The majority of enterprises are in the process of recognizing that business data analytics have the potential
to transform their daily operations and make them extremely effective at addressing business challenges,
identifying new market trends and embracing new ways to engage customers. Such analytics are in most
cases related with the processing of data coming from various data sources that include structured and
unstructured data. In order to get insight through the analysis results, appropriate input has to be provided
that in many cases has to combine data from cross-sectorial and heterogeneous public or private data
sources. Thus, there is inherent a need for applying novel techniques in order to harvest complex and
heterogeneous datasets, turn them into insights and make decisions. In this paper, we present an approach
for the production of added-value business analytics through the consumption of interlinked versions of data
and the exploitation of linked data principles. Such interlinked data constitute valuable input for the
initiation of an analytics extraction process and can lead to the realization of analysis that was not envisaged
in the past. In addition to the production of analytics based on the consumption of linked data, the proposed
approach supports the interlinking of the produced results with the associated input data, increasing in this
way the value of the produced data and making them discoverable for further use in the future. The designed
business analytics and data mining component is described in detail, along with an indicative application
scenario combining data from the governmental, societal and health sectors.
[1]
S. S. Sherekar,et al.
Comparative Analysis of Data Mining Tools and Techniques for Evaluating Performance of Database System
,
2013
.
[2]
Luke Vale,et al.
Quality assessment form for case series
,
2015
.
[3]
ISO / IEC 25010 : 2011 Systems and software engineering — Systems and software Quality Requirements and Evaluation ( SQuaRE ) — System and software quality models
,
2013
.
[4]
Sally A. McKee,et al.
Characterizing and subsetting big data workloads
,
2014,
2014 IEEE International Symposium on Workload Characterization (IISWC).
[5]
Neal Leavitt.
Bringing big analytics to the masses
,
2013,
Computer.
[6]
Gregory Piatetsky-Shapiro,et al.
Discovery, Analysis, and Presentation of Strong Rules
,
1991,
Knowledge Discovery in Databases.