Models and issues in data stream systems

By Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, Jennifer Widom. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2002

3,026 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e83a416fa52c3ecb22b0e1419a1f5ce4c9467155

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Citations in the corpus

A Framework for Projected Clustering of High Dimensional Data Streams (2004, methodology): “While the problem of clustering has recently been studied in the data stream environment [3] [8] [11], these methods are for the case of full dimensional clustering.”
Efficient Computation of Frequent and Top-k Elements in Data Streams (2005, background): “proximate stream handling algorithms that incrementally analyze arriving data in one pass, answer approximate queries, and store summaries for future usage [4].”
Towards Expressive Publish/Subscribe Systems (2006, background): “Traditionally this is performed by sharing operators and query results [7, 8, 11, 20].”
Mining Frequent Patterns in Data Streams at Multiple Time Granularities (2002, background): “However, join is a typical blocking operator [Babcock et al.2002] which cannot be performed over stream data since one can only observe at any moment a very limited size window of a data stream.”
Chain: operator scheduling for memory minimization in data stream systems (2003, background): “…that limit the applicability of standard relational database technology: (1) the volume of data is extremely high, and (2) on the basis of the data, decisions are arrived at and acted upon in close * Supported in part by a Rambus Corporation Stanford Graduate Fellowship and NSF…”
A Framework for Clustering Evolving Data Streams (2003, background): “The data stream problem has been extensively researched in recent years because of the large number of relevant applications [1, 3, 6, 8, 13].”
Query Processing, Approximation, and Resource Management in a Data Stream Management System (2003, background): “We refer the reader to our recent survey paper [3], which provides extensive coverage of related work.”
High-availability algorithms for distributed stream processing (2005, background): “emerging class of applications that require sophisticatedan timely processing of high-volume data streams, often originating in distributed environments.”
Learning from Time-Changing Data with Adaptive Windowing (2007, methodology): “To reduce these costs we present a new version ADWIN2 using ideas developed in data stream algorithmics [1, 15, 2, 6] to find a good cutpoint quickly.”
Maintaining variance and k-medians over data stream windows (2003, methodology): “The sliding window model [2, 7] is the other commonly-used mechanism for discounting stale data.”
The CQL continuous query language: semantic foundations and query execution (2006, methodology): “The stream-sample operator currently is used only for system-managed load shedding [9], although we also intend to implement a sample clause in our query language [8].”
Data streams: algorithms and applications (2005, background): “The associated pape r [11] is well worth reading, in particular, for a database perspective.”
Open challenges for data stream mining research (2014, methodology): “Data stream mining is a research field that studies methods and algorithms for extracting knowledge from volatile streaming data [14; 5; 1].”
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].”
MillWheel: Fault-Tolerant Stream Processing at Internet Scale (2013, background): “Streaming SQL systems [1] [2] [5] [6] [21] [24] provide succinct and simple solutions to many streaming problems, but intuitive state abstractions and complex application logic (e.g. matrix multiplication) are more naturally expressed us-ing the operational ﬂow of an imperative language rather than…”
Data Streams: Models and Algorithms (Advances in Database Systems) (2006, background): “[8] Aggarwal C. (2006). On Biased Reservoir Sampling in the presence of stream evolution.”
No pane, no gain: efficient evaluation of sliding-window aggregates over data streams (2005, background): “Managing and processing streams gives rise to challenges that have been extensively discussed and recognized [3, 4, 6, 7, 12].”
Efficient elastic burst detection in data streams (2003, background): “An excellent survey of models and issues in data stream can be found in [3].”