Outlier detection used for identifying wrong values in data is typically applied to single datasets to search them for values of unexpected behavior. In this work, we instead propose an approach which combines the outcomes of two independent outlier detection runs to get a more reliable result and to also prevent problems arising from natural outliers which are exceptional values in the dataset but nevertheless correct. Linked Data is especially suited for the application of such an idea, since it provides large amounts of data enriched with hierarchical information and also contains explicit links between instances. In a first step, we apply outlier detection methods to the property values extracted from a single repository, using a novel approach for splitting the data into relevant subsets. For the second step, we exploit owl:sameAs links for the instances to get additional property values and perform a second outlier detection on these values. Doing so allows us to confirm or reject the assessment of a wrong value. Experiments on the DBpedia and NELL datasets demonstrate the feasibility of our approach.
In order to efficiently use the ever growing amounts of structured data on the web, methods and tools for quality-aware data integration should be devised. In this paper we propose an approach to automatically learn the conflict resolution strategies, which is a crucial step in large-scale data integration. The approach is implemented as an extension of the Sieve data quality assessment and fusion framework. We apply and evaluate our approach on the use case of fusing data from 10 language editions of DBpedia, a large-scale structured knowledge base extracted from Wikipedia. We also propose a method for extracting rich provenance metadata for each DBpedia fact, which is later used in data fusion.
Software systems are becoming an integral part of everyday life influencing organizational and social activities. This aggravates the need for a socio-technical perspective for requirements engineering, which allows for modelling and analyzing the composition and interaction of hardware and software components with human and organizational actors. In this setting, alternative requirements models have to be evaluated and selected finding a right trade-off between the technical and social dimensions. To address this problem, we propose a tool-supported process of requirements analysis for socio-technical systems, which adopts planning techniques for exploring the space of requirements alternatives and a number of social criteria for their evaluation. We illustrate the proposed approach with the help of a case study, conducted within the context of an EU project.
Abstract. The quest for designing secure and trusted software has led to refined Software Engineering methodologies that rely on tools to support the design process. Automated reasoning mechanisms for requirements and software verification are by now a well-accepted part of the design process, and model driven architectures support the automation of the refinement process. We claim that we can further push the envelope towards the automatic exploration and selection among design alternatives and show that this is concretely possible for Secure Tropos, a requirements engineering methodology that addresses security and trust concerns. In Secure Tropos, a design consists of a network of actors (agents, positions or roles) with delegation/permission dependencies among them. Accordingly, the generation of design alternatives can be accomplished by a planner which is given as input a set of actors and goals and generates alternative multiagent plans to fulfill all given goals. We validate our claim with a case study using a state-of-the-art planner.
Abstract. At the early stages of the cooperative information system development one of the major problems is to explore the space of alternative ways of assignment and delegations of goals among system actors. The exploration process should be guided by a number of criteria to determine whether the adopted alternative is good-enough. This paper frames the problem of designing actor dependency networks as a multi-agent planning problem and adopts an off-the-shelf planner to offer a tool (P-Tool) that generates alternative actor dependency networks, and evaluates them in terms of metrics derived from Game Theory literature. As well, we offer preliminary experimental results on the scalability of the approach.
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