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Management of process execution data

Management of process execution data topic folder

Data Science and Big Data: What is the role of process (management)?

There is a rapidly growing interest in Data Science and Big Data. Unfortunately, lion's share of attention goes to topics such as MapReduce, NSA, and Hadoop (instead of BPM-related topics). What is the role of processes? How will BPM be influenced by the abundance of event data?
Although our capabilities to store and process data have been increasing exponentially since the 1960-ties, suddenly many organizations realize that survival is not possible without exploiting available data intelligently. Out of the blue, "Big Data" has become a topic in board-level discussions. The abundance of data will change many jobs across all industries and will definitely also change the BPM discipline. Data science aims to use the different data sources to answer questions grouped into the following four categories: • Reporting: What happened? • Diagnosis: Why did it happen? • Prediction: What will happen? • Recommendation: What is the best that can happen? The above questions are highly generic and can be applied in very different domains. Wikipedia states that "Data science incorporates varying elements and builds on techniques and theories from many fields, including mathematics, statistics, data engineering, pattern recognition and learning, advanced computing, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products". Many alternative definitions of data science have been suggested. The term "process" (or process management or BPM) does not appear in them. However, the relations between the above four questions and BPM are obvious. If you have ideas to embed BPM better in Big Data & Data Science or if you see novel ways of using event data in BPM, please let us know: through this blog or through your BPM 2014 submission.

Understanding process execution data?

Another important topic in this context is how to make particularly large process execution data accessible to users, e.g., process analysts. Is "pure" visualization sufficient or do have to find new ways of conveying the data?

Management of process execution data: Submit your work to BPM 2014

We, Wil van der Aalst and Stephanie Rinderle-Ma, encourage you to submit papers related to process execution data to BPM 2014 in Haifa. This first post discusses the scope of this topic area and list examples of successful BPM 2013 submissions within this scope.

The management of process execution data is increasing in importance in the BPM community. This is clearly shown in the survey  http://www.hindawi.com/isrn/se/2013/507984/ (Business Process Management: A Comprehensive Survey) which analyzes a decade of BPM conferences. It is also reflected by the papers presented at BPM 2013 and the interest in topics like process mining.

Topics in this track include:

  • — Process tracing and monitoring
  • — Process performance measurement
  • — Process mining
  • — Process data warehousing
  • — Data streaming in business processes
  • — Process data analytics and visualization

If you visit the conference in Beijing, please check out papers like:

  • Thomas Baier and Jan Mendling. Bridging Abstraction Layers in Process Mining by automated Matching of Events and Activities
  • Joos Buijs, Boudewijn Van Dongen and Wil Van Der Aalst. Mining Configurable Process Models from Collections of Event Logs
  • Chathura Ekanayake, Marlon Dumas, Luciano García-Bañuelos and Marcello La Rosa. Slice, Mine and Dice: Complexity-Aware Automated Discovery of Business Process Models
  • Simon Razniewski, Werner Nutt and Marco Montali. Verification of Query Completeness over Processes
  • Andreas Meyer, Luise Pufahl, Dirk Fahland and Mathias Weske. Modeling and Enacting Complex Data Dependencies in Business Processes
  • Stefan Appel, Sebastian Frischbier, Tobias Freudenreich and Alejandro Buchmann. Event Stream Processing Units in Business Processes
  • Geetika Lakshmanan, Szabolcs Rozsnyai and Fei Wang. Investigating Clinical Care Pathways Correlated With Outcomes
  • Massimiliano de Leoni and Wil Van Der Aalst. Aligning Event Logs and Process Models for Multi-Perspective Conformance Checking: An Approach Based on Integer Linear Programming
  • Jorge Munoz-Gama, Josep Carmona and Wil M.P. van der Aalst. Conformance Checking in the Large: Partitioning and Topology
  • David Knuplesch, Walid Fdhila, Manfred Reichert and Stefanie Rinderle-Ma. On Enabling Compliance of Cross-organizational Business Processes
  • Nicolas Poggi, Vinod Muthusamy, David Carrera and Rania Khalaf. Business Process Mining from Ecommerce Web logs
  • Fabrizio Maria Maggi, Marlon Dumas, Luciano Garcia-Banuelos and Marco Montali. Discovering Data-Aware Declarative Process Models from Event Logs
  • Jagadeesh Chandra Bose Rantham Prabhakara, Fabrizio Maggi and Wil Van Der Aalst. Enhancing Declare Maps Based on Event Correlations

We hope that next year there will again be many successful submissions related to the management of process execution data!!!

 

Wil van der Aalst and Stephanie Rinderle-Ma

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