Implementing imperfect information in fuzzy databases

Information in real-world applications is often vague, imprecise and uncertain. Ignoring the inherent imperfect nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several substantial information, which may be very useful in several data-intensive applications. In database context, several fuzzy database models have been proposed. In these works, fuzziness is introduced at different levels. Common to all these proposals is the support of fuzziness at the attribute level. This paper proposes first a rich set of data types devoted to model the different kinds of imperfect information. The paper then proposes a formal approach to implement these data types. The proposed approach was implemented within a relational object database model but it is generic enough to be incorporated into other database models.ou

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

Conceptual design and implementation of the fuzzy semantic model

FSM is one of few database models that support fuzziness, uncertainty and impreciseness of real-world at the class definition level. FSM authorizes an entity to be partially member of its class according to a given degree of membership that reflects the level to which the entity verifies the extent properties of this class. This paper deals with the conceptual design of FSM and adresses some implementation issues.ou

Some notes on an extended query language for FSM

FSM is a database model that has been recently proposed by the authors. FSM uses basic concepts of classification, generalization, aggregation and association that are commonly used in semantic modelling and supports the fuzziness of real-world at attribute, entity, class and relations intra and inter-classes levels. Hence, it provides tools to formalize and conceptualize real-world within a manner adapted to human perception of and reasoning about this real-word. In this paper we briefly review basic concepts of FSM and provide some notes on an extended query language adapted to it.ou

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

THE DECISION SUPPORT SYSTEMS FOR THE INFORMATION SOCIETY (i-Society)

The globalization process needs exact information flows that should be collected in due time. The Information Society ensures the communication between people with different expertise from various geographical areas that have similar interests. The increase of the companies’ activities leads implicitly to the increase of the volume and the complexities of databases, as well as the continuous modernization of the integrated information systems in order to collect the information in due time, that is requested by the decision takers and the frequent use of DSS. The paper presents the DSS structure, the main facilities offered by the associated software products, an evolution of the databases technologies, as well as a list of the program products used to process the statistical data and data mining in order to obtain the main sources of information that is necessary to take decisions.Information Society (i-Society); Data Base; Information Systems; Decision Support Systems (DSS); Statistical Package, Portal technology