Data summarization techniques for big data - a survey

Hesabi, Z.R., Tari, Z., Goscinski, A., Fahad, A., Khalil, I. and Queiroz, C. 2015, Data summarization techniques for big data - a survey. In Khan, Samee U. and Zomaya, Albert Y. (ed), Handbook on data centers, Springer, New York, N.Y., pp.1109-1152, doi: 10.1007/978-1-4939-2092-1_38.

Attached Files
Name Description MIMEType Size Downloads

Title Data summarization techniques for big data - a survey
Author(s) Hesabi, Z.R.
Tari, Z.
Goscinski, A.
Fahad, A.
Khalil, I.
Queiroz, C.
Title of book Handbook on data centers
Editor(s) Khan, Samee U.
Zomaya, Albert Y.
Publication date 2015
Chapter number 38
Total chapters 46
Start page 1109
End page 1152
Total pages 44
Publisher Springer
Place of Publication New York, N.Y.
Keyword(s) database management
communications engineering
systems and data security
data storage representation
Summary In current digital era according to (as far) massive progress and development of internet and online world technologies such as big and powerful data servers we face huge volume of information and data day by day from many different resources and services which was not available to human kind just a few decades ago. This data comes from available different online resources and services that are established to serve customers. Services and resources like Sensor Networks, Cloud Storages, Social Networks and etc., produce big volume of data and also need to manage and reuse that data or some analytical aspects of the data. Although this massive volume of data can be really useful for people and corporates it could be problematic as well. Therefore big volume of data or big data has its own deficiencies as well. They need big storage/s and this volume makes operations such as analytical operations, process operations, retrieval operations real difficult and hugely time consuming. One resolution to overcome these difficult problems is to have big data summarized so they would need less storage and extremely shorter time to get processed and retrieved. The summarized data will be then in "compact format" and still informative version of the entire data. Data summarization techniques aim then to produce a "good" quality of summaries. Therefore, they would hugely benefit everyone from ordinary users to researches and corporate world, as it can provide an efficient tool to deal with large data such as news (for new summarization).
ISBN 9781493920921
9781493920914
Language eng
DOI 10.1007/978-1-4939-2092-1_38
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082873

Document type: Book Chapter
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 6 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 498 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Fri, 15 Apr 2016, 15:51:41 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.