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

The need for explicit time representation in workflow environments has been recently identified. Although the concept of time is an intrinsic part of a workflow application, time management has been treated as an issue of ordering the constructs of the workflow specification. This traditional approach aids mainly in the monitoring of activity deadlines. There is limited support for modelling and representation of time constraints associated with the processes and their activities or cases. This paper presents a temporal formalism for representing indeterminacy to workflow specifications. We further argue that integrating workflows with temporal databases can further enhance the workflow specification. Finally we utilise agents as part of a temporal workflow environment. Particular attention is paid in the allocation of agents to workflow activities with reference to the properties of conflict resolution and moving hierarchies.

Purpose – Data preparation plays an important role in data mining as most real life data sets contained missing data. This paper aims to investigate different treatment methods for missing data. Design/methodology/approach – This paper introduces, analyses and compares well‐established treatment methods for missing data and proposes new methods based on naive Bayesian classifier. These methods have been implemented and compared using a real life geriatric hospital dataset. Findings – In the case where a large proportion of the data is missing and many attributes have missing data, treatment methods based on naive Bayesian classifier perform very well. Originality/value – This paper proposes an effective missing data treatment method and offers a viable approach to predict inpatient length of stay from a data set with many missing values.

A theory of the Generalized nets was used to construct a generalized net model which describes the possibility to evaluate the time of the occurrence or completion of the events. Twenty predicates for checking the validity of the special circumstances, related to time-moments of two events were introduced. The model can be used to analyze the specific moments in which the facts have happened. In result we can answer questions such as: “Is the fact valid?” “How many facts it contradicts”, “How many facts it confirms?”

Sensorial analysis based on the utilisation of human senses, is one of the most important investigation methods in food and chemical analysis. Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. An array of gas sensors has been employed to identify in vivo urine samples from patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK Health Laboratory environment. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on intelligent techniques has been developed. The implementation of an advanced neural network scheme and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been adopted in this study. The experimental results confirm the validity of the presented methods. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

In the present paper is constructed a Generalized net model of a process of sequential pattern mining by a generalized sequential pattern algorithm. Sequence pattern mining is a technique used for predictive data mining. It is used for discovering of frequent sequences in the databases. A sequence is regarded as frequent when it occurs in the data above a previously user defined minimum support within the applied time constraints. The analysis is an extension of frequent pattern mining technique that extracts frequent itemsets. They can be used for the creation of association rules. GSP algorithm was realized with metrological observations from weather databases, infrared camera and smoke detector to determine the possibility of forest fire. The proposed Generated net model can be used to monitor the sequence pattern mining process depending on meteorological parameters.

The internet is undoubtedly the biggest data source ever with tons of data from different sources following different formats. One of the main challenges in computer science is how to make data sharing and exchange between these sources possible; or in other words, how to develop a system that can deal with all these differences in data representation and extract useful knowledge from there. And since XML is the de facto standard for representing data on the internet, XML query matching has gained so much popularity recently. In this paper we present new types of fuzzy arc matching that can match a pattern arc to a schema arc as long as the correspondent parent and child nodes are there and have reachability between them. Experimental results shown that the proposed approach provided better results than previous works.

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.

As XML is becoming the de facto standard for data representation and communication, especially on the web, a need has been identified for efficient XML querying techniques that can overcome the dilemma of heterogeneity in XML data sources. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching, making the query results include not just the XML data trees that exactly match the query, but also the ones that partially match it. We believe that our approach has a potential to return useful query answers while pertaining good performance.

As the popularity of XML as a de facto standard for data representation and communication is rising, a need has been identified for efficient XML querying techniques that can overcome the dilemma of diversity in the structure of XML data sources. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching, making the query result include not just the XML data trees that exactly match the query, but also the ones that partially match it. Our approach has a potential to return useful query answers while pertaining good performance. Users will have the option to choose between quick and less accurate results or time costing and more accurate results.

Data Warehouse (DWH) Models are based on static dimensions having single hierarchies. With the evolution of World Wide Web, including external knowledge can enrich those models and provide better results of data analysis. Therefore, it would be useful to have intelligent transformation utilities that can mine various data structures and extract useful knowledge participating in the construction of flexible DWH models. This paper proposes a new approach towards intelligent transformation utilities that will allow the utilisation of multiple hierarchical schemata when defining the dimensions of data-warehouses. By allowing a particular dimension to have multiple but semantically close definitions, we allow same users to query same data with the aid of different semantics. To put it differently users are allowed to change or refine the axis of analysis with respect to a particular query, in their effort to achieve a more meaningful answer. We make use of Intuitionistic Fuzzy Logic to soften the rules of calculating the similarity between different hierarchies which in turn is used to decide if the hierarchies can be included in the definition of data warehouse dimension. Data transformations are used to transform the data from one hierarchy to another. With the aid of external data, some sort of estimation is used to estimate values of new hierarchy levels and then based on the user's request the desired hierarchy is used to view an OLAP cube.

In this paper we revise the context of ldquovalue imprecisionrdquo, as part of an knowledge-based environment. We present our approach for including value imprecision as part 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 ldquokind ofrdquo relation is taken into account by propagating the degree associated with 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.

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.

Traditional data repositories are typically focused on the storage and querying of crisp-precise domains of data. As a result, current commercial data repositories have no facilities for either storing or querying imprecise-approximate data. However, when considering scientific data (i.e. medical data, sensor data etc) value uncertainty is inherited to scientific measurements. In this paper we revise the context of “value uncertainty”, and examine common models related to value uncertainty as part of the OLAP model. We present our approach for extending the OLAP model to include treatment of value uncertainty as part of a multidimensional model inhabited by flexible date and non-rigid hierarchical structures of organisation.

In this paper we revise the context of "value uncertainty", as part of an OLAP based environment. A new multidimensional-cubic model named as the if-cube is introduced which is able to operate over data with imprecision either in the facts or in the dimensional hierarchies. These query requirements led us to introduce the concept of closure of an intuitionistic fuzzy set over a universe that has a hierarchical structure, H-IFS. We introduce the automatic recommendation of analysis according to the concepts defined as part of a domain ontology in order to guide the decision making with the aid of H-IFS.

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.

There is increasing worldwide awareness that bionics and artificial intelligence will play an important role in microbial analysis. An intelligent data-warehouse system consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on neural networks and genetic algorithms have been applied as part of a microbial analysis. The microbiological warehouse environment has, also adopted the concept of fusion of multiple classifiers dedicated to specific feature parameters. The experimental results confirm the soundness of the presented methods.

The paper presents MDDQL as a query language suitable for multi-lingual conceptual querying of collections of databases from a graphical user interface or from an application programming one. The query language, however, has been specified and implemented with the parametric theory of linguistic diversity in mind such that syntactic and semantic parsing of multi-lingual query expressions becomes quite simple and guarantees identical query results regardless the preferred natural language. We present a parsing algorithm, which shows that it is quite easy to formulate a query regardless the underlying type order of a natural language, be it Subject-Object-Verb, Subject-Verb-Object, or Object-Verb-Subject, etc., and still being able to grasp the meaning of the query at a minimal computational effort possible.

It is well accepted that many real-life datasets are full of missing data. In this paper we introduce, analyze and compare several well known treatment methods for missing data handling and propose new methods based on Naive Bayesian classifier to estimate and replace missing data. We conduct extensive experiments on datasets from UCI to compare these methods. Finally we apply these models to a geriatric hospital dataset in order to assess their effectiveness on a real-life dataset.

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.

In this paper we informally and formally defined what we mean by uncertain- ignorant information in relational databases and data warehouses. We classify proposed extensions to the relational data model that can represent and retrieve incomplete information. There are many different kinds of temporal ignorant information including information that is fuzzy, imprecise, indeterminate, indefinite, missing, partial, possible, probabilistic, unknown, uncertain, or vague. We will explore each variety of temporal ignorant information in detail with reference to database and data-warehouse design.

This paper is an attempt to provide an XML based framework for modelling and representing multi-source electronic patient records (EPR) as part of an integrated single source environment. The main focus of this framework is to capture the dynamic features of EPR data sources for further analysis and knowledge discovery with the aid of OLAP and data mining.

The need for explicit time representation in workflow environments has been recently identified. Although the concept of time is an intrinsic part of a workflow application, time management has been treated as an issue of ordering the constructs of the workflow specification. This traditional approach aids mainly in the monitoring of activity deadlines. There is limited support for modelling and representation of time constraints associated with the processes and their activities or cases. This paper presents a temporal formalism for representing indeterminacy to workflow specifications. We further argue that integrating workflows with temporal databases can further enhance the workflow specification.

Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed “artificial nose” based on chemoresistive sensors has been employed to identify in vivo urine samples from 45 patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK Public Health Laboratory environment. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on a hybrid system of genetic algorithms, neural networks and multivariate techniques such as principal components analysis and discriminant function analysis-cross validation. The experimental results confirm the validity of the presented method.

Workflows are activities involving the coordinated execution of multiple tasks performed by entities, in the enterprise environment. Workflow systems support the encoding and execution of workflows. A workflow management system is proposed, in which a considerable attention is paid on the utilisation of the services of a flexible database formalism. Flexible database formalisms must permit the modelling of trends, seasonality (periodicity), cyclic variations of trends and irregular activities (no predictable patterns) in the enterprise environment Furthermore, the formalism must permit the, expression of enterprise concepts founded in various conceptual levels. This paper will be restricted in workflow management system design, and activity scheduling, in a post relational environment whereas relational operators utilise paths (links between domains), in order to express interdependencies between activities.

An information system is used for representing and managing indicative information from multiple sources describing the state of an enterprise. Most information 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, where data are imperfect and multiple conflicting sources of information do exist. In such cases, information can be imperfect and/or temporal, or any possible combination of each two of them. In domains where information is perfect, all information sources are absolutely reliable. In more speculative domains, like diagnosis, information may be asserted relatively to some time intervals in which it is possibly defined and probably believed. In such domains different sources of information may be assigned different degrees of reliability. Value imperfection and/or temporal indeterminacy may cause uncertainty. Temporal information may be recurring, periodical or definite.

In this paper we are concerned with the problem of designing of a conceptual framework for the construction of a flexible query answering mechanism that is adequate for selecting useful answers to queries, considering a multi-source and conflicting community of information sources. We put forward a proposal to provide representations with that kind of functionality through flexible reasoning mechanisms for handling query descriptions and retrieving perspective answers. We start by positioning conceptual schema, database with regard to this problem. Then we distinguish evidential assessment as the central task in query answering considering the conceptual, instance level always with respect to the dynamism-dimension of data.

The task of an information system is the representation and management of indicative information from multiple sources describing the state of some enterprise. Most snapshot database models represent enterprises that are crisp where all relationships are fixed, and all attributes are atomic valued. In temporal databases, approaches are dealing mainly with precise absolute times. Very few approaches deal with imprecise absolute times or with infinite absolute times. No consideration has been given to imprecise infinite times. Many algebraic models are dealing with temporal or value imperfection, with no description of the semantics of uncertain information. This paper suggests an application independent conceptual framework describing the semantics of temporal and incomplete information and translates this to database representation.

One of the main uses of an information system is the representation and management of large amounts of indicative information from multiple sources describing the state of some enterprise. Most current information systems model enterprises that are crisp. A crisp enterprise is defined as one that is highly quantifiable; all relationships are fixed, and all attributes are atomic valued. The premises on which this paper is based are precise enterprises, where data are imperfect. In such cases information can be certain, imprecise or uncertain, temporal, or any possible combination of each two of them, depending on the application domain. Additionally, in domains where the information is perfect, all information sources are absolutely reliable and trustworthy. In more speculative domains, like diagnosis, information may be asserted relatively to some time intervals in which it is possibly defined and probably believed. In such domains different sources of information may be assigned different degrees of reliability. This paper is presenting a framework for the conceptual integration and uniform treatment of all these types of information.

Current information systems model enterprises that are crisp. A crisp enterprise is defined as one that is highly quantifiable; all relationships are fixed, and all attributes are atomic valued. This paper is based on precise enterprises, where data are imperfect and where multiple and possibly conflicting sources of information do exist. Imperfection may be related to the actual values or the time that something happened within an organisation. In such domains different sources of information may be assigned different degrees of reliability. Factual imperfection and/or temporal indeterminacy may cause uncertainty. Temporal information may be recurring, periodical or definite. This paper is presenting a conceptual and algebraic framework for a uniform treatment and representation of all these types of information.