Abstract
Modern datastream management system (DSMS) assume sensor measurements to be constant valued until an update is measured. They do not consider continuously changing measurement values, although a lot of real world scenarios exist that need this essential property. For instance, modern cars use sensors, like radar, to periodically detect dynamic objects like other vehicles. The state of these objects (position and bearing) changes continuously, so that it must be predicted between two measurements. Therefore, in our work we develop a new bi-temporal stream algebra for processing continuously changing stream data. One temporal dimension covers correct order of stream elements and the other covers continuously changing measurements. Our approach guarantees deterministic query results and correct optimizability. Our implementation shows that prediction functions can be processed very efficiently.
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References
Arasu, et al.: STREAM: The Standford Stream Data Manager. IEEE Data Engineering Bulletin 26(1) (2003)
Abadi, et al.: The Design of the Borealis Stream Processing Engine. In: CIDR 2005 (2005)
Chandrasekaran, et al.: TelegraphCQ: Continuous Dataflow Processing for an Uncertain World. In: CIDR 2003 (2003)
Krämer, J., Seeger, B.: Semantics and implementation of continuous sliding window queries over data streams. ACM Trans. on Database Syst. 34(1) (2009)
Morzy, M.: Mining frequent trajectories of moving objects for location prediction. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 667–680. Springer, Heidelberg (2007)
Goel, S., Imielinski, T.: Prediction-based monitoring in sensor networks: taking lessons from MPEG. SIGCOMM Comput. Commun. Rev. 31(5) (2001)
Lee, C.C., Chiang, Y.C., Shih, C.Y., Tsai, C.L.: Noisy time series prediction using m-estimator based robust radial basis function neural networks with growing and pruning techniques. Expert Syst. Appl. 36(3) (2009)
Ilarri, S., Wolfson, O., Mena, E., Illarramendi, A., Sistla, P.: A query processor for prediction-based monitoring of data streams. In: EDBT 2009. ACM, New York (2009)
Snodgrass, R.T., Kucera, H.: The TSQL2 Temporal Query Langauage. Kluwer Academic Publishers, Dordrecht (1995)
Bettini, C., Dyreson, C.E., Evans, W.S., Snodgrass, R.T., Wang, X.S.: A glossary of time granularity concepts. In: Temporal Databases, Dagstuhl (1997)
Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In: SIGMOD 2004. ACM, New York (2004)
Jacobi, J., Bolles, A., Grawunder, M., Nicklas, D., Appelrath, H.J.: A physical operator algebra for prioritized elements in data streams. Computer Science - Research and Development 1(1) (2009)
Brinkhoff, T.: Generating network-based moving objects. In: SSDBM 2000. IEEE Computer Society, Los Alamitos (2000)
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Bolles, A., Grawunder, M., Jacobi, J., Nicklas, D., Appelrath, H.J. (2010). Prediction Functions in Bi-temporal Datastreams. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15364-8_20
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DOI: https://doi.org/10.1007/978-3-642-15364-8_20
Publisher Name: Springer, Berlin, Heidelberg
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