StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time

By Yunyue Zhu, Dennis E. Shasha. Very Large Data Bases Conference, 2002

783 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/17e9e234637aadd93fd91f0e52383b67719b8972

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Citations in the corpus

Warping indexes with envelope transforms for query by humming (2003, methodology): “In addition to DFT [1, 25, 35], Discrete Wavelet Transform (DWT) [6, 30, 23], Singular Value Decomposition (SVD)[16], Piecewise Aggregate Approximation (PAA)[32, 13] and Adaptive Piecewise Constant Approximation [14] approaches have also been proposed for similarity searching.”
Online Machine Learning in Big Data Streams (2018, background): “ in multiple, rapid, time-varying, possibly unpredictable, and unbounded streams appears to yield some fundamentally new research problems. Examples of such applications include ﬁnancial applications [241], network monitoring [18, 1], security, sensor networks [66], Twitter analysis [25], and more [”
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].”
Forecasting Big Time Series: Old and New (2018, background): “Time series data occur naturally in countless domains including medical analysis [24], sensor network monitoring [27], ﬁnancial analysis [43], social activity mining [23, 22] and database systems [35, 19].”
k-Shape: Efficient and Accurate Clustering of Time Series (2015, methodology): “) Finally, cross-correlation was used to transform fMRI data into features for clustering [31], as well as for stream mining, pattern extraction, and timeseries monitoring [61, 73, 85, 90].”
Mining data streams: a review (2005, methodology): “[54] Y. Zhu and D. Shasha.”
Data stream clustering: A survey (2013, methodology): “There are three commonly studied models in data streams [Zhu and Shasha 2002]: (i) sliding windows; (ii) damped windows; and (iii) landmark windows.”
AdaRNN: Adaptive Learning and Forecasting of Time Series (2021, background): “Time series (TS) data occur naturally in countless domains including financial analysis [55], medical analysis [29], weather condition prediction [46], and renewable energy production [5].”
Streaming Pattern Discovery in Multiple Time-Series (2005, methodology): “StatStream [41] uses the DFT to summarise streams within a finite window and then compute the highest pairwise correlations among all pairs of streams, at each timestamp.”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “Applications where information naturally occurs as a stream of data values include sensor data processing [7, 32], Internet traffic analysis [13, 24], financial tickers [9, 33], and analysis of various transaction logs such as Web server logs and telephone call records [10].”
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (2020, background): “2014), economics and finance forecasting (Zhu and Shasha 2002), evolution of agriecosystems, climate change forecasting, and variations in air pollution.”
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting (2023, background): “Researches related to time series forecasting are widely applied in the real world, such as sensor network monitoring (Papadimitriou & Yu., 2006), weather forecasting, economics and finance (Zhu & Shasha, 2002), and disease propagation analysis (Matsubara et al., 2014) and electricity forecasting.”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, background): “It implements the sliding window and the dampened window models (Zhu and Shasha 2002) which keep a user-specified number (window length) of the most recent data points of the stream.”
Flux: an adaptive partitioning operator for continuous query systems (2003, background): “Variants of this operator exist [31] where both th e statistic computed and the notion of recent history can vary .”
Efficient Computation of Frequent and Top-k Elements in Data Streams (2005, background): “Currently, data stream management systems are used for monitoring click streams [31], stock tickers [11, 46], sensor readings [7], telephone call records [15], network packet traces [17], auction bidding patterns [2], traffic management [3], network-aware clustering [12], and security against DoS [1”