Download Big Data Concepts, Theories, and Applications by Shui Yu, Song Guo PDF
By Shui Yu, Song Guo
This publication covers 3 significant elements of huge info: options, theories and functions. Written via world-renowned leaders in colossal info, this booklet explores the issues, attainable strategies and instructions for large info in examine and perform. It additionally makes a speciality of excessive point thoughts equivalent to definitions of huge information from varied angles; surveys in examine and purposes; and latest instruments, mechanisms, and structures in perform. each one bankruptcy is autonomous from the opposite chapters, permitting clients to learn any bankruptcy directly.
After reading the sensible part of massive info, this e-book offers theoretical views. The theoretical study levels from immense facts illustration, modeling and topology to distribution and measurement decreasing. Chapters additionally examine the numerous disciplines that contain titanic information, equivalent to records, information mining, desktop studying, networking, algorithms, protection and differential geometry. The final component to this ebook introduces gigantic facts functions from assorted groups, comparable to company, engineering and science.
Big info innovations, Theories and Applications is designed as a reference for researchers and complex point scholars in machine technological know-how, electric engineering and arithmetic. Practitioners who specialise in details structures, enormous info, info mining, company research and different comparable fields also will locate this fabric worthy.
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Additional resources for Big Data Concepts, Theories, and Applications
Sample text
On the other hand, the iterative nature of many applications causes streams to be highly redundant. To address this problem, stream collection must be coupled with efficient automatic cleaning techniques that can avoid redundancy and information loss. Existing, event stream approaches and systems seem to be adapted for dealing with velocity but do not completely scale when volume becomes big. Efficient parallel algorithms exploiting computing resources provided by architectures like the cloud can be used to address, velocity at the different phases of Big stream cycle: collection, cleaning and analysis.
Jakobson G, Weissman MD (1993) Alarm correlation. IEEE Netw 7:52–59 51. Liu G, Mok AK, Yang EJ (1999) Composite events for network event correlation. In: Proceedings of the 6th IFIP/IEEE international symposium on integrated network management, pp 247–260 52. Wu P, Bhatnagar R, Epshtein L, Bhandaru M, Shi Z (1998) Alarm correlation engine (ACE). In: Proceedings of the IEEE/IFIP network operation and management symposium, pp 733–742 53. Nygate YA (1995) Event correlation using rule and object based techniques.
Finally, we discuss the future works happening in this space to cover the newer patterns, tools and platforms to be watched for implementation of Big Data use cases. 1 Introduction The technology space in Big Data has grown in leaps and bounds in last 10 years or so with various genres of technologies attempting to address the key aspects of Big Data related problems. The characterization of ‘what is Big’ in Big Data is actually relative to a particular context. Today in a typical organization, the Big Data Problems are identified as the situations where the existing software tools are incapable of storing, refining, and processing targeted high volume of data (‘Volume’) of any arbitrary semantics and structure (‘Variety’) within a stipulated time (‘Velocity’) with required accuracy (‘Veracity’) and at reasonable cost (‘Value’) [57].