[AISWorld] SIGDSA Mini-track – Big Data Driven Process Mining and Innovation

[Mann, Arti]

Dear Colleagues,

Consider submitting to the SIGDSA Mini-track – Big Data Driven Process Mining and Innovation (AMCIS 2017, Boston; August 20-12, 2017)

One of the main aspects of business analytics is process innovation driven by the use of data generated from the day-to-day business operations of an organization. Process innovation involves workflow re-design and resource re-configuration for higher efficiency, better quality and effectiveness; improving decision making processes for better information flow and decision-enablement. Process mining, a relatively new research discipline, may play a significant role in enabling such innovations.

As organizations delve into more data and analytics centric approaches for improving quality, efficiency, and effectiveness, more research is needed to inform us about the underlying challenges and opportunities. Process Innovation entails new or significantly improved methods of production and/or delivery. The objective of Process Mining is to discover, monitor and improve actual business processes by extracting knowledge from voluminous event logs generated as a result of the execution of those processes.

The aim of this mini-track is to promote theoretical and empirical research addressing the aforementioned aspects of process innovation.

Potential topics (suggestions, but are not limited to those listed):

·         design of data driven decision making processes,

·         case studies and empirical evaluation of data-driven process innovation,

·         process mining approaches and algorithms.

DEADLINE FOR SUBMISSION: March 1, 2017

Submission Information at https://amcis2017.aisnet.org/



Mini-track Co-Chairs:

Sagnika Sen, Pennsylvania State University, sagnika.sen at psu.edu<mailto:sagnika.sen at psu.edu>

Arti Mann, University of Houston Clear Lake, mann at uhcl.edu<mailto:mann at uhcl.edu>