Paper Info
Title
A Goal-Oriented Big Data Analytics Framework for Aligning with Business
Abstract
Big data analytics is the hottest new technology which helps turn hidden insights in big data into business value to support a better decision-making. However, current big data analytics has many challenges to do it since there is a big gap between big data analytics and business. This is mainly because lack of business context around the data, lack of expertise to connect the dots, and implicit business objectives. In this paper, we present IRIS - a big data analytics framework for aligning with business in a goal-oriented approach. It is composed of ontology for a business context model, analytics methods for connecting big data with business, an action process for collaborative work and an assistant tool utilizing Spark. In this framework, problems of the current process and solutions for the future process are hypothesized in an explicit business context model and validated them by using diverse analytics methods implemented on top of Spark libraries. Also, a goal-oriented approach enables to explore and select alternatives among potential problems and solutions. A business process for clearance pricing decision is used to show how big data analytics can be turned into business value by using our framework which align big data to business goals, as well as for an initial understanding of the applicability of IRIS.
Year
DOI
Venue
2017
10.1109/BigDataService.2017.29
2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)
Keywords
Field
DocType
Big Data Analytics,Big Data,Goal-Orientation,Business Alignment,Business Process
Artifact-centric business process model,Data science,Data mining,Business analytics,Computer science,Knowledge management,Business process modeling,Analytics,Business intelligence,Big data,Business rule,Business Process Model and Notation
Conference
ISBN
Citations 
PageRank 
978-1-5090-6319-2
5
0.55
References 
Authors
5
4
Name
Order
Citations
PageRank
Grace Park1746.14
Lawrence Chung223636.31
Liping Zhao310515.07
Sam Supakkul414715.24