By Shui Yu, Song Guo
This publication covers 3 significant elements of massive info: options, theories and functions. Written via world-renowned leaders in massive info, this booklet explores the issues, attainable recommendations and instructions for giant information in examine and perform. It additionally makes a speciality of excessive point techniques equivalent to definitions of massive facts from diversified angles; surveys in study and functions; and latest instruments, mechanisms, and structures in perform. every one bankruptcy is self reliant from the opposite chapters, permitting clients to learn any bankruptcy directly.
After interpreting the sensible facet of huge facts, this publication offers theoretical views. The theoretical study levels from massive information illustration, modeling and topology to distribution and measurement lowering. Chapters additionally examine the various disciplines that contain sizeable facts, equivalent to information, info mining, laptop studying, networking, algorithms, safeguard and differential geometry. The final component of this ebook introduces monstrous info purposes from various groups, reminiscent of enterprise, engineering and science.
Big info strategies, Theories and Applications is designed as a reference for researchers and complicated point scholars in laptop technology, electric engineering and arithmetic. Practitioners who concentrate on details structures, vast info, information mining, enterprise research and different comparable fields also will locate this fabric worthy.
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Additional resources for Big Data Concepts, Theories, and Applications
Cao H, Wolfson O, Xu B, Yin H (2005) Mobi-dic: mobile discovery of local resources in peer-to-peer wireless network. IEEE Data Eng Bull 28:11–18 26 G. Vargas-Solar et al. 17. Mokbel MF, Xiong X, Aref WG, Hambrusch S, Prabhakar S, Hammad M (2004) Place: a query processor for handling real-time spatio-temporal data streams (demo). In: Proceedings of the 30th conference on very large data bases (VLDB), Toronto, Canada 18. Hellerstein JM, Franklin MJ, Chandrasekaran S, Deshpande A, Hildrum K, Madden S, Raman V, Shah MA (2000) Adaptive query processing: technology in evolution.
Data mining is sometimes defined as the process of navigating through the data and trying to find out patterns and finally establishing all relevant relationships. Consequently, the event-mining goal is to identify patterns that potentially indicate the production of an event within large event data sets. Event mining adopts data mining techniques for the recognition of event patterns, such as association, classification, clustering, forecasting, etc. Therefore, events within a history can be mined in a multitude of ways: unwanted events are filtered out, patterns of logically corresponding events are aggregated into one new composite event, repetitive events are counted and aggregated into a new primitive event with a count of how often the original event occurred, etc.
Ends: (E1 ends E2 ) The end of event e1 of type E1 and event e2 of type E2 are simultaneous. The ends relation is commutative. The occurrence interval of (e1 ends e2 ) is [earliest (startT-e1, startT-e2), endT-e2]. 2Selection Operators Selection operators allow searching occurrences of an event type in the event history. The selection E[i] defines the occurrence of the ith element of a sequence of events of type E, i 2 N; where N is a natural number greater than 0, during a predefined time interval I.
Big Data Concepts, Theories, and Applications by Shui Yu, Song Guo