Ontology mapping of business process modeling based on formal temporal logic

A business process is the combination of a set of activities with logical order and dependence, whose objective is to produce a desired goal. Business process modeling (BPM) using knowledge of the available process modeling techniques enable a common understanding and analysis of a business process. Industry and academics use informal and formal methods respectively to represent business processes (BP), having the main objective to support an organization. Despite both are aiming at BPM but the methods used are quite different in their semantics. While carrying out literature research, it has been found that there is no general representation of business process modeling is available that is expressive than the commercial modeling tools and techniques. Therefore, it is primarily conceived to provide an ontology mapping of modeling terms of Business Process Modeling Notation (BPMN), Unified Modeling Language (UML) Activity Diagrams (AD) and Event Driven Process Chains (EPC) to temporal logic. Being a formal system, first order logic assists in thorough understanding of process modeling and its application. However, our contribution is to devise a versatile conceptual categorization of modeling terms/constructs and also formalizing them, based on well accepted business notions, such as action, event, process, sub-process, connector and flow. It is demonstrated that the new categorization of modeling terms mapped to formal temporal logic, provides the expressive power to subsume business process modeling techniques i.e. BPMN, UML AD and EPC

A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows

This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes. Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques. The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base. The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete. After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system. A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling

Automatic deduction of temporal information

In many computer based applications, temporal information has to be stored, retrieved, and related to other temporal information. Several time models have been proposed to manage temporal knowledge in the fields of conceptual modeling, database systems, and artificial intelligence. In this paper we present TSOS, a system for reasoning about time that can be integrated as a time expert in environments designed for broader problem­ solving domains. The main intended goal of TSOS is to allow a user to infer further information on the temporal data stored in the database through a set of deduction rules handling various aspects of time. For this purpose, TSOS provides the capability of answering queries about temporal specifications it has in its temporal database. Distinctive time modeling features of TSOS are the introduction of temporal modalities, i.e., the possibility of specifying if a piece of information is always true within a time interval or if it is only sometimes true, and the capability of answering about the possibility and the necessity of the validity of sorne information at a given time, the association of temporal knowledge both to instances o{ data and to types o{ data, and the development of a time calculus for reasoning on temporal data. Another relevant feature of TSOS is the ca­ pability to reason about temporal data specified at different time granularitie

Automatic Deduction of Temporal Information

In many computer-based applications, temporal information has to be stored, retrieved, and related to other temporal information. Several time models have been proposed to manage temporal knowledge in the fields of conceptual modeling, database systems, and artificial intelligence. In this paper we present TSOS, a system for reasoning about time that can be integrated as a time expert in environments designed for broader problem-solving domains. The main intended goal of TSOS is to allow a user to infer further information on the temporal data stored in the database through a set of deduction rules handling various aspects of time. For this purpose, TSOS provides the capability of answering queries about the temporal specifications it has in its temporal database. Distinctive time-modeling features of TSOS are the introduction of temporal modalitites , i.e., the possibility of specifying if a piece of information is always true within a time interval, or if it is only sometimes true, and the capability of answering about the possibility and the necessity of the validity of some information at a given time, the association of temporal knowledge both to instances of data and to types of data , and the development of a time calculus for reasoning on temporal data. Another relevant feature of TSOS is the capability to reason about temporal data specified at different time granularities. </jats:p