作者: Arteriography 時(shí)間: 2025-3-21 20:40 作者: Condyle 時(shí)間: 2025-3-22 01:34
Quantiles and Equi-depth Histograms over Streams the 99-percentile, or the quartiles of a set are examples of quantile queries. Many database optimization problems involve approximate quantile computations over large data sets. Query optimizers use quantile estimates to estimate the size of intermediate results and choose an efficient plan among 作者: 轉(zhuǎn)向 時(shí)間: 2025-3-22 08:10
Join Sizes, Frequency Moments, and Applicationslem is at the heart of a wide variety of other problems, both in databases/data streams and beyond, including approximating range-query aggregates, quantiles, and heavy-hitter elements, and building approximate histograms and wavelet representations. Our discussion focuses on efficient, sketch-based作者: JUST 時(shí)間: 2025-3-22 09:13
Top-, Frequent Item Maintenance over Streamsthat occur most frequently in one pass over the data stream using a small amount of storage space. Such problems arise in a variety of settings. For example, a search engine might be interested in gathering statistics about its query stream and in particular, identifying the most popular queries. An作者: 等待 時(shí)間: 2025-3-22 16:33 作者: 等待 時(shí)間: 2025-3-22 20:56 作者: 光滑 時(shí)間: 2025-3-22 22:31
Clustering Data Streamsch that, under some definition of “similarity,” similar items are in the same group and dissimilar items are in different groups. In this chapter we focus on clustering in a streaming scenario where a small number of data items are presented at a time and we cannot store all the data points. Thus, o作者: 規(guī)章 時(shí)間: 2025-3-23 01:54
Mining Decision Trees from Streams. Mining these continuous data streams brings unique opportunities, but also new challenges. We present a method that can semi-automatically enhance a wide class of existing learning algorithms so they can learn from such high-speed data streams in real time. The method works by sampling just enough作者: Accessible 時(shí)間: 2025-3-23 05:56 作者: humectant 時(shí)間: 2025-3-23 13:42 作者: 符合規(guī)定 時(shí)間: 2025-3-23 15:52 作者: 槍支 時(shí)間: 2025-3-23 20:50 作者: 肉體 時(shí)間: 2025-3-23 23:23 作者: 燈絲 時(shí)間: 2025-3-24 04:32 作者: 品嘗你的人 時(shí)間: 2025-3-24 07:06 作者: 弄臟 時(shí)間: 2025-3-24 11:22
Stable Distributions in Streaming Computationsow how such . norms can be efficiently estimated for massive vectors presented in the streaming model. This is achieved by making succinct . of the data, which can be used as synopses of the vectors they summarize.作者: acolyte 時(shí)間: 2025-3-24 15:31 作者: DUST 時(shí)間: 2025-3-24 21:47 作者: CON 時(shí)間: 2025-3-24 23:53 作者: Hyperopia 時(shí)間: 2025-3-25 05:07 作者: SLING 時(shí)間: 2025-3-25 08:23 作者: 紡織品 時(shí)間: 2025-3-25 14:36
Clustering Data Streamsocus on clustering in a streaming scenario where a small number of data items are presented at a time and we cannot store all the data points. Thus, our algorithms are restricted to a single pass. The space restriction is typically sublinear, ., where the number of input points is ..作者: photophobia 時(shí)間: 2025-3-25 16:06 作者: Outmoded 時(shí)間: 2025-3-25 21:20
Ron Elber,Benoit Roux,Roberto Olenderm. This chapter surveys some basic sampling and inference techniques for data streams. We focus on general methods for materializing a sample; later chapters provide specialized sampling methods for specific analytic tasks.作者: 抱狗不敢前 時(shí)間: 2025-3-26 02:36
https://doi.org/10.1007/978-3-319-60919-5other application is to detecting network anomalies by monitoring network traffic. We describe a variety of approaches that have been proposed to solve these problems. Our goal is to give a flavor of the various techniques that have been used in this area.作者: Kaleidoscope 時(shí)間: 2025-3-26 07:38
Multiscale Computational Materials Science data from the data stream to make each decision required by the learning process. The method is applicable to essentially any induction algorithm based on discrete search. In this chapter, we illustrate the use of our method by applying it to what is perhaps the most widely used form of data mining: decision tree induction.作者: Biomarker 時(shí)間: 2025-3-26 12:00 作者: 滑稽 時(shí)間: 2025-3-26 16:29 作者: 令人不快 時(shí)間: 2025-3-26 20:39
Data-Stream Sampling: Basic Techniques and Resultsm. This chapter surveys some basic sampling and inference techniques for data streams. We focus on general methods for materializing a sample; later chapters provide specialized sampling methods for specific analytic tasks.作者: Projection 時(shí)間: 2025-3-26 22:05 作者: 謙卑 時(shí)間: 2025-3-27 02:47
Mining Decision Trees from Streams data from the data stream to make each decision required by the learning process. The method is applicable to essentially any induction algorithm based on discrete search. In this chapter, we illustrate the use of our method by applying it to what is perhaps the most widely used form of data mining: decision tree induction.作者: 使殘廢 時(shí)間: 2025-3-27 07:08 作者: 玷污 時(shí)間: 2025-3-27 13:22
Temporal Dynamics of On-Line Information Streamsill be manageable, but the data stream perspective takes what has generally been a static view of a problem and adds a strong temporal dimension to it. Our focus here is on some of the challenges that this latter issue raises in the settings of text mining, on-line information, and information retrieval.作者: Obstruction 時(shí)間: 2025-3-27 16:26
https://doi.org/10.1007/978-3-642-58360-5antile summaries of data streams using small space. We highlight connections among these ideas, and how techniques developed for one setting sometimes naturally lend themselves to a seemingly different setting.作者: 原告 時(shí)間: 2025-3-27 19:05 作者: Antagonism 時(shí)間: 2025-3-28 02:01 作者: 沉積物 時(shí)間: 2025-3-28 02:38
Sketch-Based Multi-Query Processing over Data Streamsto improve the quality of the approximation provided by our algorithms. The key idea is to intelligently partition the domain of the underlying attribute(s) and, thus, decompose the sketching problem in a way that provably tightens our guarantees.作者: 梯田 時(shí)間: 2025-3-28 08:34
Textbook 2016lgorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains..A short introd作者: 抒情短詩(shī) 時(shí)間: 2025-3-28 14:27 作者: 得意人 時(shí)間: 2025-3-28 14:58
Textbook 2016frequent itemsets). Part III discusses a number of advanced topics on stream processingalgorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of作者: falsehood 時(shí)間: 2025-3-28 19:47 作者: Mindfulness 時(shí)間: 2025-3-29 00:49
Attilio Nebuloni,Giorgio Vignatin time instant and window size refers to N. This chapter presents a general technique, called the Exponential Histogram (EH) technique, that can be used to solve a wide variety of problems in the sliding-window model; typically problems that require us to maintain statistics. We will showcase this t作者: 相符 時(shí)間: 2025-3-29 05:43
Masatoshi Hamanaka,Keiji Hirata,Satoshi Tojothis chapter, we provide a brief introduction to the distributed data streaming model and the Geometric Method (GM), a generic technique for effectively tracking complex queries over massive distributed streams. We also discuss several recently-proposed extensions to the basic GM framework, such as 作者: 產(chǎn)生 時(shí)間: 2025-3-29 08:42
Data Stream Management: A Brave New World,ry chapter, we provide a brief summary of some basic data streaming concepts and models, and discuss the key elements of a generic stream query processing architecture. We then give a short overview of the contents of this volume.作者: 壯麗的去 時(shí)間: 2025-3-29 13:37
The Sliding-Window Computation Model and Resultsn time instant and window size refers to N. This chapter presents a general technique, called the Exponential Histogram (EH) technique, that can be used to solve a wide variety of problems in the sliding-window model; typically problems that require us to maintain statistics. We will showcase this t作者: COLON 時(shí)間: 2025-3-29 16:44
Tracking Queries over Distributed Streamsthis chapter, we provide a brief introduction to the distributed data streaming model and the Geometric Method (GM), a generic technique for effectively tracking complex queries over massive distributed streams. We also discuss several recently-proposed extensions to the basic GM framework, such as 作者: CLASH 時(shí)間: 2025-3-29 20:41
Data Stream Management978-3-540-28608-0Series ISSN 2197-9723 Series E-ISSN 2197-974X 作者: 纖細(xì) 時(shí)間: 2025-3-30 02:48
https://doi.org/10.1007/978-3-319-60919-5am problems studied: In the mid-1980’s, Flajolet and Martin gave an effective algorithm that uses only logarithmic space. Recent work has built upon their technique, improving the accuracy guarantees on the estimation, proving lower bounds, and considering other settings such as sliding windows, distributed streams, and sensor networks.作者: defeatist 時(shí)間: 2025-3-30 04:58 作者: morale 時(shí)間: 2025-3-30 10:11
Data-Centric Systems and Applicationshttp://image.papertrans.cn/d/image/263155.jpg作者: 是剝皮 時(shí)間: 2025-3-30 14:04 作者: Horizon 時(shí)間: 2025-3-30 20:03
978-3-662-56837-8Springer-Verlag Berlin Heidelberg 2016作者: EXALT 時(shí)間: 2025-3-30 22:12
Ray Meddis,Enrique A. Lopez-Povedadated several times throughout their lifetime. For several emerging application domains, however, data arrives and needs to be processed on a continuous basis, without the benefit of several passes over a static, persistent data image. Such . arise naturally, for instance telecom and IP network moni作者: 危機(jī) 時(shí)間: 2025-3-31 04:26
