標題: Titlebook: Big Data Infrastructure Technologies for Data Analytics; Scaling Data Science Yuri Demchenko,Juan J. Cuadrado-Gallego,Marharyta Book 2024 [打印本頁] 作者: 冰凍 時間: 2025-3-21 17:11
書目名稱Big Data Infrastructure Technologies for Data Analytics影響因子(影響力)
書目名稱Big Data Infrastructure Technologies for Data Analytics影響因子(影響力)學科排名
書目名稱Big Data Infrastructure Technologies for Data Analytics網(wǎng)絡公開度
書目名稱Big Data Infrastructure Technologies for Data Analytics網(wǎng)絡公開度學科排名
書目名稱Big Data Infrastructure Technologies for Data Analytics被引頻次
書目名稱Big Data Infrastructure Technologies for Data Analytics被引頻次學科排名
書目名稱Big Data Infrastructure Technologies for Data Analytics年度引用
書目名稱Big Data Infrastructure Technologies for Data Analytics年度引用學科排名
書目名稱Big Data Infrastructure Technologies for Data Analytics讀者反饋
書目名稱Big Data Infrastructure Technologies for Data Analytics讀者反饋學科排名
作者: 傻瓜 時間: 2025-3-21 22:37 作者: 邊緣 時間: 2025-3-22 00:46
William E. Tunmer,Robert Grieveve their main business goals and get their financial profit and ROI (Return On Investment). This will typically require some investments both in Big Data Infrastructure (computer hardware and software), business process adjustment/re-organisation and staff development or hiring.作者: 導師 時間: 2025-3-22 07:03
https://doi.org/10.1007/978-3-030-76900-0eful results such as hidden patterns and relationships in the data. We will provide the mathematical foundations of data validation, along with practical recommendations for its effective implementation.作者: Tailor 時間: 2025-3-22 09:36 作者: Pruritus 時間: 2025-3-22 15:18 作者: 庇護 時間: 2025-3-22 20:51 作者: 使迷醉 時間: 2025-3-22 22:10 作者: CHART 時間: 2025-3-23 01:44 作者: 萬花筒 時間: 2025-3-23 06:48 作者: GEST 時間: 2025-3-23 10:01 作者: OATH 時間: 2025-3-23 14:29 作者: 暫時過來 時間: 2025-3-23 19:39
Jennifer Sclafani,Alexander Nikolaou PMML, ONNX and TensorFlow will help understanding the necessary steps in the transition from the model development stage to deployment and operation. The chapter finishes with a short overview of the Data Science and ML development platform by the major cloud and Big Data providers.作者: Bereavement 時間: 2025-3-23 22:56
Data Science Projects Management, DataOps, MLOPs, PMML, ONNX and TensorFlow will help understanding the necessary steps in the transition from the model development stage to deployment and operation. The chapter finishes with a short overview of the Data Science and ML development platform by the major cloud and Big Data providers.作者: Introduction 時間: 2025-3-24 03:22 作者: 輕信 時間: 2025-3-24 06:43
Cloud and Big Data Service Providers and Platforms,ortant for general professional competences to understand the current level of technology development. It is beneficial for future decision making on selecting a cloud services provider for a specific project or use case. Different cloud providers may offer different benefits in providing specific services in different regions.作者: 疼死我了 時間: 2025-3-24 14:13
Research Data Management,prise or public data management. Open Science and Open Access are two of the movements that shape today’s research data management. We will pay attention to this at the beginning to provide context to further discussion how they are implemented technically.作者: champaign 時間: 2025-3-24 14:53 作者: inhibit 時間: 2025-3-24 21:52
Introduction,iple infrastructure components. This creates a continuous trend to use Big Data services and applications which are generally cloud based. Effective design, development and operation of modern data driven applications and services must start from the beginning and involve the whole services lifecycl作者: nephritis 時間: 2025-3-24 23:45
Big Data Technologies Foundation: Definition, Reference Architecture, Use Cases,mation Technologies (IT). We will discuss the Big Data properties: Volume, Velocity, Variety, Variability, Value, and Veracity, often called Big Data 3V or 6V. We will also discuss the main components of the Big Data ecosystem where data play a driving role. The chapter will discuss Big Data use cas作者: 接合 時間: 2025-3-25 06:51 作者: inspired 時間: 2025-3-25 07:45
Cloud and Big Data Service Providers and Platforms,r cloud and Big Data service providers, including Amazon Web Services (AWS), Microsoft Azure Cloud (Azure), and Google Cloud Platform (GCP), to understand how the main Cloud Computing properties are implemented and what services are offered..Knowledge of the cloud and Big Data services market is imp作者: STELL 時間: 2025-3-25 12:10
Big Data Algorithms, MapReduce and Hadoop ecosystem,will look at the MapReduce algorithm for parallel processing of large amounts of data that provides a basis for many other algorithms and applications working with Big Data..The MapReduce programming model and its implementation in Hadoop were invented to address limitations of the traditional high-作者: NIP 時間: 2025-3-25 18:48
Data Structures for Big Data, Modern Big Data SQL and NoSQL Databases,cludes discussion the ACID properties of the SQL databases, BASE properties of NoSQL databases, and CAP Theorem for distributed systems applied to NoSQL databases and their classification. The chapter describes examples of the NoSQL databases capable of processing Big Data.作者: 留戀 時間: 2025-3-25 23:14 作者: 避開 時間: 2025-3-26 02:43
Research Data Management, metadata tools, and registries. Current research data management is built around FAIR data principles: research data must be Findable, Accessible, Interoperable, Reusable. The chapter also reviews Open Science and Open Access principles..Research Data Management has many specifics compared to enter作者: calorie 時間: 2025-3-26 06:09
Finding Data on the Web, Data Sets, Web Scraping, Web API, dataset collections, data dumps, Web API, and the web itself. Familiarity with this topic and the ability to effectively collect data from the web, also called webscraping, is a demanded competence and skill in Data Science and Big Data job vacancies. Collecting data from external sources and from 作者: refine 時間: 2025-3-26 11:37
Data Science Projects Management, DataOps, MLOPs, production environment. We discuss the importance of using research methods for effectively planning and managing Data Science projects. We also explain importance of applying best practices in DevOps for software development to Data Science projects that defined as DataOps, primarily focused on da作者: 補充 時間: 2025-3-26 12:37 作者: 一再遛 時間: 2025-3-26 20:45 作者: Cirrhosis 時間: 2025-3-26 22:49
Akinori Yonezawa,Satoshi MatsuokaThis chapter discusses the generic streaming data processing architecture and Apache Hadoop stack components Kafka, Flume and Spark that support main stages of the streaming data processing. Simple programming examples are presented. The chapter also refers to the popular Spark libraries and platforms such as Spark MLlib and Databricks Spark.作者: HILAR 時間: 2025-3-27 05:11
William E. Tunmer,Robert GrieveThis chapter discusses Big Data security and privacy issues. Security of data has always been a concern of organisations and individuals when implementing new technologies that increasingly use distributed systems and facilities such as cloud computing.作者: 溫和女人 時間: 2025-3-27 08:59
Streaming Analytics and Spark,This chapter discusses the generic streaming data processing architecture and Apache Hadoop stack components Kafka, Flume and Spark that support main stages of the streaming data processing. Simple programming examples are presented. The chapter also refers to the popular Spark libraries and platforms such as Spark MLlib and Databricks Spark.作者: coalition 時間: 2025-3-27 12:15
Big Data Security and Compliance, Data Privacy Protection,This chapter discusses Big Data security and privacy issues. Security of data has always been a concern of organisations and individuals when implementing new technologies that increasingly use distributed systems and facilities such as cloud computing.作者: fringe 時間: 2025-3-27 14:09
Metalearning for Deep Neural Networksiple infrastructure components. This creates a continuous trend to use Big Data services and applications which are generally cloud based. Effective design, development and operation of modern data driven applications and services must start from the beginning and involve the whole services lifecycl作者: 節(jié)約 時間: 2025-3-27 18:12 作者: arboretum 時間: 2025-3-28 00:02 作者: 口味 時間: 2025-3-28 02:56 作者: FUSC 時間: 2025-3-28 06:51
Metalepse im Werk von Günter Grasswill look at the MapReduce algorithm for parallel processing of large amounts of data that provides a basis for many other algorithms and applications working with Big Data..The MapReduce programming model and its implementation in Hadoop were invented to address limitations of the traditional high-作者: 偽造 時間: 2025-3-28 10:52 作者: 龍蝦 時間: 2025-3-28 15:48 作者: 額外的事 時間: 2025-3-28 21:06
Michael L. Herriman,Marion E. Myhill metadata tools, and registries. Current research data management is built around FAIR data principles: research data must be Findable, Accessible, Interoperable, Reusable. The chapter also reviews Open Science and Open Access principles..Research Data Management has many specifics compared to enter作者: HATCH 時間: 2025-3-29 00:25 作者: 高歌 時間: 2025-3-29 05:46
Jennifer Sclafani,Alexander Nikolaou production environment. We discuss the importance of using research methods for effectively planning and managing Data Science projects. We also explain importance of applying best practices in DevOps for software development to Data Science projects that defined as DataOps, primarily focused on da作者: 發(fā)怨言 時間: 2025-3-29 10:38
https://doi.org/10.1007/978-3-030-76900-0r does not have to deal with hardware setup, patching, management, backups etc. All this is taken care of by the service provider. The user can choose from a wide variety of computing instance types that are optimized for different tasks, and can even set up automatic hardware scaling depending on t作者: output 時間: 2025-3-29 11:29 作者: 不在灌木叢中 時間: 2025-3-29 16:39
William E. Tunmer,Judith A. Boweycludes discussion the ACID properties of the SQL databases, BASE properties of NoSQL databases, and CAP Theorem for distributed systems applied to NoSQL databases and their classification. The chapter describes examples of the NoSQL databases capable of processing Big Data.作者: 小教堂 時間: 2025-3-29 19:49 作者: 晚間 時間: 2025-3-30 01:45
Yuri Demchenko,Juan J. Cuadrado-Gallego,Marharyta Explains how modern cloud-based platforms and tools can be integrated to form a coherent solution with business benefits.Reveals the major Big Data infrastructure technologies that data science and an作者: 柔聲地說 時間: 2025-3-30 06:58
http://image.papertrans.cn/b/image/192666.jpg作者: Negligible 時間: 2025-3-30 10:21 作者: fulcrum 時間: 2025-3-30 12:59
