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Titlebook: Data Streams; Models and Algorithm Charu C. Aggarwal Book 2007 Springer-Verlag US 2007 algorithm.algorithms.data.data streams.database.freq

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樓主: ETHOS
51#
發(fā)表于 2025-3-30 11:30:29 | 只看該作者
52#
發(fā)表于 2025-3-30 15:25:11 | 只看該作者
The Sliding-Window Computation Model and Results,l and pertinent than older data. In such cases, we would like to answer questions about the data only over the last . most recent data elements (. is a parameter). We formalize this model of computation and answer questions about how much space and computation time is required to solve certain problems under the sliding-window model.
53#
發(fā)表于 2025-3-30 19:31:38 | 只看該作者
54#
發(fā)表于 2025-3-30 21:56:25 | 只看該作者
https://doi.org/10.1007/978-1-0716-3230-7ay. Many existing data mining methods cannot be applied directly on data streams because of the fact that the data needs to be mined in one pass. Furthermore, data streams show a considerable amount of temporal locality because of which a direct application of the existing methods may lead to mislea
55#
發(fā)表于 2025-3-31 02:07:47 | 只看該作者
Sevdalina Kandilarova,Igor Rie?anskyto as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. Most conventional data mining techniques have to be adapted to run in a streaming environment, because of the underlying resource constraints in
56#
發(fā)表于 2025-3-31 06:14:18 | 只看該作者
57#
發(fā)表于 2025-3-31 09:46:23 | 只看該作者
58#
發(fā)表于 2025-3-31 15:44:24 | 只看該作者
António R.C. Paiva,Il Park,José C. Príncipeant characteristic: .. To discover high-level dynamic and evolving characteristics, one may need to perform multi-level, multi-dimensional on-line analytical processing (OLAP) of stream data. Such necessity calls for the investigation of new architectures that may facilitate on-line analytical proce
59#
發(fā)表于 2025-3-31 17:37:02 | 只看該作者
Dylan A. Simon,Nathaniel D. Daw may vary over time. In this chapter, we focus on one particular type of adaptivity: the ability to gracefully degrade performance via “l(fā)oad shedding” (dropping unprocessed tuples to reduce system load) when the demands placed on the system cannot be met in full given available resources. Focusing o
60#
發(fā)表于 2025-4-1 00:47:42 | 只看該作者
Zeb Kurth-Nelson,A. David Redishl and pertinent than older data. In such cases, we would like to answer questions about the data only over the last . most recent data elements (. is a parameter). We formalize this model of computation and answer questions about how much space and computation time is required to solve certain probl
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