UCIRVINEDonald Bren School of Information and Computer Sciences

Siripen Pongpaichet

PhD Candidate, Academic Advisor Prof. Ramesh Jain

Contact: spongpai@uci.edu

Interest: complex event stream processing, multimedia information system, large scale data management, having fun doing research

Fundamental ProblemWeb 1.0 Connecting People to Documents

Web 2.0 Connecting People to People

“Social Life Network”

Connecting Needs to ResourcesEffectively, Efficiently, and Promptly

In given situations.

3

Related Services

7/03/2013

http://google.org/crisismap/sandy-2012

Mash Up: Google Crisis Maps

one-touch SOS

Mobile Applications

EventShop : Global Situation Detection

Situation Recognition

Evolving Global Situation

Personal Situation

Recognition

Personal EventShop

Evolving Personal Situation

Need- Resource Matcher

Recommendation Engine

PersonaDatabase

Resources

Needs

Data Ingestion

Wearable Sensors

Calendar

Location….

Dat

a So

urce

s

….

Data Ingestion

and aggregation

Database Systems

Satellite

Environmental Sensor Devices

Social Network

Internet of Things

Actionable Information

Big Challenges• Data Ingestion to efficiently extract data from

the Web and make them available for later computation is not-trivial.

• Stream Processing Engine to bridge the semantic gap between high level concept of situations and low level data streams.

• Situation Recognition as the next step in concept recognition.

05/02/2023 7

History of EventShop

• Building as part of SLN framework• Environment and visualization tool for analyzing

heterogeneous data streams in macro scale• Help non (CS) technical experts in various domains to easily

conduct experiments for detecting real-world situations• Representing geo-spatial data in grid structure called E-mage• Generic set of operators for detecting situations• Pioneers: Vivek Singh (Rutgers University), Mingyan Gao

(Google), Ish Rishabh (Live Nation Entertainment)

8

EventShop UI

11/13/2013

Example Notification / Alerts:

You are currently in the area where there is a high chance of flooding,

these are available shelters within 10 miles around you.Space

Time Situation

Resources

People

05/02/2023

OutputIngestor

Data Source Parser

Data Adapter

Emage Generator

(+resolution mapper)

Processing

EvShop Storage

Query Parser

Query Rewriter

Event Stream Processing Executor

Action Parser

Register Data Source Register Continuous Query

Situation

Emage

Visualization (e.g., Sticker from NICT)

Actuator Communication

Action Control

Event Property & Other Information

(e.g., spatio-temporal pattern)

ᴨ

ᴨµ

Data Access Manager

Live StreamArchived Stream

Situation Stream

EventShop Architecture

Physical Data Source (e.g., sensor

streams, geo-image streams)

Logical Data Source

(e.g., preprocessing data streams, social

media streams)

Raw Event

EventWarehouseNICT - Japan

• STT Observation is represented as:STT = <latitude,longitude,timeStamp,theme,value>

Point(40,-76), TimeStamp(12-12-12 12:00:00PT), Shelter-Availability, 1600

• A flow of STTs becomes a STT Stream:STT Stream = {STT0, ..., STTi, ...}

• E-mage is represented as:E-mage = <SW,NE,latUnit,longUnit,TimeStamp,Theme,2D Grid>

SW(40,-125), NE(50,-115), 0.1latUnit, 0.1longUnit, TimeStamp(12-12-12 12:00:00PT), Shelter-Availability, [0,0,0, 1000, 2000, …]

• A flow of E-mages forms an E-mage Stream:E-mage Stream = {E-mage0, ..., E-magei, ...}

• The cell together with STT information is called stel (spatio-temporal element),stel = <SW,NE,latUnit,longUnit,timeStamp,theme,value>

EventShop Data Representation

Situation Detection Operators

Pattern Matching

Aggregation

Characterization

∏ Filter

Segmentation

72%

+

+

Growth Rate = 125%

DataSupporting

parameter(s) OutputOperator Type

+

Segmentation methods

Property required

Pattern

Mask

Conversion

@

↔

Interpolation~

+ConversionMethods

(e.g., Coarse2Fine)

+Interpolation

Methods(e.g., linear Inter.)

14

Input: EvWHHigh change PM2.5 Event

Input: TwitterAllergy Event

Input: AirNowPM2.5 Level

Input: AirNowAir Quality Index

Raw Allergy Tweets

Count #of

Tweets

PM2.5 Emage

AQI Emage

Processing

CA

S

Output“Sticker” Allergy Risk Level

Interactive MAP

Alert Message via CPCC Apps

Email Notification Situation

PM2.5 Change Event

Properties

Segmentation: Threshold

Average

N Normalization N N

Correlation

Requirement of an unified Event Model

by UCI/NICT

App1: Allergy Management

App2: Thai Flood Emergency Response

Multi-Spatio-Temporal Bounding Boxes and Granularities

• “Pyramid of E-mage” resolution is introduced to represent the real world in E-mage at different (zoom) levels.

• Each Stel (a pixel in the E-mage) represents a single fixed ground location.

• Precision vs Computational Cost

Rasterization and Error Propagation

• Data Error Factors:– Uncertainty of data stream– Data loss during data aggregation– Uncertainty during data conversion– Data error during data conversion

• To design the situation recognition model, we need to find the new cost evaluation method that will consider both data accuracy and computational cost.

Enrich Personalized Asthma Risk

• Predict air quality at air quality measuring sites.

• Interpolate air quality at the locations not covered by measuring sites.

• Predict personalized asthma risk by using EventShop and Personal EventShop.

Daily Ozone Data

Ref- http://www.arb.ca.gov/aqmis2/aqmis2.php

EventShop : Global Situation Detection

Situation Recognition

Evolving Global Situation

Personal Situation

Recognition

Personal EventShop

Evolving Personal Situation

Need- Resource Matcher

Recommendation Engine

PersonaDatabase

Resources

Needs

Data Ingestion

Wearable Sensors

Calendar

Location….

Dat

a So

urce

s

….

Data Ingestion

and aggregation

Database Systems

Satellite

Environmental Sensor Devices

Social Network

Internet of Things

Actionable Information

05/02/2023 22

Calendar PESi

FMB (Individual’s Feeling)Accelerometer

Location

Fitness Data(Nike, Fitbit) Data

Ingestion & Aggregation

Heart RateLocation (Move)

Food Log

FMB (People’s Feeling, Location)

ESOzoneCO2SO2PM 2.5

Pollen (Tree, Grass)

Air Quality Index

Data Ingestion & Aggregation

Social Media (News, Tweets)

Weather

Macro Situation Recognition

Predictive Analytics

PersonalSituation Recognition

Persona

Asthma Allergy App Server

Data Collection

Mac

ro S

ituati

onPe

rson

al S

ituati

on

Need and Resources Recommendation

SLN Use Case

More +++

• Website– http://eventshop:8004/sln

• Demo– http://auge.ics.uci.edu/eventshop

• Open Source– https://github.com/eventshop

• Collaborations