Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Testing GUI-based Software with Undetermined Input Spaces

Most software applications feature a Graphical User Interface (GUI) front-end as the main, and often the only, method for the user to interact with the software. System-testing a software application requires it to be tested as a whole through the GUI. Testers need to generate sequences of GUI events (e.g., mouse clicks and menu selections) to exercise various behaviors of the application. Because the input space of a typical GUI (i.e., the space of all possible GUI events and their interactions) is often enormous, manual GUI testing is impractical. Model-based testing is a new approach that automatically and systematically generates a large number of test cases by leveraging a formal model representing the GUI input space. Unfortunately, modern applications often have a ``context-sensitive reachability GUI,'' in which the GUI components are only reachable with some particular state or environment constraints. Thus, it is challenging to determine the GUI input space and and obtain a GUI model for automated GUI testing. This research proposes new testing techniques to tackle the challenges in model-based GUI testing. The central thesis is this: GUI-based applications can be effectively and efficiently tested by systematically and incrementally leveraging the application runtime execution observations. To explore the thesis, a novel model-based testing paradigm called Observer-Model-Exercise* (OME*) is developed. This paradigm relies on the opportunistic observations obtained during test case execution to incrementally explore the GUI input space and construct a GUI model for test case generation. To evaluate OME*, an open-source automated model-based GUI testing framework called GUITAR is developed. An empirical study with 8 widely-used open-source applications demonstrated that the OME* approach is feasible. Compared to previous model-based testing approaches, OME* was able to increase the GUI input space discovered by as much as 1,044%. As a result, 34 new faults were detected in the subject applications

Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions

With the rise of Systems Biology as a new paradigm for understanding biological processes, the development of quantitative models is no longer restricted to a small circle of theoreticians. The dramatic increase in the number of these models precipitates the need to exchange and reuse both existing and newly created models. The Systems Biology Markup Language (SBML) is a free, open, XML-based format for representing quantitative models of biological interest that advocates the consistent specification of such models and thus facilitates both software development and model exchange.

Principally oriented towards describing systems of biochemical reactions, such as cell signalling pathways, metabolic networks and gene regulation etc., SBML can also be used to encode any kinetic model. SBML offers mechanisms to describe biological components by means of compartments and reacting species, as well as their dynamic behaviour, using reactions, events and arbitrary mathematical rules. SBML also offers all the housekeeping structures needed to ensure an unambiguous understanding of quantitative descriptions.

This is Release 1 of the specification for SBML Level 2 Version 4, describing the structures of the language and the rules used to build a valid model. SBML XML Schema and other related documents and software are also available from the SBML project web site, "http://sbml.org/":http://sbml.org/

Broadening the Scope of Security Usability from the Individual to the Organizational : Participation and Interaction for Effective, Efficient, and Agile Authorization

Restrictions and permissions in information systems -- Authorization -- can cause problems for those interacting with the systems. Often, the problems materialize as an interference with the primary tasks, for example, when restrictions prevent the efficient completing of work and cause frustration. Conversely, the effectiveness can also be impacted when staff is forced to circumvent the measure to complete work -- typically sharing passwords among each other. This is the perspective of functional staff and the organization. There are further perspectives involved in the administration and development of the authorization measure. For instance, functional staff need to interact with policy makers who decide on the granting of additional permissions, and policy makers, in turn, interact with policy authors who actually implement changes. This thesis analyzes the diverse contexts in which authorization occurs, and systematically examines the problems that surround the different perspectives on authorization in organizational settings. Based on prior research and original research in secure agile development, eight principles to address the authorization problems are identified and explored through practical artifacts

A Language for Designing Process Maps

Business Process Management (BPM) is often adopted by organizations as a method to increase awareness and knowledge of their business processes. Business process modeling is used as a method to represent business processes in form of business process models. The number of organizations adopting BPM is quickly increasing. By this means, so is the number of business process models as result of a BPM initiative. Within a single organization the number of business process models often ranges from hundreds to even thousands. In order to handle such large amount of business process models, organizations structure them by the help of a process architecture. It includes a process map, which is considered as the top-most view of the process architecture where the organization's business processes and the relations between them are visually and abstractly depicted. The details of each business process shown on the process map are stored in the lower levels of the corresponding process architecture. The purpose of a process map is to provide an overview of how an organization operates as a whole without necessarily going into the process details. Therefore, the design of a process map is vital not only for the understanding of the company's processes, but also for the subsequent detailed process modeling. This is primarily because, a process map is often the result of the process identification phase of the BPM lifecycle, and is used as a foundation for the subsequent phases, where the detailed process modeling and process improvement takes place. Despite their importance, the design of process maps is still more art than science, essentially because there is no standardized modeling language available for process map design. As a result, we are faced with a high heterogeneity of process map designs from practice, although they all serve a similar purpose. This has accordingly been our main motivation for pursuing the research presented in this thesis. The research question for this thesis is the following: How to effectively model processes on an abstract level? In this thesis, we document the development of a language for designing process maps. In particular, we provide the following contributions. First, we present a holistic reference BPM framework. It is a consolidation of procedural frameworks introduced by prominent BPM researchers. The framework includes eleven BPM elements, each holding activities organizations need to consider when adopting BPM. Second, we provide a method for assessing cognitive effectiveness of process maps used in practice. For this, we follow the nine principles for cognitively effective visual notations introduced by Moody cite{moody2012physics}. In addition, we employ the cognitive fit theory to check whether the design of process maps has an effect on the BPM success in the respective organization. Second, we conduct a systematic literature review on the quality of modeling languages and models. We use the quality requirements we found as basis for developing the language for designing process maps. Third, we define the abstract syntax, semantics, and concrete syntax of the language for process maps. We follow an explorative method, hence we rely on empirical data for the language development. Accordingly, we reuse symbols in our language which have already been used in practice as part of process maps. We follow this approach in order to ensure the language will consist of elements already familiar to organizations. We evaluate the language by means of an experiment, in which we assess the effectiveness and efficiency of process maps designed using elements from our language against process maps that have not been designed using our language. Last, this thesis provides a method for testing the suitability of existing languages for specific purposes. (author's abstract

A novel technique for high-resolution frequency discriminators and their application to pitch and onset detection in empirical musicology

This thesis presents and evaluates software for simultaneous, high-resolution time-frequency discrimination. Whilst this is a problem that arises in many areas of engineering, the software here is developed to assist musicological investigations. In order to analyse musical performances, we must first know what is happening and when; that is, at what time each note begins to sound (the note onset) and what frequencies are present (the pitch). The work presented here focusses on onset detection, although the representation of data used for this task could also be used to track the pitch. A potential method of determining pitch on a sample-to-sample basis is given in the final chapter. Extant software for onset detection uses standard signal processing techniques to search for changes in features like the spectrum or phase. These methods struggle somewhat, as they are constrained by the uncertainty principle, which states that, as time resolution is increased, frequency resolution must decrease and vice versa. However, we can hear changes in frequency to a far greater time resolution than the uncertainty principle would suggest is possible. There is an active process in the inner ear which adds energy and enables this perceptual acuity. The mathematical expression which describes this system is known as the Hopf bifurcation. By building a bank of tuned resonators in software, each of which operates at a Hopf bifurcation, and driving it with audio, changes in frequency can be detected in times that defy the uncertainty relation, as we are not seeking to directly measure the time-frequency features of a system, rather it is used to drive a system. Time and frequency information is then available from the internal state variables of the system. The characteristics of this bank of resonators - called a 'DetectorBank' - are investigated thoroughly. The bandwidth of each resonator ('detector') can be as narrow as 0.922Hz and the system bandwidth is extended to the Nyquist frequency. A nonlinear system may be expected to respond poorly when presented with multiple simultaneous input frequencies; however, the DetectorBank performs well under these circumstances. The data generated by the DetectorBank is then analysed by an OnsetDetector. Both the development and testing of this OnsetDetector are detailed. It is tested using a repository of recordings of individual notes played on a variety of instruments, with promising results. These results are discussed, problems with the current implementation are identified and potential solutions presented. This OnsetDetector can then be combined with a PitchTracker to create a NoteDetector, capable of detecting not only a single note onset time and pitch, but information about changes that occur within a note. Musical notes are not static entities: they contain much variation. Both the performer's intonation and the characteristics of the instrument itself have an effect on the frequency present, as well as features like vibrato. Knowledge of these frequency components, and how they appear or disappear over the course of the note, is valuable information and the software presented here enables the collection of this data

A Deep Learning Approach to Business Process Mining

Competing and evolving markets force organisations to continuously monitor, evaluate, and optimise their business processes. To do the task at scale, organisations often turn to automatic mining of process execution logs constantly generated by various information systems. Many open-source and commercial tools have been developed in recent years to help organisations perform various process mining tasks using process execution logs (often called event logs), such as process discovery, conformance checking, and detecting drifts in processes. Compared to traditional process mining techniques such as Petri nets and Business Process Model and Notation (BPMN), deep learning methods such as Recurrent Neural Networks and Long Short-Term Memory (LSTM) in particular have proven to achieve better performance in terms of accuracy and generalising ability when predicting sequences of activities performed as part of business processes based on event logs. However, unlike traditional network-based process mining techniques that can be used to visually present all activity sequences of the discovered business process, existing deep learning-based methods for process mining lack a mechanism explaining how the activity sequence predictions are made. To address this limitation, this thesis proposes an extensible process mining solution that combines the benefits of interpretable graph-based methods and more accurate but implicit deep learning methods. The main contributions of this research are: (i) building an LSTM model for predicting business process activity sequences from event logs that outperforms existing state-of-the-art deep learning solutions; (ii) proposing a graph-based approach to explaining the decision-making process of the LSTM model when predicting business process activity sequences; and (iii) developing methods for detecting and localising sudden concept drift in event logs (i.e., offline) and event streams (i.e., online) using deep learning and graph-based approaches. The proposed methods have been extensively evaluated by conducting experiments using real-life and artificial event logs and have been demonstrated to outperform existing state-of-the-art solutions in many cases