Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.) without any labeled data. Modern neural networks have outstanding ability in modeling complex time series. Self-supervised models in particular tackle unsupervised TSAD by transforming the input via various augmentations to create pseudo anomalies for training. However, their performance is sensitive to the choice of augmentation, which is hard to choose in practice, while there exists no effort in the literature on data augmentation tuning for TSAD without labels. Our work aims to fill this gap. We introduce TSAP for TSA “on autoPilot”, which can (self-)tune augmentation hyperparameters end-to-end. It stands on two key components: a differentiable augmentation architecture and an unsupervised validation loss to effectively assess the alignment between augmentation type and anomaly type. Case studies show TSAP’s ability to effectively select the (discrete) augmentation type and associated (continuous) hyperparameters. In turn, it outperforms established baselines, including SOTA self-supervised models, on diverse TSAD tasks exhibiting different anomaly types.

Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility, and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group provides a suitable solution for externalising decisions from processes and automating decision enactments for processes. This article introduces a systematic way of tackling the separation of the decision modelling concern from process modelling by providing a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA) which approaches decisions as automated and externalised services that processes need to invoke on demand to obtain the decision outcome. The DaaS mechanism is elucidated by a formalisation of DMN constructs and the relevant layer elements. Furthermore, DaaS is evaluated against the fundamental characteristics of the SOA paradigm, proving its contribution in terms of abstraction, reusability, loose coupling, and other pertinent SOA principles. Additionally, the benefits of the DaaS design on process-decision modelling and mining are discussed. Finally, the DaaS design is illustrated on a real-life event log of a bank loan application and approval process, and the SOA maturity of DaaS is assessed.

Many designers and User Interface (UI) educators discuss principles to be followed when designing the functional aspects of a UI. However, many UI principles have been proposed, scattered in scientific papers and teaching books. Some principles are different, and many are somehow similar or overlapping with others. This makes it very difficult to comprehend where to pay attention to when designing a UI. In this paper, we perform a systematic literature review to first identify the most relevant authors in the domain of functional UI design principles. Focusing on the three most cited works of these authors, we extracted 257 principles. We next analyzed all these principles, unified their variants, and, considering their scientific influence, finally derived a shorter and core selection of 36 principles. This core selection provides educators and UI designers with a clear path to teach, evaluate, learn and improve the UI functional design.

The Internet of Things (IoT) has seen a vast surge in recent years, both in academia and in industry. Business processes are starting to take advantage of IoT to interact with the increasingly connected and dynamic physical environment. Recent literature has focused on analysing how this interaction between processes and IoT devices can be modelled, i.e. how the process can be described for discussion purposes. However, little attention is given to configuring the execution of these IoT-based processes. IoT-enhanced processes should indeed be able to incorporate IoT devices in the endeavour of process execution. This paper discusses research challenges in IoT process configuration and execution using the executable Business Process Model and Notation 2.0 (BPMN 2.0) standard. As such, we constitute an agenda for future research.

User Interface (UI) design and software engineering complement each other to develop useful and usable interactive software systems. However, the body of knowledge for the development of an application and for the design of its UI are not always well integrated. The problem starts in the education of both subjects, which are normally taught independently of each other. Although an integrative teaching approach can significantly contribute to the development of better interactive software systems, there is a lack of concrete and proven approaches for such way of teaching. This paper presents a fully functional didactic tool for filling this gap. This tool provides the learner with feedback about how to develop an application and how to design a proper UI for it. Applying Model Driven Engineering principles, the tool automatically generates a working prototype of the interactive software system from its specification models, allowing the learner to try out the final application and validate the requirements. An experiment with novice developers demonstrates the advantages of this didactic tool.

Task Models (TMs) that can adapt to context are a state-of-the-art executable modeling language that has proven to be successful in the automation and support of daily user tasks in pervasive systems. TMs are intuitive and easy to use to design such systems, however, they do not provide any verification technique for ensuring their correctness. To enable verification checking, we investigate and define mappings that can translate TMs to Context-Adaptive Petri Nets (CAPNs). CAPN is a recently proposed formalism to support Petri Nets (PNs) that take into account their execution context. Using PN as a base, CAPN provides powerful techniques for behavior simulation and verification. By applying the defined mappings, pervasive system's behavior can be intuitively represented using task models and then translated to their equivalent CAPNs. These CAPNs can be then used to perform an exhaustive checking of the represented behavior at design time in order to ensure a proper and safe system execution at runtime.

Providing individual and immediate feedback to students is a critical factor for improving knowledge and skills acquisition in higher education. However, a growing number of students with very different heterogeneous profiles and unequal problem solving skills, as well as the lack of teaching resources, make it very challenging and sometimes even impossible to give immediate feedback at individual level. This paper presents a synthesis and progress of a long-term project that addresses this challenge in the context of conceptual modelling by developing SAiLE@CoMo, a smart and adaptive learning environment. By crafting innovative process analytics techniques and expert knowledge on feedback automation, SAiLE@CoMo can automatically provide personalized and immediate feedback to leaners.

Context: Empirical Software Engineering (ESE) replication researchers need to store and manipulate experimental data for several purposes, in particular analysis and reporting. Current research needs call for sharing and preservation of experimental data as well. In a previous work, we analyzed Replication Data Management (RDM) needs. A novel concept, called Experimental Ecosystem, was proposed to solve current deficiencies in RDM approaches. The empirical ecosystem provides replication researchers with a common framework that integrates transparently local heterogeneous data sources. A typical situation where the Empirical Ecosystem is applicable, is when several members of a research group, or several research groups collaborating together, need to share and access each other experimental results. However, to be able to apply the Empirical Ecosystem concept and deliver all promised benefits, it is necessary to analyze the software architectures and tools that can properly support it.

Large systems engineering projects involve the cooperation of various stakeholders from different engineering disciplines. Individual stakeholders apply various tools and related data storage approaches that (a) might hinder seamless interoperability and (b) include limited capability to support data versioning. Project-level concepts enable the mapping of engineering data coming from different disciplines. However, it remains open how to store data on project level that enable flexible and efficient data access from different disciplines in different environments and enable backtracking to previous versions in case of defects and/or human errors. While semantic data integration provides fundamental solutions for bridging semantic gaps between common project-level concepts and the local tool concepts used by each discipline, semantic storages have been developed to query and reason over gathered data rather than versioning frequent instance changes inherent to such engineering projects in distributed and heterogeneous environments. In this paper we evaluate three software architectures using ontologies in different ways and compare selected quality attributes, i.e., performance and scalability, in the context of an industrial scenarios. Main results suggest that architectures relying on a relational database for versioning individuals still outperforms traditional ontology storages.

Ambient Media" are a fundamentally new way how to define media in the ubiquitous era, and are contribute to the field of human-computer-interaction (HCI), computer science, experience design, media management, and media studies.Ambient Media have been defined, explored, and conceptualized in [2][3][4][5]10].This special issue devotes to the exploration of phenomena of ambient media, in particular on aspects of search and retrieval, interactive interfaces, designing experiences, applying new interaction concepts, the design of experiences, educational issues, and the future prospective of this novel media form.For this special issue, we accepted six papers exploring these issues.A very special feature of this special issue is, that the contributing authors collaboratively worked on the analysis of the containing articles.This article shall give a holistic overview of the current status of Ambient Media, and outlines its potential future till 2020.The paper contributed by A. Scherp contributes with the aspect of search of open social media data via mobile phones.Interactive user interfaces are explored by M. Golja in his work, and particularly addresses the design of interfaces for digital interactive TV and their limitations.New learning strategies, and especially how libraries transform from their analogue to their digital counterpart has been explored by M. Bilandzic.Experience as a phenomena in social living contexts as e.g. at home, has been explored by D. Obal, who devoted his research to the

[Context] Replication Data Management (RDM) aims at enabling the use of data collections from several itera-tions of an experiment. However, there are several major chal-lenges to RDM from integrating data models and data from em-pirical study infrastructures that were not designed to cooperate, e.g., data model variation of local data sources. [Objective] In this paper we analyze RDM needs and evaluate conceptual RDM approaches to support replication researchers. [Method] We adapted the ATAM evaluation process to (a) analyze RDM use cases and needs of empirical replication study research groups and (b) compare three conceptual approaches to address these RDM needs: central data repositories with a fixed data model, heterogeneous local repositories, and an empirical ecosystem. [Results] While the central and local approaches have major issues that are hard to resolve in practice, the empirical ecosys-tem allows bridging current gaps in RDM from heterogeneous data sources. [Conclusions] The empirical ecosystem approach should be explored in diverse empirical environments.

Automation systems development projects typically require the integration of heterogeneous local tool data models that come from various disciplines and sources. Semantic data integration provides solutions for bridging semantic gaps between common project-level concepts and the local tool concepts used by each discipline. The following use cases represent the foundation for efficient data integration: (a) data insertion in the local tool models, (b) transformation of data between the local models and a common model, and (c) querying across concepts from different local models by using the common model. The selection of a proper semantic data storage for storing the data has a strong impact on efficiently executing these use cases. Three different important types of semantic storages have been identified: ontology file storages, triple storages, and relational databases storages. In this paper, we evaluate them, and identify their drawbacks and advantages in the context of the presented integration use cases' requirements.

The manufacturing industry is still struggling with predictability of quality, duration and costs for the ramp-up of new products. However, new product ramp-up becomes increasingly crucial due to further shortening of product life-cycles and the pressure on more sustainable and efficient manufacturing. In this paper, we introduce an approach based on a centralized knowledge base that comprises product and process master data, equipment structure, historical measurement results and means to apply quality management methods. The approach uses a knowledge base driven workflow that ensures knowledge exchange and reuse of existing knowledge within the production line. Based on a real-world use case from semiconductor manufacturing we evaluate how well this workflow enables knowledge reuse to guide product and process engineers to support a shift from a risky ramp-up project to a predictable ramp-up process.

[EN] One of the most important goals of pervasive systems is to help users in their daily life by automating their behaviour patterns. To achieve this, pervasive services must be dynamically coordinated, executed and adapted to context according to user behaviour patterns. In this work, we propose a model-driven solution to meet this challenge. We propose a task model and a context ontology to design context-adaptive coordination of services at a high level of abstraction. This design facilitates the coordination analysis at design time and is also reused at runtime. We propose a software architecture that interprets the models at runtime in order to coordinate the service execution that is required to support user behaviour patterns. This coordination is done in a context-adaptive way and decoupled from service implementation. This approach makes the models the only representation of service coordination, which facilitates the maintenance and evolution of the executed service coordination after deployment.