Continuous queries over data streams

By Shivnath Babu, Jennifer Widom. SGMD, 2001

837 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/616bd65d24ef364184f083fede314b59b434977c

How researchers see this paper

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Citations in the corpus

Monitoring Streams - A New Class of Data Management Applications (2002, background): “The Streams project [5] attempts to provide complete DBMS functionality along”
StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time (2002, background): “For the performance to be suÆciently good however, \Data Stream Management Systems" (DSMSs) [3], whatever their logical model, should exploit the following characteristics of the application:”
Exploiting Punctuation Semantics in Continuous Data Streams (2003, neutral): “Thus, [2] [4] [6] S means 2 È 4 È 6 È S.”
Data Streams: Models and Algorithms (Advances in Database Systems) (2006, background): “[I81 Guha S., Rastogi R., Shim K. (1998). CURE: An Efficient Clustering Algorithm for Large Databases.”
Evaluating window joins over unbounded streams (2003, background): “In [12], Babu and Widom proposed architecture for a general purpose stream data management system and identified research problems in continuous query processing over streams.”
Continuously adaptive continuous queries over streams (2002, background): “[9] is a “vision” paper presenting many high-level goals for continuous query systems over streams.”
Models and issues in data stream systems (2002, background): “The issues raised in this section will frame the discussion in the rest of the paper.”
Processing flows of information: From data stream to complex event processing (2012, methodology): “Probably the most complete model to describe DSMSs comes from the authors of Stream [Babu and Widom 2001]; it greatly in.uenced the design of our own model for a generic IFP system.”
Processing complex aggregate queries over data streams (2002, methodology): “Other related studies have proposed techniques for incrementally maintaining equi-depth histograms [14] and Haar wavelets [22], maintaining samples and simple statistics over sliding windows [8], as well as general, high-level architectures for stream database systems [ 4 ].”
Flexible time management in data stream systems (2004, background): “Flexible Time Management in Data Stream Systems* Utkarsh Srivastava Stanford University usriv@cs.stanford.edu ABSTRACT Continuous queries in a Data Stream Management System (DSMS) rely on time as a basis for windows on streams and for de.ning a consistent semantics for multiple streams and…”
Operator Scheduling in a Data Stream Manager (2003, methodology): “Similar to Aurora, the STREAM project [6] also attempts to provide comprehensive data stream management and processing functionality.”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “Whereas most crowdsourced data processing tasks have a relatively short request-response microtask format, the participant described something closer to a continuous query [29] in streaming database systems.”
Multi-Dimensional Regression Analysis of Time-Series Data Streams (2002, background): “Recently, several research papers have been published on the management and querying of stream data [6, 14, 15, 13], and data mining (classi cation and clustering) on stream data [20, 16].”
Diagnosing Changes in An Ontology Stream: A DL Reasoning Approach (2012, methodology): “Initially tackled by the Database community (Babu and Widom 2001), these challenges have been recently exposed in the semantic web as Copyright c© 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org).”