On computing correlated aggregates over continual data streams

By Johannes Gehrke, Flip Korn, Divesh Srivastava. ACM SIGMOD Conference, 2001

350 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/575265124fe11196c1e2e77a5466d48a988024d6

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In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Processing complex aggregate queries over data streams (2002, background): “ments for different problems; examples include quantile and orderstatistics computation [16, 21], estimating frequency moments and join sizes [3, 2], data clustering and decision-tree construction [10, 18], estimating correlated aggregates [13], and computing one-dimensional (i.”
Exploiting Punctuation Semantics in Continuous Data Streams (2003, background): “Many current stream processing systems [ 9 ], [16], [24] use fixed windows and sliding windows for the same reasons we propose punctuated streams: They regulate the size of state and they allow blocking operators to emit results before the end of the stream.”
Efficient elastic burst detection in data streams (2003, methodology): “There are three well-known window models that are the subjects of many research projects [10, 11, 12, 22].”
Evaluating window joins over unbounded streams (2003, background): “Recently a good deal of research has been conducted in this area: for example, stream sampling [27], updating summary data structures [36][40][37][41][38], and maintaining stream statistics [11].”
Streaming Queries over Streaming Data (2002, methodology): “Other recent research[LSM99, GKS01 , SH98] has focused on developing algorithms to perform specific functions on sequenced data.”
Models and issues in data stream systems (2002, background): “It is also a good approach when data streams are bursty.”
Data streams: algorithms and applications (2005, background): “For what f ’s and g’s (by sampling or otherwise) can such queries be approximated on data streams ? See [87] for the motivation.”
Monitoring Streams - A New Class of Data Management Applications (2002, background): “, [12]), data reduction, and summary techniques [6, 9], where result accuracy is traded for efficiency.”
Approximate Frequency Counts over Data Streams (2012, background): “Algorithms over data streams that pertain to aggregation include approximate quantiles [MRL99, GK01], Voptimal histograms [GKS01b], wavelet based aggregate queries [GKMS01, MVW00], and correlated aggregate queries [GKS01a].”
TelegraphCQ: Continuous Dataflow Processing for an Uncertain World (2003, background): “Two popular windowing schemes in the context of stream query processing are landmark and sliding windows [ GKS01 ].”
Issues in data stream management (2003, background): “In many cases, only an excerpt of a stream is of interest at any given time, giving rise to window models, which may be classified according the the following three criteria [14, 33]:”
Efficient decision tree construction on streaming data (2003, background): “More recently, the database community has focused on a new model of data processing, in which data arrives in the form of continuous streams [3, 4, 9, 12, 14, 16, 23, 30].”
StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time (2002, background): “In the spirit of the work in [10, 11], we generalize the three kinds of temporal spans for which the statistics of time series are calculated.”
Fjording the stream: an architecture for queries over streaming sensor data (2002, background): “For instance, [7] showed single pass algorithms to compute complex, correlated aggregates over sets of streams.”
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries (2001, methodology): “Within the database community, one-pass algorithms have been designed for getting median, quantiles and other order statistics [25, 17], correlated aggregate queries [15], mining [14], etc.”