From Grown to Structured - Reducing unnecessary Variability of Technology Architectures in large-scale IT Landscapes

Die IT-Landschaft in einem Unternehmen entwickelt sich typischerweise über viele Jahre und Jahrzehnte. Um den steigenden Bedarf an Softwarelösungen zur Unterstützung unterschiedlichster Geschäftsfunktionen zu realisieren, werden so über die Zeit immer mehr Anwendungssysteme geschaffen und in die bestehende Landschaft integriert. In der Konsequenz können solche gewachsenen IT-Landschaften aus hunderten oder gar tausenden von Softwaresystemen bestehen, die ein breites Spektrum unterschiedlichster Technologien verwenden. So setzen zwar viele von ihnen gleiche Kerntechnologien (z.B. Java, .Net oder SAP) ein, unterscheiden sich jedoch häufig in den verwendeten technologischen Komponenten (z.B. verschiedene Betriebssysteme, Datenbanksysteme oder Applikationsserver). Diese technologischen Varianten sind aus Architektursicht nicht immer erforderlich und verursachen unnötige Variabilität in den Technologiearchitekturen der betrachteten Systeme, was zu einer höheren Komplexität, einer geringeren Anpassungsfähigkeit sowie zu steigenden Kosten und höherem Aufwand für die Wartung und Weiterentwicklung der gesamten IT-Landschaft führt. Um diesen Herausforderungen zu begegnen, ist es erforderlich, dass die Variabilität von technologisch verwandten Softwaresystemen reduziert wird. Da hiermit komplexe Tätigkeiten verbunden sind, die bisher manuell von Experten durchgeführt werden müssen, sind sie für gewachsene IT-Landschaften kaum durchführbar. Zur Lösung dieses Problems werden in dieser Dissertation drei wissenschaftlichen Beiträge vorgestellt: (1) Ein Mining-Verfahren zur Bestimmung von Variabilität in Technologiearchitekturen, welches eine beliebige Anzahl an verwandten IT-Systemen analysiert und alle Variabilitätsbeziehungen zwischen ihnen bestimmt. (2) Ein regelbasierter Ansatz zur Ableitung von Restrukturierungsempfehlungen, welcher unnötige Variabilität in den betrachteten Technologiearchitekturen identifiziert und geeignete Maßnahmen zur Reduzierung dieser Variabilität vorschlägt. (3) Ein Ansatz zur Simulation und Bewertung von abgeleiteten Restrukturierungsempfehlungen, welcher Experten bei der Entscheidungsfindung zur konkreten Restrukturierung von betrachteten Technologiearchitekturen unterstützt. Alle Beiträge wurden mit Experteninterviews und Fallstudien evaluiert. Für diese Evaluation standen uns verschiedene Experten eines Industriepartners sowie Daten von realen Technologiearchitekturen zur Verfügung.A company's IT landscape typically evolves over years and even decades. By satisfying the growing demand for software solutions, the number of software systems increases with a company’s requirements to support various business functions. As such an evolution is normally uncoordinated, the realization of new requirements often results in additional software systems. As a consequence, grown IT landscapes can consist of hundreds or thousands of different software systems with a large variety of technologies. Although such software systems typically utilize similar core technologies (e.g., Java, .Net, or SAP), they often differ in implemented technology components which are required to run the software system (e.g., different operating systems, database systems or application server). From an architectural point of view, such technical variants are not always necessary and might lead to unnecessary variability in the technology architectures of regarded software systems. This results in increasing costs, a reduced adaptability and higher effort for maintaining and evolving existing software solutions and the entire IT landscape. To cope with these challenges, the variability of technically related software systems has to be reduced. As this requires manual and complex analyzes, it is not feasible for a large number of software systems. Thus, experts continuously face the tedious challenge of making reasonable restructuring decisions for large-scale IT landscapes. To solve the described problems, this doctoral thesis comprises three different scientific contributions: (1) An automated mining approach for determining variability in technology architectures, which is capable of analyzing a large number of software systems and determining the inherent variability relations. (2) A rule-based approach for deriving restructuring recommendations, which identifies unnecessary variability in the considered technology architectures and suggests appropriate restructuring measures to reduce this variability. (3) An approach for evaluating and simulating derived restructuring recommendations, which supports experts in taking reasonable decisions for restructuring of analyzed technology architectures. All contributions were evaluated by means of expert interviews and several case studies. For this purpose, we had access to various experts as well as real-word data from our industry partner

Towards Reducing the Complexity of Enterprise Architectures by Identifying Standard Variants Using Variability Mining

For decades, Enterprise Architectures (EAs) of car manufacturers have been constantly evolved to respond to growing requirements. As a consequence, EAs have often reached a very high level of complexity, which leads to problems in adapting EAs to new environmental condi­tions. Such a new condition is, for instance, digitalization of society (e.g., social media, Internet of Things) which has a huge effect on the automotive industry and the grown EA. Resulting changes in complex EAs have long implementation cycles, require enormous communica­tion efforts, and lead to high development costs. To alle­viate these problems, in this paper, we present a concept to reduce the complexity of grown EAs by adapting the Family Mining approach. This approach is originally used to compare block-oriented models, such as MATLAB/Si­mulink models, and to identify commonalities and diffe­rences between these models. In our concept, we utilize the Family Mining approach to analyze the variability of a particular EA and to identify the contained variants. All information about the variability and the variants will be used to derive standard variants representing default so­lutions for different issues. Using these standard variants, the existing EA will be restructured involving economic considerations (e.g., which standard variant yields best benefits under certain circumstances). Hence, applying this concept to a complex EA should allow reducing the complexity of the EA, alleviating related problems and making suitable design decisions for future extensions

EuGMS Task and Finish group on Fall-Risk-Increasing Drugs (FRIDs): Position on Knowledge Dissemination, Management, and Future Research

Falls are a major public health concern in the older population, and certain medication classes are a significant risk factor for falls. However, knowledge is lacking among both physicians and older people, including caregivers, concerning the role of medication as a risk factor. In the present statement, the European Geriatric Medicine Society (EuGMS) Task and Finish group on fall-risk-increasing drugs (FRIDs), in collaboration with the EuGMS Special Interest group on Pharmacology and the European Union of Medical Specialists (UEMS) Geriatric Medicine Section, outlines its position regarding knowledge dissemination on medication-related falls in older people across Europe. The EuGMS Task and Finish group is developing educational materials to facilitate knowledge dissemination for healthcare professionals and older people. In addition, steps in primary prevention through judicious prescribing, deprescribing of FRIDs (withdrawal and dose reduction), and gaps in current research are outlined in this position paper