EventShop ISG talk 140213
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This document discusses EventShop, a system for real-time macro situation recognition from heterogeneous data streams. EventShop ingests data from sources like sensors, social media, satellites and uses it to detect situations. It represents spatial data as "emages" on a grid and uses operators to detect situations. The document outlines EventShop's architecture, collaboration with NICT on tools like EventWarehouse and Sticker, work on multi-granularity emages and predictive analytics, and applications for social life networks.
![6/6/2016 2
Web
Location Based
Mobile Applications
Ongoing Archived
Database System
satelliteCloud
resources
Environmental
Sensor Devices
Internet of Things
Social Media
Billions of
geo-location and
time based devices
Social Life
Network
Real-time
Information sharing
&
decision making
Experts
People
Governmental
Agencies
Situations
[Jain 2011] Social Life Network](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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EventShop ISG talk 140213
- 1. EventShop Real-Time Macro Situation Recognition from Heterogeneous Streams Siripen Pongpaichet UCI ISG Talk 02/14/2014 6/6/2016 1
- 2. 6/6/2016 2 Web Location Based Mobile Applications Ongoing Archived Database System satelliteCloud resources Environmental Sensor Devices Internet of Things Social Media Billions of geo-location and time based devices Social Life Network Real-time Information sharing & decision making Experts People Governmental Agencies Situations [Jain 2011] Social Life Network
- 3. Examples of (Specific) System in SLN approach 6/6/2016 3 one-touch SOS Emergency SituationDaily Situation Social Life Network Connect People to real-world Resources effectively, efficiently, and promptly in given Situations.
- 4. EventShop : Global Situation Detection Situation Recognition Evolving Global Situation …. Data Ingestion and aggregation Database Systems Satellite Environmental Sensor Devices Social Network Internet of Things 6/6/2016 4 00 Need- Resource Matcher Recommendation Engine Actionable Information Resources Needs Personal Situation Recognition Personal EventShop: Personal Situation Detection Evolving Personal Situation Data Ingestion Wearable Sensors Calendar Location…. DataSources
- 5. 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 (MIT), Mingyan Gao (Google) 6/6/2016 5
- 6. EventShop UI 11/13/2013 6 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
- 7. Current State and Next Steps • Enhance EventShop Architecture • Collaboration Research (with NICT): – Sticker 3D visualization tool, – EventWarehouse • Multi Granularity E-mage • Predictive Analytics • SLN Use Case 6/6/2016 7
- 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 (Dashboard) 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
- 9. From Heterogeneous Data to Situation Recognition in EventShop 2.0 11/13/2013 9
- 10. EvS Input ManagerExternal Event Preprocessing (EvWarehouse) Real-Time Sensor Streams e.g., Cloud Satellite Pictures, Gridding Data Real-Time Sensor Streams e.g., Wind Speed, Traffic Flow Real-Time Sensors Event Model Wrapper 1D STT to Emage Event Model Wrapper 2D Data Adapter Emage Generator Emage Emage Factory STT Emage Raw Social Media Streams e.g., Twitter, News RSS Feed Near Real-Time Sensors Event Model Wrapper STT to Emage Data Adapter Emage Generator Emage Emage Factory STT Topic Event Detection Abnormal Event Detection Raw Sensor Streams e.g., PM2.5 data “EventModel” Streams e.g., suddenly change of data trend within time window Emage Store STT Store Metadata Store EventSource Parser Interface ES internal storage (Optional) RealTime Emage Streams NearRealTime Emage Streams Processing Manager ES Descriptor ES Control (Start/Stop/ View ES) Users Input Data/Events Flow Theme AdapterType SourceURL TimeWindow Parameters InitialResolution AggregationFunc Metadata 6/6/2016 10
- 11. Stream Processing Engine Operators Manager Built-in Operators User-Defined Operators ᴨ ᴨ µ Data Access ᴨ ᴨ µ Data Access ᴨ ᴨ µ Data Access Input Manager Event Stream Executor Operators Nodes Storage AsterixDB, SciDB, MongoDB Emage Store STT Store Metadata Store Query Parser Interface Query Descriptor Query Control (Start/Stop/ View) Real-time/ near real-time Emage Streams Archived Emage Streams Situation Streams Emage Interpolation Function Emage Conversion Final Resolution, Interpolation Func Parameter Operators Operators Store Parameters Retrieve Parameters Query Rewriter Execution Plan 6/6/2016 11
- 12. Current State and Next Steps • Enhance EventShop Architecture • Collaboration Research (with NICT): – Sticker 3D visualization tool, – EventWarehouse • Multi Granularity E-mage • Predictive Analytics • SLN Use Case 6/6/2016 12
- 13. Sticker & EventWarehouse 6/6/2016 13 NICT
- 14. EvShop and EvWarehouse Interface 1. Retrieve EventModel stream – Option1: EvShop periodically sends request to EvWH to access new events stored in EventModel table (MPQL) – Option 2: EvWH pushes new events to EvShop (listener) 2. Access EventModel stream’s metadata 3. Create new EventModel Stream 6/6/2016 14
- 15. Example of MPQL SELECT MIN(observation),MAX(observation),SUM(observation), AVG(observation) FROM LiveERestflCO2Sensor GROUP BY TIME('2013-10-01T00:00:00','2013-10-02T00:00:00', 12 HOUR ), SPACE( 130.0,30.0,140.0,40.0, 5,5 ) 6/6/2016 15 SELECT observation FROM STREAM LiveERestflCO2Sensor
- 16. Current State and Next Steps • Enhance EventShop Architecture • Collaboration Research (with NICT): – Sticker 3D visualization tool, – EventWarehouse • Multi Granularity E-mage • Predictive Analytics • SLN Use Case 6/6/2016 16
- 17. Multi Granularity E-mage • data is created and collected in different forms • different sensors cover different sized spaces, produce data at different rates • data is produced and consumed at different spatial, temporal, and symbolic granularities 6/6/2016 17
- 18. Pyramid of E-mage Resolution 6/6/2016 18 Level Stel Size 1 78 km 2 39 km 3 19.6 km 4 9.8 km 5 4.9 km 6 2.4 km 7 1.2 km 8 611 m 9 306 m 10 153 m 11 76 m 12 39 m 13 19 m 14 10 m 15 5 m 16 2.4 m 17 1.2 m 18 60 cm 19 30 cm 20 15 cm Inspired by the most popular service like Google Maps, Bing Maps, and OGC WMTS They provide the standard of the granularity level of the world map
- 19. Multi Granularity E-mage 6/6/2016 19 Time t1 t2 t3 t4 Space DS1: update every 10 mins DS2: update every 5 mins DS3: update every 30 mins The situation model is processed every 10 mins E-mage spatial transformation are categorized into two main types 1) Coarse2Fine: nearest-neighbor interpolation, linear interpolation, bilinear interpolation, and split uniform. 2) Fine2Coarse: summation, maximum value, minimum value, average, majority.
- 20. Multi Granularity E-mage • How to dynamically adjust appropriate granularity? – Guarantee the quality of the results – Data error propagation • Uncertainty of data stream, data loss during data conversion, etc. – Source selection 6/6/2016 20
- 21. Rasterization Errors Prediction • The regression model depicts the relationships between rasterization errors and their affecting factors – Equal area conversion (EAC) algorithm is used for rasterization of vector polygons – Rasterization errors calculated from Error Evaluation Method Based on Grid Cells (EEM-BGC) – The factors includes both the complexity of polygons perimeter index (e.g., density of arcs length (DA) and density of polygon (DP)) and the size of gird cells (SG). 6/6/2016 21 Relative area error = Area before conversion – Area after conversion Area Before conversion )ln(456.931.0418.0499.58 SGDPDAE For vector data of county level boundary of Beijing [Liao 2012] Error Prediction for Vector to Raster Conversion Based on Map Load and Cell Size
- 22. Current State and Next Steps • Enhance EventShop Architecture • Collaboration Research (with NICT): – Sticker 3D visualization tool, – EventWarehouse • Multi Granularity Emage • Predictive Analytics • SLN Use Case 6/6/2016 22
- 23. Predictive Analytics 6/6/2016 23 Situation An actionable abstraction of observed or extrapolated spatio-temporal characteristics - Ish Rishabh
- 24. Current State and Next Steps • Enhance EventShop Architecture • Collaboration Research (with NICT): – Sticker 3D visualization tool, – EventWarehouse • Multi Granularity Emage • Predictive Analytics • SLN Use Case 6/6/2016 24
- 25. 6/6/2016 25 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
- 26. Please Stay Tuned! Open Source (Next week) 6/6/2016 26
Editor's Notes
- The Web now has enormous volume of heterogeneous data being continuously reported by different sensors and humans from different locations. The web is become a universal medium or data, information, and knowledge exchange Real world phenomena are now being observed by multiple media streams which are available in real-time over the web, and increasingly the majority of these has space and time semantic . We believe: A significant fraction of the data regularly created is location-sensitive data streams. Many emerging applications are related to taking actions in real time and depend on emerging ‘situations’ and contexts. Lots of data about emerging situations and contexts is already available and more is becoming available. Examples: Disaster Management detect effected area Health environment hazard What is the point to create generic framework????? Definition of Situation and Events???? Situation is actionable abstraction of observed spatio-temporal descriptor Internet of things Data base system Global sensor (fields sensors) Satellite Social network Mobile application IaaS is the most basic level of the cloud computing service models. It offers the virtual (as well as physical) machines, servers, storage options, load balancers, networks, and more. PaaS is next in line, focusing more on operating systems, databases, web servers, development tools, etc. This is where IT development happens.
- Situation recognition is a central component of an SLN. Situations are the result of interactions among several related events. Events are the results of some happenings that are due to signficant state changes. Based on the situation at a place, the system Identies needs and available resources to satisfy those needs. The situation recognition is always in a context of a specfic application and so are all other operations. The data sources used by the system are also those publicly available or specically made available in the context of the application. The system closes the loop by sending actuation or action information to appropriate needs and resources as a result of the matching. A SLN system is shown in Figure 2. Here, not only people, but other objects like mobile applications (e.g. body activity monitors if allowed by the owner), databases, and the Internet of Things (e.g., trac sensors) also observe, store and report information about the state of entities in the world. In this setting, we conceive of a world where (a) a signifcant body of information today come from sensors, (b) the number of sensors is huge and the number of events generated by them are even larger, (c) a large fragment of data, both human and device generated, have associated locational information, (d) most situation and needs assessment decisions are for controlling and managing real time and evolving situations, and (e)keeping pace with the real-time nature of our problem space, planning and decision processes need to be viewed like a real-time control system that interoperates with the publish- subscribe and update-propagation model of standard social networks. A user update in a social network is analyzed to create a microevent (or a personal event), which is then fed to the situation recognizer. The situation recognizer evaluates this microevent with respect to other events from different sources and creates an action (e.g., a message, a recommendation, an alert) that goes back to the sender or a potential resource that can service the needs of the original message sender.
- 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
- Depending on the applications, geospatial data streams used in models may be needed at different bounding boxes and resolutions. For instance, users studying traffic patterns near Los Angeles area may require pollution data at level of every 50 yards for every 30 seconds. However, experts who study climate of US may need the same data for the whole US every 10 miles for every day. I will only talk about operators over grid data structure -> but tomorrow you will hear more talk from Ish in other models. Stream processing -> tuple based GIS -> grid, graph, line Context on grid array -> advantages, unique behavior or characteristic
- 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)
- First, the current version favors data input in grid or raster form. We realize that much data is created and collected in different forms. Also, data is produced and consumed at different spatial, temporal, and symbolic granularities. Different sensors cover different sized spaces, produce data at different rates. Many data sources use geo-political concepts and relationships in producing and representing data. This means that we must use GIS structures as input as well as output while doing most computations still in grid format. This introduces many computational as well as quality of results related issues. In addition, data streams used in these models are generated by data sources available on the Web, access of which usually suffers from various processing and network constraints. Reckless use of these data services without careful planning will eventually make it impossible for the system to access data streams. Also, duplicate access to data sources and redundant computation can also waste huge amounts of machine and network resources.
- In GIS, the diverse Web Map Service (WMS) specification [1] of the Open Geospatial Consortium (OGC) Zoom level is just for presen Every Stel at any detail level represents a single fixed ground location. The ground resolution indicates the distance on the ground that’s represented by a single Stel. For example, at a ground resolution of 10 meters/Stel, each Stel represents a ground distance of 10 meters. The ground resolution varies depending on the level of detail and the latitude at which it’s measured. tation or visualization framework I would like to bring this concept/standard to our computational framework to represent the real world in Emage.
- The factors includes density of arcs length, density of polygons, and the size of gird cells. The first two represents the complexity of the polygon. Calculate EEM-BGC General rasterization erros, Das, DPs were calculated by using the ARCGIS software, The relationships were analyze density of arcs length (DA) -> m/0.001km^2 density of polygon (DP) -> n/100km^2 size of gird cells (SG) -> km
- EventShop integrates heterogeneous real-time data streams and detects actionable situations. Sit-uation recognition based on latest observations alone is too late for taking appropriate actions to prevent critical events. It is important to guide people based on expected situations in the near future, rather than on the situation that just occurred.
- Secondly, it is good to recognize situations after they happened. But it is much better if we can predict situations, event just a bit in advance, and act accordingly. This is the essence of closed loop control systems. Application of predictive analytics in EventShop will increase its applicability. We address these issues in this proposal. The real-time data streams are in nature of time series. Autore-gressive (AR), moving average (MA), and autoregressive moving average (ARMA) models [95] are often used for time series prediction. Multivariate spatio-temporal autoregressive model takes into account both spatial and temporal correlations but requires to estimation of a large number of parameters. In order to reduce the number of parameters, [89] and [97] consider the spatial cor-relation in neighbors of distance. EventShop involves complex relationship between large number of data streams, so the selection of data streams correlated to predicted target is very important to improve the prediction accuracy. We propose to include feature selection in EventShop. There are many feature selection methods, such as L1 norm, L2 norm, group lasso and so on. Here, we use Lasso, penalized regression model (4) to eciently solve the feature selection problem.
- The data streams over the web (e.g. tweets, weather.gov feeds) are translated into a unified format. Based on application logics, multiple spatio-temporal analysis operators are formed to generate different situation recognition models. The uniform data streams are continuously fed into the models to detect and recognize real-time situations. Then, the detected situations (e.g. ‘flu outbreak’ in New England) can be combined with user parameters (e.g. ‘high temperature’, and location). Again, based on application logic, the situation based controller is constructed to send out personalized action alerts (e.g. ‘Report to CDC center on 4th street’) to each individual. In addition, the analytic reports are also available to a central analyst who can then take large-scale (state, nation, corporate, or world-wide) decisions.
- There are two main components in EventShop framework which are Data Ingestor and Stream Processing Engine. A workflow of moving from heterogeneous raw data streams to actionable situations is shown here. In the Data Ingestor component, original raw spatio-temporal data from the Web are translated into unified STT (Space-Time-Theme) format along with their numeric values using an appropriate Data Wrapper. Based on users’ defined spatio-temporal resolutions, the system aggregates each STT stream to form an E-mage stream. These E-mage streams are then transferred to the Stream Processing Engine component for processing. Based on situation recognition model determined by the domain expert, appropriate operators are applied on the E-mage streams to detect situation. The final step is a segmentation operation that uses domain knowledge to assign appropriate class to each pixel on the E-mage. This classification results in a segmentation of an E-mage into areas characterized by the situation there. Once we know the situation, appropriate actions can be taken.