The goal of the Special Session on Process Mining of the 2015 IEEE Symposium on Computational Intelligence and Data Mining Symposium is to allow experts in the
area of process mining and (big) data analysis to share new techniques,
applications and case studies. This session is organized by the IEEE
Task Force on Process Mining.
We now live in a
time where the amount of data created daily goes easily beyond the
storage and processing capabilities of nowadays systems: organizations,
governments but also individuals generate large amounts of data at a
rate that has started to overwhelm the ability to timely extract useful
knowledge from it. Nevertheless the strategic importance of the
knowledge hidden in such data, for effective decision making is
paramount and need to be extracted quickly in order to effectively react
to dynamic situations. Efficient stream processing approaches for real
time analysis are crucial for enabling the predictive capabilities
required by today's dynamically and rapidly evolving enterprises.
Moreover, since the work of medium-large enterprises is typically
governed by business processes, it is very common to have event data
generated as result of such process executions.
Process
mining is a relatively young research discipline that sits between
computational intelligence and data mining on the one hand and process
modeling and analysis on the other hand. The idea of process mining is
to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today's systems.
Process
mining provides an important bridge between data mining and business
process analysis. Under the Business Intelligence (BI) umbrella many
buzzwords have been introduced to refer to rather simple reporting and
dashboard tools, such as BAM, CEP, CPM, CPI, BPI, TQM and Six Sigma.
These approaches have in common that processes are "put under a
microscope" to see whether further improvements are possible. Process
mining is an enabling technology for CPM, BPI, TQM, Six Sigma, and the
like.
Over the last decade,
event data have become readily available and process mining techniques
have matured. Process mining algorithms have been implemented in various
academic and commercial systems. Today, there is an active group of
researchers working on process mining and it has become one of the "hot
topics" in Business Process Management (BPM) research. Moreover, there
is a huge interest from industry in process mining. More and more
software vendors are adding process mining functionality to their tools.
Finally
the level of maturity and the relative low-cost of distributed
approaches for storage and processing of information has not been fully
exploited by the process mining community. There are very few research
results on distributed storage methods and process mining algorithms.
Considering
all these aspects, a special session on process mining can improve the
value of the conference by enhancing awareness of typical problems and
issues of process mining. In addition, it is possible to get inspired
from classical data mining approaches and methodologies in order to
improve analysis of data coming from information systems.
Topics of Interest
OrganizersAndrea Burattin, University of Innsbruck, Austria |