The VLDB Journal (2004) 13: 317 / Digital Object Identiﬁer (DOI) 10.1007/s00778-004-0141-5 1 2,3 Johannes Gehrke , Joseph M. Hellerstein Department of Computer Science, Cornell University; Ithaca, NY 14853, USA; e-mail: firstname.lastname@example.org Computer Science Division, University of California, Berkeley; Berkeley, CA 94720, USA; e-mail: email@example.com Intel Research, Berkeley; Berkeley, CA; 94704, USA Published online: November 12, 2004 – c Springer-Verlag 2004 Data stream management techniques have been a hot research The fourth paper is an experience paper. It describes the area in the database community for the last 5 years. To our latest lessons from the design and implementation of the Au- call for papers for this special issue with a deadline of October rora stream processing engine, and it describes the authors’ 2003 we received 23 submissions that covered a wide range vision for their next system. of ongoing data stream research. In two rounds of review, The issue concludes with an article on data stream pro- we selected ﬁve papers that represent the diversity and depth cessing in sensor networks. Sensor nodes are different from of this research. Early work in data streams concentrated on traditional computers since energy is one of the limiting fac- developing efﬁcient algorithms for speciﬁc data stream queries tors. The authors propose two methods for saving energy. First, such as sampling, join size estimation, and quantiles. This they propose a group-aware network construction that mini- issue shows that current data stream research has matured and mizes network trafﬁc. Second, they allow queries to specify transcended pure algorithmic research to novel data types such that approximate query results (within user-speciﬁed bounds) as XML and to core systems issues. are sufﬁcient, a further opportunity to reduce trafﬁc. The stream considered in the ﬁrst paper consists of XML Overall, we believe that these papers are an excellent snap- user queries rather than traditional data records. The paper shot of the state of the data stream community as of early 2004, considers how to efﬁciently mine frequent XML query pat- and we hope that you will enjoy reading the papers as much terns. As it is not feasible to keep all queries in main memory, as we did. the authors give efﬁcient algorithms to incrementally maintain frequent user queries. Acknowledgements. We would like to thank Tamer Ozsu, our editor- The second paper considers how a data stream manage- in-chief, for his advice and support throughout the process, and we ment system can deal with load spikes by carefully scheduling would like to thank Stacey Shirk for administrative support. Our operators in the system. The suggested scheduling method, biggest thanks go to the authors whose contributions created the issue chain scheduling, keeps the output latency within a given that you are reading. bound while minimizing queuing memory. The third paper shows how to give approximate answers to aggregate queries over datasets undergoing constant change. In particular, this paper focuses on dealing with a stream that includes not only insertions of new data but also deletions of Johannes Gehrke old data. Joseph M. Hellerstein
The VLDB Journal – Springer Journals
Published: Dec 1, 2004
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