65,047 research outputs found
A genetic programming based business process mining approach
As business processes become ever more complex there is a need for companies to understand the processes they already have in place. To undertake this manually would be time consuming. The practice of process mining attempts to automatically construct the correct representation of a process based on a set of process execution logs. The aim of this research is to develop a genetic programming based approach for business process mining. The focus of this research is on automated/semi automated business processes within the service industry (by semi automated it is meant that part of the process is manual and likely to be paper based). This is the first time a GP approach has been used in the practice of process mining. The graph based representation and fitness parsing used are also unique to the GP approach. A literature review and an industry survey have been undertaken as part of this research to establish the state-of-the-art in the research and practice of business process modelling and mining. It is observed that process execution logs exist in most service sector companies are not utilised for process mining. The development of a new GP approach is documented along with a set of modifications required to enable accuracy in the mining of complex process constructs, semantics and noisy process execution logs. In the context of process mining accuracy refers to the ability of the mined model to reflect the contents of the event log on which it is based; neither over describing, including features that are not recorded in the log, or under describing, just including the most common features leaving out low frequency task edges, the contents of the event log. The complexity of processes, in terms of this thesis, involves the mining of parallel constructs, processes containing complex semantic constructs (And/XOR split and join points) and processes containing 20 or more tasks. The level of noise mined by the business process mining approach includes event logs which have a small number of randomly selected tasks missing from a third of their structure. A novel graph representation for use with GP in the mining of business processes is presented along with a new way of parsing graph based individuals against process execution logs. The GP process mining approach has been validated with a range of tests drawn from literature and two case studies, provided by the industrial sponsor, utilising live process data. These tests and case studies provide a range of process constructs to fully test and stretch the GP process mining approach. An outlook is given into the future development of the GP process mining approach and process mining as a practice.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Process discovery techniques return process models that are either formal
(precisely describing the possible behaviors) or informal (merely a "picture"
not allowing for any form of formal reasoning). Formal models are able to
classify traces (i.e., sequences of events) as fitting or non-fitting. Most
process mining approaches described in the literature produce such models. This
is in stark contrast with the over 25 available commercial process mining tools
that only discover informal process models that remain deliberately vague on
the precise set of possible traces. There are two main reasons why vendors
resort to such models: scalability and simplicity. In this paper, we propose to
combine the best of both worlds: discovering hybrid process models that have
formal and informal elements. As a proof of concept we present a discovery
technique based on hybrid Petri nets. These models allow for formal reasoning,
but also reveal information that cannot be captured in mainstream formal
models. A novel discovery algorithm returning hybrid Petri nets has been
implemented in ProM and has been applied to several real-life event logs. The
results clearly demonstrate the advantages of remaining "vague" when there is
not enough "evidence" in the data or standard modeling constructs do not "fit".
Moreover, the approach is scalable enough to be incorporated in
industrial-strength process mining tools.Comment: 25 pages, 12 figure
Applications of concurrent access patterns in web usage mining
This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access pat-terns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
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