Business Process Analytics and Big Data Systems: A Roadmap to Bridge the Gap

被引：24

作者：

Sakr, Sherif ^{[1
]}

Maamar, Zakaria ^{[2
]}

Awad, Ahmed ^{[1
,3
]}

Benatallah, Boualem ^{[4
]}

Van Der Aalst, Wil M. P. ^{[5
]}

机构：

[1] Univ Tartu, Inst Comp Sci, EE-50409 Tartu, Estonia

[2] Zayed Univ, Coll Technol Innovat, Dubai, U Arab Emirates

[3] Cairo Univ, Giza, Egypt

[4] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2015, Australia

[5] Rhein Westfal TH Aachen, Dept Comp Sci, D-52074 Aachen, Germany

来源：

IEEE ACCESS | 2018年 / 6卷

关键词：

Business process analytics; Big Data systems; process data-intensive operations; EVENT CORRELATION; DISCOVERY; MAPREDUCE; FRAMEWORK;

D O I：

10.1109/ACCESS.2018.2881759

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Business processes represent a cornerstone to the operation of any enterprise. They are the operational means for such organizations to fulfill their goals. Nowadays, enterprises are able to gather massive amounts of event data. These are generated as business processes are executed and stored in transaction logs, databases, e-mail correspondences, free form text on (enterprise) social media, and so on. Taping into these data, enterprises would like to weave data analytic techniques into their decision making capabilities. In recent years, the IT industry has witnessed significant advancements in the domain of Big Data analytics. Unfortunately, the business process management (BPM) community has not kept up to speed with such developments and often rely merely on traditional modeling-based approaches. New ways of effectively exploiting such data are not sufficiently used. In this paper, we advocate that a good understanding of the business process and Big Data worlds can play an effective role in improving the efficiency and the quality of various data-intensive business operations using a wide spectrum of emerging Big Data systems. Moreover, we coin the term process footprint as a wider notion of process data than that is currently perceived in the BPM community. A roadmap towards taking business process data intensive operations to the next level is shaped in this paper.

引用

页码：77308 / 77320

页数：13

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