International Journal of Cooperative Information Systems

Cooperative information systems (CIS) are at the core of a digital economy that is dedicated to providing hyper-connected citizens with access to intuitive and sophisticated technology. No longer are commercial applications the only driver of development activities, but wider societal and individual deployment areas are now explored. CIS researchers have for more than two decades studied the nature and requirements of cooperative systems linking latest technologies with hybrid user bases. However, the current opportunity-rich landscape of digital technologies such as mobile and social computing, advanced data analytics, robotic process automation or smart devices has triggered entire new, exciting research questions. In light of this setting, the 23rd International Conference on Cooperative Information Systems took place within the context of the 14th OnTheMove Federated Conferences and Workshops in Rhodes, Greece, in October 2015. This Special Issue features the carefully selected and comprehensively reviewed best papers from this event.

Today’s emerging trends like factory of the future, big data, Internet-of-things, intelligent traffic solutions, cyber-physical systems, wireless sensor networks, smart home, smart city and smart grid raise new challenges on software development. They are characterized by high concurrency, distribution and dynamics as well as huge numbers of heterogeneous devices, resources and users that must collaborate in a reliable way. The management of the interactions and dependencies between the participants is a complex task posing massive coordination problems. The here proposed approach is twofold: (i) to analyze similarities in the communication and synchronization behavior of such applications and to identify coordination patterns; and (ii) to give a precise specification of them by means of a suitable coordination model which enables the development of coordination pattern-based software components as solutions. The vision is to compose advanced cooperative information systems from proven, configurable, reusable, generic components that run on a suitable target platform, in order to reduce software development time, risks and costs. In this paper we delimit the idea of “coordination patterns” from other related pattern approaches and motivate the need for a well-defined model to specify them. Several coordination models to achieve this goal are discussed, and the advantages of a new coordination model termed the “Peer Model” are pointed out. The feasibility of the approach to identify coordination patterns, to model them and to provide generic components that can be reused in different scenarios through configuration and composition is evaluated by means of a coordination pattern found in several industrial use cases.

Business processes describe and implement the business logic of companies, control human interaction, and invoke heterogeneous services during runtime. Therefore, ensuring the correct execution of processes is crucial. Existing work is addressing this challenge through process verification. However, the highly dynamic aspects of the current processes and the deep integration and frequent invocation of third party services limit the use of static verification approaches. Today, one frequently utilized approach to address this limitation is to apply process tests. However, the complexity of process models is steadily increasing. So, more and more test cases are required to assure process model correctness and stability during design and maintenance. But executing hundreds or even thousands of process model test cases lead to excessive test suite execution times and, therefore, high costs. Hence, this paper presents novel coverage metrics along with a genetic test case selection algorithm. Both enable the incorporation of user-driven test case selection requirements and the integration of different knowledge sources. In addition, techniques for test case selection computation performance optimization are provided and evaluated. The effectiveness of the presented genetic test case selection algorithm is evaluated against five alternative test case selection algorithms.

Increasing attention has been paid to the detection and analysis of “deviant” instances of a business process that are connected with some kind of “hidden” undesired behavior (e.g. frauds and faults). In particular, several recent works faced the problem of inducing a binary classification model (here named deviance detection model ) that can discriminate between deviant traces and normal ones, based on a set of historical log traces (labeled as either deviant or normal). Current solutions rely on applying standard classifier-induction methods to a feature-based representation of the given traces, where the features include sequence-based patterns extracted from the corresponding sequences of activities. However, there is no consensus on which kinds of patterns are the most suitable for such a task. On the other hand, mixing multiple pattern families together may produce a heterogenous, redundant and sparse representation of the traces that likely leads to poor deviance detection models. In this paper, we propose an ensemble-learning method for solving this problem, where multiple base classifiers are trained on different feature-based views of the log (each obtained by mapping the traces onto a distinguished collection of patterns). A stacking procedure is used to combine the discovered base models into an overall probabilistic model that associates any new trace with an estimate of the probability that it reflects a deviant process instance. This helps the analyst prioritize the inspection of the cases that are more likely to be deviant. The method also takes advantage of all nonstructural data available in the log, and employs a resampling mechanism to deal with the rarity of deviances in the training log. It has been conceived as the core of a comprehensive framework for detecting and analyzing business process deviances. The framework supports the analyst to investigate suspect deviances, and provides some feedback to the learning method for improving the accuracy of the discovered deviance detection models. Tests on several real-life datasets proved the validity of the approach, as concerns its capability to discover an accurate deviance detection model, and to effectively exploit new (originally unlabeled) traces via active learning and self-training mechanisms.

Competition in today’s globalized markets forces organizations to collaborate within dynamic business networks to provide mass-customized integrated solutions for customers. The collaboration within dynamic business networks necessitates forming dynamic networked business processes (DNBPs). Networked business processes need to be supported by high quality information that is exchanged in a trustworthy environment. Information governance (IG) is described as a holistic approach to different mechanisms that support high quality and secure information exchanges. However, dynamism of networked business processes causes IG issues like unsecured information access and low quality information products to emerge. In this paper, a comprehensive list of the IG issues in DNBPs is identified through structured steps. The identified IG issues are characterized within four main categories, respectively, information product quality, information service quality, information security, and metadata issues. For the evaluation of the practical significance of the identified IG issues, a case study is conducted in a business network that provides mobility solutions. In this way, the paper closes the gap between studies on IG, which have mostly concentrated on IG within the borders of a single organization or IG in stable business networks, and studies on dynamic business networks, which have addressed the formation of dynamic inter-organizational interactions without paying rigorous attention to information artefacts that are exchanged.