PROCESS MODELS DISCOVERY AND TRACES CLASSIFICATION: A FUZZY-BPMN MINING APPROACH.

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain process, and describes causal dependencies between the various activities as performed within the process execution environment. In principle, one can use process discovery to obtain process models that describes reality. To this end, the work in this artcle presents a Fuzzy-BPMN mining approach that uses training events log representing 10 different real-time business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the accuracy and performance of the employed approach. The method focuses on carrying out a classification task to determine the traces, i.e. individual cases that makes up the test event logs in order to determine which traces that can be replayed by the original model. Thus, the paper aim is to provide a technique for process models discovery which is as good in balancing between “overfitting” and “underfitting” as it is able to correctly classify the traces that can be replayed (i.e allowed) or non-replayable (disallowed) by the model. In other words, the study shows through the Fuzzy-BPMN replaying notation and the series of validation experiments - how given any classified trace (for the test events log) and discovered process model (the training log) it can be unambiguously determined whether or not the traces found can be replayed on the discovered model

Linked Open Data: State-of-the-Art Mechanisms and Conceptual Framework

Today, one of the state-of-the-art technologies that have shown its importance towards data integration and analysis is the linked open data (LOD) systems or applications. LOD constitute of machine-readable resources or mechanisms that are useful in describing data properties. However, one of the issues with the existing systems or data models is the need for not just representing the derived information (data) in formats that can be easily understood by humans, but also creating systems that are able to process the information that they contain or support. Technically, the main mechanisms for developing the data or information processing systems are the aspects of aggregating or computing the metadata descriptions for the various process elements. This is due to the fact that there has been more than ever an increasing need for a more generalized and standard definition of data (or information) to create systems capable of providing understandable formats for the different data types and sources. To this effect, this chapter proposes a semantic-based linked open data framework (SBLODF) that integrates the different elements (entities) within information systems or models with semantics (metadata descriptions) to produce explicit and implicit information based on users’ search or queries. In essence, this work introduces a machine-readable and machine-understandable system that proves to be useful for encoding knowledge about different process domains, as well as provides the discovered information (knowledge) at a more conceptual level

Semantic process mining of enterprise transaction data

Process mining technologies provide capabilities for discovering and describing multiple perspectives of the real business process flows in an organization. Enterprise Resource Planning (ERP) systems are commonly stated in research as promising areas for process mining. ERP systems are application packages that have received wide industrial adoption, and they contain extensive amounts of data related to business process performance. However, very little research work describes actual experience from applying process mining in such industrial environments. In the work presented in this thesis, we have conducted studies on applying process mining techniques on real life ERP transaction data and we have explored technical opportunities targeting challenges introduced by the real world. Specifically, this thesis answers the following four research questions: RQ1. How can ontologies be applied to harmonize and interpret ERP transaction data? RQ2. Can reliable business process traces be extracted from large-scale transaction logs in ERP systems? RQ3. To what extent can semantic search techniques enrich process mining with explorative knowledge discovery? RQ4. How can ontologies be used to lift process mining from the technical level to a conceptual business level? The main contributions of this thesis are: C1. Ontology driven harmonization of event log structures from ERP data. C2. Ontology driven search for explorative investigations of process executions. C3. Techniques for annotating unlabeled transaction sequences with business process definitions. C4. Use of ontologies to manage perspectives of process mining models, define trace clusters and to extend the number of dimensions for data mining. C5. Value of search and semantics in business process mining on ERP transaction data