Boyan Kolev

An intuitionistic fuzzy component based approach for identifying Web usage patterns

This work presents a framework for Web mining, which is developed to support and assist existing data mining algorithms in order to preliminarily refine browsing pattern with relevant constraints. Intuitionistic fuzzy sets are used to represent the possibility that backward steps are used while searching for the pages of interest.

An Application of Intuitionistic Fuzzy Relational Databases in Football Match Result Predictions

This paper presents a model for prediction of football league matches. We use intuitionistic fuzzy SQL (IFSQL) and intuitionistic fuzzy relational databases (IFRDB) to store and manage data about football matches and predictions. We take advantage of intuitionistic fuzzy sets by relating the degree of indefiniteness to an uncertainty about the estimation for a team’s capabilities. The uncertainty is produced by insufficient statistical data.

An extension of the relational model to intuitionistic fuzzy data quality attribute model

The model we suggest makes the data quality an intrinsic feature of an intuitionistic fuzzy relational database. The quality of the data is no more determined by the level of user complaints or ad hoc sql queries prior to the data load but it is stored explicitly in relational tables and could be monitored and measured regularly. The quality is stored on an attribute level basis in supplementary tables to the base user ones. The quality is measured along preferred quality dimensions and is represented by intuitionistic fuzzy degrees. To consider the preferences of the user with respect to the different quality dimensions and table attributes we create additional tables that contain the weight values. The user base tables are not intuitionistic fuzzy but we have to use an intuitionistic fuzzy RDBMS to represent and manipulate data quality measures.

Intuitionistic fuzzy generalized net analysis of periodic deadlock detection in database systems

This paper presents an intuitionistic fuzzy generalized net model of a transaction database system, which uses the 2PL protocol with periodic deadlock detection. It defines probabilities for a transaction to be granted a requested lock, held back by another transaction or deadlocked, which are integrated with the intuitionistic fuzzy predicates. We can use this model to simulate transaction processing and to analyze the efficient time for useful work and the time wasted in holding back transactions.

Intuitionistic Fuzzy Dependency Framework

This paper proposes an Intuitionistic Fuzzy Dependency Framework (IFDF) model as a flexible tool for analyzing the cause and effect of events occurring in systems, where causation dependencies might be partial or vague. A core data model with basic operations is introduced. A static approach for dependency analyses is presented using traversals of the dependency graph. A dynamic approach using generalized nets as a simulation tool is also presented for the case of systems with temporal dependencies.

Intuitionistic Fuzzy Data Quality Attribute Model and Aggregation of Data Quality Measurements

The model we suggest makes the data quality an intrinsic feature of an intuitionistic fuzzy relational database. The quality of the data is no more determined by the level of user complaints or ad hoc sql queries prior to the data load but it is stored explicitly in relational tables and could be monitored and measured regularly. The quality is stored on an attribute level basis in supplementary tables to the base user ones. The quality is measured along preferred quality dimensions and is represented by intuitionistic fuzzy degrees. To consider the preferences of the user with respect to the different quality dimensions and table attributes we create additional tables that contain the weight values. The user base tables are not intuitionistic fuzzy but we have to use an intuitionistic fuzzy RDBMS to represent and manipulate data quality measures.

Representation of Value Imperfection with the Aid of Background Knowledge: H-IFS

An Integrated Data Management System is required for representing and managing indicative information from multiple sources describing the state of an enterprise. In such environments, information may be partially known because the related information from the real world corresponds to a set of possible values including the unknown. Here, we present a way to replace unknown values using background knowledge of data that is often available arising from a concept hierarchy, as integrity constraints, from database integration, or from knowledge possessed by domain experts. We present and examine the case of H-IFS to represent support contained in subsets of the domain as a candidate for replacing unknown values mostly referred in the literature as NULL values.

Merging Probabilistic & Null Values Utilising an Intuitionistic Fuzzy Relational Mediator

An integrated data management system is required for representing and managing indicative information from multiple sources describing the state of an enterprise. Current data management systems model enterprises that are crisp. A crisp enterprise is one that is highly quantifiable; relationships are fixed and attributes are atomic valued. The premises for this paper are precise enterprises, data maybe uncertain; multiple sources of information do exist, but uncertainty may be described using different models