research activities towards intelligent society · generate a database of the detected events for...

Copyright 2013 FUJITSU LABORATORIES LTD.

Research Activities

towards Intelligent Society

May 30, 2013

FUJITSU Laboratories Ltd.

Software Technologies Laboratories

Dr. Hirotaka HARA

Copyright 2013 FUJITSU LABORATORIES LTD.

New IT Strategy of Japanese Government

New Industry Creation

and Growth of all Industry

The world most comfortable

and safest country

One-stop public services

For anyone anywhere

Open Data/Big Data

Agriculture based on IT

Regional Vitalization Framework

Community Healthcare

Disaster Prevention/ Damage reduction

Work-Life Balance

e-Government

Cloud based Government IT Systems

IT Governance

Education IT Infrastructure Cyber Security R&D

Strong IT Environment

Japanese government just announced a new IT strategy as a key policy for economic growth of Japan on 24th May

Copyright 2013 FUJITSU LABORATORIES LTD. DIGITAL DISTRIBUTION OF THIS FILE IS PROHIBITED Copyright 2013 FUJITSU LABORATORIES LTD. 2

Big Data Business Trend in Japan

Big data market in Japan is €1.5B and will be €15B in 2020

The largest application area of Big data is “Marketing” and “Product Planning” and “Strategic Planning” are future expected areas

(%) (%) Application fields of Big Data (Now) Application fields of Big Data (Future)

(Nomura Research Institute, http://www.nri.co.jp/news/2012/121225.html)

0 10 20 30

Others

Maintenance

HR Management

Production

Procurement

Inner Control

Indirect Operations

Logistics

Services

Strategic Planning

Sales Promotion

Sales

Product Planning

Management

Marketing

0 20 40 60

Others

Factory Planning

HR Management

Internal Control

Maintenance

Procurement

Logistics

Indirect Operation

Services

Sales Promotion

Sales

Strategic Planning

Management

Product Planning

Marketing


What is Big Data? High spatial and temporal resolution of data creates a new

business value. Personal activity data, environmental data, etc.

Variety of “Big Data” Social Media and other consumer generated data

•Twitter：400million/day （2012/06）, Facebook：1 billion users （2012）

Sensor Data for personal activities and environment

•Smartphone shipments ：1B （2014）, smart meter ：150M （2015）

Open Data from governments and public sectors

•Economic impact of open data in Europe : over €40B （VicKery,2011）

Mesh size Update interval Data volume

AMeDAS 21Km-mesh 10min 1

C-band radar 1Km-mesh 5min 103

X-band MP radar 250m-mesh 1min 105

(Weather data)


Cloud Fusion

Fujitsu’s Vision: Human-Centric Intelligent Society

Environment Energy

Food Business

Efficiencies

Health Safety Security

Real

world

Various Kinds of Information

from Human Awareness

and Sensing Devices

Real-world value creation through human-centric ICT

New

Services

&

Solutions

New

Services

&

Solutions


Fujitsu Labs’ Research Areas toward HCIS

Sensor Data GPS, weather

Social Media

Industory

Big Data

Twitter, Blogs, etc.

2.Analysis

Data/Text Mining Analysis Platform Optimization Simulation

4.Data processing

Fast processing of Large-Scale Data

1.Collection

3.Application Transportation Energy Disaster Recovery Marketing

(2)Social Media Analysis

Analytic Templates

Optimal Area Discovery

Privacy Security

Smart Energy

Transportation Simulation

Social Simulation (1)Linked Open Data

Incremental Data Processing

Parallel CEP

(3)Stream Data Aggregation

Open Data goverment

Distributed Data

Collection

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LOD Platform: Search Interface Stores LOD around the world and provide a high-speed search

Collaborative Research with Digital Enterprise Research Institute, Ireland


LOD Application in Financial Domain


Example of Linked Data Application in Japan

I-Scover(http://i-scover.ieice.org) provides metadata search of the IEICE transaction papers, Technical Report and international conference papers.

Search results of keyword metadata

6 metadata: article, person, organization, keyword, event, publication.

> 150,000 articles

External website (CiNii, ACM IEEE, DBpedia)

dynamic publishing aggregate

信学太郎

IEICE metadata

IEICE papers external search result

related metadata metadata detail

IEICE: The Institute of Electronics, Information and Communication Engineers


Social Media Analitics

9

Business/Industry

Society/Social Ties

Daily Life

Discovering causal relationships hidden in diverse sets of heterogeneous data

Identifying patterns of human behavior from unstructured data such as social media and sensor data

Future Predictions

Compound Data Analysis

Semantic Analysis

Business Data （sales／stock prices）

Sensor Data （GPS, etc.）

Social Media （news, blog, etc.）

User benefits Strategic management decisions based on Massive data around enterprises’

activities

Quantitative, objective, and accurate analyses and predictions, instead of relying on intuition and experience of experts


Sales Prediction of New Products from Social Media

[Number of Sales.]

release date

[Opinion on social media.]

Predict sales of new products before their release:

-Extract consumer’s reputation from social media

-Discover prediction rules from relations between the reputation and actual sales results of past products

booking expect

real thing

worry

different

anxious

look forward to cancel

Compound data analysis extracts correlation between the amount of sales

and consumer’s behaviors/opinions

Semantic analysis extracts Consumer’s behaviors and opinions on social media


Social Event Visualization

Use twitter as huge number of sensors for event detection

Generate a database of the detected events for map generation

Filtering and Selecting tweets for a target topic

Classify selected tweets into sub-categories

Identify Locations of the events in the tweets

Launch a new product for media companies in 2013

①

②

Select tweets related to crime

Infer Crime type and Location

All tweets Crime DB

③

Crime Map

Machine learning

+

Natural language

processing


Social Event Visualization


XeSTR: Very Fast Aggregation for Stream Data

Improve both of time complexity and memory complexity with special tree-based data structure named “full blossom tree”

More than 100 times faster than traditional CEP engine

Real-time aggregation of rainfall from XRAIN1

Performance: 100mil. recs from 0.5mil. meshes within 10+ seconds, enabling update every 1min.

1hour 6hours 12hours

Immediately switch wide/local views Downpours Landslide Useable to different kinds of disasters

Map data @OpenStreetMap


XeSTR: Example for Rain Radar


Cloud Fusion

Fujitsu’s Vision: Human-Centric Intelligent Society

Environment Energy

Food Business

Efficiencies

Health Safety Security

Real

world

Various Kinds of Information

from Human Awareness

and Sensing Devices

Real-world value creation through human-centric ICT

New

Services

&

Solutions

New

Services

&

Solutions


16

Copyright 2010 FUJITSU LIMITED


Tourism Cloud: Access to hidden local attractions

Use Web Route Guide for in-car

internet-based journey planning Use mobile device as local

guide at the destination

Tourism cloud Web Route Guide Mobile Route Guide

Information service based on user contributions

Experience facilities Government bodies Shops and restaurants

Local information, topical information, hidden discoveries


Laptop Battery Control for Peak Power Reduction

Consumption-recharging control Cloud

Battery data

Consumption-recharging schedule

User 1 User 2 ・・・・・・

PC usage pattern Power demand forecasting

Power data 電力需要パタン電力需要パタン Power demand pattern

Office

Controllable

things

Consumption-recharging schedule

AC powered

+ charging Battery powered AC powered

＋

－

＋

－

Multiple demand patterns

Combining Prediction and Optimization to overcome uncertainty from human activities

Predict multiple demand patterns and generate the control plans of batteries that can reduce peak demand for any predicted pattern

Fundamental technology for many kinds of urban operation optimization


Simulation Results

19時13時 14時 15時 20時 21時16時 17時 18時8時 9時 10時 11時 12時

Peak power demand reduction rate: 10.4%

Po

we

r De

ma

nd

Po

we

r co

nfig

ura

tion

of e

ach

no

teb

ook P

C

Control results Forecasts

AC駆動+充電 AC駆動バッテリー駆動AC powered with charging AC powered Battery powered

8 9 10 11 12 13 14 15 16 17 18 19 20 21 [time]

8 9 10 11 12 13 14 15 16 17 18 19 20 21 [time]


Social Simulation/Multi Agent Simulation

Support decision making on layout design of checkout counters in a supermarket

New agent models to simulate real customer’s behaviors consist of agent interactions through Queue Recognition, Selection and Movement

Business trials started for introducing new type self-checking counters

Even queues

Uneven queues

1 2 3 4 5 6

Too far to find

shortest queue Select near and

short queue

Agent model for Recognition/Selection/Movement

1 2 3 4 5 6 Avoid each other


Social Simulation


XeSTR: Very Fast Aggregation for Stream Data

Improve both of time complexity and memory complexity with special tree-based data structure named “full blossom tree”

Compute not only sum/average of values in a window, but also deal with minimum/maximum/median values

More than 100 times faster than traditional CEP engine

(千葉幕張,400)

(千葉木更津,200) (東京汐留,200)

(神奈川川崎,300)

(千葉幕張,600)

(千葉木更津,300)

(千葉木更津,400)

神

奈

川

千

葉

木幕

張

東

(神奈川川崎,800)

(神奈川新横浜,100)

(神奈川新横浜,400)

Order of processing

0

5

10

15

20

25

0 250000 500000実行時間[sec]

集計期間内データ数

XeSTR Esper

ウィンドウ内のデータ数（件）

レスポンス時間

(秒)

1/144倍

本技術既存技術

Window size (number of data )

cp

u t

ime

(s

ec

)

100 times faster

Esper XeSTR

Full Blossom Tree XeSTR vs. Esper


Benefit: Application to Rain Radar

Real-time aggregation of rainfall from XRAIN1

Performance: 100mil. recs from 0.5mil. meshes within 10+ seconds, enabling update every 1min.

Featu

res

and B

enefits

Computation without re-doing and re-reading • computation time invariable with aggregation intervals

• dynamically changeable mesh sizes

Applic

ation

(M

etrolo

gy)

1hour 6hours 12hours

Expected domain (other than metrology and disaster reduction) e.g. retailer: immediate notification of hot-selling goods by updating sales reports every hour.

Immediately switch wide/local views Downpours Landslide

Enabling real-time surveillance • of various disasters(e.g. downpours, landslides),

• for both wide and local area.

Useable to different kinds of disasters

Map data @OpenStreetMap

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