Paper Info
Title
IRIS: A goal-oriented big data analytics framework on Spark for better Business decisions
Abstract
Big data analytics is the hottest new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business expectations because of lack of business context and lack of expertise to connect the dots, inaccurate scope and batch-oriented Hadoop system. In this paper, we present IRIS - a goal-oriented big data analytics framework using Spark for better business decisions, which consists of a conceptual model which connects a business side and a big data side providing context information around the data, a claim-based evaluation method which enables to focus the most effective solutions, a process on how to use IRIS framework and an assistant tool using Spark which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big queries or big data analytics. As an empirical study, a shipment decision process is used to show how IRIS can support better business decisions in terms of comprehensive understanding both on business and data analytics, high priority and fast decisions.
Year
DOI
Venue
2017
10.1109/BIGCOMP.2017.7881719
2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
Keywords
Field
DocType
Big Data Analytics,Goal-Orientated Approach,Business Process,Big Data,Conceptual Model
Data science,Artifact-centric business process model,Business analytics,Software analytics,Computer science,Web analytics,Analytics,Business intelligence,Big data,Business rule
Conference
ISSN
ISBN
Citations 
2375-933X
978-1-5090-3016-3
2
PageRank 
References 
Authors
0.45
4
4
Name
Order
Citations
PageRank
Grace Park1746.14
Sooyong Park2120778.34
Latifur Khan32323178.68
Lawrence Chung423636.31