Knowledge Management & E-Learning 8(1), pp. 138-157.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Objective: Big Data Analytics is a panoply of techniques the principal intention of which is to ferret out dimensions or factors from certain data streamed or available over the WWW. We offer a subset or "second" stage protocol of Big Data Analytics (BDA) that uses these dimensional datasets as benchmarks for profiling related data. We call this Specific Context Benchmarking (SCB). Method: In effecting this benchmarking objective, we have elected to use a Digital Frequency Profiling (DFP) technique based upon the work of Newcomb and Benford, who have developed a profiling benchmark based upon the Log10 function. We illustrate the various stages of the SCB protocol using the data produced by the Academic Research Libraries to enhance insights regarding the details of the operational benchmarking context and so offer generalizations needed to encourage adoption of SCB across other functional domains. Results: An illustration of the SCB protocol is offered using the recently developed Benford Practical Profile as the Conformity Benchmarking Measure. ShareWare: We have developed a Decision Support System called: SpecificContextAnalytics (SCA:DSS) to create the various information sets presented in this paper. The SCA:DSS, programmed in Excel™ VBA®, is available from the corresponding author as a free download without restriction to its use. Conclusions: We note that SCB effected using the DFPs is an enhancement not a replacement for the usual statistical and analytic techniques and fits very well in the BDA milieu.
Bibtex:
@article{,
author = {Halperin, Michael and Lusk, Edward J.},
year = {2016},
month = {03},
pages = {138--157},
title = {Navigating the Benford labyrinth: A big-data Analytic protocol illustrated using the Academic library Context},
volume = {8},
journal = {Knowledge Management & E-Learning},
url = {https://www.researchgate.net/publication/303301953_Navigating_the_Benford_labyrinth_A_big-data_Analytic_protocol_illustrated_using_the_Academic_library_Context},
}
Reference Type: Journal Article
Subject Area(s): Computer Science, General Interest