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A windowed query operator breaks a data stream into possibly overlapping subsets of data and computes a result over each. Many stream systems can evaluate window aggregate queries. However, current stream systems suffer from a lack of an explicit definition of window semantics. As a result, their implementations unnecessarily confuse window definition with(More)
Windows queries are proving essential to data-stream processing. In this paper, we present an approach for evaluating <i>sliding-window</i> aggregate queries that reduces both space and computation time for query execution. Our approach divides overlapping windows into disjoint <i>panes,</i> computes sub-aggregates over each pane, and "rolls up" the(More)
Peer-to-peer (P2P) architectures are commonly used for file-sharing applications. The reasons for P2P's popularity in file sharing – fault tolerance, scalability, and ease of deployment – also make it a good model for distributed data management. In this paper, we introduce a scalable P2P framework for distributed data management applications using mutant(More)
What does a data stream mean? Much of the extensive work on query operators and query processing for data streams has proceeded without the benefit of an answer to this question. While such impreci-sion may be tolerable when dealing with simple cases, such as flat data, guaranteed physical order and element-wise operations, it can lead to ambiguities when(More)
Many stream-processing systems enforce an order on data streams during query evaluation to help unblock blocking operators and purge state from stateful operators. Such in-order processing (IOP) systems not only must enforce order on input streams, but also require that query operators preserve order. This order-preserving requirement constrains the(More)
To address increasing traffic congestion and its associated consequences, traffic managers are turning to intelligent transportation management. The <i>latte</i> project is extending data stream technology to handle queries that combine live streams with large data archives, motivated by needs in the Intelligent Transportation Systems (ITS) domain. In(More)
SQL Server 2012 introduced two innovations targeted for data warehousing workloads: column store indexes and batch (vectorized) processing mode. Together they greatly improve performance of typical data warehouse queries, routinely by 10X and in some cases by a 100X or more. The main limitations of the initial version are addressed in the upcoming release.(More)