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Seismo-Surfer a tool for collecting, querying, and mining seismic data

Seismo-Surfer a tool for collecting, querying, and mining seismic data. Yannis Theodoridis University of Piraeus ytheod@unipi.gr. Outline of the presentation. Concepts and motivation The Seismo-Surfer tool Architecture Functionality Current status Future work Conclusions.

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Seismo-Surfer a tool for collecting, querying, and mining seismic data

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  1. Seismo-Surfera tool for collecting, querying, and mining seismic data Yannis Theodoridis University of Piraeus ytheod@unipi.gr

  2. Outline of the presentation • Concepts and motivation • The Seismo-Surfer tool • Architecture • Functionality • Current status • Future work • Conclusions

  3. Concepts and motivation (1) • Seismic data are recorded by seismologists (geologists etc.) in order to study tectonic activity. • This kind of data is characterized by several attribute types • alphanumeric (e.g. magnitude) • spatial (epicenter, depth) • temporal (time of occurrence)

  4. Concepts and motivation (2) Goal: to build a prototype system (tool) of practical impact that combines results of latest research trends in the fields of • Non-traditional databases (spatio-temporal) • Data warehousing • Data mining by using state-of-the-art DBMS technology. … all this into a user-friendly environment.

  5. Concepts and motivation (3) • Such a tool could be useful to • researchers of geophysical sciences (e.g. for constructing seismic profiles). • key personnel in public administration (e.g. for visualizing epicenters and relating them with other spatial entities). • simple users (e.g. web-surfers seeking for maps of seismic activity).  The Seismo-Surfer tool

  6. Seismo-Surfer Architecture

  7. Seismo-Surfer Functionality (1) • Non-traditional queries (spatial and spatio-temporal) • “find all epicentres ofearthquakes within distance no more than 50Km from Athens in the last 10 years” • Data warehouse functionality by supporting summarized views of data in different levels of abstraction • spatial (e.g. province, country, continent) • temporal (e.g. month, year, ten year period) • Data mining operations • finding / visualizing clusters • seeking association rules

  8. Seismo-Surfer Functionality (2) • Remote data sources integration (e.g. from the web). • Example: only summaries of seismic data could be stored locally and additional data could be loaded, from the remote (web) source, on demand • Phenomena extraction • Example: automatic extraction of semantics from stored data, suchas • the mainshock and • possible intensiveaftershocks in shock sequences

  9. Seismo-Surfer Current Status (1) • A prototype has been implemented using Oracle (9i DB & Spatial Data Cartridge) and Java technologies. • Two web sources have been integrated and the local database is auto-updated • Greek events (source: Inst. of Geodynamics @ the Nat’l Observatory of Athens www.gein.noa.gr ) • Global events (source: US Geological Survey www.usgs.gov ). • Extra map layers with geographical entities of Greece (populated places, islands etc.) have been also integrated. • (source: US NIMA www.nima.mil )

  10. Seismo-Surfer Current Status (2) Current functionality includes • Spatiotemporal Queries (exploiting the R-tree indexing technique): • Range queries (epicenters in a region) • Nearest-Neighbor Queries (epicenters closest to a point on the map) • Distance Queries (epicenters at a distance lees than X) • Closest-Pair Queries (epicenters closest to Greek cities) (cont’d)

  11. Seismo-Surfer Current Status (3) (cont’d) • Data Mining Operations • Currently, a single clustering algorithm (k-means) • Various visualization features • Maps, plots and GUI tools that assist the user to the query formulation process and allow viewing the selected or analyzed data in a number of different ways. Screenshots …

  12. 1) Spatio-temporal queries

  13. 1a) Closest-pair queries

  14. 2) Clustering

  15. 3) Plotting facilities

  16. Future Work • Data warehouse functionality • summarized views of data • spatial (e.g. province, country, continent) • temporal (e.g. month, year, ten year period) • aggregations stored locally; detailed data fetched from web sources, on demand • More data mining operations • more clustering techniques • algorithms for classification and correlation rules • More data sources and layers • ‘smart’ filters for feeding local DB • Semi-structured data?

  17. Conclusions • Seismo-Surfer is a prototype data management and mining system for seismic data • Combines latest research trends in database management, data warehousing and data mining • Integrates data from remote (web) sources • Two versions will be available soon: • A desktop version of full functionality • A web interface (light version) For more information: http://isl.cs.unipi.gr/seismo/

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