Abstract
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.
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References
Arasu, A., et al.: STREAM: The Stanford Stream Data Manager (demonstration description). In: SIGMOD, pp. 665–665 (2003)
Abadi, D.J., et al.: Aurora: A Data Stream Management System. In: SIGMOD 2003, p. 666 (2003)
Abadi, D.J., et al.: An Integration Framework for Sensor Networks and Data Stream Management Systems. In: VLDB 2004, pp. 1361–1364 (2004)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: PODS, pp. 1–16 (2002)
Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, New York (1988)
Behrend, A., Dorau, C., Manthey, R., Schüller, G.: Incremental View-Based Analysis of Stock Market Data Streams. In: IDEAS, pp. 269–275 (2008)
Blackmann, S.S., Populi, R.: Design and Analysis of Modern Tracking Systems. Artech House, Boston (1999)
Chan, M., Leong, H.V., Si, A.: Incremental Update to Aggregated Information for Data Warehouses over Internet. In: DOLAP, pp. 57–64 (2000)
Ceri, S., Widom, J.: Deriving Production Rules for Incremental View Maintenance. In: VLDB, pp. 577–589 (1991)
Dellaert, F.: The Expectation Maximization Algorithm. Technical Report GIT-GVU-02-20, Georgia Institute of Technology, Atlanta, USA (2002)
Ghanem, T.M.: Incremental Evaluation of Sliding-Window Queries over Data Streams. IEEE Trans. on Knowl. and Data Eng. 19(1), 57–72 (2007)
Golab, L., Özsu, M.T.: Issues in Data Stream Management. SIGMOD Record 32(2), 5–14 (2003)
Griffin, T., Libkin, L.: Incremental maintenance of views with duplicates. In: SIGMOD 1995, San Jose, May 23-25, pp. 328–339 (1995)
Gupta, A., Mumick, I.S. (eds.): Materialized Views: Techniques, Implementations, and Applications. MIT Press, Cambridge (1999)
Gupta, A., Mumick, I.S., Subrahmanian, V.S.: Maintaining Views Incrementally. In: SIGMOD, pp. 157–166 (1993)
Manthey, R.: Reflections on Some Fundamental Issues of Rule-based Incremental Update Propagation. In: DAISD, pp. 255–276 (1994)
Madden, S., Franklin, M.J.: Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data. In: ICDE 2002, pp. 555–566 (2002)
Qian, X., Wiederhold, G.: Incremental Recomputation of Active Relational Expressions. Knowledge and Data Engineering 3(3), 337–341 (1991)
Seltzer, M.: Beyond Relational Databases. Communications of the ACM 51(7), 52–58 (2008)
Stonebraker, M., Cetintemel, U.: “One Size Fits All”: An Idea Whose Time Has Come and Gone. In: ICDE, pp. 2–11 (2005)
Streit, R., Luginbuhl, T.E.: Probabilistic Multihypothesis Tracking. Tech. Rep. NUWC-NPT/10/428, Naval Undersea Warfare Center, Newport USA (1995)
Subramanian, S., et al.: Continuous Queries in Oracle. In: VLDB, pp. 1173–1184 (2007)
Tanner, M.A.: Tools for Statistical Inference. Springer, New York (1996)
Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing Uncertainty in Moving Objects Databases. ACM Trans. Database Syst. 29(3), 463–507 (2004)
Wald, A.: Sequential Analysis. John Wiley & Sons, New York (1947)
Wieneke, M., Koch, W.: The PMHT: Solutions for some of its Problems, pp. 1–12. SPIE (2007)
Wieneke, M., Koch, W.: On Sequential Track Extraction within the PMHT Framework. EURASIP Journal on Advances in Signal Processing (2008)
Wolfson, O., Xu, B., Chamberlain, S., Jiang, L.: Moving Objects Databases: Issues and Solutions. In: SSDBM, pp. 111–122 (1998)
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Behrend, A., Manthey, R., Schüller, G., Wieneke, M. (2009). Detecting Moving Objects in Noisy Radar Data Using a Relational Database. In: Grundspenkis, J., Morzy, T., Vossen, G. (eds) Advances in Databases and Information Systems. ADBIS 2009. Lecture Notes in Computer Science, vol 5739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03973-7_21
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DOI: https://doi.org/10.1007/978-3-642-03973-7_21
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