Cost-Based Optimization of Integration Flows

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation

Mathematical modeling of human brain physiological data

In this work two extensions of a model, that simulates the haemodynamical processes and oxygen transport of the human brain, where proposed. The general behavior of the original model and its two modifications were examined in great detail, as well as the differences between the diverse realizations. The quality of the different models was judged by their ability to reproduce particular measurements gained from patients with severe head trauma. Inside these time segments, the ABP and TiPO2 are correlated, whereas the ABP and ICP are anti-correlated. This type of behavior was not fully understood up to now, but does occur in a significant amount of time intervals, as correlation analyses demonstrate. It could be shown, that the original model, on which all proposed extensions were based on, was not able to reproduce this behavior. For a quantitative reproduction of this effect both extensions are necessary. As a result a meaningful physiological explanation for this kind of data can be given

Multiphase Stirred Tank Bioreactors – New Geometrical Concepts and Scale‐up Approaches

Mainly with respect to biotechnological cases, current developments in the field of impeller geometries and findings for multistage configurations with a specific view on aerated stirred tanks are reviewed. Although often the first choice, in the given case the 6‐straight blade disc turbine is usually not the best option. Furthermore, quantities usable for scale‐up, specifically applicable in this field are discussed. Only quantities taking local conditions into account appear to be able to actually compare different stirrer types and scales.DFG, 56091768, TRR 63: Integrierte chemische Prozesse in flüssigen MehrphasensystemenDFG, 315464571, Interaktion der mechanischen Beanspruchung und der Produktivität von biologischen Agglomeraten in RührfermenternDFG, 256647858, Stoffübergang von aufsteigenden Blasen in reagierenden FlüssigphasenTU Berlin, Open-Access-Mittel - 201

On the Usefulness of Weight-Based Constraints in Frequent Subgraph Mining

Frequent subgraph mining is an important data-mining technique. In this paper we look at weighted graphs, which are ubiquitous in the real world. The analysis of weights in combination with mining for substructures might yield more precise results. In particular, we study frequent subgraph mining in the presence of weight-based constraints and explain how to integrate them into mining algorithms. While such constraints only yield approximate mining results in most cases, we demonstrate that such results are useful nevertheless and explain this effect. To do so, we both assess the completeness of the approximate result sets, and we carry out application-oriented studies with real-world data-analysis problems: software-defect localization, weighted graph classification and explorative mining in logistics. Our results are that the runtime can improve by a factor of up to 3.5 in defect localization and classification and 7 in explorative mining. At the same time, we obtain an even slightly increased defect-localization precision, stable classification precision and obtain good explorative mining results

Analyzing Measures for the Construct “Energy-Conscious Driving”: A Synthesized Measurement Model to Operationalize Eco-Feedback

During the last several years, a large number of studies have dealt with eco-driving and have defined rules for driving vehicles more ecologically, eco-friendly, and energy efficiently. These rules are vague or insufficient for achieving their purpose, and the construct “energy- conscious driving” is unsatisfactorily defined. To structure available research and develop a more extensive concept of energy-conscious driving, a measurement model for energy- conscious driving is introduced. The model stems from a literature review conducted to identify six groups of measures for energy-conscious driving, and a synthesis of these groups to identify dependencies between them. This paper contributes to theory by building on existing knowledge on eco-driving through an analysis of available literature and describing dependencies between our six measures of energy-conscious driving. Based on our model, researchers can evaluate different eco-feedback designs and practitioners can implement more specific eco-feedback systems for improved user performance

The influence of situational interest on the appropriate use of cognitive learning strategies

This study explores the role of two facets of situational interest, interestingness and personal significance, as predictors of the adequate use of three types of cognitive learning strategies (rehearsal strategies, organizational strategies, and elaboration strategies). In order to attain this goal, it introduces a new measure of the adequacy of the use of cognitive learning strategies by using the distance between teachers’ estimates of appropriate use of learning strategies for a specific task and students’ reported strategic behavior. Based on a theoretical model of the use of cognitive learning strategies, the study shows, by means of structural equation modeling, that different facets of situational interest play different roles in predicting students’ surface and deep processing. In summary, it was found that experienced personal significance played a major role in predicting deep-processing strategies for a significant proportion of the 34 tasks in this study, whereas interestingness fell short of expectations. Limitations did arise owing to some missing values, which may blur the findings at the lower interest and achievement end for the student sample. Nevertheless, suggestions have been made for future research, which can help teachers of history classes to determine components of success, namely experienced personal significance, when designing tasks and consequently provide effective learning tasks to their classes

Auswirkungen von Vorhydratation auf die Leistungsfähigkeit von Zementen unter Berücksichtigung verschiedener Klinkereigenschaften

In Deutschland werden derzeit zur Zementmahlung meist Kugelmühlen eingesetzt (über 100 Mühlen, teilweise über 40 Jahre alt). Die Fertigmahlung von Zementen auf deutlich energieeffizienteren Vertikal-Wälzmühlen ist international insbesondere bei Neubauprojekten bereits heute verbreitet, wird in Deutschland jedoch kaum praktiziert (weniger als fünf Mühlen). Ein Grund dafür sind immer noch fehlende Kenntnisse, wie durch den Mahlbetrieb in Vertikal-Wälzmühlen auftretende Veränderungen der Zementeigenschaften durch Vorhydratation systematisch entgegengewirkt werden kann, um möglichst gleiche Zementeigenschaften wie von auf Kugelmühlen hergestellten Zementen zu erhalten. Das Forschungsvorhaben verfolgte zwei Ziele. Zum ersten sollte untersucht werden, ob bestimmte zementchemische Eigenschaften einen Klinker robuster gegenüber einer Vorhydratation machen. Dazu wurde im Versuchsprogramm eine Auswahl unterschiedlicher Klinker verwendet, die ein breites mineralogisches Spektrum abdeckt. Es sollte geklärt werden, in welcher Größenordnung sich die Effekte einer Vorhydratation in Abhängigkeit vom Klinker, seiner Feinheit und der Intensität der Vorhydratation bei Hydratationsreaktionen und Zementeigenschaften bemerkbar machen. Klinkermehle wurden systematisch vorhydratisiert. Aus den entstandenen Materialien wurden Laborzemente hergestellt, deren Hydratationsreaktionen und Normeigenschaften charakterisiert wurden. Zum zweiten sollte untersucht werden, mit welchen Maßnahmen möglichen negativen Auswirkungen entgegengewirkt werden kann, insbesondere durch Anpassung der Sulfatträgerzusammensetzung. Dazu wurden aus ausgewählten vorhydratisierten Klinkern Serien an Laborzementen mit unterschiedlicher Sulfatträgerzusammensetzung hergestellt und untersucht. Besonders die kleinen und mittelständischen Unternehmen der deutschen Zementindustrie würden dadurch in ihrer Wettbewerbsfähigkeit gestärkt, da sie nicht auf Erfahrungen mit Vertikal-Wälzmühlen an anderen Standorten zurückgreifen können, um Investitionsentscheidungen zu treffen oder Strategien zur Optimierung von Zementen zu entwickeln

CAPTURE AND ANALYSIS OF SENSOR DATA FOR ASTHMA PATIENTS

Worldwide more than 230 million people suffer from asthma. Reliable and timely guidance for indi-viduals to minimize their risk for asthma attacks is not available. This is largely due to the fact that asthma symptoms are often caused by multiple environmental and personal factors. Many of them are neither captured nor systematically analysed. This is addressed by the project ActOnAir. It aims at a comprehensive capture of health factors and the environmental exposure of individuals, as well as a subsequent analysis in real-time. For this purpose the ActOnAir system provides a mobile sensor box for data collection, a sensor data integration and processing platform, a data mining component and a smartphone application for patients. This contribution outlines the design objectives of the ActOnAir system and discusses corresponding key requirements. The related system architecture is introduced and first results from a prototype implementation are sketched

Forcasting Evolving Time Series of Energy Demand and Supply

Real-time balancing of energy demand and supply requires accurate and efficient forecasting in order to take future consumption and production into account. These balancing capabilities are reasoned by emerging energy market developments, which also pose new challenges to forecasting in the energy domain not addressed so far: First, real-time balancing requires accurate forecasts at any point in time. Second, the hierarchical market organization motivates forecasting in a distributed system environment. In this paper, we present an approach that adapts forecasting to the hierarchical organization of today’s energy markets. Furthermore, we introduce a forecasting framework, which allows efficient forecasting and forecast model maintenance of time series that evolve due to continuous streams of measurements. This framework includes model evaluation and adaptation techniques that enhance the model maintenance process by exploiting context knowledge from previous model adaptations. With this approach (1) more accurate forecasts can be produced within the same time budget, or (2) forecasts with similar accuracy can be produced in less time