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

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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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 expressions. 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 understand? We address this problem by presenting a natu-ral language interface to semantic web querying. The interface allows formulat-ing queries in Attempto Controlled English (ACE), a subset of natural English. Each ACE query is translated into a discourse representation structure – a vari-ant of the language of first-order logic – that is then translated into an N3-based semantic web querying language using an ontology-based rewriting framework. As the validation shows, our approach offers great potential for bridging the gap between the logic-based semantic web and its real-world users, since it al-lows users to query the semantic web without having to learn an unfamiliar formal language. Furthermore, we found that users liked our approach and de-signed good queries resulting in a very good retrieval performance (100% pre-cision and 90% recall).

Traditional semantic web query languages support a logic-based access to the semantic web. They offer a retrieval (or reasoning) of data based on facts. On the traditional web and in databases, however, exact querying often provides an incomplete answer as queries are overspecified or the mix of multiple ontologies/modelling differences requires “interpretational flexibility.” This paper introduces iRDQL – a semantic web query language with support for similarity joins. It is an extension to RDQL that enables the user to query for similar resources in an ontology. A similarity measure is used to determine the degree of similarity between two semantic web resources. Similar resources are ranked by their similarity and returned to the user. We show how iRDQL allows to extend the reach of a query by finding additional results. We quantitatively evaluated one measure of SimPack – our library of similarity measures for the use in ontologies – for its usefulness in iRDQL within the context of an OWL-S semantic web service retrieval test collection. Initial results of using iRDQL indicate that it is indeed useful for extending the reach of the query and that it is able to improve recall without overly sacrificing precision.

In this work, fundamental properties of Bayes decision rule using general loss functions are derived analytically and are verified experimentally for automatic speech recognition. It is shown that, for maximum posterior probabilities larger than 1/2, Bayes decision rule with a metric loss function always decides on the posterior maximizing class independent of the specific choice of (metric) loss function. Also for maximum posterior probabilities less than 1/2, a condition is derived under which the Bayes risk using a general metric loss function is still minimized by the posterior maximizing class. For a speech recognition task with low initial word error rate, it is shown that nearly 2/3 of the test utterances fulfil these conditions and need not be considered for Bayes risk minimization with Levenshtein loss, which reduces the computational complexity of Bayes risk minimization. In addition, bounds for the difference between the Bayes risk for the posterior maximizing class and minimum Bayes risk are derived, which can serve as cost estimates for Bayes risk minimization approaches.

The key aim of this thesis work is to design and prototypically implement the Accounting module for an AAA server, based on the Generic AAA Architecture defined in RFC 2903 and Diameter protocol specifications. The resulting protocol and architecture shall provide a solution for offering accounting services to a Mobile Grid. It will also be used at a later stage together with various charging models for creating a charging mechanism for future mobile grids. A mobile grid environment, by its heterogenos nature brings new challenges to all the tree A-s in a traditional AAA environment. Regarding the accounting proccess, new types of resources have to be accounted for which require new parameters that have to be present in accounting records. Besides the traditional accounting of time, bytes, and packets, a grid service might need to account for CPU usage, memory consumption, or even accessed/containing information. The accounting module shall provide generic interfaces for possibly different monitoring entities that adapt to the type of resource being accounted for. The access to accounted for data shall be secure and reliable. Secure in this context means that accounted for records for a certain service can be created by certain entities that are aproved by that service provider. This requirement can be realized using X.509 certificates or other kind of credential tokens. Encryption of accounting messages shall be offered as a communication option between the AAA client and the AAA server. Reliability refers to the posibility of retreiving the accurate accounting information for a certain service/resource usage for charging consumptions.

Good similarity measures are central for techniques such as retrieval, matchmaking, clustering, data-mining, ontology translations, automatic database schema matching, and simple object comparisons. Measures for the use with complex (or aggregated) objects in ontologies are, however, rare, even though they are central for semantic web applications. This paper first introduces SimPack, a library of similarity measures for the use in ontologies (of complex objects). The measures of the library are then experimentally compared with a similarity ``gold standard'' established by surveying 94 human subjects in two ontologies. Results show that human and algorithm assessments vary (both between people and across ontologies), but can be grouped into cohesive clusters, each of which is well modeled by one of the measures in the library. Furthermore, we show two increasingly accurate methods to predict the cluster membership of the subjects providing the foundation for the construction of personalized similarity measures.

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This is the initial technical report of the Semantic Web Services Language (SWSL) Committee of the Semantic Web Services Initiative (SWSI). This report consists of the following four top-level documents, with four related appendices. * Semantic Web Services Framework (SWSF) Overview * The Semantic Web Services Language (SWSL) * The Semantic Web Services Ontology (SWSO) * SWSF Application Scenarios Appendices (of the Ontology document): * PSL in SWSL-FOL and SWSL-Rules * Axiomatization of the FLOWS Process Model * Axiomatization of the Process Model in SWSL-Rules * Reference Grammars

A data mining (DM) process involves multiple stages. A simple, but typical, process might in-clude preprocessing data, applying a data-mining algorithm, and postprocessing the mining re-sults. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. Extending notions developed for statistical expert systems we present a prototype Intelligent Discovery Assistant (IDA), which provides users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to show that an IDA can indeed provide useful enumerations and effective rankings in the context of simple classification processes. We discuss how an IDA could be an important tool for knowledge sharing among a team of data miners. Finally, we illustrate the claims with a com-prehensive demonstration of cost-sensitive classification using a more involved process and data from the 1998 KDDCUP competition.

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 understand? We address this problem by presenting a natural language front-end to semantic web querying. The front-end allows formulating queries in Attempto Controlled English (ACE), a subset of natural English. Each ACE query is translated into a discourse representation structure – a variant of the language of first-order logic – that is then translated into the semantic web querying language PQL. As examples show, our approach offers great potential for bridging the gap between the semantic web and its real-world users, since it allows users to query the semantic web without having to learn an unfamiliar formal language.