Geospatial Web Technology for Accessing Environmental...
Transcript of Geospatial Web Technology for Accessing Environmental...
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Media Watch on Climate Change
Geospatial Web Technology for Accessing Environmental Online Resources
http://www.ecoresearch.net/climate
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IDIOM ProjectIDIOM Project
• Scientific Partner:• MODUL University Vienna• Technical University Graz • Vienna University for Economics and Business Administration
(Coordinator)
• Commercial Partner:• Gentics• Prisma• Österreichwerbung
• Project • Total Cost: 773,113 EUR • WU-CM + WU-TLS: 316,200 EUR
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Research QuestionsResearch Questions
• How widespread is content redundancy, and what influences content replication within and across social networks?
• Does media interactivity influence information diffusion? Can existing models such as hub-and-spoke, syndication and peer-to-peer explain this influence?
• Which content placement strategies increase the impact on the target audience and support self-reinforcing content propagation in virtual communities (viral marketing)?
• Impact of macroscopic information flows on environmental awareness and public opinion? What are appropriate methods to measure and model the extent, dynamics and latency of this process?
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US Election 2004 Web MonitorUS Election 2004 Web Monitor
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Time Magazine, Sep 27, 2004 Vol. 164 No. 13
US Election 2004 Web MonitorUS Election 2004 Web Monitor
Source: ABC News/Washington Post, CBS News/
New York Times, CNN/USA Today/Gallup
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US Election 2004 Web MonitorUS Election 2004 Web Monitor
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Web Mining ArchitectureWeb Mining Architecture
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Media Watch on Climate ChangeMedia Watch on Climate Change
• Public Web Portalhttp://www.ecoresearch.net/climate/
• Extraction of Environmental Knowledge
150 Anglo-American News Media Sites 200,000 Documents (Mirrored in Weekly Intervals) 10,000 Environmental Articles
• Contextualized Information Space Spatial Annotate Source and Target Geography
BBC: „Vienna Marking Mozart Milestone“Source: Europe | United Kingdom | LondonTarget: Europe | Austria | Vienna
Semantic Classify Documents, Assign Ontology Concepts
Temporal Add Timestamps to Publications, Events, etc.
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Climate Change Media WatchClimate Change Media Watch
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Interface ServicesInterface Services
• Semantic Interfaces• Perceptual Maps (US Election Monitor 2004)
• Information Landscapes (Aureka, VisIslands)
• Ontology-based Visualizations
WebMap Technologies
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Perceptual MapsPerceptual Maps
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Thematic MapsThematic Maps
Aureka
VisIslandsInfoSky
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Ontology ServicesOntology Services
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Interface ServicesInterface Services
• Semantic Interfaces• Perceptual Maps (US Election Monitor 2004)• Information Landscapes (Aureka, VisIslands)• Ontology-based Visualizations
• Geospatial InterfacesThe Geospatial Web “may ultimately be the big disruptive innovation of the coming decade” (Erle et al. 2005, xxv).• 2D Platforms
• MapQuest• Google Maps• Yahoo! Maps
• 3D Platforms• NASA World Wind• Google Earth• MS Virtual Earth
CIA World Factbook
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www.austria.infowww.austria.info
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Geospatial MappingGeospatial Mapping
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The Geospatial WebThe Geospatial Web
• Integrates• Cartographic Data• Environmental Indicators• Geotagged Hypermedia
• Knowledge Planets• Topology of Information Landscape
• Peak = Cluster of Documents on a Specific Topic
• Valley = Sparsely Populated Part of the Information Space
• Projecting Information Landscapes onto Virtual Globes• Initial Arrangement and Stability of Topics• Navigation Across Layers and the 0° Meridian Line
• Download | www.geospatialweb.com/chapter-1Scharl, A. (2007). “Media Platforms for Managing Geotagged Knowledge Repositories”, The Geospatial Web – How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society. Eds. A. Scharl and K. Tochtermann. London: Springer. 3-14.
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Analytical ServicesAnalytical Services
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IDIOM System ArchitectureIDIOM System Architecture
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Processing TechniquesProcessing Techniques
• Infrastructure• Virtualization (XEN)• Host Systems
Siemens RX200/300 Server mit je 8 CPU's und 32 GB RAM• Eases administrative tasks• PC7 cluster
• Tasks• Mirroring• Tagging (Spatial, Temporal, Named Entities)• Semantic Maps• Ontology Extension
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Distributed Computing – hadoopDistributed Computing – hadoop
• Developed by the Apache project (Lucene subproject)
• Implements MapReduce + a distributed Filesystem
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Distributed Computing – hadoopDistributed Computing – hadoop
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Keep things simpleKeep things simple
• Overhead ↓ -> Processing Time ↓• Avoid costly operations• Database writes, Networking, IO
• DFS Input Data -> DFS Output Data• NFS about twice as fast • file names• Handle output of redeployed tasks
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Use your Database and Data Structures wiselyUse your Database and Data Structures wisely
• INSERT vs. COPY • Example: 4 million DB writes (prepared statements, stored
procedures, ...) - 2.5 days without results vs. - 30 seconds(!) + 2 min (COPY)
• Constraints and Indices
• Cache data (!)• Example: Computation of the Semantic Map
- 3 hours -> 18 min (10x)
• Use prepared statements (DB Planer)• Approx. 10x
• Data Structures:e.g. ArrayLists vs. HashSet; List vs. Set
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