IIS & Dept. of Civil Engineering
Univ. of Tokyo

Data Mining Strategy
Kenji Taniguchi
Dept. of Civil Engineering, Univ. of Tokyo

IIS & Dept. of Civil Engineering
Univ. of Tokyo

IIS & Dept. of Civil Engineering
Univ. of Tokyo
Back Grounds
PresentNear Future
Same number…
Same abilities…
Researchers

Researchers

Too huge amount of data…
Data
Data

How to exploit HUGE data…??

Keywords

Visual Data Mining•Data Base
•Visualization•Targeting•Data Mixing•Artificial Intelligence•Interface, Recognition

IIS & Dept. of Civil Engineering
Univ. of Tokyo- Framework -TOP
Single Data
Visualization

Spatial Distribution -1D plot, 2D, 3D
Time Series – animation
Spatial-Temporal Coupled – 3D-4D animation

Multi-Data•Analysis Function•Interface•AI Function

Pre-processing

Selection
Data mixing – pixel matching(coordinate, resolution)
Analysis – Correlation, Regression, SVD,
Composite, etc
Visualization

Data Analysis

Regionalization, Clustering,Anomaly -2D, Animation, etc
Composite -Raw, Spatial mean
Periodicity -Auto-correlation, Fourier Transform
Correlation Analysis
Other (EOF, etc)

AnalysisExtreme events, Tele-connection, Oscillation

AIUser Log→ Path Detection → Success Scenario↓Analysis supporting System
Event detection
Physical relationshipHypothesis test/verification

IIS & Dept. of Civil Engineering
Univ. of Tokyo- Interface and Process of Analysis (general) -TOP
User Log↓Analyzing Path

Single Data

Multi-DataAIAI
Step 1

Step 0

Step 1(Data select supporting)Single Data Analysis

Multi-Data Analysis

Method 1

Method 1

Method 2

Step 2

Method 3

Method 2
Method 3AI

IIS & Dept. of Civil Engineering
Univ. of Tokyo- Interface of Analysis (Single Data) -Single Data Analysis
Visualization

Analyzed Viewing

Advanced Analyzing

Raw Data Viewing

Analyzed Viewing

Advanced Analyzing

2D Plot

Anomaly

El Niño Find modelTime SeriesComposite???

Cross Section

Periodicity

Hovmöller

Correlation(What can do with GrADS)

EOF…

Other Success Scenario

AI

IIS & Dept. of Civil Engineering
Univ. of Tokyo- Interface of Analysis (Analyzed Viewing) -Analyzed Viewing
Anomaly

Composite

Anomaly

Composite

Correlation

Objective Years

Area Selection

Memorize the years,
and apply to other data

Averaging Time

2D Plot
Time Series
Time Resolutionetc.

Some feedback function
to reflect the result oftime series analysisPeriodicity

Correlation

Periodicity

Tele-connectionetc.

Auto-correlation
Fourier Transformetc.

IIS & Dept. of Civil Engineering
Univ. of Tokyo- Development of AI Part –AI PartTOP

Data Selection
Log

How to select Data? Some Physical Relationship between data?
After which data, which analysis?
Analyzing Path

Analysis part
Finding

Finding

Log

Given (ex. Anomaly→Periodicity→Composit…
Anomaly→Correlation, Comparison)

Selected Path (method)
Finding

Method 1

Method 3

Physical/Dynamical Relationship
Goal
What kind of findings, goals?

Method 2

Method 4

Finding

FindingGoal

Applying derived analyzing path
Automatic Analysis Function

Goal/Finding

Speculation/Evaluation

IIS & Dept. of Civil Engineering
Univ. of Tokyo-Present & Future¾Current Situation
・Developing On-line analysis system-Display Interface-Analyzing methods¾Future ~for further Dream
・How to apply this specific fruit for general user?
・It is indispensable to include some knowledge ofCognition Sciences.
・We should get an Excellent result by using this system
to public our activities and involve researchers globally.