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  1. Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '10)
  2. Microsoft framework for geospatial stream processing
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Microsoft framework for geospatial stream processing
Geostreaming at IBM: infosphere streams and intelligent traffic systems
The Oracle platform for real time streaming event driven architecture based solutions
Spatiotemporal summarization of traffic data streams
A data stream-based evaluation framework for traffic information systems
Framing the question: detecting and filling spatial-temporal windows
Modeling and prediction of moving region trajectories
AIMS: an SQL-based system for airspace monitoring
OpenSense: open community driven sensing of environment
Querying streaming point clusters as regions
A programming framework for integrating web-based spatiotemporal sensor data with MapReduce capabilities
Exploratory novelty identification in human activity data streams

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Microsoft framework for geospatial stream processing

Content Provider ACM Digital Library
Author Katibah, Ed
Abstract Microsoft StreamInsight is a platform for developing and deploying streaming applications. StreamInsight embraces a temporal stream model to unify and further enrich query language features, handle imperfections in event delivery and define consistency guarantees on the output. With its extensibility framework, StreamInsight enables developers to integrate their domain expertise within the query pipeline as user defined functions, operators and aggregates. The addition of the Microsoft SQL Server Spatial Library enables StreamInsight to deliver Geostreaming support. With StreamInsight analyzing and summarizing input data, SQL Server and SQL Azure has the ability to persist these summaries in the data center or the cloud. These summaries can be used as feedback to StreamInsight for future query processing or used as the basis for historical data analysis and reporting. This framework enables organizations to seamlessly consume, use, and extend all form of stream data including geographic data.
Starting Page 1
Ending Page 1
Page Count 1
File Format PDF
ISBN 9781450304313
DOI 10.1145/1878500.1878501
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-11-02
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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