標(biāo)題: Titlebook: Handbook of Big Data Technologies; Albert Y. Zomaya,Sherif Sakr Book 2017 Springer International Publishing AG 2017 Big Data.MapReduce.Had [打印本頁(yè)] 作者: 不服從 時(shí)間: 2025-3-21 17:31
書目名稱Handbook of Big Data Technologies影響因子(影響力)
書目名稱Handbook of Big Data Technologies影響因子(影響力)學(xué)科排名
書目名稱Handbook of Big Data Technologies網(wǎng)絡(luò)公開度
書目名稱Handbook of Big Data Technologies網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Handbook of Big Data Technologies讀者反饋
書目名稱Handbook of Big Data Technologies讀者反饋學(xué)科排名
作者: 客觀 時(shí)間: 2025-3-21 21:54 作者: 石墨 時(shí)間: 2025-3-22 02:22 作者: 預(yù)感 時(shí)間: 2025-3-22 08:30
Big Data Analysis on Cloudstorage, process and analysis capabilities. Those data volumes, commonly referred as Big Data, can be exploited to extract useful information and to produce helpful knowledge for science, industry, public services and in general for humankind. Big Data analytics refer to advanced mining techniques ap作者: 字謎游戲 時(shí)間: 2025-3-22 09:45
Data Organization and Curation in Big Data analytics are getting more complex, the advances in big data applications are no longer hindered by their ability to collect or generate data. But instead, by their ability to efficiently and effectively manage the available data. Therefore, numerous scalable and distributed infrastructures have be作者: COST 時(shí)間: 2025-3-22 13:58
Big Data Query Enginesare used in several big data applications ranging from the generation of simple reports to running deep and complex query workloads. The insights drawn by running big data analytics depend primarily on the capabilities of the underlying query engine, which is responsible for translating user queries作者: 牢騷 時(shí)間: 2025-3-22 21:07 作者: 使腐爛 時(shí)間: 2025-3-22 21:37
Semantic Data Integrationuly useful, scientists need not only to be able to access it, but also be able to interpret and use it. Doing this requires semantic context. Semantic Data Integration is an active field of research, and this chapter describes the current challenges and how existing approaches are addressing them. T作者: 消耗 時(shí)間: 2025-3-23 01:46 作者: 高談闊論 時(shí)間: 2025-3-23 09:24
Non-native RDF Storage Enginesn be stored according to many different data storage models. Some of these attempt to use general purpose database storage techniques to persist Linked Data, hence they can leverage existing data processing environments (e.g., big Hadoop clusters). We therefore look at the multiplicity of Linked Dat作者: 惡名聲 時(shí)間: 2025-3-23 12:25 作者: 領(lǐng)袖氣質(zhì) 時(shí)間: 2025-3-23 16:19 作者: cruise 時(shí)間: 2025-3-23 19:17
Searching the Big Data: Practices and Experiences in Efficiently Querying Knowledge Basesbase of the world’s knowledge and typically use the entity-relational model. Most of the existing knowledge bases make their data in the RDF format. Tools including querying, inferencing and reasoning on facts are developed to consume the knowledge. In this chapter, we introduce a client-side cachin作者: 免除責(zé)任 時(shí)間: 2025-3-24 01:43
Management and Analysis of Big Graph Data: Current Systems and Open Challengeso analyze such graph data should meet a number of challenging requirements including support for an expressive graph data model with heterogeneous vertices and edges, powerful query and graph mining capabilities, ease of use as well as high performance and scalability. In this chapter, we survey cur作者: 胰島素 時(shí)間: 2025-3-24 06:12 作者: frugal 時(shí)間: 2025-3-24 10:21 作者: 防水 時(shí)間: 2025-3-24 12:59
https://doi.org/10.1007/978-3-642-72414-5data model captures the logical representation and structures for data processing and management. Understanding storage and data model together is essential for understanding the built-on big data ecosystems. In this chapter we are going to investigate and compare the key storage and data models in 作者: Intruder 時(shí)間: 2025-3-24 18:35
Indikationen für die Digitalistherapierograms. Programming models normally the core feature of big data frameworks as they implicitly affects the execution model of big data processing engines and also drives the way for users to express and construct the big data applications and programs. In this chapter, we comprehensively investigat作者: Gustatory 時(shí)間: 2025-3-24 19:33
Dokumentation und Arzneimittelsicherheittechniques with key features like scalability, fault tolerance, efficient task distribution, usability and processing speed. In this chapter, we first provide a comprehensive survey of the requirements, give an overview and classify existing big data programming platforms based on different dimensio作者: Anonymous 時(shí)間: 2025-3-25 00:25
,Pflanzliche durchblutungsf?rdernde Mittel,torage, process and analysis capabilities. Those data volumes, commonly referred as Big Data, can be exploited to extract useful information and to produce helpful knowledge for science, industry, public services and in general for humankind. Big Data analytics refer to advanced mining techniques ap作者: Organonitrile 時(shí)間: 2025-3-25 06:19 作者: 遺忘 時(shí)間: 2025-3-25 09:55
https://doi.org/10.1007/978-3-642-79546-6are used in several big data applications ranging from the generation of simple reports to running deep and complex query workloads. The insights drawn by running big data analytics depend primarily on the capabilities of the underlying query engine, which is responsible for translating user queries作者: Endearing 時(shí)間: 2025-3-25 12:06 作者: Hallmark 時(shí)間: 2025-3-25 16:22
https://doi.org/10.1007/978-3-642-85362-3uly useful, scientists need not only to be able to access it, but also be able to interpret and use it. Doing this requires semantic context. Semantic Data Integration is an active field of research, and this chapter describes the current challenges and how existing approaches are addressing them. T作者: 演繹 時(shí)間: 2025-3-25 21:46 作者: frivolous 時(shí)間: 2025-3-26 01:00 作者: daredevil 時(shí)間: 2025-3-26 07:44 作者: Allure 時(shí)間: 2025-3-26 11:39 作者: notice 時(shí)間: 2025-3-26 12:41 作者: 甜得發(fā)膩 時(shí)間: 2025-3-26 18:17 作者: LVAD360 時(shí)間: 2025-3-26 23:00 作者: Iniquitous 時(shí)間: 2025-3-27 01:26
Evidenz in der Geburtshilfe und Gyn?kologieve volume, high streaming rate, and uncertainty inherent in the data, raise several challenges that require new efforts for smarter and faster graph analysis. With the advent of complex networks such as the World Wide Web, social networks, knowledge graphs, genome and scientific databases, Internet 作者: 面包屑 時(shí)間: 2025-3-27 07:44 作者: 血友病 時(shí)間: 2025-3-27 10:49 作者: Directed 時(shí)間: 2025-3-27 14:59
http://image.papertrans.cn/h/image/420873.jpg作者: Melodrama 時(shí)間: 2025-3-27 21:25 作者: 過去分詞 時(shí)間: 2025-3-27 22:35 作者: unstable-angina 時(shí)間: 2025-3-28 04:44
978-3-319-84138-0Springer International Publishing AG 2017作者: 敲竹杠 時(shí)間: 2025-3-28 09:15 作者: Ophthalmoscope 時(shí)間: 2025-3-28 14:23
ying and mining mechanisms in domains such as social networks. ?Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT)978-3-319-84138-0978-3-319-49340-4作者: 填料 時(shí)間: 2025-3-28 15:25
,Pflanzliche durchblutungsf?rdernde Mittel,terms of platforms, programming tools, frameworks, and data mining algorithms) spring up everyday to cope with the growing scope of interest in Big Data. This chapter discusses models, technologies and research trends in Big Data analysis on Clouds. In particular, the chapter presents representative作者: 無孔 時(shí)間: 2025-3-28 22:08 作者: Allure 時(shí)間: 2025-3-29 02:18
https://doi.org/10.1007/978-3-642-79546-6s realized by partitioning data across multiple machines that communicate via a high speed interconnect layer. These systems often rely on dedicated expensive hardware resources in order to scale-out query processing and provide fault tolerance. With the emergence of Hadoop, it became possible to us作者: 爵士樂 時(shí)間: 2025-3-29 05:51 作者: 急性 時(shí)間: 2025-3-29 09:20
Der Formalismus der Negativsprachee produced in various ways for a specific scenario. Such heterogeneous data can incorporate knowledge on provenance, which can be further leveraged to provide users with a reliable and understandable description of the way the query result was derived, and improve the query execution performance due作者: SNEER 時(shí)間: 2025-3-29 13:44
https://doi.org/10.1007/978-3-662-58248-0rithms to reduce the identification time. We then prefetch and cache the results of these queries aiming to improve the overall querying performance. We also develop a forecasting method, namely Modified Simple Exponential Smoothing, to implement the cache replacement. Our approach has been evaluate作者: Paradox 時(shí)間: 2025-3-29 18:24 作者: 愛社交 時(shí)間: 2025-3-29 23:04
Evidenz in der Geburtshilfe und Gyn?kologiechapter, we shall describe the emerging graph queries and mining problems, their applications and resolution techniques. We emphasize the current challenges and highlight some future research directions.作者: superfluous 時(shí)間: 2025-3-30 01:55
Big Data Analysis on Cloudsterms of platforms, programming tools, frameworks, and data mining algorithms) spring up everyday to cope with the growing scope of interest in Big Data. This chapter discusses models, technologies and research trends in Big Data analysis on Clouds. In particular, the chapter presents representative作者: 我正派 時(shí)間: 2025-3-30 07:46
Data Organization and Curation in Big Dataques for optimized storage and organization of data that have big influence on query performance, namely the ., and . techniques. However, in the cases of non-traditional workloads where queries have special execution and data-access characteristics, the standard indexing and layout techniques may f作者: 指令 時(shí)間: 2025-3-30 12:08
Big Data Query Enginess realized by partitioning data across multiple machines that communicate via a high speed interconnect layer. These systems often rely on dedicated expensive hardware resources in order to scale-out query processing and provide fault tolerance. With the emergence of Hadoop, it became possible to us作者: 烤架 時(shí)間: 2025-3-30 15:24 作者: scrutiny 時(shí)間: 2025-3-30 18:32 作者: stroke 時(shí)間: 2025-3-30 22:10
Searching the Big Data: Practices and Experiences in Efficiently Querying Knowledge Basesrithms to reduce the identification time. We then prefetch and cache the results of these queries aiming to improve the overall querying performance. We also develop a forecasting method, namely Modified Simple Exponential Smoothing, to implement the cache replacement. Our approach has been evaluate作者: 散開 時(shí)間: 2025-3-31 03:00 作者: blight 時(shí)間: 2025-3-31 05:16 作者: ABIDE 時(shí)間: 2025-3-31 10:15
