87 research outputs found

    Flexible hierarchies and fuzzy knowledge-based OLAP

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    The notion of H-IFS in data modelling

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    In this paper we revise the context of "value imprecision", as part of an knowledge-based environment We present our approach for including value imprecision as pan of a non-rigid hierarchical structures of organization. This led us to introduce the concept of closure of an Intuitionistic fuzzy set over a universe that has a hierarchical structure. Intuitively, in the closure of this Intuitionistic fuzzy set, the "kind of" relation is taken into account by propagating the degree associated wit an element to its sub-elements in the hierarchy. We introduce the automatic analysis according to concepts defined as part of a knowledge hierarchy in order to guide the query answering as part of an integrated database environment with the aid of hierarchical intuitionistic fuzzy sets

    The notion of H-IFS -an approach for enhancing query capabilities in Oracle10g

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    Query answering requirements for a knowledge based treatment of user requests led us to introduce the concept of closure of an Intuitionistic fuzzy set over a universe that has a hierarchical structure. We introduce the automatic analysis of queries according to concepts defined as part of a knowledge based hierarchy in order to guide the query answering as part of an integrated database environment with the aid of hierarchical Intuitionistic fuzzy sets, H-IFS. In this paper based on the notion of H-IFS we propose an ad-hoc utility build on top of Oracle10g that allows us to enhance the query capabilities of by providing better and knowledgeable answers to user’s requests. The theoretical aspects as well the practical issues and achieved results are presented throughout the rest of the paper

    Bipolar querying of valid-time intervals subject to uncertainty

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    Databases model parts of reality by containing data representing properties of real-world objects or concepts. Often, some of these properties are time-related. Thus, databases often contain data representing time-related information. However, as they may be produced by humans, such data or information may contain imperfections like uncertainties. An important purpose of databases is to allow their data to be queried, to allow access to the information these data represent. Users may do this using queries, in which they describe their preferences concerning the data they are (not) interested in. Because users may have both positive and negative such preferences, they may want to query databases in a bipolar way. Such preferences may also have a temporal nature, but, traditionally, temporal query conditions are handled specifically. In this paper, a novel technique is presented to query a valid-time relation containing uncertain valid-time data in a bipolar way, which allows the query to have a single bipolar temporal query condition

    Definitive Integral on the Interval of IF Sets

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    Intercriteria Analysis and Arithmetic Functions

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    The possibility to apply the intercriteria analysis over normalized data is discussed. An example, related to evaluation of six arithmetic functions, is given

    Approximate XML query matching and rewriting using Intuitionistic Fuzzy Trees

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    XML is undoubtedly becoming the predominant de facto standard for data representation and communication, especially on the web, which in turn is causing XML data repositories to grow rapidly. Current XML Query languages, such as Xquery, have limited capabilities in querying multiple data sources with different structures (schemas) which is inefficient. Therefore, an urgent need has been identified for XML querying techniques that can overcome the rising diversity in XML data schemas. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching by considering a novel approach of matching arcs as basic units of data schemas. Additionally, we provide an algorithm for rewriting the original query to be able to retrieve data from local data sources. Our approach was tested using synthetic data sources with high degree of structural diversity, and it proved useful result while pertaining good performance and low memory usage
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