ทำไม่ได้”
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Misconception pattern

distribution ของ distractorบอกว่าเข้าใจผิดอย่างไร
WHAT IT MEASURES AND WHY

บอกว่าเข้าใจผิดอย่างไร

SOURCE INPUT
activity · assessment · sequence
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
answers=[0,1,1,1]; times=[50,42,35,31] sec; concept=OSI routingUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. distribution ของ distractor
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
mastery=3/4=75%; time slope=(31−50)/3=−6.33 sec/attemptA compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
misconception_pattern__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(events); sequence model O(L)–O(L²); dashboard มัก sub-second หลัง aggregateActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
คะแนนคือหลักฐานบางส่วน ไม่ใช่ตัวตนผู้เรียน; ปกป้องข้อมูลและแยก “ยังไม่เรียน” จาก “ทำไม่ได้”
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

answers=[0,1,1,1]; times=[50,42,35,31] sec; concept=OSI routing
CALCULATION

From data to feature

mastery=3/4=75%; time slope=(31−50)/3=−6.33 sec/attempt
COMPUTE TIME

How cost grows

aggregation O(events); sequence model O(L)–O(L²); dashboard มัก sub-second หลัง aggregate

12

meter · weather · tariff · grid

Energy & Smart Grid

พลังงานและโครงข่ายอัจฉริยะ

Important features—from source observation to a reusable feature contract

Select a feature to open its data, calculation, output contract, computational cost and interpretation boundary.

01

Load factor

average load/peak loadบอกความสม่ำเสมอของการใช้กำลัง
WHAT IT MEASURES AND WHY

บอกความสม่ำเสมอของการใช้กำลัง

SOURCE INPUT
meter · weather · tariff · grid
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
hourly load=[80,100,120,100] MW; renewable=90 MWh; total=400 MWhUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. average load/peak load
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
avg=100; peak=120; load_factor=0.833; renewable_share=22.5%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
load_factor__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(T); forecast features across S sites O(ST); streaming ≈ ms/eventActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
อากาศ วันหยุด tariff และ outage ต้องตรงตามเวลาที่รู้จริง; meter revision ต้อง version ได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
02

Ramp rate

ΔMW/Δtimeวัดความเร็วที่ระบบต้องปรับตัว
WHAT IT MEASURES AND WHY

วัดความเร็วที่ระบบต้องปรับตัว

SOURCE INPUT
meter · weather · tariff · grid
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
hourly load=[80,100,120,100] MW; renewable=90 MWh; total=400 MWhUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ΔMW/Δtime
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
avg=100; peak=120; load_factor=0.833; renewable_share=22.5%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
ramp_rate__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(T); forecast features across S sites O(ST); streaming ≈ ms/eventActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
อากาศ วันหยุด tariff และ outage ต้องตรงตามเวลาที่รู้จริง; meter revision ต้อง version ได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
03

Degree hours

ความต่างอุณหภูมิจากฐานสะสมแทน demand จากอากาศ
WHAT IT MEASURES AND WHY

แทน demand จากอากาศ

SOURCE INPUT
meter · weather · tariff · grid
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
hourly load=[80,100,120,100] MW; renewable=90 MWh; total=400 MWhUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. ความต่างอุณหภูมิจากฐานสะสม
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
avg=100; peak=120; load_factor=0.833; renewable_share=22.5%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
degree_hours__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(T); forecast features across S sites O(ST); streaming ≈ ms/eventActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
อากาศ วันหยุด tariff และ outage ต้องตรงตามเวลาที่รู้จริง; meter revision ต้อง version ได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
04

Peak/off-peak ratio

พลังงานช่วง peak/off-peakอ่านรูปแบบเวลาและ tariff
WHAT IT MEASURES AND WHY

อ่านรูปแบบเวลาและ tariff

SOURCE INPUT
meter · weather · tariff · grid
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
hourly load=[80,100,120,100] MW; renewable=90 MWh; total=400 MWhUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. พลังงานช่วง peak/off-peak
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
avg=100; peak=120; load_factor=0.833; renewable_share=22.5%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
peak_off-peak_ratio__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(T); forecast features across S sites O(ST); streaming ≈ ms/eventActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
อากาศ วันหยุด tariff และ outage ต้องตรงตามเวลาที่รู้จริง; meter revision ต้อง version ได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
05

Renewable share

renewable/total generationบริบทความผันผวนของ supply
WHAT IT MEASURES AND WHY

บริบทความผันผวนของ supply

SOURCE INPUT
meter · weather · tariff · grid
Declare entity, source, unit, event time and availability time. A similarly named field from another system may not be equivalent.
SMALL DATA EXAMPLE
hourly load=[80,100,120,100] MW; renewable=90 MWh; total=400 MWhUse only records available at the feature timestamp.
CALCULATION WORKFLOW
  1. Filter the declared entity and time window.
  2. Validate units, missing values and impossible measurements.
  3. renewable/total generation
  4. Attach timestamps, parameters and code version.
WORKED EXAMPLE
avg=100; peak=120; load_factor=0.833; renewable_share=22.5%A compact arithmetic example; production data needs a larger validated reference window.
OUTPUT CONTRACT
renewable_share__value
entity_id · as_of · available_at · window · unit · versionThe metadata makes the value reproducible and comparable.
COMPUTATIONAL TIME
aggregation O(T); forecast features across S sites O(ST); streaming ≈ ms/eventActual time depends on volume, window, indexing, hardware, implementation and reuse. Benchmark on the target system.
INTERPRETATION BOUNDARY
อากาศ วันหยุด tariff และ outage ต้องตรงตามเวลาที่รู้จริง; meter revision ต้อง version ได้
One feature is one piece of evidence—not the complete state of a person, machine, market or system.
DATA EXAMPLE

A small observation

hourly load=[80,100,120,100] MW; renewable=90 MWh; total=400 MWh
CALCULATION

From data to feature

avg=100; peak=120; load_factor=0.833; renewable_share=22.5%
COMPUTE TIME

How cost grows

aggregation O(T); forecast features across S sites O(ST); streaming ≈ ms/event

CROSS-DOMAIN LESSON

A good feature preserves meaning across time, teams and systems.

Observation

Define entity, field, unit, source and measurement process.

Time

Separate event time, processing time and availability time.

Version

Keep formula, parameters, reference population and code version.

Validation

Test ranges, missingness, drift, parity and leakage.