A genetic programming based business process mining approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "A genetic programming based business process mining
  author =       "Christopher James Turner",
  year =         "2009",
  school =       "School of applied Sciences, Cranfield University",
  address =      "UK",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://dspace.lib.cranfield.ac.uk/bitstream/1826/4471/1/Christopher_Turner_thesis_2009.pdf",
  URL =          "http://hdl.handle.net/1826/4471",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=27&uin=uk.bl.ethos.515106",
  size =         "303 pages",
  bibsource =    "OAI-PMH server at dspace.lib.cranfield.ac.uk",
  contributor =  "Ashutosh (supervisor) Tiwari",
  language =     "en",
  oai =          "oai:dspace.lib.cranfield.ac.uk:1826/4471",
  abstract =     "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 used for process

                 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, using 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.",
  notes =        "uk.bl.ethos.515106 Supervisor: Ashutosh Tiwari",

Genetic Programming entries for Christopher James Turner