Results 41 to 50 of about 935,434 (195)

Variability in Data Streams [PDF]

Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2016
We consider the problem of tracking with small relative error an integer function $f(n)$ defined by a distributed update stream $f'(n)$. Existing streaming algorithms with worst-case guarantees for this problem assume $f(n)$ to be monotone; there are very large lower bounds on the space requirements for summarizing a distributed non-monotonic stream ...
Felber, David, Ostrovsky, Rafail
openaire   +2 more sources

Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table [PDF]

International Journal of Computational Intelligence Systems, 2014
Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication.
Le Wang, Lin Feng, Bo Jin
doaj   +1 more source

Mining developer communication data streams

, 2014
This paper explores the concepts of modelling a software development project as a process that results in the creation of a continuous stream of data.
Connor, Andy M., Finlay, Jacqui, Pears, Russel   +2 more
core   +1 more source

A review on data stream classification [PDF]

, 2018
At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science.
A. A Haneen, A. Noraziah, Aggarwal C.C., Aggarwal C.C., Amini A., Amini A., Amini A., Ankerst M., Boden B., Cao F., Chen Y., Esfandani G., Forestiero A., Hu W., Huang T.-Q., Kholghi M., Mohd Helmy Abd Wahab, Nakata Y., Namadchian A., Rajaraman A., Sun Z., Xiong Z.   +21 more
core   +2 more sources

Statistical data reduction for streaming data [PDF]

2017 New York Scientific Data Summit (NYSDS), 2017
Bulk of the streaming data from scientific simulations and experiments consists of numerical values, and these values often change in unpredictable ways over a short time horizon. Such data values are known to be hard to compress, however, much of the random fluctuation is not essential to the scientific application and could therefore be removed ...
Wu, Kesheng, Lee, Dongeun, Sim, Alex, Choi, Jaesik   +3 more
openaire   +3 more sources

Batch-Incremental Learning for Mining Data Streams [PDF]

, 2004
The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival.
Bainbridge, David, Holmes, Geoffrey, Kirkby, Richard Brendon   +2 more
core   +1 more source

Stellar Streams as Probes of Dark Halo Mass and Morphology: A Bayesian Reconstruction

, 2011
Tidal streams provide a powerful tool by means of which the matter distribution of the dark matter halos of their host galaxies can be studied. However, the analysis is not straightforward because streams do not delineate orbits, and for most streams ...
A. Varghese, Allgood, Bailin, Belokurov, Bett, Binney, Bullock, Carlberg, Combes, Conn, Conn, Conn, Debattista, Dehnen, Dehnen, Dehnen, Dubinski, Dubinski, Dubinski, Eyre, Eyre, Frenk, G. F. Lewis, Gregory, Grillmair, Grillmair, Helmi, Hoekstra, Ibata, Ibata, Ibata, Iodice, Jing, Kazantzidis, Koposov, Kuijken, Law, Law, Mackey, Majewski, Mandelbaum, Martínez-Delgado, Martínez-Delgado, Martínez-Delgado, Mayer, McConnachie, Merrifield, Mouhcine, Navarro, Newberg, Odenkirchen, Odenkirchen, Olling, O’Brien, O’Brien, O’Brien, O’Brien, Pfenniger, R. Ibata, Read, Sackett, Sackett, Springel, Teuben, White   +64 more
core   +1 more source

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

International Journal of Digital Earth, 2017
Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities.
Sergio Trilles, Òscar Belmonte, Sven Schade, Joaquìn Huerta   +3 more
doaj   +1 more source

Monitoring Threshold Functions over Distributed Data Streams with Node Dependent Constraints

Algorithms, 2012
Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed ...
Yaakov Malinovsky, Jacob Kogan
doaj   +1 more source

A Cross-Modal Dynamic Attention Neural Architecture to Detect Anomalies in Data Streams from Smart Communication Environments

Applied Sciences, 2023
Detecting anomalies in data streams from smart communication environments is a challenging problem that can benefit from novel learning techniques. The Attention Mechanism is a very promising architecture for addressing this problem.
Konstantinos Demertzis, Konstantinos Rantos, Lykourgos Magafas, Lazaros Iliadis   +3 more
doaj   +1 more source