冒失
发表于 2025-3-26 23:40:13
A Survey of Synopsis Construction in Data Streams,ill provide a survey of the key synopsis techniques, and the mining techniques supported by such methods. We will discuss the challenges and tradeoffs associated with using different kinds of techniques, and the important research directions for synopsis construction.
MANIA
发表于 2025-3-27 02:17:51
Algorithms for Distributed Data Stream Mining,sure to the literature and illustrates the behavior of this class of algorithms by exploring two very different types of techniques—one for the peer-to-peer and another for the hierarchical distributed environment. The chapter also briefly discusses several different applications of these algorithms.
概观
发表于 2025-3-27 05:45:41
Dimensionality Reduction and Forecasting on Streams,do this quickly, with no buffering of stream values and without comparing pairs of streams. Moreover, it is any-time, single pass, and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies, to do efficient forecasting and, more generally, to dramatically simplify further data processing.
蛤肉
发表于 2025-3-27 11:38:08
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前奏曲
发表于 2025-3-27 17:06:32
R. Gabasov,N. V. Balashevich,F. M. Kirillova terms):At any time ., the set of output tuples generated thus far by the join betweentwo streams .. and .. should be the same as the result of the relational (non-streaming) join between the sets of input tuples that have arrived thus far in ..and ...
demote
发表于 2025-3-27 18:05:31
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打包
发表于 2025-3-28 00:01:31
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1FAWN
发表于 2025-3-28 02:41:58
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anniversary
发表于 2025-3-28 09:27:46
https://doi.org/10.1007/978-1-0716-3230-7ering as a general summarization technology to solve data mining problems on streams. Our discussion illustrates the importance of our approach for a variety of mining problems in the data stream domain.
jettison
发表于 2025-3-28 11:41:21
Sevdalina Kandilarova,Igor Riečanskýpective, it is a much more challenging problem in the data stream domain. In this chapter, we will re-visit the problem of classification from the data stream perspective. The techniques for this problem need to be thoroughly re-designed to address the issue of resource constraints and concept drift