標題: Titlebook: Big Data Processing Using Spark in Cloud; Mamta Mittal,Valentina E. Balas,Raghvendra Kumar Book 2019 Springer Nature Singapore Pte Ltd. 20 [打印本頁] 作者: Hoover 時間: 2025-3-21 16:42
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書目名稱Big Data Processing Using Spark in Cloud讀者反饋
書目名稱Big Data Processing Using Spark in Cloud讀者反饋學科排名
作者: Exterior 時間: 2025-3-21 21:41 作者: 正式通知 時間: 2025-3-22 00:46
Studies in Big Datahttp://image.papertrans.cn/b/image/185658.jpg作者: shrill 時間: 2025-3-22 06:34 作者: 同謀 時間: 2025-3-22 10:32
978-981-13-4448-0Springer Nature Singapore Pte Ltd. 2019作者: 伸展 時間: 2025-3-22 16:42
Michael Bonitz,Norman Horing,Patrick Ludwigation is immensely expanding inside each ten minutes and it is difficult to oversee it and it offers ascend to the term Big data. This paper depicts the enormous information and its difficulties alongside the advancements required to deal with huge data. This moreover portrays the conventional metho作者: 小蟲 時間: 2025-3-22 17:52 作者: 衰老 時間: 2025-3-22 23:32 作者: Cognizance 時間: 2025-3-23 04:53
Eigenspaces and Regular Elements,he hardware, software, and systems. Cloud Computing allows healthy and wider efficient computing services in terms of providing centralized services of storage, applications, operating systems, processing, and bandwidth. Cloud Computing is a type of architecture which helps in promotion of scalable 作者: judiciousness 時間: 2025-3-23 08:00
https://doi.org/10.1007/978-3-319-51744-5ata generation begins with the fact that there is vast information to capture and store. The rate of mounting of data on the Internet was one of the important factors in giving rise to the concept of big data. However, it is related to Internet but its existence is due to growing unstructured data w作者: SLAY 時間: 2025-3-23 11:03 作者: 縱欲 時間: 2025-3-23 17:03 作者: 提升 時間: 2025-3-23 20:30
https://doi.org/10.1007/978-3-031-17646-3This insulin is helpful in reducing the risk of diabetes. Diabetes Mellitus is a disorder of metabolism; it is one of the highest occurring diseases in the world, having affected over 422 million people. Diabetic level in person depends on various factors; if their values are kept in control, a diab作者: 痛得哭了 時間: 2025-3-23 23:22
https://doi.org/10.1007/978-3-031-17646-3sing layer, an open-source cluster (in-memory) computing platform, unified data processing engine, faster and reliable in a cutting-edge analysis for all types of data. It has a potent to join different datasets across multiple disparate data sources. It supports in-memory computing and enables fast作者: chapel 時間: 2025-3-24 05:54
Introduction to Computational Biologyl as big data repository possesses some peculiar attributes. Perhaps, analysis of big data is a common phenomenon in today’s scenario and there are many approaches with positive aspects for this purpose. However, they lack the support to deal conceptual level. There are numerous challenges related t作者: GEAR 時間: 2025-3-24 08:44 作者: Certainty 時間: 2025-3-24 14:23 作者: Lacunar-Stroke 時間: 2025-3-24 15:31
Book 2019fies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses作者: 造反,叛亂 時間: 2025-3-24 20:19 作者: 一個姐姐 時間: 2025-3-25 01:33
Big Data Streaming with Spark,vides a framework which enables such scalable, error tolerant streaming with high throughput. This chapter introduces many concepts associated with Spark Streaming, including a discussion of supported operations. Finally, two other important platforms and their integration with Spark, namely Apache Kafka and Amazon Kinesis are explored.作者: dithiolethione 時間: 2025-3-25 07:05
Michael Bonitz,Norman Horing,Patrick Ludwigdologies which were utilized before, to manage information their impediments and how it is being overseen by the new approach Hadoop. It additionally portrays the working of Hadoop along with its pros cons and security on huge data.作者: BRAND 時間: 2025-3-25 08:08
Imaging Diagnostics in Dusty Plasmasvides a framework which enables such scalable, error tolerant streaming with high throughput. This chapter introduces many concepts associated with Spark Streaming, including a discussion of supported operations. Finally, two other important platforms and their integration with Spark, namely Apache Kafka and Amazon Kinesis are explored.作者: 解開 時間: 2025-3-25 15:18
https://doi.org/10.1007/978-3-319-51744-5ssing and analysis frameworks. In this chapter, data processing frameworks Hadoop MapReduce and Apache Spark are used and the comparison between them is shown in terms of data processing parameters as memory, CPU, latency, and query performance.作者: cornucopia 時間: 2025-3-25 19:06
Data Processing Framework Using Apache and Spark Technologies in Big Data,ssing and analysis frameworks. In this chapter, data processing frameworks Hadoop MapReduce and Apache Spark are used and the comparison between them is shown in terms of data processing parameters as memory, CPU, latency, and query performance.作者: 態(tài)學 時間: 2025-3-25 23:42
2197-6503 stsworking on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing978-981-13-4448-0978-981-13-0550-4Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: JOG 時間: 2025-3-26 00:44
Big Data Analysis in Cloud and Machine Learning,d, in order to harvest maximum benefits of the available big data. Furthermore, we are also aware that conventional analytics tools are incapable to capture the full value of big data. Hence, machine learning seems to be an ideal solution for exploiting the opportunities hidden in big data. In this 作者: Painstaking 時間: 2025-3-26 08:02
,Cloud Computing Based Knowledge Mapping Between Existing and Possible Academic Innovations—,-,ion program, but the situation is now changing. There are many potential to offer Cloud Computing in Indian educational segment. This paper is conceptual in nature and deals with the basic of Cloud Computing; its need, features, types existing, and possible programs in the Indian context. Paper also作者: 推崇 時間: 2025-3-26 10:34
Implementing Big Data Analytics Through Network Analysis Software Applications in Strategizing High media networks) represent the links or relationships between content generators as they look, react, comment, or link to one another’s content. There are many forms of computer-mediated social interaction which includes SMS messages, emails, discussion groups, blogs, wikis, videos, and photo sharin作者: 鼓掌 時間: 2025-3-26 15:27 作者: 成份 時間: 2025-3-26 16:54 作者: debouch 時間: 2025-3-26 22:40
,Processing Using Spark—A Potent of BD Technology,(Data processing paradigm supports columnar storage), and Recommendation systems with MlLib. All libraries operate on RDDs as the data abstraction is very easy to compose with any applications. RDDs are a fault-tolerant computing engine (RDDs are the major abstraction and provide explicit support fo作者: reperfusion 時間: 2025-3-27 02:38
Recent Developments in Big Data Analysis Tools and Apache Spark,erent sources. In addition, analytic uncertainty is also hard to predict the aspects for which the data is useful for the purpose of analysis. The main focus of this chapter is to illustrate different tools used for the analysis of big data in general and Apache Spark (AS) in particular. The data st作者: Buttress 時間: 2025-3-27 05:55 作者: Visual-Field 時間: 2025-3-27 11:19 作者: 蹣跚 時間: 2025-3-27 16:59 作者: MOTTO 時間: 2025-3-27 20:16 作者: relieve 時間: 2025-3-27 22:54 作者: Fillet,Filet 時間: 2025-3-28 05:09
Venn Diagrams and Logical Connectives,ent progress over researches with respect to machine learning for big data analytic and different techniques in the context of modern computing environments for various societal applications. Specifically, our aim is to investigate opportunities and challenges of ML on big data and how it affects th作者: 占線 時間: 2025-3-28 10:12 作者: delusion 時間: 2025-3-28 11:11
https://doi.org/10.1007/978-3-031-17646-3(Data processing paradigm supports columnar storage), and Recommendation systems with MlLib. All libraries operate on RDDs as the data abstraction is very easy to compose with any applications. RDDs are a fault-tolerant computing engine (RDDs are the major abstraction and provide explicit support fo作者: Chemotherapy 時間: 2025-3-28 17:20
Introduction to Computational Biologyerent sources. In addition, analytic uncertainty is also hard to predict the aspects for which the data is useful for the purpose of analysis. The main focus of this chapter is to illustrate different tools used for the analysis of big data in general and Apache Spark (AS) in particular. The data st作者: Exaggerate 時間: 2025-3-28 22:31 作者: 參考書目 時間: 2025-3-29 02:34 作者: 向外供接觸 時間: 2025-3-29 04:52 作者: Initial 時間: 2025-3-29 10:25 作者: MINT 時間: 2025-3-29 12:35
Big Data Analysis in Cloud and Machine Learning,business organization, as it is the data that streams into actionable insights of businesses. The data available with the organizations are so much in volume that it is popularly referred as big data. It is the hottest buzzword spanning the business and technology worlds. Economies over the world is作者: Induction 時間: 2025-3-29 17:31
,Cloud Computing Based Knowledge Mapping Between Existing and Possible Academic Innovations—,-,he hardware, software, and systems. Cloud Computing allows healthy and wider efficient computing services in terms of providing centralized services of storage, applications, operating systems, processing, and bandwidth. Cloud Computing is a type of architecture which helps in promotion of scalable 作者: frivolous 時間: 2025-3-29 22:06
Data Processing Framework Using Apache and Spark Technologies in Big Data,ata generation begins with the fact that there is vast information to capture and store. The rate of mounting of data on the Internet was one of the important factors in giving rise to the concept of big data. However, it is related to Internet but its existence is due to growing unstructured data w作者: bronchiole 時間: 2025-3-30 01:40 作者: 千篇一律 時間: 2025-3-30 07:07
Machine Learning on Big Data: A Developmental Approach on Societal Applications,m input examples to make data-driven predictions or decisions. The growing concept “Big Data” need to be brought a great deal accomplishment in the field from claiming data science. It gives data quantifiability in a variety of ways that endow into data science. ML techniques have made huge societal作者: coalition 時間: 2025-3-30 10:58
Personalized Diabetes Analysis Using Correlation-Based Incremental Clustering Algorithm,This insulin is helpful in reducing the risk of diabetes. Diabetes Mellitus is a disorder of metabolism; it is one of the highest occurring diseases in the world, having affected over 422 million people. Diabetic level in person depends on various factors; if their values are kept in control, a diab
