Big Data Concepts, Theories, and Applications
| Verlag | Springer |
| Auflage | 2016 |
| Seiten | 437 |
| Format | 15,6 x 24,5 x 2,6 cm |
| Gewicht | 840 g |
| Artikeltyp | Englisches Buch |
| ISBN-10 | 3319277618 |
| EAN | 9783319277615 |
| Bestell-Nr | 31927761A |
Thisbook covers three major parts of Big Data: concepts, theories andapplications. Written by world-renowned leaders in Big Data, this book exploresthe problems, possible solutions and directions for Big Data in research andpractice. It also focuses on high level concepts such as definitionsof Big Data from different angles; surveys in research and applications; andexisting tools, mechanisms, and systems in practice. Each chapter is independentfrom the other chapters, allowing users to read any chapter directly.
After examining the practical sideof Big Data, this book presents theoretical perspectives. The theoreticalresearch ranges from Big Data representation, modeling and topology todistribution and dimension reducing. Chapters also investigate the manydisciplines that involve Big Data, such as statistics, data mining, machinelearning, networking, algorithms, security and differential geometry. The lastsection of this book introduces Big Data applications from diff erentcommunities, such as business, engineering and science.
Big Data Concepts, Theories and Applications is designed as a reference for researchersand advanced level students in computer science, electrical engineering andmathematics. Practitioners who focus on information systems, big data, datamining, business analysis and other related fields will also find thismaterial valuable.
Inhaltsverzeichnis:
Big Continuous Data:Dealing with Velocity by Composing Event Streams.- Big Data Tools andPlatforms.- Traffic Identification in Big Internet Data.- Security Theories andPractices for Big Data.- Rapid Screening of Big Data against InadvertentLeaks.- Big Data Storage Security.- Cyber Attacks on MapReduce Computation Timein a Hadoop Cluster.- Security and Privacy for Big Data.- Big Data Applicationsin Engineering and Sciences.- Geospatial Big Data for Environmental andAgricultural Applications.- Big Data in Finance.- Big Data Applications inBusiness Analytics.