標(biāo)題: Titlebook: Data Streams; Models and Algorithm Charu C. Aggarwal Book 2007 Springer-Verlag US 2007 algorithm.algorithms.data.data streams.database.freq [打印本頁(yè)] 作者: ETHOS 時(shí)間: 2025-3-21 18:35
書目名稱Data Streams影響因子(影響力)
書目名稱Data Streams影響因子(影響力)學(xué)科排名
書目名稱Data Streams網(wǎng)絡(luò)公開度
書目名稱Data Streams網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Streams被引頻次
書目名稱Data Streams被引頻次學(xué)科排名
書目名稱Data Streams年度引用
書目名稱Data Streams年度引用學(xué)科排名
書目名稱Data Streams讀者反饋
書目名稱Data Streams讀者反饋學(xué)科排名
作者: 愚蠢人 時(shí)間: 2025-3-21 20:28 作者: 手術(shù)刀 時(shí)間: 2025-3-22 01:44
A Survey of Classification Methods in Data Streams,to 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作者: caldron 時(shí)間: 2025-3-22 07:42
Frequent Pattern Mining in Data Streams, data streams have attracted a lot of research interests. Compared with other streaming queries, frequent pattern mining poses great challenges due to high memory and computational costs, and accuracy requirement of the mining results..In this chapter, we overview the state-of-art techniques to mine作者: Amenable 時(shí)間: 2025-3-22 11:41 作者: 線 時(shí)間: 2025-3-22 16:25
Multi-Dimensional Analysis of Data Streams Using Stream Cubes,ant 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作者: 線 時(shí)間: 2025-3-22 18:22 作者: Fluctuate 時(shí)間: 2025-3-22 22:26
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 probl作者: ADORE 時(shí)間: 2025-3-23 03:52
A Survey of Synopsis Construction in Data Streams,ining algorithms require efficient execution which can be difficult to achieve with a fast data stream. In many cases, it may be acceptable to generate . for such problems. In recent years a number of . have been developed, which can be used in conjunction with a variety of mining and query processi作者: 大約冬季 時(shí)間: 2025-3-23 08:54 作者: epicardium 時(shí)間: 2025-3-23 10:15 作者: NEX 時(shí)間: 2025-3-23 15:12 作者: 尾隨 時(shí)間: 2025-3-23 18:34 作者: SLAY 時(shí)間: 2025-3-23 22:37 作者: 迅速飛過(guò) 時(shí)間: 2025-3-24 04:20
A Survey of Stream Processing Problems and Techniques in Sensor Networks,nsor nodes are capable of communicating their readings, typically through wireless radio. Sensor nodes produce streams of data, that have to be processed in-situ, by the node itself, or to be transmitted through the network, and analyzed offline. In this chapter we describe recently proposed, effici作者: 臥虎藏龍 時(shí)間: 2025-3-24 08:19
1386-2944 ata Streams: Models and Algorithms primarily discusses issues related to the mining aspects of?data streams.? Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using 作者: deadlock 時(shí)間: 2025-3-24 14:18
https://doi.org/10.1007/978-3-0348-8497-6do 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.作者: NAG 時(shí)間: 2025-3-24 17:36
Computational Phonogram Archivingsed in-situ, by the node itself, or to be transmitted through the network, and analyzed offline. In this chapter we describe recently proposed, efficient distributed techniques for processing streams of data collected with a network of sensors.作者: meditation 時(shí)間: 2025-3-24 21:16
Book 2007 makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams...This volume covers mining asp作者: Indolent 時(shí)間: 2025-3-25 02:22
A Survey of Join Processing in Data Streams, 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 ...作者: Ganglion-Cyst 時(shí)間: 2025-3-25 06:49 作者: Dorsal-Kyphosis 時(shí)間: 2025-3-25 08:58 作者: 包庇 時(shí)間: 2025-3-25 11:49 作者: Innovative 時(shí)間: 2025-3-25 17:40 作者: 擴(kuò)音器 時(shí)間: 2025-3-25 20:04 作者: 鴿子 時(shí)間: 2025-3-26 01:57 作者: 截?cái)?nbsp; 時(shí)間: 2025-3-26 06:03 作者: Insatiable 時(shí)間: 2025-3-26 10:34
Lecture Notes in Computer Sciencesure 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.作者: Coordinate 時(shí)間: 2025-3-26 14:35 作者: ordain 時(shí)間: 2025-3-26 17:48 作者: 冒失 時(shí)間: 2025-3-26 23:40
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.作者: MANIA 時(shí)間: 2025-3-27 02:17
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.作者: 概觀 時(shí)間: 2025-3-27 05:45
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.作者: 蛤肉 時(shí)間: 2025-3-27 11:38 作者: 前奏曲 時(shí)間: 2025-3-27 17:06
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 ...作者: demote 時(shí)間: 2025-3-27 18:05 作者: 打包 時(shí)間: 2025-3-28 00:01 作者: 1FAWN 時(shí)間: 2025-3-28 02:41 作者: anniversary 時(shí)間: 2025-3-28 09:27
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.作者: jettison 時(shí)間: 2025-3-28 11:41
Sevdalina Kandilarova,Igor Rie?anskypective, 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作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-28 18:19
Springer Optimization and Its Applicationsion. We also discuss the problem of change detection in the context of graph data, and illustrate that it may often be useful to determine communities of evolution in graph environments..The presence of evolution in data streams may also change the underlying data to the extent that the underlying d作者: ANTE 時(shí)間: 2025-3-28 18:55
António R.C. Paiva,Il Park,José C. Prínciped data in a multi-resolution model: The more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage requirements of time-related data and adapts nicely to the data analysis tasks commonly encountered作者: endocardium 時(shí)間: 2025-3-29 01:15 作者: exacerbate 時(shí)間: 2025-3-29 05:23 作者: 思想上升 時(shí)間: 2025-3-29 09:57 作者: 即席 時(shí)間: 2025-3-29 14:39 作者: AUGER 時(shí)間: 2025-3-29 17:16
Multi-Dimensional Analysis of Data Streams Using Stream Cubes,d data in a multi-resolution model: The more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage requirements of time-related data and adapts nicely to the data analysis tasks commonly encountered作者: Exuberance 時(shí)間: 2025-3-29 21:36 作者: CEDE 時(shí)間: 2025-3-30 00:50
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 problems under the sliding-window model.作者: Indurate 時(shí)間: 2025-3-30 06:18 作者: 虛弱 時(shí)間: 2025-3-30 11:30 作者: Pde5-Inhibitors 時(shí)間: 2025-3-30 15:25
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.作者: Fresco 時(shí)間: 2025-3-30 19:31 作者: ESO 時(shí)間: 2025-3-30 21:56
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作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-31 02:07
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作者: entitle 時(shí)間: 2025-3-31 06:14 作者: 漂亮才會(huì)豪華 時(shí)間: 2025-3-31 09:46 作者: Armada 時(shí)間: 2025-3-31 15:44
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作者: 有危險(xiǎn) 時(shí)間: 2025-3-31 17:37
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作者: overture 時(shí)間: 2025-4-1 00:47
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
