EventShop Demo
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Siripen Pongpaichet is a PhD candidate at UC Irvine studying complex event stream processing, multimedia information systems, and large scale data management. Their research focuses on EventShop, an open source platform for detecting situations from heterogeneous data streams. EventShop represents data spatially using "E-mages" and detects situations through stream processing operators. It aims to bridge semantic concepts and low-level data to provide actionable information. Challenges include efficient data ingestion and developing a stream processing engine to recognize situations from data streams.
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EventShop Demo
- 1. UCIRVINE Donald 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
- 2. Fundamental Problem Web 1.0 Connecting People to Documents Web 2.0 Connecting People to People “Social Life Network” Connecting Needs to Resources Effectively, Efficiently, and Promptly In given situations.
- 3. Related Services 7/03/2013 3 http://google.org/crisismap/sandy-2012 Mash Up: Google Crisis Maps one-touch SOS Mobile Applications
- 4. 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…. DataSources …. Data Ingestion and aggregation Database Systems Satellite Environmental Sensor Devices Social Network Internet of Things Actionable Information
- 5. 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.
- 6. 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) 6/6/2016 7
- 7. EventShop UI 11/13/2013 8 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
- 8. 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 ᴨ µ Data Access Manager Live Stream Archived Stream Situation Stream EventShop Architecture 6/6/2016 Physical Data Source (e.g., sensor streams, geo-image streams) Logical Data Source (e.g., preprocessing data streams, social media streams) Raw Event EventWarehouse NICT - Japan
- 9. • 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
- 10. Situation Detection Operators Pattern Matching Aggregation Characterization ∏ Filter Segmentation 72% + + Growth Rate = 125% Data Supporting parameter(s) OutputOperator Type + Segmentation methods Property required Pattern Mask Conversion @ ↔ Interpolation ~ + Conversion Methods (e.g., Coarse2Fine) + Interpolation Methods (e.g., linear Inter.)
- 11. Input: EvWH High change PM2.5 Event Input: Twitter Allergy Event Input: AirNow PM2.5 Level Input: AirNow Air Quality Index Raw Allergy Tweets Count #of Tweets PM2.5 Emage AQI Emage Processing C A 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 14
- 13. App2: Thai Flood Emergency Response
- 14. 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
- 15. 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.
- 16. 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.
- 17. Daily Ozone Data Ref- http://www.arb.ca.gov/aqmis2/aqmis2.php
- 18. 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…. DataSources …. Data Ingestion and aggregation Database Systems Satellite Environmental Sensor Devices Social Network Internet of Things Actionable Information
- 19. 6/6/2016 22 Calendar PESi FMB (Individual’s Feeling) Accelerometer Location Fitness Data (Nike, Fitbit) Data Ingestion & Aggregation Heart Rate Location (Move) Food Log FMB (People’s Feeling, Location) ESOzone CO2 SO2 PM 2.5 Pollen (Tree, Grass) Air Quality Index Data Ingestion & Aggregation Social Media (News, Tweets) Weather Macro Situation Recognition Predictive Analytics Personal Situation Recognition Persona Asthma Allergy App Server Data Collection MacroSituationPersonalSituation Need and Resources Recommendation
- 20. More +++ • Website – http://eventshop:8004/sln • Demo – http://auge.ics.uci.edu/eventshop • Open Source – https://github.com/eventshop • Collaborations
Editor's Notes
- During the first-generation of the Web (Web 1.0) in 1990s, the focus was primarily on building the Web, and making it accessible by connecting people to online documents. In the the second-generation (Web 2.0) in 2000s, the growth of social networking sites, wikis, communication tools and folksonomies brought a new experience to our society, by connecting people to people. In addition, the emergence of the mobile Internet and mobile devices was a significant platform driving the adoption and growth of the Web. Effectively, the Web became a universal medium for data, information, and knowledge exchange. In the third-generation Web (Web 3.0), the innovation shifted toward upgrading the back-end Web infrastructure level making the Web more connected, more exposed, and more intelligent. The Web is transformed from a network of separately siloed applications and content repositories to a more seamless and interoperable as a whole. The Web now can be used to establish a new network called “Social Life Networks (SLN)” [11] by connecting people to real-world (life) resources for decision making at both individual and societal levels.
- Mashing-up is a website or application that combines content from more than one source into an integrated experience The data used in a mash-up is usually accessed via an API. The goal is to bring together in a single place data sources that tend live in their won data silos. Only visual integration, and do not provide any sophisticated analysis capabilities. Mobile apps that allow users to contribute to the society and share information to the love one in their network during the crisis situation Life 360 let family set up a private network, then with a click of a button, they can let each other know where they are and if they're safe. You can also enable background tracking so everyone in your private network can continuously share their locations with one another. The app also has a panic alert feature you can activate to immediately contact family members via text, email and a voice call to give your location at the moment you need help. There are also options for regular feature phones. For family members without a phone, there is an additional GPS device that can be provided for a fee. SOS+ Waze: crowd-sourcing which allow user to report the current situation as you can see these type of data include both space time and theme None of them can really connect people to the real world resources based on detected situation. It is time consuming by manually browsing on the web. The comprehensive development tools and computational frameworks for effectively combining and processing these available heterogeneous streams are lacking
- Bring Predictive here.
- The data on the Web are not only generated in different media format (e.g., KML, JSON, image, table, and sensor signal), but the properties of them are also very different (e.g., measuring weather, and traffic).
- Given the geo-spatial continuity, we believe that a spatial grid structure is naturally suitable for representing various geo-spatial data, where each cell of the grid stores value of observations at the corresponding geo-location and in turn represents evolving situation at a location in space. We adopt the grid structure, and call it E-mage (an event data based analog of image) [19]. The
- Emage Generator: Transform data from Data Adapter into Emage representation and is responsible for both making this data directly available to the executor, as well as writing it to the disk recent buffer, and emage resolution mapper, The queries run in the executor and can access the live data directly from emage generator and the historical data from the disk. Data access method is used to handle disk overload – data reduction/ user define function etc. and create Emage stream from disk. In addition, other information such as spatial and temporal pattern, and other properties Query rewriter -> source selection
- In the physical sensor networks, sensors are built to observe the real world environment; for example, space satellite, remote sensing, laser scanning, acoustic sensing, motion sensing and camera sensing. Most of the information is time series of measurements. A sensor reports a measurement over a given time period, while its coverage area is often fixed and promoted to the metadata. The measurement area can be represented in variety of GIS structures including point (latitude, longitude coordinate), vector polygon (region), vector line (arc), and raster (grid) areas. In actuated network, sensors report data only when they have been triggered or detect an event. In the logical sensor networks, geospatial data are generated from the cyber world to represent events in the real world. The data are reported mostly by human via variety types of service such as location based service, social network sensing (e.g., Twitter, Facebook, Flickr), statistical reports, and news. Since these data are naturally available in unstructured format and could have significant noise and missing data, it is nontrivial how to extract meaningful information from them. Many researchers have studied and contributed into this aspect including data mining, entity extraction, topic discovery, and sentiment analysis.
- Accessing External Data Accessing Internal Data (via EvS Internal Storage)
- Building block of the 2D grid in an E-mage is a single cell.
- Bring Predictive here.