Tracking and data association
Maintaining views incrementally
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Incremental maintenance of views with duplicates
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Materialized views: techniques, implementations, and applications
Materialized views: techniques, implementations, and applications
Incremental update to aggregated information for data warehouses over Internet
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incremental Recomputation of Active Relational Expressions
IEEE Transactions on Knowledge and Data Engineering
Deriving Production Rules for Incremental View Maintenance
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Moving Objects Databases: Issues and Solutions
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Issues in data stream management
ACM SIGMOD Record
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a data stream management system
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
"One Size Fits All": An Idea Whose Time Has Come and Gone
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incremental Evaluation of Sliding-Window Queries over Data Streams
IEEE Transactions on Knowledge and Data Engineering
An integration framework for sensor networks and data stream management systems
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Communications of the ACM - Web science
On sequential track extraction within the PMHT framework
EURASIP Journal on Advances in Signal Processing
Incremental view-based analysis of stock market data streams
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
AIMS: an SQL-based system for airspace monitoring
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
Supporting phase management in stream applications
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
Towards a universal tracking database
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Efficient tracking of moving objects using a relational database
Information Systems
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In moving object databases, many authors assume that number and position of objects to be processed are always known in advance. Detecting an unknown moving object and pursuing its movement, however, is usually left to tracking algorithms outside the database in which the sensor data needed is actually stored. In this paper we present a solution to the problem of efficiently detecting targets over sensor data from a radar system based on database techniques. To this end, we implemented the recently developed probabilistic multiple hypothesis tracking approach using materialized SQL views and techniques for their incremental maintenance. We present empirical measurements showing that incremental evaluation techniques are indeed well-suited for efficiently detecting and tracking moving objects from a high-frequency stream of sensor data in this particular context. Additionally, we show how to efficiently simulate the aggregate function product which is fundamental for combining independent probabilistic values but unsupported by the SQL standard, yet.