The biggest issue with map-reduce and geostatistics is that map process assumes no inter-record dependencies. Which means you need flow control and multiple MR jobs (why cascading makes so much sense).
Meet customer demands for handling larger data, faster data and more data types
Transcript of "The State of Big Data for Geo - ESRI Big Data Meetup"
The State of Big Datafor Geo @ajturner CTO DC Dev Center @seangorman Strategist DC Dev Center
MapReduce Geo (MrGeo)• DIA project initiated by Terry Busch to extend geoprocessing to very large data sets• Built by SPADAC -> GeoEye -> Digital Globe• Uses HDFS and MapReduce to store, process, and index geospatial imagery and vector data• Interoperable with: – ArcGIS Desktop – COMET – Google Earth – WMS clients – Adobe Flex and Silverlight environments• Listed on the Hadoop Apache page to be open sourced
GCEP• Geospatial complex event processing• Extends the to include the ability to use Geospatial constructs in the rules for filtering events• The ability to utilize the OGC Geospatial Functions within Espers Event Processing Language (EPL). –Contains, within, disjoint, intersects, overlaps, crosses, intersection, touches, buffer, relate, union, convex hull
Neo4j Spatial• Utilities for importing from ESRI Shapefile as well as Open Street Map files• Support for all the common geometry types• An RTree index for fast searches on geometries• Support for topology operations during the search (contains, within, intersects, covers, disjoint, etc.)• The possibility to enable spatial operations on any graph of data, regardless of the way the spatial data is stored, as long as an adapter is provided to map from the graph to the geometries.• Ability to split a single layer or dataset into multiple sub- layers or views with pre-configured filters