Issues in data stream management

By Lukasz Golab, M. Tamer Özsu. 2003

1,079 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

FOSTER: Feature Boosting and Compression for Class-Incremental Learning (2022, background): “The real world is constantly changing, with new concepts and categories continuously springing up [14,35,46,48].”
Co-Transport for Class-Incremental Learning (2021, background): “in our ever-changing world, data often comes from an open environment, which is with stream format [11] or available temporarily due to privacy issues [9].”
PyCIL: a Python toolbox for class-incremental learning (2021, background): “However, in our ever-changing world, data often comes from open environment, which is with stream format (Golab and Özsu, 2003) or available temporarily due to privacy issues (Delange et al.”
Processing flows of information: From data stream to complex event processing (2012, background): “There exist a few works that describe and classify such systems, emphasizing common features and ¨ open issues [Babcock et al. 2002; Golab and Ozsu 2003].”
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].”
A review on time series data mining (2011, background): “R-tree proposed by Guttman (1984)) for the representation.”
A Survey of Classification Methods in Data Streams (2007, neutral): “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.”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “As surveyed in [4, 15], there has been a great deal of recent interest in developing novel data management techniques and adapting traditional database technology to the data stream model; Cougar [32], Aurora [7], TelegraphCQ [8, 22], and STREAM [4, 23] are some examples.”
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 updata”
Time-series data mining (2012, background): “Several papers review research issues for data streams mining [Gaber et al. 2005] or management [Golab and Ozsu 2003].”
Mining data streams: a review (2005, background): “The following is a discussion of both addressed and open research issues [17, 21, 26, 37].”
Efficient Computation of Frequent and Top-k Elements in Data Streams (2005, neutral): “[27] reviewed the literature.”
Deep Class-Incremental Learning: A Survey (2023, background): “However, training data is often with stream format in the open world [6], [7].”
The CQL continuous query language: semantic foundations and query execution (2006, background): “There has been a considerable surge of research in many aspects of processing continuous queries over unbounded data streams [24, 25].”
Density-based clustering for real-time stream data (2007, background): “Examples such as the transactions in a supermarket and the phone records of a mobile phone company illustrate that, the raw data typically have massive volume and can only be scanned once following the temporal order [7, 8].”
Theory and Practice of Bloom Filters for Distributed Systems (2012, background): “Duplicate element detection is an important problem, especially pertaining to data stream processing [36].”