2,793 research outputs found
Bipolar SQLf: a Flexible Querying Language for Relational Databases
International audienceFlexible querying of information systems allows expressing complex preferences in user queries. Such preferences can be modeled by fuzzy bipolar conditions which are made of constraints c and wishes w and interpreted as "to satisfy c and if possible to satisfy w".We de ne in this article the main elements of the Bipolar SQLf language, which is an SQL-like querying language based on a bipolar relational algebra. This language is an extension of the SQLf language. Basic statements (projection, selection, etc.) are firstly defined in terms of syntax, evaluation and calibration. Then, complex statements, such as bipolar queries based on nesting operators are studied in terms of expression, evaluation, query equivalence and backward compatibility with the SQLf language
Relational Algebra for In-Database Process Mining
The execution logs that are used for process mining in practice are often
obtained by querying an operational database and storing the result in a flat
file. Consequently, the data processing power of the database system cannot be
used anymore for this information, leading to constrained flexibility in the
definition of mining patterns and limited execution performance in mining large
logs. Enabling process mining directly on a database - instead of via
intermediate storage in a flat file - therefore provides additional flexibility
and efficiency. To help facilitate this ideal of in-database process mining,
this paper formally defines a database operator that extracts the 'directly
follows' relation from an operational database. This operator can both be used
to do in-database process mining and to flexibly evaluate process mining
related queries, such as: "which employee most frequently changes the 'amount'
attribute of a case from one task to the next". We define the operator using
the well-known relational algebra that forms the formal underpinning of
relational databases. We formally prove equivalence properties of the operator
that are useful for query optimization and present time-complexity properties
of the operator. By doing so this paper formally defines the necessary
relational algebraic elements of a 'directly follows' operator, which are
required for implementation of such an operator in a DBMS
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
Fuzzy querying with the use of interval-valued fuzzy sets
The paper deals with flexible queries in relational databases. Conditions included in queries are modeled with the use of interval-valued fuzzy sets. Each value returned by a query is associated with a subinterval of [0,1] which expresses a membership degree. The bounds of membership intervals have been determined for different operations of relational algebra and different SQL operators
Bipolarity in ear biometrics
Identifying people using their biometric data is a problem that is getting increasingly more attention. This paper investigates a method that allows the matching of people in the context of victim identification by using their ear biometric data. A high quality picture (taken professionally) is matched against a set of low quality pictures (family albums). In this paper soft computing methods are used to model different kinds of uncertainty that arise when manually annotating the pictures. More specifically, we study the use of bipolar satisfaction degrees to explicitly handle the bipolar information about the available ear biometrics
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