Integrating computer log files for process mining: a genetic algorithm inspired technique

Process mining techniques are applied to single computer log files. But many processes are supported by different software tools and are by consequence recorded into multiple log files. Therefore it would be interesting to find a way to automatically combine such a set of log files for one process. In this paper we describe a technique for merging log files based on a genetic algorithm. We show with a generated test case that this technique works and we give an extended overview of which research is needed to optimise and validate this technique

Inheritance of interorganizational workflows : how to agree to disagree without loosing control?

Intemet-based technology, E-commerce, and the rise of networked virtual enterprises have fueled the need for interorganizational workflows. Although XML allows trading partners to exchange information, it cannot be used to coordinate activities in different organizational entities. Business-to-business processes are hindered by the lack of a common language to support collaboration. This paper describes the P2P (Public-To-Private) approach which addresses some of the problems using a notion of inheritance. The approach consists of three steps: (1) create a common understanding of the interorganizational workfiow by specifying the shared public workflow, (2) partition the public workflow over the organizational entities involved, and (3) for each organizational entity: create a private workflow which is a subclass of the relevant part of the public workfiow. This paper shows that this approach avoids typical anomalies in business-to-business collaboration (e.g., deadlocks and livelocks) and yields an interorganizational workfiow which is guaranteed to realize the behavior specified in the public workflow

Desire lines in big data : using event data for process discovery and conformance checking

Recently, the Task Force on Process Mining released the Process Mining Manifesto. The manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active contributions from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing relevance of process mining as a bridge between data mining and business process modeling. This paper summarizes the manifesto and explains why process mining is a highly relevant, but also very challenging, research area. This way we hope to stimulate the broader IS (Information Systems) and KM (Knowledge Management) communities to look at process-centric knowledge discovery. This paper summarizes the manifesto and is based on a paper with the same title that appeared in the December 2011 issue of SIGKDD Explorations (Volume 13, Issue 2)

"Mine your own business" : using process mining to turn big data into real value

Like most IT-related phenomena, also the growth of event data complies with Moore’s Law. Similar to the number of transistors on chips, the capacity of hard disks, and the computing power of computers, the digital universe is growing exponentially and roughly doubling every 2 years. Although this is not a new phenomenon, suddenly many organizations realize that increasing amounts of "Big Data" (in the broadest sense of the word) need to be used intelligently in order to compete with other organizations in terms of efficiency, speed and service. However, the goal is not to collect as much data as possible. The real challenge is to turn event data into valuable insights. Only process mining techniques directly relate event data to end-to-end business processes. Existing business process modeling approaches generating piles of process models are typically disconnected from the real processes and information systems. Data-oriented analysis techniques (e.g., data mining and machines learning) typically focus on simple classification, clustering, regression, or rule-learning problems. This keynote paper provides pointers to recent developments in process mining thereby clearly showing that process mining provides a natural link between processes and data on the one hand and performance and compliance on the other hand. Keywords: Process Mining, Process Discovery, Conformance Checking, Business Process Management

Desire lines in big data : using event data for process discovery and conformance checking

Recently, the Task Force on Process Mining released the Process Mining Manifesto. The manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active contributions from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing relevance of process mining as a bridge between data mining and business process modeling. This paper summarizes the manifesto and explains why process mining is a highly relevant, but also very challenging, research area. This way we hope to stimulate the broader IS (Information Systems) and KM (Knowledge Management) communities to look at process-centric knowledge discovery. This paper summarizes the manifesto and is based on a paper with the same title that appeared in the December 2011 issue of SIGKDD Explorations (Volume 13, Issue 2)

Process cubes : slicing, dicing, rolling up and drilling down event data for process mining

Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also signicant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.Keywords: OLAP, Process Mining, Big Data, Process Discovery, Conformance Checkin