Contributions published at Dynamic and Distributed Information Systems (Abraham Bernstein)

Mobile Devices such as PDAs and Smartphones find more and more access in our private and business life. It is thereby automatically assumed that this new technology increases productivity. But is this really so? The diploma thesis attempts to identify the effects and impacts of ubiquitous computing on labor productivity and offers a theoretical concept for the research and analysis of the changes in intra-corporate productivity (labor productivity) when ubiquitous computing is implemented. Furthermore a concept is provided which can serve as a basis for working out other experimental situations as well for the analysis of the impact of ubiquitous computing on productivity as for the impact of other IT tools. On the basis of this concept a design for an experiment will be prepared, which will be realised in the next future and will provide some data for a statement about the impact of ubiquitous computing on labor productivity.

The goal of this thesis is the detection and the identification of simultaneous and time-shifted regime drifts in economic data. To achieve this objective artificial and economic datasets have been created. The dependencies between their features and their inner structures will be described. As the economic dataset needed to be prepared for the later analysis, all the necessary steps will be explained. The problem of regime drift detection is a new field in data mining research. For this reason tools will be described which can handle familiar problems and in addition new methods will be introduced. Their results on the datasets will be presented and illustrated. It will be shown that these methods are capable of identifying simultaneous regime drifts. Although the time-shifted problem could not be solved, a major understanding for the underlying problematic has been built up.

This thesis introduces a generic data management framework capable of dealing with distributed knowledge represented in Semantic Web languages. Persistent data storage, data querying, retrieval, annotation, versioning and security in terms of authentication and authorization are key features and are thoroughly discussed in regard to traditional software principles such as distribution, openness, robustness and scalability. This work emerged from a process support system project named NExT whose architecture called for a novel data management framework approach. First, the envisioned framework's specific requirements are determined. In the second part, appropriate overall concepts are elaborated and subsequently a complete system architecture is presented. The thesis closes with the presentation of a reference implementation that can be used by the NExT system. The implementation furthermore reveals the architecture's feasibility as a proof-of-concept prototype.

On the job one is exposed to different sources of disruption. The goal of this paper ist to reduce these disturbances. In earlier attempts it was already shown that one can determine the interruptability degree of a person with sensor data and the assistance of data mining algorithms. But these values could be acquired only afterwards. Therefore a framework has to be developed, which can manage the hole procedures in real time. Apart from the programming of the software components the whole application also has to be evaluated on its suitability for daily use.

Risk Predictors currently used in the field of Acute Myocardial Infarction (AMI) were developed on data cohorts collected in the early 90’s using traditional statistical methods. Considering the progress in the therapy of AMI as well as in the field of Data Mining, it was hypothesized that a better Risk Predictor could be developed. Working on the AMIS PLUS registry (n=7520) existing scores were evaluated and a new Risk Prediction Model developed, using the AODE algorithm fromthe Bayes family. The most accepted Risk Score (TIMI Risk Score for ST-Elevation) yielded an Area under the ROC Curve (AUC) of 0.803. The newly developed Risk Model called AMIS Model achieved an AUC of 0.875 using less input variables. Tests showed that the prediction capacity of the AMIS Model was especially good with patients undergoing PCI treatment (AUC=0.885 compared to AUC=0.783 of TIMI Risk Score).

In our daily lifes, mobile communication devices attain very high and still growing significance. This gives us the ability to reach anybody, everywhere at anytime. Consequently, also insignificant calls reach and disturb us in unfavorable situations. The goal of this Thesis is to determine the key parameters for a long-term experiment to gather data on user behavior. The analysis of these data may help to predict the interruptability level of a user and, subsequently, taking appropriate measures. These key parameters are determined on the one hand by the experimental method and on the other hand by the features of the implementation environment. An application has been created in order to assess the feasibility of the experiment setup.

The Semantic Web is the vision of the linking of various databases and the enrichment of those using semantic information. Users are able to query those databases using formal query languages. The major goal of the activities concerning the Semantic Web is to provide a collection of data that can be sought by intelligent systems rather than using simple full text search engines. The main focus in the development of the Semantic web therefore lies in making the semantic data accessible to machines. Most people are not used to query databases through formal languages. A possible solutions to deal with this complaint is the use of Natural Language Processing Systems. This thesis engages in a system that is capable of translating natural language sentences into a formal query language for OWL ontologies called SPARQL. The developed conceptual design has been implemented and evaluated in aJava prototype.

This diploma thesis deals with the research field ""Context Awareness"". While common research focuses on the determination of context per se, delivering context information to the environment has taken less into account. To increase productivity, it is very important to consider delivery and use of this information. The goal of this thesis is to make context information available in a very intuitive way. The thesis is applied to an office-based-setup. The derived context information corresponds to the availability or interruptability of the person in the office. This information is passed on to visitors. The developed application is based on actual research findings about user interface design and usability.

Computing fixpoints of increasing sequences of sets is an important problem in many areas of computer science including algorithmic verification, program analysis, inductive inference and systems biology. For most problems, the fixpoint computation does not terminate, so an approximate solution has to be found. Widening is a technique to compute an over-approximation of an infinite, increasing sequence of sets. In this thesis, we present a framework for constructing widening operators for fixpoint computations over sets represented as automata. Many widening operators for automata that appear in the literature are instances of our framework. Moreover, two inductive inference algorithms in the literature naturally fall out as instances of this framework. We identify general criteria that characterise the effect of widening and use these criteria to study various properties of widening operators. We also provide several new results and generalise existing results about widening operators and inductive inference algorithms. Finally, we show how a widening operator defined in our framework can be combined with algorithms for automated verification of infinite state systems and provide a heuristic for generating counterexamples if verification fails.

A recent publication has presented the AMIS model, a novel model for risk prediction in acute myocardial infarction (AMI). The model proposed therein is based on AODE, a probabilistic bayesian classifier. It outperforms TIMI, a widely accepted prediction model in the field, when classifying patients on their expected in-hospital survival or non-survival. It was hypothesised that the score which serves as a basis for the classification could be used as a probability estimator, allowing a more fine-graned stratification of patients into different mortality classes. An evaluation method for the fit of probabilistic models is developed and applied to the AMIS model. In the evaluation, the AMIS model clearly outperformes TIMI as a risk estimator.

In the era of wireless communication, so-called smartphones have inexorably pervaded our way of live. Through their ubiquity, the user is exposed to recurrent interruptions, oftentimes unwanted ones. Consequently, the question emerges whether and to what extent a smartphone truly could become smart in order to minimize these interruptions. To find an answer to this question, we enabled a smartphone with the possibility to ascertain its own surroundings, that is to become context-aware. The goal of this thesis is the preparation of an experiment approaching the vision of a truly “smart” phone. This consists in the design, the implementation and the evaluation of an application capable of collecting and persistently storing data originating from sensors mounted in and around a smartphone.

Distractions by negative external influences in the daily business routines are not to be underestimated since they can lead to severe productivity losses. Depending on the interruptability level, already minor disturbances may discontinue the workflow. One of today’s scarcely controlled origins of such disturbances is the telephone. A system that filters incoming phone calls according to their actual context to the interruptability could solve this problem. This diploma thesis suggests a system for classification of phone calls with automatic inquiry to increase the system's performance.

NExT (Next Experiment Toolbox) is a system that provides guidance to the user during the whole lifecycle of experiments in the life science domain. These are typically composed out of many atomic complex and potentially long-running tasks, which are grounded by a heterogeneous tool set. This thesis extends NExT with a planning component for semi-automated composition of OWL-S services while coping with non-determinism and incomplete knowledge. The solution offers a set of non-standard planning features, namely synthesizing plan alternatives, imprecise planning in cases of no or too few solutions, and planning with complex goals and QoS optimization criteria. The concept decouples planning tasks from their problem solving. It proposes a novel approach to encode planning problems and user requested cooperative planning features as PDDL 3.0 planning problems, and to allocate them to appropriate distributed planners. Aside the deployment of state-of-the-art planners, planner concepts for synthesizing plan alternatives and for dealing with the no-solution case are shown.

This thesis introduces similarity measures to be used by comparing XML workflows and RDF or OWL structures. These structures are accessed and converted into a generic graph representation. Two graphs are compared by a measure to conclude in a single value indicating the similarity of the graphs. Similarity is calculated by two different similarity measures, the graph isomorphism measure and the subgraph isomorphism measure. The graph isomorphism measure detects structurally identical graphs and calculates the similarity upon the nearness of the node labels. Structurally different graphs are compared by the subgraph isomorphism measure to find matching parts. The size and the label similarity of the nodes of a matched part contribute to its similarity based upon the compared graphs. The highest similarity value of all parts is defined to be the similarity of the two graphs. Performance improvements were developed and implemented which led to a decreasing runtime. Further improvements were analyzed and proposed to be implemented at a later date.

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Traditional process support systems offer the promise of software assembled from service elements. The typical approach is a static composition of atomic processes to more powerful services. In the real world, however, processes change over time: business needs are rapidly evolving and, thus, changing the work itself and relevant information may be unknown until workflow execution run-time. Hence, the traditional, static approach does not sufficiently address the need for dynamism. Based on applications in the life science domain this paper puts forward five requirements for dynamic process support system. Specifically, these demand a focus on a tight user interaction in the process discovery, composition, and execution phases. The system and the user establish a continuous feedback loop resulting in a mixed-initiative kind approach. We also present a prototype implementation NExT, which embodies this approach and present a preliminary validation based on a real-world scenario as well as a comparison with other process support tools.

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Nowadays the usage of collaborative learning in e-Learning environments is becoming very popular. Even so, there is a lack of web-based assignment systems supporting collaborative learning. In this work, I report on a novel assignment system for organizing and realizing web-based exercises. This new flexible assignment system makes it possible to design and perform different exercises in various collaborative learning-settings. For example, exercises can be arranged as tutorials as well as peer assessments. For an effective implementation of collaborative learning environments, the learning process must be structured. That can be done through so-called collaboration scripts and statechart diagrams.

The Semantic Web presents the vision of a distributed, dynamically growing knowledge base founded on formal logic. Common users, however, seem to have problems even with the simplest Boolean expression. As queries from web search engines show, the great majority of users simply do not use Boolean expressions. So how can we help users to query a web of logic that they do not seem to under-stand? We address this problem by presenting Ginseng, a quasi natural language guided query interface to the Semantic Web. Ginseng relies on a simple question grammar which gets dynamically extended by the structure of an ontology to guide users in formulating queries in a language seemingly akin to English. Based on the grammar Ginseng then translates the queries into a Semantic Web query language (RDQL), which allows their execution. Our evaluation with 20 users shows that Ginseng is extremely simple to use without any training (as opposed to any logic-based querying approach) resulting in very good query per-formance (precision = 92.8%, recall = 98.4%). We, furthermore, found that even with its simple gram-mar/approach Ginseng could process over 40% of questions from a query corpus without modification.

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