1 Ubiquitous GIS Part III: Implementation Issues Fall 2007 Ki-Joune Li Pusan National University

STEMPusan National University STEM-PNU 2 Two Viewpoints Geographic Context Real World Application Systems How to provide Geographic Context ? How to store and search Geographic Context ? How to analyze Geographic Context ? Representation of Geographic Context Identification of Geographic Context

STEMPusan National University STEM-PNU 3 Challenges for Implementation Representation of Geographic Context Identification of Geographic Feature Providing Geographic Context Storing and Searching Geographic Context Collecting and Analyzing Geographic Context Context Modeling Context Representation Ontology Geo-LabelingGUID In-Network Processing UBGI MiddlewareStandard Contextual Reasoning and Context-Aware Mapping Data Streaming Management from Geo-Sensors

STEMPusan National University STEM-PNU 4 Context Modeling Most basic part of UBGI A Framework of Context is required to describe context Context in Linguistics in Ubiquitous Computing Context Modeling TextMeaning Context FactInterpretation Context

STEMPusan National University STEM-PNU 5 Context as Parameters DataInterpretation Spatial and Spatiotempoal Context Behavioral Context System Environment Context Human Context Others Parametric GML Contextual Parameters User-centric Meaning

STEMPusan National University STEM-PNU 6 Issues of Context Modeling Classification of Context Representation of Context Spatio-Temporal Properties of Context Parametric Approach Ontology and Context

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 8 2-D Bar Codes Home Page URL, UFID, u-Location, and Other Information

STEMPusan National University STEM-PNU 9 Virtual Geo-Labels No Physical Devices Dynamic Computation of Geo-Labels with 3-D Objects Position View Direction Velocity Real World Augmented Reality on a screen

STEMPusan National University STEM-PNU 10 Implementation of Virtual Geo-Label in 3-D Server of 3-D GIS Databases Server of Application DB Geo-Label Mobile Client Position Velocity Interest View Point Geo-Label Dynamic Computation Presentation of Useful Information Progressive Transfer Simplification of 3-D Objects to Lessen the Computation Overhead

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 12 Should be processed in Real-Time Large Number of Nodes e.g. 1 Million Nodes → 1  sec/ node Scalability and Real-Time Constraint Geographic Context Mobile Node Mobile Node Dynamic Updates of Position Context Request Mobile Node Mobile Node Mobile Node Mobile Node Mobile Node Mobile Node GIS DB Location DB stationary and mobile nodes

STEMPusan National University STEM-PNU 13 Server Geographic Context-Awareness by In-Network Processing Scalability Problem Each node has a small fraction of geographic Information. Each node exchanges geographic information by P2PSensor NetworkBroadcasting

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

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

STEMPusan National University STEM-PNU 16 Data on Air Broadcasting like DMB - Needs a Broadcasting Server - Databases are periodically broadcasted Broadcasting Geographic Context Broadcasting Server Hybrid Approach - Push-Protocol by Broadcasting - Pull-Protocol by Request on Demand

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 20 Ubiquitous Computing Architecture Heterogeneity UBGI Middleware Mobile Node Middleware Mobile Node Middleware 3-Tiers Architecture Server Middleware Client Massively Distributed Environment Binding Client and Server Binding Mobile Nodes

STEMPusan National University STEM-PNU 21 Performance Bottleneck Heterogeneity UBGI Middleware Middleware Binding Objects Geographic Binding Location Data Server (GIS) Mobile Node Middleware Mobile Node LDS Standard e.g. SensorML

STEMPusan National University STEM-PNU 22 Heterogeneity of Spaces and Reference Systems Linear Space Euclidian Space (L57,Seg22,49)(E121213,N ) Indoor Space (BD218,Room431) Heterogeneous Representation of Location User of UBGI service

STEMPusan National University STEM-PNU 23 Seamless Space Linear Space: (L57,Seg22,49) Indoor Space: (BD218,Room431)Euclidian Space : (E121213, N )

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

STEMPusan National University STEM-PNU 25 Context-Aware Mapping Traditional Map user A user B user I user D user C user F user G user H Context-Aware Mapping user A Context-Aware Mapping user B Context-Aware Mapping user C Context-Aware Mapping user D

STEMPusan National University STEM-PNU 26 Spatial and Spatiotemporal Aspects Context-Aware Mapping Geographic Information For Everyone My Geographic Information My Context My Profile My Status Interpretation Contextual Reasoning My Surroundings My H/W and S/W Context

STEMPusan National University STEM-PNU 27 Context-Aware Mapping: Example Spatial and Spatiotemporal Aspects Geographic Features around My Position 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 My Context Lunch before 30 min. On a highway Interpretation Preference to cheapest gas Small Screen, PDA Fuel for only 50 Km

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 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

STEMPusan National University STEM-PNU 30 Summary Context Modeling Heterogeneity Geo-Labeling ScalabilityIn-Network Processing UBGI Middleware Context-Aware Mapping Data Streaming Management