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Background information on the Alpha miner

For the interested reader pointers follow to the scientific papers that describe in great detail how the Alpha miner works. Note that this is extra material and this is not required for the course.

  1. W.M.P. van der Aalst, A.J.M.M. Weijters, and L. Maruster. Workflow Mining: Which Processes can be Rediscovered? BETA Working Paper Series, WP 74, Eindhoven University of Technology, Eindhoven, 2002.
  2. W.M.P. van der Aalst, A.J.M.M. Weijters, and L. Maruster. Workflow Mining: Discovery Process Models from Event Logs IEEE Transactions on Knowledge and Data Engineering, 16(9):1128-1142, 2004.

Note that many extensions of the Alpha miner exist:

  1. Alpha+: A.K.A. de Medeiros, B.F. van Dongen, W.M.P. van der Aalst, and A.J.M.M. Weijters. Process Mining for Ubiquitous Mobile Systems: An Overview and a Concrete Algorithm. In L. Baresi, S. Dustdar, H. Gall, and M. Matera, editors, Ubiquitous Mobile Information and Collaboration Systems (UMICS 2004), pages 156-170, 2004.
  2. Alpha++: L. Wen, W.M.P. van der Aalst, J. Wang, and J. Sun. Mining process models with non-free-choice constructs. Data Mining and Knowledge Discovery, 15(2):145-180, 2007.
  3. Alpha#: L. Wen, J. Wang, W.M.P. van der Aalst, B. Huang, and J. Sun. Mining Process Models with Prime Invisible Tasks. Data and Knowledge Engineering, 69(10):999-1021, 2010.

In short, the differences between the four algorithms are that the Alpha miner is of course the first and most basic implementation. The Alpha+ algorithm is an extension on the Alpha miner which can deal with short-loops and self-loops. The Alpha++ algorithm is again an extension, which is able to find additional non-free choice constructs (e.g. can discover more complex patterns). The Alpha# algorithm can also discover several invisible tasks that are not observable but which do influence the control flow.

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This article is from the free online course:

Introduction to Process Mining with ProM

Eindhoven University of Technology

Get a taste of this course

Find out what this course is like by previewing some of the course steps before you join:

  • Introduction

    Introduction to process mining: recognizing event data, what is process mining and what can process mining analyse.

  • Installing ProM lite
    Installing ProM lite

    In this step we show how to find and install the free and open source process mining tool ProM lite.

  • Using ProM lite
    Using ProM lite

    In this lecture we show the basic concepts and usage of ProM (lite): the resource, action and visualization perspectives.

  • Event logs
    Event logs

    In this lecture we explain what an event log is and how it is structured. We also explain the most common attributes found in an XES event log.

  • Event logs in ProM
    Event logs in ProM

    In this lecture we show you how you can load an event log in ProM and how you can get initial insights in the contents.

  • Converting a CSV file to an event log
    Converting a CSV file to an event log

    Most data is not recorded in event log format. In this video we explain how a CSV file can be converted to an event log.

  • Exploring event logs with the dotted chart
    Exploring event logs with the dotted chart

    After loading an event log into ProM it is important to apply the dotted chart to get initial process insights before process models are discovered.

  • Filtering event logs
    Filtering event logs

    Before good quality process models can be discovered the event log data needs to be filtered to contain only completed cases for instance.

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