Cloud computing offers readily available, scalable infrastructure to tackle problems involving high data volume and velocity. Discovering processes from event streams, especially when the business processes execute in a cloud environment, is such a problem. Event stream data is generated rapidly with varying volume and must be processed on-the-fly, making stream processing an important use case for cloud computing. This paper describes a distributed, streaming implementation of the flexible heuristics miner on Amazon Kinesis, a cloud-based event stream infrastructure, showing how mining methods can scale effortlessly to tens of millions of events per minute.

Process discovery is an approach to extract process models from event logs. Given the distributed nature of modern information systems, event logs are likely to be distributed across different physical machines. Map-Reduce is a scalable approach for efficient computations on distributed data. In this paper we present Map-Reduce implementations of two well-known process mining algorithms to take advantage of the scalability of the Map-Reduce approach. We present the design of a series of mappers and reducers to compute the log-based ordering relations from distributed event logs. These can then be used to discover a process model. We provide experimental results that show the performance and scalability of our implementations.

An important step in developing the requirements for an information system is analyzing the application domain. In this step, conceptual models are used for representing an application domain. However, while languages for software design are available and widely used, no generally accepted language exists for conceptual modeling. This work suggests the use of object-oriented software modeling languages also for conceptual modeling. Such use can support a more accurate transition from domain models to software models. As software-modeling languages were not intended for modeling application domains, their constructs lack the required semantics. While previous papers addressed the representation of structural elements of domains using object concepts, this paper addresses behavioral aspects, related to change and interaction. The proposed semantics are based on a mapping between ontological concepts that describe behavior and object-oriented constructs related to dynamics. Based on these mappings, modeling rules are proposed to guide the modeler in creating ontologically well-formed models. The mappings and rules are exemplified using UML and are demonstrated using a case study.

Schema matching is the identification of database elements with similar meaning as preparation for subsequent database integration. Over the past 20 years, different schema-matching methods have been proposed and have been shown to be successful to various degrees. However, schema matching is an ongoing research area and the problem is not yet considered to be solved. This article reviews existing schema-matching methods from the perspective of theories of meanings drawn from philosophy and psychology. It positions existing methods, raises questions for future research based on these theories, and shows how these theories can form a firm theoretical basis as well as guide future schema-matching research.

One of the central problems of database integration is schema matching, that is, the identification of similar data elements in two or more databases or other data sources. Existing definitions of "similarity" in this context vary greatly. As a result, schema matching has given rise to a large number of heuristics software tools. However, the empirical understanding of this process in humans is very limited so that little guidance can be offered to the further development of heuristics and tools. This paper presents an exploratory process tracing study of the similarity judgement process in humans. The similarity judgements of 12 data integration professionals on a range of integration problems are recorded and analyzed. Implications for future empirical and applied research in this area are discussed.

The Unified Modeling Language (UML) is widely accepted as the de-facto standard for object-oriented information systems (IS) design and software modeling. Recent research has proposed to extend the use of UML to conceptual modeling of application domains. Conceptual models serve both as the basis for communication and domain understanding among analysts, and as the starting point for IS software design. Prior research has proposed a set of modeling rules to provide the analyst or modeler with guidelines for using UML for conceptual modeling.This paper provides an empirical assessment of the benefits derived from using these modeling rules. Using an experimental study involving 53 subjects, it examines the effects of different levels of modeling rule application on application domain understanding. The results show statistically significant differences in the level of domain understanding that was derived from the various models.

Information systems are situated in and are representations of some business or organizational domain. Hence, understanding the application domain is critical to the success of information systems development. To support domain understanding, the application domain is represented in conceptual models. The correctness of conceptual models can affect the development outcome and prevent costly rework during later development stages. This paper proposes a method to restrict the syntax of a modeling language to ensure that only possible configurations of a domain can be modeled, thus increasing the likelihood of creating correct domain models. The proposed method, based on domain ontologies, captures relationships among domain elements via constraints on the language metamodel, thus restricting the set of statements about the domain that can be generated with the language. In effect, this method creates domain specific modeling languages from more generic ones. The method is demonstrated using the Unified Modeling Language (UML). Specifically, it is applied to the subset of UML dealing with object behavior and its applicability is demonstrated on a specific modeling example.