SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
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
Incremental maintenance of recursive views using relational calculus/SQL
ACM SIGMOD Record
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
Using Partial Differencing for Efficient Monitoring of Deferred Complex Rule Conditions
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Integrity Constraints Checking In Deductive Databases
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Deriving Production Rules for Incremental View Maintenance
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
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
"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
A transformational framework for the automatic control of derived data
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
ATLAS: a small but complete SQL extension for data mining and data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
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
Multisensor Data Fusion
LCS-Hist: taming massive high-dimensional data cube compression
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Detecting Moving Objects in Noisy Radar Data Using a Relational Database
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
CAMS: OLAPing Multidimensional Data Streams Efficiently
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
TinTO: a tool for the view-based analysis of streams of stock market data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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Tracking uncooperative moving objects by means of radar is a complex task due to clutter and association problems in multi-target scenarios. An approach to solve this problem is probabilistic multiple hypothesis tracking (PMHT). This method combines classical track filtering with a likelihood ratio test for the estimation of the plot-to-track association. The basics of PMHT and similar algorithms have gained much attention recently. However, the efficient implementation of real world applications of this technique still represents a challenging task. Since a common requirement in this context is the reliable storage of track data in a database, an implementation of the tracker's calculation inside a database management system (DBMS) using SQL views is desirable. A naive implementation of PMHT using a commercial DBMS, however, usually leads to performance problems because of the high frequency of measurement updates. In this paper, we propose possible optimizations for solving these performance problems. Their usage leads to a dramatic run-time improvement in our sample case and makes the implementation of PMHT in a database context feasible.