Continuously adaptive continuous queries over streams

By Samuel Madden, Mehul A. Shah, Joseph M Hellerstein, Vijayshankar Raman. ACM SIGMOD Conference, 2002

680 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/4350f7972bde4655c29d297b732edb67f36eb827

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Citations in the corpus

The CQL continuous query language: semantic foundations and query execution (2006, methodology): “example, more sophisticated techniques such as [6, 20, 32] should be applicable.”
Streaming Queries over Streaming Data (2002, methodology): “There is, however, a subtle difference between the flavors of Eddy as described by Avnur and Hellerstein [AH00] and Madden et al. [ MSHR02 ] and the PSoup Eddy.”
Maté: a tiny virtual machine for sensor networks (2002, background): “Being able to reprogram a network quickly is also very useful for issues such as data aggregation and adaptive query processing [22]; dynamically installing aggregation functions can provide a more flexible system than a predefined set of aggregates.”
A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data (2011, methodology): “Data stream management systems (DSMSs) such as STREAM [4], Aurora [15], and TelegraphCQ [26] were built to overcome limitations of traditional database management systems in supporting streaming applications [20].”
Distributed top-k monitoring (2003, background): “…Chris Olston Stanford University {babcock, olston}@cs.stanford.edu ABSTRACT The querying and analysis of data streams has been a topic of much recent interest, motivated by applications from the .elds of networking, web usage analysis, sensor instrumentation, telecommunications, and others.”
Approximate join processing over data streams (2003, background): “Adaptive query processing systems like Telegraph [18], NiagaraCQ [9], sensor database systems [5] and adaptive techniques as proposed in [7, 20, 24] aim at providing the best possible query performance in continuously changing environments like the Internet.”
Issues in data stream management (2003, methodology): “Two approaches have been proposed to execute similar continuous queries together: sharing query plans [17] and indexing query predicates [13, 52].”
Aurora: a new model and architecture for data stream management (2003, background): “Recent work [19] extended Eddies to support the processing of queries over streams, mainly by permitting Eddies systems to process multiple queries simultaneously and for unbounded lengths of time.”
Fjording the stream: an architecture for queries over streaming sensor data (2002, methodology): “In [16], we discuss techniques for combining joins to overcome this limitation.”
The design of an acquisitional query processor for sensor networks (2003, methodology): “This earlier work, as well as our own work on continuous query processing [36], inspired the periodic and event-driven features of TinyDB.”
Query Processing, Approximation, and Resource Management in a Data Stream Management System (2003, methodology): “Eddies [2, 13] uses a scheduling criterion similar to our per-operator greedy approach, but for routing individual tuples through operator queues rather than scheduling the execution of operators.”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “TelegraphCQ is designed for adaptive query processing , where query plans are re-ordered throughout the lifetime of a continuous query in response to changes in the execution environment (e.g. fluctuating stream arrival rates).”
Delay aware querying with Seaweed (2006, neutral): “Many large-scale systems route tuples through longstanding pre-installed queries [2, 3, 10, 13, 23, 30].”
Models and issues in data stream systems (2002, background): “In the next two subsections, we will touch upon several approaches to approximation, some of which are peculiar to the data stream model of computation.”
Exploiting Punctuation Semantics in Continuous Data Streams (2003, background): “Additionally, they have enhanced their adaptive query processing system [3] to support queries over data streams [17].”
TinyDB: an acquisitional query processing system for sensor networks (2005, methodology): “RELATED WORK There has been some recent publication in the database and systems communities on query processing in sensor networks [Intanagonwiwat et al. 2000; Madden et al. 2002a; Bonnet et al. 2001; Madden and Franklin 2002; Yao and Gehrke 2002].”
Maximizing the Output Rate of Multi-Way Join Queries over Streaming Information Sources (2003, result): “The MJoin approach is similar in some respects to SteMs [9].”