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Implementing Ubiquitous GIS: Challenges and Solutions

This article discusses the implementation issues of Ubiquitous GIS, including context modeling, geo-labeling, storing and searching geographic context, and in-network processing. It presents different approaches and technologies for providing and analyzing geographic context.

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Implementing Ubiquitous GIS: Challenges and Solutions

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  1. Ubiquitous GIS Part III: Implementation Issues Fall 2007 Ki-Joune Li http://isel.cs.pnu.edu/~lik Pusan National University

  2. Two Viewpoints Real World GeographicContext Application Systems Representation of GeographicContext Identification of GeographicContext How to provide Geographic Context ? How to store andsearch Geographic Context ? How to analyze Geographic Context ?

  3. Challenges for Implementation Context Modeling Ontology Representation of Geographic Context Context Representation Identification of Geographic Feature Geo-Labeling GUID Providing Geographic Context In-Network Processing UBGI Middleware Standard Storing and Searching Geographic Context Contextual Reasoning and Context-Aware Mapping Collecting and Analyzing Geographic Context Data Streaming Management from Geo-Sensors

  4. Context Modeling • Context Modeling • Most basic part of UBGI • A Framework of Context is required to describe context • Context • in Linguistics • in Ubiquitous Computing Text Meaning Context Fact Interpretation Context

  5. Context as Parameters Data Interpretation ParametricGML ContextualParameters User-centricMeaning Spatial and Spatiotempoal Context Behavioral Context System Environment Context Human Context Others

  6. Issues of Context Modeling • Classification of Context • Representation of Context • Spatio-Temporal Properties of Context • Parametric Approach • Ontology and Context

  7. Geo-Labels • Geo-Label: A label for recognizing geographic feature • Implementation • Physical Device • 2-D Bar Code • RFID • Virtual Geo-Label • Dynamic Computation from Viewpoint • Contents of Geo-Labels • UFID • u-Location • Other Information

  8. 2-D Bar Codes Home Page URL, UFID, u-Location, and Other Information

  9. Real World Augmented Realityon a screen Virtual Geo-Labels • No Physical Devices • Dynamic Computation of Geo-Labels with 3-D Objects • Position • View Direction • Velocity

  10. Implementation of Virtual Geo-Label in 3-D Geo-Label Mobile Client Server of 3-D GIS Databases Position Progressive Transfer DynamicComputation Velocity Simplification of 3-D Objects toLessen the Computation Overhead Interest View Point Server of Application DB Geo-Label Presentation of UsefulInformation

  11. Issues of Geo-Label • Implementation of Virtual Geo-Labels • iPointer TM of IST • Paper Map • Panoramic View of 3-D objects • Storing GUID in Geo-Label • GUID: Global Unique Identifier

  12. Should be processed in Real-Time Scalability and Real-Time Constraint Geographic Context Location DBstationary and mobile nodes GIS DB Dynamic Updates ofPosition ContextRequest Large Number of Nodes e.g. 1 Million Nodes → 1 sec/ node Mobile Node Mobile Node Mobile Node Mobile Node

  13. Geographic Context-Awareness by In-Network Processing Scalability Problem Server Each node has a small fraction of geographic Information. Each node exchanges geographic information by P2P Sensor Network Broadcasting

  14. In-Network Processing: Sensor Network Sensor Network Database Mobile Ad-Hoc Network (MANET) No Centralized Server Multi-Hop Databases are scattered into mobile node Needs Geographic Routing Coverage Area

  15. (x1,y1,t1), IPAddr1 (x2,y2,t2), IPAddr2 (x3,y4,t4), IPAddr3 (x4,y4,t4), IPAddr4 In-Network Processing: P2P Peer-to-Peer Originally for File Sharing Services - Examples: Napster, Gnutella, StarCraft No Centralized Server Sensor Network or Infrastructure Network - Each node has an IPv6 address - No Geographic Limit unlike sensor network Databases are scattered into mobile nodes

  16. Data on Air Data on Air Broadcasting like DMB - Needs a Broadcasting Server - Databases are periodically broadcasted Hybrid Approach - Push-Protocol by Broadcasting - Pull-Protocol by Request on Demand Broadcasting Server BroadcastingGeographic Context

  17. Issues in In-Network Processing: Indexing • Indexing • Databases are scattered into small pieces at local devices • NO GLOBAL Server storing a Global Index • Modification of • DHT (Distributed Hash Table) or • Distributed Index Structures are required

  18. Issues in In-Network Processing: Data Format • Data Format for exchange should be defined • Data Items to be included in messages • Distributed Data Structures like distributed index • Efficiency • Heterogeneity • Standards like SensorML and TransduceML • Middleware for Massively Distributed Systems • Space Heterogeneity

  19. Issues in In-Network Processing: Protocols • Distributed Algorithms • Strongly related with protocol • P2P, Sensor Network, Data on Air, and Hybrid • Example: Data on Air • Push Protocol • Tradeoff between data items and period • Determination of Data Items to Broadcast: Hotspot Analysis • Hybrid Approach • Push Protocol for Hotspot data items • Pull Protocol on demand request • Other Communication Media like WIBRO

  20. 3-Tiers Architecture Client Client Client Massively Distributed Environment Middleware Server Server Server MobileNode MobileNode MobileNode Middleware Middleware Middleware Binding Client and Server Middleware Middleware Middleware MobileNode MobileNode MobileNode Binding Mobile Nodes Heterogeneity UBGI Middleware Ubiquitous Computing Architecture

  21. Mobile Node Mobile Node Location Data Server (GIS) PerformanceBottleneck Middleware Binding Objects Geographic Binding LDS LDS Middleware Middleware Standard Mobile Node Mobile Node Heterogeneity UBGI Middleware e.g. SensorML

  22. Heterogeneity of Spaces and Reference Systems User of UBGI service Heterogeneous Representation of Location (BD218,Room431) (E121213,N3750015) (L57,Seg22,49) Linear Space Indoor Space Euclidian Space

  23. Seamless Space Linear Space: (L57,Seg22,49) Indoor Space: (BD218,Room431) Euclidian Space : (E121213, N3750015)

  24. Elevator Stairs W.C. 405 401 Emergency Bell A 4th Floor 404 406 p (F4, 401, 15, 18) Emergency Bell B Example: Indoor Space • No more Euclidian Space • Different coordinate systems and different properties. • We should rebuild Spatial DBMS for Indoor Space

  25. Context-Aware Mapping Context-Aware Mapping Context-AwareMapping user B user A userD userF Traditional Map userC userG userB Context-AwareMapping Context-AwareMapping user C user D userH userA userI

  26. Context-Aware Mapping Geographic Information For Everyone My GeographicInformation Spatial and Spatiotemporal Aspects Interpretation My Context Contextual Reasoning My H/W and S/W Context My Profile My Status My Surroundings

  27. Context-Aware Mapping: Example 1. Highway or Accessible from Highway 2. Gas stations within 50Km 3. If possible cheapest gas 4. No restaurant for 3 hours 5. GI without complicated visualization 6. GI without heavy geometric computation Geographic Features around My Position Spatial and Spatiotemporal Aspects Interpretation My Context Small Screen, PDA Lunch before 30 min. On a highway Preference to cheapest gas Fuel for only 50 Km

  28. Context-Aware Mapping: Requirements • Contextual Reasoning in Real-Time • Mapping NOT Map itself • Dynamic Context: Data Stream from Geo-Sensors • Two possible approaches • Approach 1: GI with Context-Awareness Features • Example: Extension of GML with Context-Awareness Tags • More Preprocessing and Less Runtime Contextual Reasoning • Approach 2: GI without Context-Awareness Features • Example: GML and Agent for Context-Awareness • Less Preprocessing and More Runtime Contextual Reasoning

  29. Data Stream from Geo-Sensors • Data from sensors: Stream rather than databases • Data Stream differs from Databases • Online arrival of data elements, No control over the sequence • Data elements are to be discarded after processed • Only small size of memory to store them • Continuous queries rather than “one-time” query • DSMS: Different Approaches from conventional DBMS • Query Processing, Indexing etc.. • Stream Mining rather than Data Mining

  30. Summary Context Modeling Geo-Labeling Scalability In-Network Processing UBGI Middleware Heterogeneity Context-Aware Mapping Data Streaming Management

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