Goal-Driven Multi-Process Analysis.

Extant process modeling techniques address different aspects of processes, such as activity sequencing, resource allocation, and organizational responsibilities. These techniques are usually based on graphic notation and are driven by practice rather than by theoretical foundations. The lack of theoretical principles hinders the ability to ascertain the correctness of a process model. A few techniques (notably Petri Nets) are formalized and apply verification mechanisms (mostly for activity sequencing and concurrency). However, these techniques do not deal with important aspects of process design such as process goals. As previously suggested, a formal process modeling framework, termed the Generic Process Model (GPM), has been used to define the notion of process model validity. In GPM, validity is based on the idea that the purpose of process design is to assure that an enacted process can reach its goal. In practice, often several processes work together to accomplish goals in an organizational domain. Accordingly, in this paper we extend the validity analysis of a single process to a cluster of processes related by the exchange of physical entities or information. We develop validity criteria and demonstrate their application to models taken from the Supply Chain Operations Reference-model (SCOR). We also use the formal concepts to analyze the role of an information system in inter-process communication and its possible effects on process cluster validity

Variations in Conceptual Modeling: Classification and Ontological Analysis

Conceptual models are aimed at providing formal representations of a domain. They are mainly used for the purpose of understanding and communicating about requirements for information systems.Conceptual modeling has acquired a large body of research dealing with the semantics of modeling constructs, with the goal to make models better vehicles for understanding and communication. However, it is commonly known that different people construct different models of a given domain although all may be similarly adequate. The premise of this paper is that variations in models reflect vagueness in the criteria for deciding how to map reality into modeling constructs. Exploring model variations as such can contribute to research that deals with the semantics of modeling constructs.This paper reports an exploratory study in which empirically obtained model variations were qualitatively analyzed and classified into variation types. In light of the identified variation types, we analyzed two ontology-based modeling frameworks in order to evaluate their potential contribution to a reduction in variations. Our analysis suggests that such frameworks may contribute to more conclusive modeling decision making, thus reducing variations. However, since there is no complete consistency between the two frameworks, in order to reduce variations, a single framework should be systematically applied

THE ROLE OF DOMAIN KNOWLEDGE IN REQUIREMENTS ELICITATION: AN EXPLORATORY STUDY

Requirements elicitation is the first activity in the requirements engineering process. It includes learning, surfacing and discovering the requirements of the stakeholders of the developed system. The elicitation process involves actors of different roles, backgrounds and domain knowledge. Therefore, it is a communication-intensive process. Overcoming communication barriers between analysts and stakeholders, partly caused by a gap in their domain knowledge, is essential. Various elicitation techniques exist for helping analysts extract the requirements from the different stakeholders. During the elicitation process, the analysts are not limited to one specific technique and can use different techniques according to the situation, time and resources available. Analysts may have domain knowledge prior to the elicitation process. This prior knowledge may have an impact on the elicitation process, affecting the analysts’ decisions and conduct within it. This paper reports an exploratory study in which the perceived and actual effects of prior domain knowledge on the requirements elicitation process were examined. The results indicate that domain knowledge clearly affects the elicitation process and the way the analysts conduct the elicitation. The findings provide insights as to both positive and negative effects of domain knowledge on requirements elicitation, as perceived by participants with and without domain knowledge. Furthermore, these insights can be utilized in practice for supporting analysts in the elicitation process and for forming requirements analysis teams. They highlight the different contributions that can be provided by analysts with different levels of domain knowledge in requirements analysis teams and the synergy that can be gained by forming heterogeneous teams of analysts with and without domain knowledge

Conceptualizing Routing Decisions in Business Processes: Theoretical Analysis and Empirical Testing

Business process models are widely used for purposes such as analyzing information systems, improving operational efficiency, modeling supply chains, and re-engineering business processes. A critical aspect of process representation involves a choice among alternative or parallel routes. Such choices are usually represented in process models by routing structures that appear as “split” and “merge” nodes. However, evidence indicates that modelers face difficulties representing routing options correctly. Clearly, errors in representing routing options might negatively affect the effective use of business process models. We suggest that this difficulty can be mitigated by providing process modelers with a catalog of routing possibilities described in terms that are meaningful to analysts. Based on theoretical considerations, we develop such a catalog and demonstrate that its entries have business meaning and that it is complete with respect to a defined scope of process behaviors that do not depend on resources or on software features. The catalog includes some routing cases not previously recognized. We tested experimentally the catalog in helping subjects understand process behavior. The findings demonstrate that the catalog helps modelers understand and conceptualize process behavior and that the likely reasons are its completeness and the practical terms used to describe its entries

Model-based Analysis of Data Inaccuracy Awareness in Business Processes

Problem definition: Data errors in business processes can be a source for exceptions and hamper business outcomes. Relevance: The paper proposes a method for analyzing data inaccuracy issues already at process design time, in order to support process designers by identifying process parts where data errors might remain unrecognized, so decisions could be taken based on inaccurate data. Methodology: The paper follows design science, developing a method as an artifact. The conceptual basis is the notion of data inaccuracy awareness – the ability to tell whether potential discrepancies between real and IS values may exist. Results: The method was implemented on top of a Petri net modeling tool and validated in a case study performed in a large manufacturing company of safety–critical systems. Managerial implications: Anticipating consequences of data inaccuracy already during process design can help avoiding them at runtime

Data Impact Analysis in Business Processes

Business processes and their outcomes rely on data whose values are changed during process execution. When unexpected changes occur, e.g., due to last minute changes of circumstances, human errors, or corrections of detected errors in data values, this may have consequences for various parts of the process. This challenges the process participants to understand the full impact of the changes and decide on responses or corrective actions. To tackle this challenge, the paper suggests a semi-automated approach for data impact analysis. The approach entails a trans-formation of business process models to a relational database representation, to which querying is applied, in order to retrieve process elements that are related to a given data change. Specifically, the proposed method receives a data item (an attribute or an object) and information about the current state of process execution (in the form of a trace upon which an unexpected change has occurred). It analyzes the impact of the change in terms of activities, other data items, and gateways that are affected. When evaluating the usefulness of the approach through a case study, it was found that it has the potential to assist experienced process participants, especially when the consequences of the change are extensive, and its locus is in the middle of the process. The approach contributes both to practice with tool-supported guidance on how to handle unexpected data changes, and to research with a set of impact analysis primitives and queries