H.-Jürgen Appelrath

Market-based self-organized provision of active power and ancillary services: An agent-based approach for Smart Distribution Grids

Transforming the existing power generation to renewable, distributed generation implicates an increase in complexity for the control of the overall system. We propose a distributed control method to launch products of self-organized coalitions of small active units in a power grid at markets for trading active power as well as ancillary services. Our concept combines the integration of grid restrictions into proactive scheduling of active power with provision of ancillary services, and additionally provides reactive scheduling of active power, e.g. in the case of ancillary service activation.

Odysseus: a highly customizable framework for creating efficient event stream management systems

Odysseus is a flexible, feature-rich and extensible framework to design event stream management systems and was developed to support research in event stream processing. It provides a systematic approach to define sources and queries, execution and presentation of results. Odysseus offers basic functionality for fast deployment, but due to its modular architecture, users can easily configure and expand them to meet a large set of applications and research questions.

Information and communication technologies for promoting and sustaining quality of life, health and self-sufficiency in ageing societies – outcomes of the Lower Saxony Research Network Design of Environments for Ageing (GAL)

Many societies across the world are confronted with demographic changes, usually related to increased life expectancy and, often, relatively low birth rates. Information and communication technologies (ICT) may contribute to adequately support senior citizens in aging societies with respect to quality of life and quality and efficiency of health care processes. For investigating and for providing answers on whether new information and communication technologies can contribute to keeping, or even improving quality of life, health and self-sufficiency in ageing societies through new ways of living and new forms of care, the Lower Saxony Research Network Design of Environments for Ageing (GAL) had been established as a five years research project, running from 2008 to 2013. Ambient-assisted living (AAL) technologies in personal and home environments were especially important. In this article we report on the GAL project, and present some of its major outcomes after five years of research. We report on major challenges and lessons learned in running and organizing such a large, inter- and multidisciplinary project and discuss GAL in the context of related research projects. With respect to research outcomes, we have, for example, learned new knowledge about multimodal and speech-based human–machine-interaction mechanisms for persons with functional restrictions, and identified new methods and developed new algorithms for identifying activities of daily life and detecting acute events, particularly falls. A total of 79 apartments of senior citizens had been equipped with specific “GAL technology”, providing new insights into the use of sensor data for smart homes. Major challenges we had to face were to deal constructively with GAL’s highly inter- and multidisciplinary aspects, with respect to research into GAL’s application scenarios, shifting from theory and lab experimentation to field tests, and the complexity of organizing and, in our view, successfully managing such a large project. Overall it can be stated that, from our point of view, the GAL research network has been run successfully and has achieved its major research objectives. Since we now know much more on how and where to use AAL technologies for new environments of living and new forms of care, a future focus for research can now be outlined for systematically planned studies, scientifically exploring the benefits of AAL technologies for senior citizens, in particular with respect to quality of life and the quality and efficiency of health care.

Multidimensional process mining: a flexible analysis approach for health services research

Health services research (HSR) is a scientific field analyzing personal health services regarding quality and efficiency. In particular, it focuses on discovering and analyzing health care processes. Of special interest are the factors of influence like the age of the patients and their impact on the processes. Multidimensional process mining is a way to discover health care processes according to certain factors of influence. Because the existing approach for multidimensional process mining from literature is unsatisfactory (e.g., missing separation of process model and visualization), a novel approach is developed. The basic idea is to move the multidimensional aspects directly into the event log which is a record of all events of the process activities serving as a foundation for process mining. OLAP operations are defined to select traces as input for usual process discovery algorithms. For a representation of results adequate to the user needs, several kinds of visualization are provided. During an optional step of consolidation it is possible to reduce the complexity of the mining results, e.g., by clustering process models. This paper motivates this approach and outlines its basic ideas at the early stage of PhD work.

Herakles: real-time sport analysis using a distributed data stream management system

Tactical decisions profoundly characterize team sports like soccer or basketball. Analyses of matches and training sessions (e.g., mileage or pass completion rate of a player) become more and more important for those tactical decisions. Most of the analyses are video-based, resulting in high operating expenses. Additionally, a highly specialized system with a huge amount of system resources like processors and memory is needed. Typically, analysts present the results of the video data analysis in time-outs (e.g., in the half-time break of a soccer match). However, coaches often desire to view statistics in real-time during the match. In this paper, we demonstrate Herakles, a system for live sport analysis using streaming sensor data and a Peer-to-Peer network of conventional and low-cost private machines. Since sensor data is typically of high volume and velocity, Herakles uses OdysseusP2P, a distributed data stream management system, for processing these streams in real-time. Since the results of the data stream processing are intended for coaches, the front-end of Herakles is an application for mobile devices like smartphones or tablets. With Herakles, a coach is able to view individual sport statistics during the game at the sideline to make immediate tactical decisions.

A Relational Data Warehouse for Multidimensional Process Mining

Multidimensional process mining adopts the concept of data cubes to split event data into a set of homogenous sublogs according to case and event attributes. For each sublog, a separated process model is discovered and compared to other models to identify group-specific differences for the process. For an effective explorative process analysis, performance is vital due to the explorative characteristics of the analysis. We propose to adopt well-established approaches from the data warehouse domain based on relational databases to provide acceptable performance. In this paper, we present the underlying relational concepts of PMCube, a data-warehouse-based approach for multidimensional process mining. Based on a relational database schema, we introduce generic query patterns which map OLAP queries onto SQL to push the operations (i.e. aggregation and filtering) to the database management system. We evaluate the run-time behavior of our approach by a number of experiments. The results show that our approach provides a significantly better performance than the state-of-the-art for multidimensional process mining and scales up linearly with the number of events.

Prediction functions in bi-temporal datastreams

Modern datastream management system (DSMS) assume sensor measurements to be constant valued until an update is measured. They do not consider continuously changing measurement values, although a lot of real world scenarios exist that need this essential property. For instance, modern cars use sensors, like radar, to periodically detect dynamic objects like other vehicles. The state of these objects (position and bearing) changes continuously, so that it must be predicted between two measurements. Therefore, in our work we develop a new bitemporal stream algebra for processing continuously changing stream data. One temporal dimension covers correct order of stream elements and the other covers continuously changing measurements. Our approach guarantees deterministic query results and correct optimizability. Our implementation shows that prediction functions can be processed very efficiently.

Utilizing Structured Information from Multiple External Sources in the Context of the Multidimensional Data Model

Analytical Information Systems (AIS) enable analysts to visualize and analyze large amounts of data. They are based on a Data Warehouse in whose context typically various types of metadata are used. Often these metadata slightly consider additional information especially concerning the Multidimensional Data Model (MDM) like definitions, business rules, terminology or background information. By adding this metadata, particularly regarding the linked data movement, a significant improvement in the domain of AIS can be achieved. Our approach suggests a semantic metadata layer that enhances the AIS to allow modeling additional information in form of real-world entities. These entities correlate with MDM elements and are derived and integrated from various external structured sources. As a prototype we show the feasibility of this approach through a filter component that filters classification nodes with information not covered by the MDM.

Scheduling von Datenströmen auf der Basis von Service Level Agreements

ZusammenfassungSoftware as a Service (SaaS) stellt einen Ansatz zur Bereitstellung standardisierbarer Software über das Internet dar. Auch die Verarbeitungskapazität von Datenstrommanagementsystemen (DSMS) kann auf diese Weise verschiedenen Kunden kostengünstig zugänglich gemacht werden. Der Anbieter garantiert dem Kunden eine gewisse Dienstqualität in Form von Service Level Agreements (SLAs), deren Verletzung i.d.R. finanzielle Konsequenzen hat. Der Dienstanbieter ist also zum einen daran interessiert, möglichst geringe Kosten durch die SLA-Verletzung zu verursachen, zum anderen eine optimale Systemressourcenauslastung (CPU, Hauptspeicher, etc.) zu erzielen. Diese Ressourcen werden in DSMS mit Hilfe von Schedulingverfahren den kontinuierlichen Anfragen zugewiesen. In unserer Arbeit haben wir ein aus dem Datenbankkontext stammendes Schedulingverfahren für Datenströme angepasst, das sowohl die durch Verletzungen von SLAs entstehenden Kosten aus Sicht des Anbieters berücksichtigt als auch eine effiziente Ressourcenverteilung ermöglicht. In Simulationen haben wir gezeigt, dass sich durch ein solches Scheduling die Kosten des Anbieters senken lassen.

A Case Study on a GQM-Based Quality Model for a Domain-Specific Reference Model Catalogue to Support Requirements Analysis within Information Systems Development in the German Energy Market

Within this contribution, an approach on a goal-question-metric (GQM) based quality model for a domain-specific reference model catalogue is introduced. First of all, we motivate and present an ontology-based reference model catalogue to support requirements analysis within information systems development in the German energy market. For this purpose, we describe requirements for creating such a catalogue. Based on these requirements, quality metrics, to continuously measure the quality of the catalogue during its development and extension, are presented. In addition, the application of these metrics is shown.