Querying and mining data streams: you only get one look a tutorial

By Minos N. Garofalakis, Johannes Gehrke. ACM SIGMOD Conference, 2002

310 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/57159db86dfb5f2f2be8b12eab9b44cc950b3b2a

How researchers see this paper

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In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Finding recent frequent itemsets adaptively over online data streams (2003, background): “As a result, data stream processing should satisfy the following requirements [1].”
Data streams: algorithms and applications (2005, background): “[107] DIMACS gathered working groups on data streams.”
Density-based Projected Clustering over High Dimensional Data Streams (2012, background): “Data streams impose new challenges for the clustering problem since “it is usually impossible to store an entire data stream or to scan it multiple times due to its tremendous volume” [12].”
Differentially private summaries for sparse data (2012, background): “The topic of data summarization is vast, generating many surveys and books on different approaches to the problem [19, 8, 20].”
Bursty and Hierarchical Structure in Streams (2002, background): “( Garofalakis et al., 2002 )), where the data must be processed in a single pass with very limited storage.”
Twitter Heron: Stream Processing at Scale (2015, background): “Spouts are sources of input data (e.g. a stream of Tweets), and bolts are an abstraction to represent computation on the stream.”
Storm@twitter (2014, background): “[1, 2, 3, 4]), and borrows heavily from that line of thinking.”
A Survey of Classification Methods in Data Streams (2007, unknown): “While, specific methods for stream classification will be discussed later, it is useful to understand the broad characteristics of different methods which are used to adapt conventional classification techniques to the case of data streams.”
Foundations and Advances in Data Mining (2005, methodology): “There are many such logic views, for example, deductive database systems, Datalog [21], and Decision Logic [19] among others.”
An improved data stream summary: the count-min sketch and its applications (2004, methodology): “We refer the readers to surveys [28,3], tutorials [16], as well as the general literature.”
Differentially Private Publication of Sparse Data (2011, background): “Just storing this table requires terabytes, and generate 1012 random draws from the noise distribution is extremely slow.”
What's hot and what's not: tracking most frequent items dynamically (2003, methodology): “In Section 4, we present a preliminary experimental study of our algorithm using synthetic data as well as real network data addressing the application domain cited earlier and show that our algorithm is effective and practical.”