603,161 research outputs found

    Temporal Data Modeling and Reasoning for Information Systems

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    Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

    A Review of integrity constraint maintenance and view updating techniques

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    Two interrelated problems may arise when updating a database. On one hand, when an update is applied to the database, integrity constraints may become violated. In such case, the integrity constraint maintenance approach tries to obtain additional updates to keep integrity constraints satisfied. On the other hand, when updates of derived or view facts are requested, a view updating mechanism must be applied to translate the update request into correct updates of the underlying base facts. This survey reviews the research performed on integrity constraint maintenance and view updating. It is proposed a general framework to classify and to compare methods that tackle integrity constraint maintenance and/or view updating. Then, we analyze some of these methods in more detail to identify their actual contribution and the main limitations they may present.Postprint (published version

    The Application of the Hermeneutic Process to Qualitative Safety Data: A Case Study using Data from the CIRAS project

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    This article describes the new qualitative methodology developed for use in CIRAS (Confidential Incident Reporting and Analysis System), the confidential database set up for the UK railways by the University of Strathclyde. CIRAS is a project in which qualitative safety data are disidentified and then stored and analysed in a central database. Due to the confidential nature of the data provided, conventional (positivist) methods of checking their accuracy are not applicable; therefore a new methodology was developed - the Applied Hermeneutic Methodology (AHM). Based on Paul Ricoeur's `hermeneutic arc', this methodology uses appropriate computer software to provide a method of analysis that can be shown to be reliable (in the sense that consensus in interpretations between different interpreters can be demonstrated). Moreover, given that the classifiers of the textual elements can be represented in numeric form, AHM crosses the `qualitative-quantitative divide'. It is suggested that this methodology is more rigorous and philosophically coherent than existing methodologies and that it has implications for all areas of the social sciences where qualitative texts are analysed

    Reverse engineering to achieve maintainable WWW sites

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    The growth of the World Wide Web and the accelerated development of web sites and associated web technologies has resulted in a variety of maintenance problems. The maintenance problems associated with web sites and the WWW are examined. It is argued that currently web sites and the WWW lack both data abstractions and structures that could facilitate maintenance. A system to analyse existing web sites and extract duplicated content and style is described here. In designing the system, existing Reverse Engineering techniques have been applied, and a case for further application of these techniques is made in order to prepare sites for their inevitable evolution in futur

    ARTMAP-IC and Medical Diagnosis: Instance Counting and Inconsistent Cases

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    For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking algorithm (MT+), the new algorithm (MT-) better approximates the real-time network differential equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pima Indian diabetes, breast cancer, heart disease, and gall bladder removal. ARTMAP-IC results arc equal to or better than those of logistic regression, K nearest neighbor (KNN), the ADAP perceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP dynamics are fast, stable, and scalable. A voting strategy improves prediction by training the system several times on different orderings of an input set. Voting, instance counting, and distributed representations combine to form confidence estimates for competing predictions.National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-95-J-0409, N00014-95-0657
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