Abstract
Moving object environments are characterized by large numbers of objects continuously sending location updates. At times, data arrival rates may spike up, causing the load on the system to exceed its capacity. This may result in increased output latencies, potentially leading to invalid or obsolete answers. Dropping data randomly, the most frequently used approach in the literature for load shedding, may adversely affect the accuracy of the results. We thus propose a load shedding technique customized for spatio-temporal stream data. In our model, spatio-temporal properties, such as location, time, direction and speed over time, serve as critical factors in the load shedding decision. The main idea is to abstract similarly moving objects into moving clusters which serve as summaries of their members’ movement. Based on resource restrictions, members within clusters may be selectively discarded, while their locations are being approximated by their respective moving clusters. Our experimental study illustrates the performance gains achieved by our load-shedding framework and the tradeoff between the amount of data shed and the result accuracy.
Keywords
- Data Stream
- Cluster Member
- Continuous Query
- Location Update
- Query Answer
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Babcock, B., Datar, M., Motwani, R.: Load shedding techniques for data stream systems. In: MPDS: Workshop on Management and Processing of Data Streams (2003)
Babcock, B., Datar, M., Motwani, R.: Load shedding for aggregation queries over data streams. In: ICDE, pp. 350–361 (2004)
Barbará, D., DuMouchel, W., et al.: The new jersey data reduction report. IEEE Data Eng. Bull. 20(4) (1997)
Bolch, G., et al.: Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications. John Wiley and Sons, Chichester (1998)
Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)
Carney, D., Çetintemel, U., et al.: Monitoring streams - a new class of data management applications. In: VLDB, pp. 215–226 (2002)
Chu, S.: The influence of urban elements on time-pattern of pedestrian movement. In: The 6th Int. Conf. on Walking in the 21st Cent. (2005)
Das, A., Gehrke, J., et al.: Semantic approximation of data stream joins. IEEE Trans. Knowl. Data Eng. 17(1), 44–59 (2005)
Das, A., Gehrke, J., Riedewald, M.: Approximate join processing over data streams. In: SIGMOD, pp. 40–51 (2003)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2000)
Hartigan, J.A.: Clustering Algorithms. John Wiley and Sons, Chichester (1975)
Jain, A.K., Murthy, M.N., Flynn, P.J.: Data clustering: A review. Technical Report MSU-CSE-00-16, Department of Computer Science, Michigan State University, East Lansing, Michigan (August 2000)
Jain, R.K.: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley and Sons, Chichester (1991)
Kalnis, P., Mamoulis, N., Bakiras, S.: On Discovering Moving Clusters in Spatio-temporal Data. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)
Kurose, J.F., Ross, K.: Computer Networking: A Top-Down Approach Featuring the Internet. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)
Liu, B., Zhu, Y., Rundensteiner, E.: Run-time operator state spilling for memory intensive continuous queries. In: SIGMOD Conference, pp. 347–358 (2006)
Mokbel, M.F., Aref, W.G.: Sole: Scalable online execution of continuous queries on spatio-temporal data streams. tr csd-05-016. Technical report, Purdue University (2005)
Mokbel, M.F., Aref, W.G., Hambrusch, S.E., Prabhakar, S.: Towards scalable location-aware services: requirements and research issues. In: GIS, pp. 110–117 (2003)
Mokbel, M.F., Xiong, X., et al.: Sina: Scalable incremental processing of continuous queries in spatio-temporal databases. In: SIGMOD, pp. 623–634 (2004)
Nehme, R.V., Rundensteiner, E.A.: SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-temporal Queries on Moving Objects. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 1001–1019. Springer, Heidelberg (2006)
Prabhakar, S., et al.: Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Trans. Computers 51(10) (2002)
Reiss, F., Hellerstein, J.M.: Data triage: An adaptive architecture for load shedding in telegraphcq. In: ICDE, pp. 155–156 (2005)
Rundensteiner, E.A., Ding, L., et al.: Cape: Continuous query engine with heterogeneous-grained adaptivity. In: VLDB, pp. 1353–1356 (2004)
Shah, M., Hellerstein, J., et al.: Flux: An adaptive partitioning operator for continuous query systems. cs-02-1205. Technical report, U.C. Berkeley (2002)
Sistla, A.P., Wolfson, O., et al.: Modeling and querying moving objects. In: ICDE, pp. 422–432 (1997)
Tatbul, N.: Qos-driven load shedding on data streams. In: XMLDM, pp. 566–576 (2002)
Tatbul, N., Çetintemel, U., et al.: Load shedding in a data stream manager. In: VLDB, pp. 309–320 (2003)
Tatbul, N., Zdonik, S.B.: Window-aware load shedding for aggregation queries over data streams. In: VLDB, pp. 799–810 (2006)
Tu, Y.-C., Liu, S., Prabhakar, S., Yao, B.: Load shedding in stream databases: A control-based approach. In: VLDB, pp. 787–798 (2006)
Urhan, T., Franklin, M.J.: Xjoin: A reactively-scheduled pipelined join operator. IEEE Data Eng. Bull. 23(2) (2000)
Wolfson, O., Cao, H., Lin, H., Trajcevski, G., Zhang, F., Rishe, N.: Management of Dynamic Location Information in DOMINO. In: Jensen, C.S., Jeffery, K.G., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 769–771. Springer, Heidelberg (2002)
Xiong, X., Mokbel, M.F., et al.: Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp. 643–654 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nehme, R.V., Rundensteiner, E.A. (2007). ClusterSheddy: Load Shedding Using Moving Clusters over Spatio-temporal Data Streams. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-540-71703-4_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
eBook Packages: Computer ScienceComputer Science (R0)