Information Integration - the process of integration, evolution and versioning

At present, many information sources are available wherever you are. Most of the time, the information needed is spread across several of those information sources. Gathering this information is a tedious and time consuming job. Automating this process would assist the user in its task. Integration of the information sources provides a global information source with all information needed present. All of these information sources also change over time. With each change of the information source, the schema of this source can be changed as well. The data contained in the information source, however, cannot be changed every time, due to the huge amount of data that would have to be converted in order to conform to the most recent schema.\ud In this report we describe the current methods to information integration, evolution and versioning. We distinguish between integration of schemas and integration of the actual data. We also show some key issues when integrating XML data sources

Representing uncertainty regarding satisfaction degrees using possibility distributions

Evaluating flexible criteria on data leads to degrees of satisfaction. If a datum is uncertain, it can be uncertain to which degree it satisfies the criterion. This uncertainty can be modelled using a possibility distribution over the domain of possible degrees of satisfaction. In this work, we discuss the meaningfulness thereof by looking at the semantics of such a representation of the uncertainty. More specifically, it is shown that defuzzification of such a representation, towards usability in (multi-criteria) decision support systems, corresponds to expressing a clear attitude towards uncertainty (optimistic, pessimistic, cautious, etc.

On various forms of bipolarity in flexible querying

International audienceThe paper discusses the modeling of “if possible" in requirements of the form “A and if possible B". We distinguish between two types of understanding: either i) A and B are requirements of the same nature and are viewed as constraints with different levels of priority, or ii) they are of different nature (only A induces constraint(s) and B is only used for breaking ties among items that are equally satisfying A). We indicate that the two views are related to different types of bipolarity, and discuss them in relation with possibilistic logic. The disjunctive dual of the first view (“A or at least B") is then presented in this logical setting. We also briefly mention the idea of an extension of the second view where B may refer both to bonus conditions or malus conditions that may increase or decrease respectively the interest in an item satisfying A

On nearness measures in fuzzy relational data models

AbstractIt has been widely recognized that the imprecision and incompleteness inherent in real-world data suggest a fuzzy extension for information management systems. Various attempts to enhance these systems by fuzzy extensions can be found in the literature. Varying approaches concerning the fuzzification of the concept of a relation are possible, two of which are referred to in this article as the generalized fuzzy approach and the fuzzy-set relation approach. In these enhanced models, items can no longer be retrieved by merely using equality-check operations between constants; instead, operations based on some kind of nearness measures have to be developed. In fact, these models require such a nearness measure to be established for each domain for the evaluation of queries made upon them. An investigation of proposed nearness measures, often fuzzy equivalences, is conducted. The unnaturalness and impracticality of these measures leads to the development of a new measure: the resemblance relation, which is defined to be a fuzzified version of a tolerance relation. Various aspects of this relation are analyzed and discussed. It is also shown how the resemblance relation can be used to reduce redundancy in fuzzy relational database systems

Incomplete conjunctive information

AbstractMany information systems capable of handling incomplete or fuzzy information manipulate objects with single-valued attributes. Information is then said to be disjunctive. Information is said to be conjunctive when pertaining to many-valued attributes. While a piece of incomplete disjunctive information is easily represented by means of a set of mutually exclusive possible values, modeling incomplete conjunctive information theoretically leads to consider families of sets, since attributes are then set-valued under complete information. Some proposals are made in order to efficiently and rigorously represent incomplete conjunctive information, and deal with query evaluation, especially in the case where only upper and/or lower bounds of the set of values of a many-valued attribute are known. Applications of this approach can be expected for the processing of time intervals, as well as spatial reasoning, among other topics, in knowledge base management