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Multi-level modeling is currently regaining attention in the database and software engineering community with different emerging proposals and implementations. One driver behind this trend is the need to reduce model complexity, a crucial aspect in a time of analytics in Big Data that deal with complex heterogeneous data structures. So far no standard exists for multi-level modeling. Therefore, different formalization approaches have been proposed to address multi-level modeling and verification in different frameworks and tools. In this article, we present an approach that integrates the formalization, implementation, querying, and verification of multi-level models. The approach has been evaluated in an open-source F-Logic implementation and applied in a large-scale data interoperability project in the oil and gas industry. The outcomes show that the framework is adaptable to industry standards, reduces the complexity of specifications, and supports the verification of standards from a software engineering point of view. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of transforming business specifications written in natural language into formal models suitable for use in information systems development. It proposes a method for transforming controlled natural language specifications based on the Semantics of Business Vocabulary and Business Rules standard. This approach is unique in combining techniques from Model-Driven Engineering (MDE), Cognitive Linguistics, and Knowledge-based Configuration, which allows the reliable semantic processing of specifications and integration with existing MDE tools to improve productivity, quality, and time-to-market in software development. The method first learns the vocabulary of the specification from glossary-like definitions then parses the rules of the specification and outputs the resulting formal SBVR model. Both aspects of the method are tested separately, with the system correctly learning 98% of the vocabulary and correctly interpreting 98% of the rules of an SBVR SE based example. Finally, the proposed method is compared to state-of-the-art approaches for creating formal models from natural language specifications, arguing that it meets the criteria necessary to fulfil the three goals of (1) shifting control of specification to non-technical business experts, (2) reducing the manual effort involved in formalising specifications, and (3) supporting business experts in creating well-formed sets of business vocabularies and rules. [ABSTRACT FROM AUTHOR]

Service composition is a recent field that has seen a flurry of different approaches proposed towards the goal of flexible distributed heterogeneous interoperation of software systems, usually based on the expectation that such systems must be derived from higher-level models rather than be coded at low level. In practice, achieving service interoperability nonetheless continues to require significant modelling approach at multiple abstraction levels, and existing formal approaches typically require the analysis of the global space of joint executions of interacting services. Based on our earlier work on providing locally checkable consistency rules for guaranteeing the behavioural consistency of inheritance hierarchies, a model-driven approach for creating consistent service orchestrations is proposed. Service execution and interaction is represented with a high-level model in terms of extended Petri net notation; formal criteria are provided for service consistency that can be checked in terms of local model properties, and give a multi-step design approach for developing services that are guaranteed to be interoperable. Finally, it is outlined how the presented results can be carried over and applied to modelling processes using the Business Process Modelling Notation (BPMN). [ABSTRACT FROM AUTHOR]

Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work‐integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro‐level relations. Objectives: The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. Method: This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem‐solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. Results and Conclusions: We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Implications: Applying temporal motifs as an analytics‐based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion. Lay Description: What is already known about this topic?: The development of team cohesion has been widely studied for decades in face‐to‐face educational contexts but remains underexplored in online environmentsObtrusive tools, such as questionnaire instruments are still the primary method used to examine team cohesionTeam cohesion is an emergent state that changes over time, representing the synchrony of different behavioural interactionsResearch that explores and examines the temporality of cohesion as it unfolds in team learning is limited What this paper adds?: Underlines how emerging team behaviours are characterised by their engagement, performance and cohesion development using temporal network motifsPresents an analytics‐based measure to examine cohesion using temporal network motifs as a proxyProvides a learning analytics approach for researchers and instructors to evaluate cohesion in online learning environments through online learner trace data The implications of study findings for practitioners: The paper provides insights into distinct teams with emerging learning behaviours, performance associations and cohesion in an online learning platform.The paper provides instructors with a way to monitor and support a team's cohesion over time by examining the way temporal motifs occur.The paper offers a new avenue to understanding learner engagement beyond summarisations [ABSTRACT FROM AUTHOR]

A correction to the article "Design for service compatibility" that was published in the 2012 issue is presented.

Student-facing visualisations have attracted increased attention with recent developments in data-driven tools to support individual and group work. Learning analytics dashboards (LADs), a data-enhanced feedback tool that allows students to make sense of their learning by providing insights into their learning behaviours represents one of the prominent examples of this trend. While these visualisation tools are increasingly used to study and enhance students' learning in academic contexts, current research is limited regarding the effects of the LADs in K-12 environments. There is a missed opportunity to empower teams and allow instructors and researchers to understand how teams use the LAD to regulate their learning. In this study, we developed a K-12 LAD for supporting students' collaborative work and evaluated with respect to students' perceived usefulness of the proposed LAD and the association between its use and course performance. The study followed a mixed-methods approach, combining quantitative analysis of log data from the dashboard and qualitative analysis of students' perceptions using surveys and focus groups. Our results show that different roles within teams have distinguished engagement patterns with the LAD and that the tool improves the collaborative learning experience. We postulate that the implications of this study will aid future research work when investigating the behaviours of teams and optimising their learning using LADs. • Learning analytics dashboard in K-12 collaborative learning promotes engagement and collaboration. • Team interactions with a dashboard can support instructors in assessing and improving collaborative learning behaviours. • Visuals illustrating workflow processes of teams fosters more engagement with the dashboard compared to traditional visuals. • Discovered behavioural differences between team members and leader engagement with the learning analytics dashboard. • K-12 teams perceived the learning analytics dashboard as useful in supporting team regulation in collaborative settings. [ABSTRACT FROM AUTHOR]

Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We describe a goal-oriented approach to manage this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements at runtime. We offer an extended vocabulary of goal types by presenting two novel conceptions: differential goals and integral goals, which we formalize in both linear-time and branching-time settings. We describe goal lifecycles and interactions and the extended notion of context for the representation of rapidly changing, complex operating environments. We then illustrate the working of the approach by presenting a detailed scenario of adaptation in a Kubernetes setting, in the face of a Distributed Denial-of-Service (DDoS) attack. • Goal-based approach to IT infrastructure agility can help maintain situational awareness. • Temporal goal types help capture goals relating to rate of evolution and aggregation. • Mappings between goal models and execution platform elements facilitates self-adaptation. [ABSTRACT FROM AUTHOR]

Many industry sectors have been pursuing the adoption of Industry 4.0 (I4.0) ideas and technologies, which promise to realize lean and just-in-time production through digitization and the use of smart machines. This shift is driven by technological advances, including Artificial Intelligence (AI) and machine learning, sensor networks and Internet of Things technologies, cloud computing, additive manufacturing, and the availability of large amounts of data that can be exploited by these technologies. However, the adoption of AI technologies for I4.0 varies considerably among industry sectors. This article complements broader reviews of I4.0 by examining the specific applications of IAI in several industry sectors, highlighting the issues and concerns encountered in and across different industry sectors, and discussing potential solutions that have been introduced along with opportunities and challenges for adoption. In this article, we review the literature to identify common themes and concerns related to the adoption of AI technologies in the context of I4.0 in several industry sectors. AI solutions are discussed in the context of an AI adoption pipeline that spans data collection, processing, model construction, and interpretation of results. Our findings indicate that although different industries share common issues, the adopted solutions are often specific to a particular industry sector, which may be difficult to transfer to other sectors. Moreover, industry sectors may pursue different adoption strategies due to varying experience and maturity of AI practices. These findings may inform managers, practitioners, and decision-makers who are involved in the adaptation of Industry 4.0 transformation in their respective industry sectors. [ABSTRACT FROM AUTHOR]

One of the most significant challenges in information system design is the constant and increasing need to establish interoperability between heterogeneous software systems at increasing scale. The automated translation of data between the data models and languages used by information ecosystems built around official or de facto standards is best addressed using model-driven engineering techniques, but requires handling both data and multiple levels of metadata within a single model. Standard modelling approaches are generally not built for this, compromising modelling outcomes. We establish the SLICER conceptual framework built on multilevel modelling principles and the differentiation of basic semantic relations (such as specialisation, instantiation, specification and categorisation) that dynamically structure the model. Moreover, it provides a natural propagation of constraints over multiple levels of instantiation. The presented framework is novel in its flexibility towards identifying the multilevel structure, the differentiation of relations often combined in other frameworks, and a natural propagation of constraints over multiple levels of instantiation. [ABSTRACT FROM AUTHOR]

The article presents an abstract on a paper titled "Anatomic Assessment of Pulmonary Veins and Left Atrium: Comparison of Magnetic Resonance Angiography With Gadoteric Acid, the Blood Pool Contrast Agent B22956/1 and a Non-Contrast Enhanced Imaging Approach," presented at the 2011 Society for Cardiovascular Magnetic Resonance (CMR)/Euro CMR Joint Scientific Sessions held in France.

This article describes the technical principles and representation of a constraint-based configuration method for work processes. Methods developed for the configuration of modular systems comprising components have traditionally adopted a representation where the properties and compatibility requirements are expressed as constraints associated with individual components. However, this representation does not accurately capture constraints on paths and subprocesses and is therefore unsuitable for process configuration. This article extends established constraint-based configuration methods with a constraint language for specifying properties of execution paths in work processes. A framework for semiautomated process customization is presented. It integrates the extended constraint language with a metamodel of the work processes in an organization and allows to adapt generic work processes to fit the requirements of specific development projects. Heuristic search methods are applied to build valid process configurations by incrementally resolving constraint violations. The declarative framework facilitates the adaptation of abstract work processes as well as the validation and repair of existing processes. The approach was developed in the context of a real-world system of complex design and development processes where it was shown that significant process improvements and reduction in effort required to edit process models can be achieved. [ABSTRACT FROM PUBLISHER]