Results 261 to 270 of about 7,350 (305)

Cooperation mode selection and information sharing in a live streaming e-commerce supply chain with traffic data investment. [PDF]

PLoS One
Wang H, Zhu A, Yu L, Mu D, Meng Z.
europepmc   +1 more source

Quantum Online Streaming Algorithms with Logarithmic Memory

International Journal of Theoretical Physics, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kamil Khadiev, Aliya Khadieva, Khadiev Kamil   +2 more
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SubtStream: Online subtractive stream clustering algorithm

Concurrency and Computation: Practice and Experience, 2022
AbstractReal‐time stream data processing has gained high importance with the rapid rise of big data trends in different areas such as social media, finance, business, science, and bioinformatics. Stream data can be characterized as fast, unstable, and big data sets.
Musa Milli, Hasan Bulut
openaire   +1 more source

Online ensemble learning algorithm for imbalanced data stream

Applied Soft Computing, 2021
Abstract In many practical applications, due to the inability to collect complete training data sets at one time, the adaptability of the classifier is poor. Online ensemble learning can better solve this problem. However, most of the data streams are imbalanced.
Hongle Du, Yan Zhang, Ke Gang, Lin Zhang 0038, Yeh-Cheng Chen   +4 more
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A new evolving clustering algorithm for online data streams

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2016
In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations.
Clauber Gomes Bezerra, Bruno Sielly Jales Costa, Luiz Affonso Guedes, Plamen Parvanov Angelov   +3 more
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Online Algorithms for Caching Multimedia Streams

2000
We consider the problem of caching multimedia streams in the internet. We use the dynamic caching framework of Dan et al. and Hofmann et al.. We define a novel performance metric based on the maximum number of simultaneous cache misses, andp resent near-optimal on-line algorithms for determining which parts of the streams should be cachedat any point ...
Matthew Andrews, Kamesh Munagala
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An Improved Online Stream Data Clustering Algorithm

2012 Second International Conference on Business Computing and Global Informatization, 2012
The stream data mining is a hot research topic in recent years. In order to improve the efficiency of stream data mining, this paper designs an online stream data clustering algorithm IStrAP. IStrAP considers the features of stream data, such as potentially infinity, rapidness, and inability to scan historical data repeatedly, and introduces a method ...
Lingjuan Li, Xiong Li
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A Novel HOSFS Algorithm for Online Streaming Feature Selection

2020 International Conference on System, Computation, Automation and Networking (ICSCAN), 2020
In recent days, Data stream mining is important for many of the real time and IOT based applications. Online feature selection is the one big topic of data stream mining which attracted researchers with intensive interest. This technique reduces the dimensionality of the streaming features by excluding inappropriate and redundant features.
S. Sandhiya, U. Palani
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Algorithm 908

ACM Transactions on Mathematical Software, 2010
We present a novel, online algorithm for exact summation of a stream of floating-point numbers. By “online” we mean that the algorithm needs to see only one input at a time, and can take an arbitrary length input stream of such inputs while requiring only constant memory. By “exact” we mean that the sum of the internal array of our algorithm is exactly
Yongkang Zhu, Wayne B. Hayes
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Algorithm for Online Detection of the Data Stream Based on Distance

2016
To address the inaccuracy and high time complexity of traditional data stream mining technology, this paper introduces a new algorithm of date detection based on k-distance to pruning and comentropy to detect sliding windows. When the data fills the current window, the k-distance of the data is used to prune all data in the pruning time.
Liya Yu, Jie Hu 0016, Zhonghe Wei, Guanci Yang   +3 more
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