Intelligent Database Systems Lab.[Prof. Hyun, Soon Joo]

박사과정김태훈

kingmbc@kaist.ac.kr

2017.03.07 (화)

Lab Members

• Professor– Hyun, Soon Joo

• sjhyun@kaist.ac.kr• #804, ITC Bldg. (N1)• Tel. 350-3563 (office)

• Students– 2 Ph.D. students– 2 M.S. students– Office

• #822, ITC Bldg. (N1)• Tel. 350-7763

• Homepage– http://idb.kaist.ac.kr

http://idb.kaist.ac.kr/members

• Sensor Database : Wireless Sensor Networks as Database

Data Warehouse

Research Interest (1/2): Sensor Database

Applications | Habitat monitoring, disaster surveillance, military supports, patient monitoring, etc.

Query/Result

Query/Result

Sensor Node

(Virtual) Storage

Wireless Sensor Network

Query/Result

Sensor Database

Research Interest (1/2): Sensor Database (Smart Home)

Query

Result

Research Interest (1/2): Sensor Database (Smart Home)

Research Interest (1/2): Sensor Database (Healthcare)

Illumination sensor Heartrate Blood pressure Altitude SensorAccelerometer

Sleep monitoringBMI

ECG

Step count Used calorie Moving distance

Information

Assistance

(Problem) Huge amount of sensory data is

constantly transferred!!

Current activity

Consult

Research Interest (1/2): Sensor Database (UAVs)

• How to manage (connect, control, collect) swarms of drones?

Research Interest (1/2): Sensor Database

• Sensor Network Query Language (SNQL) and Processor (SNQP)

[ON EVENT <event-predicate> | <event-name>]SELECT <select_item_expressions_list>FROM <list_of_table_references (table or inline view) >[WHERE <condition>][GROUP BY <expression_list>][SAMPLE PERIOD <time_unit> FOR <time_unit> |CASE

[WHEN <condition> THEN SAMPLE PERIOD <time_unit>]+

ELSE SAMPLE PERIOD <time_unit> FOR <time_unit>END][WITHIN <participation-percentage>]

Research Interest (1/2): Sensor Database

Mobile Sensor Data

Wireless Networking

Distributed Computing

Stream Data Processing

Energy Efficiency / Response

Time

• Research Challenge

–어떻게저많은스트림데이터를서버단에서빠르게처리/저장하지?

–각기다른형태의센서기기에데이터수집을어떻게일괄적으로할수있을까?

–데이터수집에관한사용자요구사항이바뀔때마다어떻게그것을개별기기에반영하지?

–이상한상황이발생하면나에게알려주면좋을텐데!

IoT 전력량관리모듈을통한전력계측

일상기기의스마트오브젝트화

액츄에이터설치

객체별보드설치

스마트홈커뮤니티서비스프레임워크

스마트홈커뮤니티테스트베드구축

10

N1 테스트베드구축(회의실, 휴게실)

웹캠

제습기로봇청소기

환경센서

통합제어모듈

도어센서

스마트 TV & XBOX 서비스

환경센서

통합제어모듈

가습기

사용자인식카메라

스크린

화분

프로젝터

Research Interest (2/2): Context-aware Computing

누구랑같이

있는가?

나의현재

기분은?

여기온

목적은?

현재나의

행동은?

내가가고있는

장소는?

언제떠나야하는가?

Social relationship mining from

Bluetooth

Activityrecognition from

accelerometer sensor

Sentiment analysis from

text

Task recognition from all features

Next-placeprediction from GPS

User Context

Social Implication

Temporal Implication

Spatial Implication

연구 가설/모델

설정

데이터 수집

(Collection)

데이터 전처리

(Preprocessing)

데이터 변환

(Transformation)

데이터 분석

(Mining)

데이터 해석 및

평가

Research Interest (2/2): Context-aware Computing [Nearby People Recommendation]

“우리 대학 연구소Data Mining

Position에 누구초청할 사람 없나?

“같은 학교”다닌 애가

있지 않을까?”

“애가 왜 이렇게자주 아프지? 뭐를 좀 먹어야

하나?”

“나랑 고향이같은 사람있나?”

“이번주 부산가려는데어디가

맛집이지?”

“부산 집에내려가서

맛있는 집밥먹어야지”

학

회

장

신입생

환영회장

사

내

카

페

병

원

Realization of serendipitous interaction opportunity in a place

Mobile opportunistic social matching [Terveen 2005][Mayer 2015]

“소셜 그래프마이닝에

관심있는 사람어디 없나?”

Research Interest (2/2): Context-aware Computing [Nearby People Recommendation]

ENTER

Conference Venue Shopping Mall Hospital

InteractionOpportunityInteraction

Opportunity

Serendipitous Interaction

Opportunity

LEAVE

Willingness to interact with others

Can a machine predict the degree of willingness to interact with nearby people encountered in a public place?

Research Interest (2/2): Context-aware Computing [Nearby People Recommendation]

Research Interest (2/2): Context-aware Computing [Place Ambience Prediction & Place Recommendation]

Can a machine predict the ambience of place?

Hyper-local, ambiance-driven place search and discovery • E.g., a trendy place for a night-out or a romantic place for the wedding anniversary

Data-driven recommendations for place owners to improve the presentation of their venues (e.g., architecture design and style)

Research Interest (2/2): Context-aware Computing

• Research Challenge

–사용자의 Context를알아내면무엇을할수있을까?

–어떤데이터를어떻게수집하고, 수집된데이터로어떻게사용자Context를알아내지?

–사용자의 Context를알았으면어떤서비스를어떤방식으로제공/추천하는것이좋을까?

Web / Mobile Data (Text,

Image, Sensor)

Social / Environmental

Psychology Theory

Data Mining / Machine Learning

User Satisfaction / Experience

이런신입생이면좋겠어요.

http://www.slideshare.net/evoka/freedom-responsibility-culture-49207219?related=1

Q & A

WELCOME!

N1 #822

Any Time (9∽22)

kingmbc@kaist.ac.kr