Workflow Mining

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    Workflow mining:

    A survey of issues and approaches

    W.M.P. van der Aalst, B.F. van Dongen, J. Herbst,

    L. Maruster, G. Schimm, A.J.M.M. Weijters

    Eindhoven University of Technology

    Presenter: Seyed Ziae Mousavi MojabWayne State University - 2013

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    Agenda

    Introduction Related work

    Workflow mining

    Workflow log

    Comparison and open problems

    Conclusion

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    Introduction

    Data mining:The computational process of discovering patterns in large data

    sets involving methods at the intersection of artificial

    intelligence, machine learning, statistics, and database systems

    The process of

    analyzing data from

    different

    perspectives andsummarizing it into

    useful information

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    Introduction

    Workflow Life-Cycle: to construct a workflowmodel

    to deal with limitation andparticularities of the

    workflow management

    system

    to run and execute aworkflow and collect

    diagnostic information

    to provide input for thedesign phase

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    Introduction

    The Goal ofworkflow mining:

    To reverse the process and collect data at runtime to supportworkflow design and analysis.

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    Introduction

    Workflow mining techniques:

    o To create a feedback loop to adapt the workflow modelto change circumstances and detect imperfections of the

    design

    Reengineering Vs. Improvements

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    Related work

    Process mining approaches (investigated by Cook & Wolf):

    o Neural Networks Approacho Pure Algorithmic Approach

    o Markovian Approach

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    Related work

    Process mining approaches:

    o Neural Networks are non-linear predictive models that learn through

    training and resemble biological neural networks in

    structure

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    Related work

    Process mining approaches:

    o Pure Algorithmic Approach builds a finite state machine (FSM), where states are

    fused if their futures (in terms of possible behavior in

    the next k steps) are identical.

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    Related work

    Process mining approaches:

    o Markovian Approach uses a mixture of algorithmic and statistical methods

    and is able to deal with noise

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    Related work

    Final goal of discovery approaches:

    o we want to be able to generate a concrete Petri net ratherthan a set of dependency relations between events.

    Petri net: is one of several mathematical modeling

    languages for the description of distributed systems

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    Workflow Mining

    Workflow mining:

    o The goal of workflow mining is to extract informationabout processes from transaction logs.

    o Instead of starting with a workflow design, we start by

    gathering information about the workflow processes asthey take place.

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    Workflow Mining

    Workflow mining:

    o We assume: each event refers to a task (i.e: step in a workflow)

    each event refers to a case (i.e: workflow instance)

    events are totally ordered

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    Workflow Mining

    Workflow mining:

    o Definition: The term workflow (process) mining refers to

    methods for distilling a structured process

    description from a set of real execution.

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    Workflow Mining

    Workflow mining:

    o The challenge of workflow mining is to derive goodworkflow models with as little information as possible

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    Workflow Mining

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    Workflow Mining

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    Workflow Logs

    Logs:

    o The XML format is used as input for the analysis tools

    o The DTD specifies the syntax of a workflow log

    o It's a well-formed and valid XML file with top element

    WorkFlow_log

    o consists of (optional) information about the source

    program and information about one or more workflow

    processes.

    o Both processes and cases have an id and a description ...

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    Workflow Logs

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    Comparison & Open Problems

    Process mining tools:

    o EMiT: can discover the underlying process model and

    represent it in terms of a Petri net

    o Little Thumb: is a tool that attempts to induce a

    workflow model from a possibly noisy and incomplete

    workflow log

    o InWoLvE: to deal with duplicate tasks

    o Process Miner: exploiting the properties of block-

    structured workflows through rewriting rules

    o ...

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    Comparison & Open Problems

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    Comparison & Open Problems

    Open problems:

    o Few of the tools exploit timing information

    o Only can deal with basic routing constructs such as basic

    parallelism and basic loops

    o Problem of noise

    o ...

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    Conclusion

    Summary:

    o presented an overview of the various problems,

    techniques, tools, and approaches for workflow mining

    o compared the process mining tools by focusing on nine

    aspects (Structure, Time, Basic parallelism, ...).

    o revealed differences and also pointed out problems that

    need to be tackled.

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    Thank You!