-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] AMCIS 2018 CFP | Big Data Driven Process Mining and Innovation (SIGDSA) Datum: Fri, 5 Jan 2018 20:19:47 +0000 Von: Arslan, Faruk Arslan@UHCL.edu An: aisworld@lists.aisnet.org aisworld@lists.aisnet.org

CALL FOR PAPERS

24th Americas Conference on Information Systems (AMCIS 2018) Aug. 16-18, 2018 New Orleans, LA, USA

Track: Data Science and Analytics for Decision Support

Mini-track: Big Data Driven Process Mining and Innovation (SIGDSA)

Description of Proposed Mini-track: 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. The objective of Process Mining is to discover, monitor and improve actual business processes by extracting knowledge from voluminous event logs generated because 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. Example topics may include, but are not limited to - design of data driven decision-making processes, case studies and empirical evaluation of data-driven process innovation, process mining approaches and algorithms

Fit with Track: With the proliferation of big data originating from multitude of sources such as social media, Internet of Things (IoT), sensor data, managers are exploring new ways to utilize data-driven decision making at various levels of the enterprise. 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. As such, data driven process innovation is an important subset of data-driven decision support in organizations.

Mini-track co-chairs: 1: Arti Mann, Assistant Professor, University of Northern Iowa, arti.mann@uni.edu 2: Faruk Arslan, Assistant Professor, University of Houston - Clear Lake, arslan@uhcl.edu

Submission Instructions: https://amcis2018.aisnet.org/submissions/call-for-papers/

Important Dates: January 15, 2018: Manuscript submissions open February 28, 2018: Deadline for paper submissions April 18, 2018: Authors will be notified of decisions April 25, 2018: Camera-ready submissions due

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