Abstract
Data stream and probabilistic data have been recently considered noticeably in isolation. However, there are many applications including sensor data management systems and object monitoring systems which need both issues in tandem. The existence of complex correlations and lineages prevents Probabilistic DBMSs (PDBMSs) from continuously querying temporal positioning and sensed data. Our main contribution is developing a new system to continuously run monitoring queries on probabilistic data streams with a satisfactory fast speed, while being faithful to correlations and uncertainty aspects of data. We designed a new data model for probabilistic data streams. We also presented new query operators to implement threshold SPJ queries with aggregation (SPJA queries). In addition and most importantly, we build a java-based working system, called Xtream, which supports uncertainty from input data streams to final query results. Unlike probabilistic databases, the data-driven design of Xtream makes it possible to continuously query high-volumes of bursty probabilistic data streams. In this paper, after reviewing main characteristics and motivating applications for probabilistic data streams, we present our new data model. Then we focus on algorithms and approximations for basic operators (select, project, join, and aggregate). Finally, we compare our prototype with Orion the only existing probabilistic DBMS that supports continuous distributions. Our experiments demonstrate how Xtream outperforms Orion w.r.t. efficiency metrics such as tuple latency (response time) and throughput as well as accuracy, which are critical parameters in any probabilistic data stream management system.
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Dezfuli, M.G., Haghjoo, M.S. (2012). Xtream: A System for Continuous Querying over Uncertain Data Streams. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_1
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DOI: https://doi.org/10.1007/978-3-642-33362-0_1
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