標題: Titlebook: Big Data 2.0 Processing Systems; A Systems Overview Sherif Sakr Book 2020Latest edition The Editor(s) (if applicable) and The Author(s), un [打印本頁] 作者: CLOG 時間: 2025-3-21 20:02
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書目名稱Big Data 2.0 Processing Systems網(wǎng)絡公開度學科排名
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書目名稱Big Data 2.0 Processing Systems被引頻次學科排名
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書目名稱Big Data 2.0 Processing Systems年度引用學科排名
書目名稱Big Data 2.0 Processing Systems讀者反饋
書目名稱Big Data 2.0 Processing Systems讀者反饋學科排名
作者: INCUR 時間: 2025-3-21 20:26 作者: 怒目而視 時間: 2025-3-22 02:23 作者: Induction 時間: 2025-3-22 07:57
Rüdiger Lorenz,Margareta Klinger,Mario Brocklar techniques on harnessing the power of data by achieving powerful analytical features. This chapter focuses on discussing several systems that have been developed to support computationally expensive machine learning and deep learning algorithms on top of big data processing frameworks.作者: 屈尊 時間: 2025-3-22 10:03
https://doi.org/10.1007/978-3-030-44187-6Database Management Systems; Hadoop; Stream Data Management; Graph Databases; Cloud Computing; Data Analy作者: Dna262 時間: 2025-3-22 16:01 作者: 知識分子 時間: 2025-3-22 20:49
Tau and Intracellular Transport in Neurons,a storage, and computation systems. In practice, data generation and consumption is becoming a main part of people’s daily life especially with the pervasive availability and usage of Internet technology and applications. The Big Data term has been coined under the tremendous and explosive growth of作者: Contend 時間: 2025-3-22 22:56 作者: 拉開這車床 時間: 2025-3-23 05:27 作者: 千篇一律 時間: 2025-3-23 08:38
K. D. Lerch,D. Sch?fer,J. Uelzenetween objects. Graphs have been widely used to represent datasets and encode problems across an already extensive range of application domains. The ever-increasing size of graph-structured data for these applications creates a critical need for scalable and even elastic systems that can process lar作者: inconceivable 時間: 2025-3-23 13:01
M. S. von Haken,H. P. Adams,K. Rieke new data-generating scenarios, such as the ubiquity of mobile devices, location services, and sensor pervasiveness. In general, stream processing engines enable a large class of applications in which data are produced from various sources and are moved asynchronously to processing nodes. Thus, stre作者: Feedback 時間: 2025-3-23 14:45 作者: 擴音器 時間: 2025-3-23 19:47
Jingyang Tang,Jinglu Ai,R. Loch Macdonaldmodern life including the way we live, socialize, think, work, do business, conduct research, and govern society. In this chapter, we provide an outlook for various applications to exploit big data technologies in current and future application domains. In addition, we highlight some of the open cha作者: BROW 時間: 2025-3-24 02:07
research and development.Describes an entire range of engine.This book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and indus作者: 接合 時間: 2025-3-24 03:03 作者: exclusice 時間: 2025-3-24 09:12
Frédéric Saudou,Sandrine Humbertth the analysis of multi-structured data from other sources such as clickstreams, call detail records, application logs, or text from call center records. This chapter provides an overview of various general-purpose big data processing systems which empower its user to develop various big data processing jobs for different application domains.作者: maculated 時間: 2025-3-24 12:11
Jingyang Tang,Jinglu Ai,R. Loch Macdonaldok for various applications to exploit big data technologies in current and future application domains. In addition, we highlight some of the open challenges which addressing them will further improve the power of big data technologies.作者: 前面 時間: 2025-3-24 14:53
Introduction,rvasive availability and usage of Internet technology and applications. The Big Data term has been coined under the tremendous and explosive growth of the world digital data which is generated from various sources and in different formats. This chapter gives an overview of the main concepts, sources, and technologies for the big data phenomena.作者: Bravado 時間: 2025-3-24 19:01
General-Purpose Big Data Processing Systems,th the analysis of multi-structured data from other sources such as clickstreams, call detail records, application logs, or text from call center records. This chapter provides an overview of various general-purpose big data processing systems which empower its user to develop various big data processing jobs for different application domains.作者: botany 時間: 2025-3-25 02:58 作者: deficiency 時間: 2025-3-25 06:36
M. S. von Haken,H. P. Adams,K. Rieke cancellation. The main focus of this chapter is to cover several systems that have been designed to provide scalable solutions for processing big data streams in addition to other set of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems.作者: 中世紀 時間: 2025-3-25 07:43
Large-Scale Stream Processing Systems, cancellation. The main focus of this chapter is to cover several systems that have been designed to provide scalable solutions for processing big data streams in addition to other set of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems.作者: 山崩 時間: 2025-3-25 12:34
Large-Scale Processing Systems of Structured Data,g engine. This chapter provides an overview of various systems that have been introduced to support the SQL flavor on top of the Hadoop-like infrastructure and provide competing and scalable performance on processing large-scale structured data.作者: Delude 時間: 2025-3-25 16:04
Large-Scale Graph Processing Systems,ge amounts of it efficiently. In general, graph processing algorithms are iterative and need to traverse the graph in a certain way. This chapter focuses on discussing several systems that have been designed to tackle the problem of large-scale graph processing.作者: LIKEN 時間: 2025-3-25 20:41
Book 2020Latest edition framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured dat作者: osculate 時間: 2025-3-26 02:25
M. S. von Haken,H. P. Adams,K. Riekeg engine. This chapter provides an overview of various systems that have been introduced to support the SQL flavor on top of the Hadoop-like infrastructure and provide competing and scalable performance on processing large-scale structured data.作者: 縮影 時間: 2025-3-26 06:39 作者: 貨物 時間: 2025-3-26 11:10
Book 2020Latest editionve been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while t作者: 暴行 時間: 2025-3-26 15:13
de competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while t978-3-030-44189-0978-3-030-44187-6作者: 青石板 時間: 2025-3-26 18:12
Introduction,a storage, and computation systems. In practice, data generation and consumption is becoming a main part of people’s daily life especially with the pervasive availability and usage of Internet technology and applications. The Big Data term has been coined under the tremendous and explosive growth of作者: PON 時間: 2025-3-27 00:05
General-Purpose Big Data Processing Systems,, and machine learning. Moreover, the process might involve the analysis of structured data from conventional transactional sources, in conjunction with the analysis of multi-structured data from other sources such as clickstreams, call detail records, application logs, or text from call center reco作者: 生命 時間: 2025-3-27 03:39
Large-Scale Processing Systems of Structured Data,onse time of milliseconds or few seconds. In addition, many programmers may be unfamiliar with the Hadoop framework and they would prefer to use SQL as a high-level declarative language to implement their jobs while delegating all of the optimization details in the execution process to the underlyin作者: 高腳酒杯 時間: 2025-3-27 05:33
Large-Scale Graph Processing Systems,etween objects. Graphs have been widely used to represent datasets and encode problems across an already extensive range of application domains. The ever-increasing size of graph-structured data for these applications creates a critical need for scalable and even elastic systems that can process lar作者: diskitis 時間: 2025-3-27 13:31
Large-Scale Stream Processing Systems, new data-generating scenarios, such as the ubiquity of mobile devices, location services, and sensor pervasiveness. In general, stream processing engines enable a large class of applications in which data are produced from various sources and are moved asynchronously to processing nodes. Thus, stre作者: Immunization 時間: 2025-3-27 17:07 作者: 秘傳 時間: 2025-3-27 19:24 作者: 擴大 時間: 2025-3-27 23:58
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