eprintid: 6774
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/67/74
datestamp: 2023-11-09 16:18:34
lastmod: 2023-11-09 16:18:34
status_changed: 2023-11-09 16:07:37
type: conference_item
metadata_visibility: show
creators_name: Abdullahi, A.U.
creators_name: Ahmad, R.B.
creators_name: Zakaria, N.M.
title: Proposed adaptive indexing for Hive
ispublished: pub
keywords: High level languages; Indexing (of information); Query languages, Computational time and memory; Hadoop; High-level abstraction; High-level queries; Hive; Indexing approaches; Map-reduce; Map-reduce programming, Big data
note: cited By 1; Conference of 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 ; Conference Date: 19 May 2015 Through 20 May 2015; Conference Code:124374
abstract: The value of Big Data largely relies on its analytical outcomes; and MapReduce has so far been the most efficient tool for performing analysis on the data. However, the low level nature of MapReduce programming necessitates the development of High-level abstractions, i.e., High Level Query Languages (HLQL), such as Hive, Pig, JAQL and others. These languages can be categorized as either dataflow based or OLAP-based. For OLAP-based HLQL, in particular Hive, at the moment, the speed of retrieval of big data for the analysis is still requiring improvement. Hence, indexing is one of the techniques used for this purpose. Yet, the indexing approach still has its loopholes since it is performed manually and externally using the approach of index inclusion and two-way data scanning. It requires huge computational time and space and hence not scalable for future potential scale of big data. Thus, an adaptive indexing framework is proposed for improving both the computational time and memory usage of the indexing process. The technique shall check the user queries to determine the necessity for indexing and use internal indexing with one-way data scanning approach for the indexing strategy. In this paper, the initial framework of the technique is presented and discussed. © 2015 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995545978&doi=10.1109%2fISMSC.2015.7594057&partnerID=40&md5=e158e437b797845f1106d19805df9df5
id_number: 10.1109/ISMSC.2015.7594057
full_text_status: none
publication: 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 - Proceedings
pagerange: 226-231
refereed: TRUE
isbn: 9781479978946
citation:   Abdullahi, A.U. and Ahmad, R.B. and Zakaria, N.M.  (2016) Proposed adaptive indexing for Hive.  In: UNSPECIFIED.