Monday, February 20, 2017, E.1.05

8.30am, Dr. Andreas Mauthe, Lancaster University, School of Computing and Communications

Title: Resilience in Dynamic, Opportunistic Systems-of-Systems

Abstract: Distributed computing environments nowadays are becoming increasingly diverse and differentiated in nature. They are moving from traditional distributed systems comprising PCs/mobiles + IP networks to ecosystems that include Internet of Things (IoT) installations (e. g. smart cities and buildings), environmental sensor and actuator networks (e. g. WSNs) using non-IP protocols, cluster-based cloud systems, ad-hoc networks such as MANETs and VANETs, and virtualised systems supported by network overlays (e. g. Clouds). In order to exploit the full potential of these various „systems“ they need to be dynamically interconnected, so they can interact with, and respond to each other. For example, VANETs talk to smart cities, WSNs process data in back-end clouds, and overlay-based systems adjust their resilience properties when their underlying IP environment changes. It is becoming recognised in many research communities that this can be best achieved through a „systems-of-systems“ approach. However, this also requires a new approach towards system resilience and security considering the autonomic characteristics and dynamic nature of systems-to-systems approach. We have developed a programming approach called tecton, which is a distributed representation of a potentially-opportunistically-interacting distributed system. It provides support for opportunistic, rule-based interaction with low overhead.

In this presentation I will introduce the tecton abstraction, and in particular discuss how resilience can be ensured within a dynamic systems-of-systems ecosystem. The resilience mechanisms are based on D²R²+DR framework originally conceived within an autonomic networking concept. Resilience is provided through structural means but also active components call Resilience Managers. Through these, independent systems can interact while ensuring that security and resilience are maintained. A central component of this framework is anomaly detection that operates in conjunction with remediation to provide operational resilience considering the environmental dynamics as well as novel threats.

The talk will also provide and outlook on further research challenges in the context of critical systems infrastructure, Internet of Things (IoT) and Cyber Physical Systems. Further, it will outline how the research relates to teaching and collaborative research opportunities within AAU.

10:45am, Dr. Stefan Schulte, TU Wien, Fakultät für Informatik

Title: Elastic Computing

Abstract: Especially in volatile IT environments, there is a constant necessity to adapt computational resources to fulfil the resource and quality demands of service users. Permanently providing resources which are able to handle peak loads is not the best solution for this, as these capacities will not be fully utilized most of the time, leading to unnecessary cost. However, permanent underprovisioning of computational resources is obviously also not an option.

Within this talk, elastic computing will be discussed as a solution to satisfy volatile demands for computational resources. A special focus will be on elastic processes and elastic stream processing. Also, a brief outlook on how Internet of Things devices could be used to provide computational resources for services will be given.

2:00pm, Dr. Vlado Handziski, TU Berlin / TU Dresden

Title: The Ever-changing Landscape of Distributed Systems

Abstract: During the last two decades the distributed systems research had to constantly adapt to a rapidly changing technological landscape bringing novel challenges and questioning long-held assumptions. The talk will provide a brief overview of this evolution, focusing on several important paradigm shifts like the emergence of resource-constrained wireless devices with rich sensing and actuation capabilities, the rise of the smartphone platform, the cloud-centric architecture and big-data processing frameworks, the microservices model and the renewed interest in data-centric middleware. I will elaborate on the major milestones as part of this evolution and highlight some of my own contributions to the field. In the final part of the talk I will concentrate on the future of distributed systems and explore some novel challenges from new application fields (e.g. cyber-physical systems), new technologies (e.g. blockchains) and new algorithms (e.g. deep learning).

4:15pm, Dr. Thomas Zinner, Universität Würzburg, Institute für Informatik

Title: SDN, NFV & DASH: Leistungsbewertung neuer Mechanismen zur Netz- und Anwendungs­steuerung

Abstract: Das heutige Internet hat sich über Jahrzehnte zu einer globalen, universalen Kommunikations- und Diensteplattform entwickelt. Der Erfolg des Internets wird vor allem vorangetrieben durch das vielfältige Angebot an Anwendungen, einhergehend mit der steigenden Verbreitung durch immer schnelleren Breitband-Internetzugang.  Leider kommt es aufgrund der starren Architektur, fehlender Flexibilität, und mangelnder Ressourcensteuerung immer wieder zu Beeinträchtigung der Dienstqualität und damit der vom Nutzer wahrgenommenen Dienstgüte. Neue Technologien und Konzepte wie Cloud Computing, Software Defined Networking oder die Virtualisierung von Netzfunktionen ermöglichen eine Flexibilisierung der Kommunikationsarchitektur und damit die Überwindung dieser Nachteile.

Der Vortrag befasst sich mit Leistungsuntersuchungen neuartiger Mechanismen zur Netz- und Anwendungssteuerung, die sich durch diese Technologien ergeben.

Tuesday, February 21, 2017, E.1.05

8:30am, Dr. Radu Prodan, Universität Innsbruck, Institut für Informatik

Title: Multi-objective Modelling and Optimization of Scientific and Industrial Applications on Distributed Computing Infrastructures

Abstract: The presentation is structured in four parts. It starts with an overview of the current research conducted by the applicant at the University of Innsbruck in the field of distributed and parallel systems. The second part introduces the ASKALON environment, the main research product of the applicant, designed to simplify and support the development cycle of scientific applications on distributed computing infrastructures. Afterwards, it presents a selection of the latest research methods and results obtained in two important areas: modelling, prediction and simulation of energy consumption and multi-objective optimisation and scheduling of scientific applications on distributed computed infrastructures with respect to time, cost, energy, robustness and storage as simultaneous conflicting objectives. The third part of the talk presents the research conducted by the applicant in the area of industrial applications, with focus on massively multiplayer online gaming on distributed computing resources. The talk concludes with an outlook into the future research plans.

10:45am, Dr. Karin Anna Hummel, Universität Linz, Department of Telecooperation

Title: The Interplay of Physical Mobility and Wireless Networked Systems

Abstract: Physical device mobility challenges networked systems as it causes degraded quality of wireless communication links, disconnections, and mobility management overhead, e.g. in the cellular network. The effects of physical mobility are on the one hand observable in the network, on the other hand, mobility context can be exploited to improve networking. In this talk, I explain the relation between physical mobility and networked systems and present two lines of my recent research. In the first part, I focus on the question whether the cellular network can be used as a reliable ubiquitous sensor of physical mobility. As the observation of signaling caused by mobile devices comes with considerable spatial uncertainties, characterizing mobility is challenging. I present a robust method that allows to counteract these uncertainties for vehicular traffic estimation. The outcomes of a one-month study based on real cellular data on a European highway reveal that our method can detect road congestion episodes reliably, and even faster than methods based on traditional road observation technologies. In the second part, I discuss how delay-tolerant networks can take smarter packet forwarding decisions about when, where, and to whom to transmit by being aware of the devices’ mobility context. In this frame, I detail results of several experimental studies with our fleet of micro aerial vehicles and demonstrate that leveraging mobility information and moreover anticipating mobility characteristics improves single link transmissions and data forwarding performance over multiple links.

24. November 2016, Lakeside, Raum L 1.014

09:00-11.00, Martin Atzmüller

Title: Data Analytics in Social and Ubiquitous Knowledge Spaces

Abstract: In the age of digital transformation, information and data is becoming ubiquitous. Social Media, Web 2.0, mobile and ubiquitous computing, as well as Industry 4.0 and smart production enable diverse options for the analysis and mining of the emerging complex knowledge spaces. The talk presents according approaches and methods for comprehensive analytics of the respective information and knowledge processes.

11:00-13:00, Patrick Delfmann

Title: Comprehensible Predictive Models for Business Processes

Abstract: Predictive modeling approaches provide a way to analyze, plan, streamline, and control the structure and behavior of business processes. For instance, they can inform decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The talk introduces a new predictive modeling technique for business processes based on probabilistic finite automatons. Fitting such a probabilistic model to a data set of past behavior makes it possible to predict how currently running process instances will behave in the future. To clarify how this technique works and to facilitate its adoption, the talk also shows a way to visualize the probabilistic models. The effectiveness of the technique is assessed via an experimental evaluation with synthetic and real-world data. Finally, the talk reports on current applications of the technique in the areas of classic business process management, corporate communication and collaboration and the Internet of Things.

14:00-16:00, Alexander Felfernig

Title: Recommendation and Decision Technologies for Complex Products and Services

Abstract: Recommendation technologies are widely used for increasing the accessibility of large item assortments – application examples thereof are online platforms such as amazon.com or netflix.com. Although already widely used in industrial environments, there are still many challenges to be tackled to further increase the applicability of these technologies. Especially when providing recommenders for complex products and services such as financial services, electronic equipment, and software artefacts, understandability and manageability issues arise with regard to the underlying knowledge bases. In scenarios where groups of users are interacting with such systems, mechanisms have to be provided that assure fairness and consensus in the underlying decision process. Furthermore, knowledge about human decision making has to be taken into account when developing recommendation systems, for example, mechanisms have to be included that are able to counteract decision biases. Finally, recommendation technologies are not only used for recommending products and services but also act as key-enabling technologies for achieving persuasive goals. This talk will focus on a discussion of solutions that help to tackle the mentioned challenges and have already shown to be useful in industry. In this context, new research results in terms of algorithms and techniques will be presented. The talk will be concluded with an overview of major issues for future research.

16:00-18:00, Sabine Graf

Title: How to make educational information systems more adaptive, intelligent and personalized?

Abstract: Information systems are prevalent in our daily lives and research showed that making such systems more adaptive, intelligent and personalized by enabling them to automatically consider users’ current situations, characteristics and preferences and providing them with actionable information can make these systems more effective and easier to use. An important domain for such information systems is education. Although research showed that education works best when it is tailored to individual people’s needs, the information systems that are widely used by educational institutions (e.g., learning management systems such as Moodle) provide very limited to no personalization, adaptivity or intelligent support. Furthermore, while these systems typically gather and store huge amounts of data about how people use these systems, even with the advancements in data mining, business intelligence and decision support systems in recent years, in the educational sector, such data are rarely used to inform and benefit users (e.g., instructors, learners, etc.). This talk will discuss how educational information systems can be enhanced to provide adaptive recommendations, intelligent support and customized information to learners, instructors and educational institutions. More concretely, the talk will focus on how to detect and retrieve a variety of useful information from raw data as well as how to present such information to users, for example, through dashboards and visualizations, or in the form of adaptive interfaces, intelligent support and personalized recommendations, using techniques from different fields such as data mining, user modelling, data/knowledge analytics, recommender systems, decision support systems, business intelligence and others. Results and findings will be discussed, showing that such enhanced information systems allow to better support users such as learners, instructors and the administration of educational institutions. In addition, future research directions will be outlined, to continue addressing some of the prevalent challenges in blended and technology enhanced education, such as increasing learner performance, improving user satisfaction and providing the right information to the right person at the right time. Furthermore, potential opportunities to apply our research to other domains such as e-commerce, business, tourism, etc. will be discussed.

25. November 2016, Lakeside, Raum L 1.014

9:00-11.00, Christian Huemer

Title: Model-Driven ERP System Development

Abstract: In the discipline of accounting, the resource-event-agent (REA) ontology is a well accepted conceptual accounting framework to analyze the economic phenomena within and across enterprises. We used REA as a basis for developing a domain specific modeling language and corresponding tool support for ERP system development. Evidently, a model-driven approach requires transforming the REA-DSL artifacts to code. Thus, we developed the transformation of the REA-DSL to a relational database for an ERP system. This approach offers the advantage that a domain expert verifies the relevant data in an „accounting language“, whereas the IT expert is able to work with traditional data base structures.

In an alternative approach, we target the adaptation of an ERP system to specific company’s needs by configuration and customization which usually is cost-intensive and requires experts with specialized know-how. Therefore, we developed a model-driven approach that configures a generic ERP system to customer needs by means of models in order to provide a less costly solution. In the so-called REAlist approach, the REA business models serve as configuration information to be interpreted at run-time by the generic, domain-agnostic ERP engine.

This talk will first introduce the REA-DSL that provides the foundation for both approaches and will afterwards highlight the REA-DSL transformation to data base structures and elaborate in detail on the REAlist architecture. In order to demonstrate the future research portfolio, the presented work is further discussed in the context of Industrie 4.0 and will highlight open research challenges for horizontal and vertical integration of ERP systems in production processes.

11:00-13.00, Dietmar Jannach

Titel: Empfehlungssysteme – Mehr als smarte Algorithmen zur Matrixvervollständigung

Kurzfassung: Automatisierte Empfehlungen und Personalisierung sind heutzutage ein allgegenwärtiger Bestandteil unserer Nutzererfahrung sowohl im World Wide Web als auch in mobilen Apps. In der Informatikforschung wurden in diesem Umfeld in den letzten Jahren enorme Fortschritte gemacht. Die Aufgabe der entwickelten Algorithmen ist es zumeist, möglichst exakt vorherzusagen, wie relevant ein Produkt, ein Service oder allgemein ein Objekt für den einzelnen Benutzer ist. Das Empfehlungsproblem wird in diesen Arbeiten oft als Problem der Vervollständigung einer dünn besetzten Matrix aus Benutzern und Objekten betrachtet. In diesem Vortrag wird anhand aktueller Forschungsergebnisse darauf eingegangen, warum Algorithmen mit einer hohen Vorhersagegenauigkeit auf Vergangenheitsdaten nicht unbedingt diejenigen sind, die zu den nützlichsten Empfehlungen für die Benutzer in der Praxis führen. Ausgehend von diesen Erkenntnissen wird ein konzeptueller Rahmen vorgeschlagen, der von einer algorithmenorientierten Vorgehensweise zu einem interdisziplinären ziel- und nutzenorientierten Forschungsansatz führt.

14:00-16:00, Agnes Koschmider

Titel: Vorrausschauende Analysen für smarte Umgebungen

Kurzfassung: Trotz der heute zu beobachtenden Verbreitung von Sensoren in unterschiedlichsten Geräten und Umgebungen, die Daten für individuelle Objekte (Auto, Person, etc.) mit personenbezogenen Verhaltensprofilen sammeln, existieren zur Prädiktion individuellen Verhaltens überraschend wenige wissenschaftliche Ergebnisse und praktische Ansätze. Dabei hätten Anwendungen solcher Verfahren potentiell einen hohen wirtschaftlichen als auch sozialen Mehrwert. Denkt man beispielsweise an Smart Home Umgebungen zur Unterstützung der Heimpflege älterer Menschen, ließen sich z.B. individuelle Wahrscheinlichkeiten zum Wechsel in eine andere Pflegestufe innerhalb einer gewissen Zeit prognostizieren. Im Kontext von Connected Cars könnten durch Auswertung menschlichen Verhaltens individuelle Gefahrensituationen prognostiziert werden, die den Eingriff des Fahrers ins System erfordern. Existierende Forschungsarbeiten im Bereich prädiktive Verhaltensanalyse beschränken sich weitgehend auf strukturierte Prozesse, bei denen die Reihenfolge einzelner Aktivitäten vordefiniert ist und sich alle individuellen Objekte nach dem gleichen Prozessschema richten. Diese Vorbedingungen des strukturierten menschlichen Verhaltens trifft in smarten Umgebungen, bei denen Daten aus Sensoren gesammelt werden (wie Smart Home oder Connected Cars) nicht zu, hier sprechen wir von unstrukturierten Entscheidungsfindungsprozessen.

16:00-18:00, Thomas Setzer

Title: Robust Integration of Judgmental and Statistical Predictions in Forecasting Support Systems

Abstract: The accuracy of forecasting plays a pivotal role in enterprise decision making as forecasts usually form part of an IS on which business functions, such as marketing or resource planning depend. Today, forecasts are typically derived by expert judgment supported by model-based predictions that are provided within forecasting support systems (FSS). There is, however, evidence that this integration is often carried out poorly and the resulting predictions are regularly biased. In this talk, I propose an alternative forecast generation process that – in the spirit of crowd wisdom and forecast blending – combines individual model-based and judgmental forecasts ex-post to derive less biased and more robust predictions. The core of the combination method is the analytical derivation of the previously unknown bias–variance decomposition of a combination’s mean squared error (MSE). In our recent work we could derive this decomposition for an important class of linear combinations (weighting schemes) of individual forecasts, which allows for determining the weighting scheme with minimum expected MSE. Empirical results will be presented that show significantly lower MSE when applying the approach compared the judgmental forecasts or commonly used weighting schemes. The outlook of the talk will discuss a novel kind of debiasing support system. The system is aimed at a cognitive engineering of experts (to reduce bias over time) and avoiding high-impact errors introduced by adjusting originally sound judgmental expectations through the combination. Based on an expert’s error history, the system provides information on the bias suggested to drive the difference and prompts him/her for action and explanation in case of strong deviation between the combined forecast and his/her expectation.