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Data Stream Management Systems (DSMS) provide real-time data processing in an effective way, but there is always a tradeoff between data quality (DQ) and performance. We propose an ontology-based data quality framework for relational DSMS that includes DQ measurement and monitoring in a transparent, modular, and flexible way. We follow a threefold approach that takes the characteristics of relational data stream management for DQ metrics into account. While (1) Query Metrics respect changes in data quality due to query operations, (2) Content Metrics allow the semantic evaluation of data in the streams. Finally, (3) Application Metrics allow easy user-defined computation of data quality values to account for application specifics. Additionally, a quality monitor allows us to observe data quality values and take counteractions to balance data quality and performance. The framework has been designed along a DQ management methodology suited for data streams. It has been evaluated in the domains of transportation systems and health monitoring.

With the rise of Cloud Computing, many predicted a paradigmatic change of IT-based business processes. However, extant research is primarily focusing on technical aspects, such as security and scalability, hence the assumed paradigm shift has not been explored in more detail yet. Focusing on Software as a Service (SaaS) as underlying Cloud model, we conducted an extensive literature review to derive a conceptual model presenting the influence of flexible, SaaS-based business processes on operational agility. Moreover, these processes were classified into three categories; Enterprise Resource Planning Services, Work Support Services, and Decision Support Services. In addition, we conducted ten in-depth interviews with Cloud experts to reflect on potentially missing categories and to substantiate the categories derived from extant literature. The theoretical and empirical parts of our paper reveal that SaaS-enabled processes have a positive influence on operational agility of enterprises which provides first preliminary evidence for the predicted paradigm shift.

Abstract. This paper describes an approach to transform a Structural Operational Semantics given as a set of deduction rules to a Linear Process Specification. The transformation is provided for deduction rules in De Simone format and extended to incorporate predicates. The Linear Process Specifications are specified in syntax of the language mCRL2, that, with help of the underlying (higher-order) re-writer/toolset, can be used for simulation, labelled transition system generation and verification of behavioural properties. We illustrate the technique by showing the effect of the transformation from the Structural Operational Semantics specification of a simple process algebra to aLinear Process Specification.

The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.

Supervisory machine control is the high-level control in advanced manufacturing machines that is responsible for the coordination of manufacturing activities. Traditionally, the design of such control systems is based on finite state machines. An alternative, more flexible approach is based on task-resource models. This paper describes an approach for the migration of supervisory machine control architectures towards this alternative approach. We propose a generic migration approach based on model transformations that includes normalisation of legacy architectures before their actual transformation. To this end, we identify a number of key concerns for supervisory machine control and a corresponding normalised design idiom. As such, our migration approach constitutes a series of model transformations, for which we define transformation rules. We illustrate the applicability of this model-driven approach by migrating (part of) the supervisory control architecture of an advanced manufacturing machine: a wafer scanner developed by ASML. This migration, towards a product-line architecture, includes a change in architectural paradigm from finite state machines to task-resource systems.

Abstract. This paper describes the process of formalizing an existing, industrial domain specific language (dsl) that is based on the taskresource paradigm. Initially, the semantics of this dsl is defined informally and implicitly through an interpreter. The formalization starts by projecting the existing concrete syntax onto a formal abstract syntax that defines the language operators and process terms. Next, we define the dynamic operational semantics at the level of individual syntactical notions, using structural operational semantics (sos) as a formal metalanguage. Here, the impact of the formalization process on the dsl is considered in terms of disambiguation of underlying (semantic) language design decisions.

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