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Article
Publication date: 29 April 2021

Mohamed Haddache, Allel Hadjali and Hamid Azzoune

The study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool…

Abstract

Purpose

The study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.

Design/methodology/approach

The problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.

Findings

An appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.

Research limitations/implications

The authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.

Practical implications

The tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.

Social implications

This work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.

Originality/value

This study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 14 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 20 December 2018

Sanjay Jharkharia and Chiranjit Das

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration…

Abstract

Purpose

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration decisions pertaining to emission reduction.

Design/methodology/approach

The authors’ model includes a fuzzy c-means (FCM) clustering algorithm and a fuzzy formal concept analysis. First, a set of suppliers were classified according to their carbon performances through the FCM clustering algorithm. Then, the fuzzy formal concepts were derived from a set of fuzzy formal contexts through an intersection-based method. These fuzzy formal concepts provide the relative level of investments and collaboration decisions for each identified supplier cluster. A case from the Indian renewable energy sector was used for illustration of the proposed analytical model.

Findings

The proposed model and case illustration may help manufacturing firms to collaborate with their suppliers for improving their carbon performances.

Research limitations/implications

The study contributes to the low carbon supply chain management literature by identifying the decision criteria of investments toward low carbon supplier development. It also provides an analytical model of collaboration for low carbon supplier development. Though the purpose of the study is to illustrate the proposed analytical model, it would have been better if the model was empirically validated.

Originality/value

Though the earlier studies on green supplier development program evaluation have considered a set of criteria to decide whether or not to invest on suppliers, these are silent on the relative level of investment required for a given set of suppliers. This study aims to fulfill this gap by providing an analytical model that will help a manufacturing firm to invest and collaborate with its suppliers for improving their carbon performance.

Details

Benchmarking: An International Journal, vol. 26 no. 1
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 20 August 2018

Sebastião M. Neto, Sérgio Dias, Rokia Missaoui, Luis Zárate and Mark Song

In recent years, the increasing complexity of the hyper-connected world demands new approaches for social network analysis. The main challenges are to find new…

Abstract

Purpose

In recent years, the increasing complexity of the hyper-connected world demands new approaches for social network analysis. The main challenges are to find new computational methods that allow the representation, characterization and analysis of these social networks. Nowadays, formal concept analysis (FCA) is considered an alternative to identifying conceptual structures in a social network. In this FCA-based work, this paper aims to show the potential of building computational models based on implications to represent and analyze two-mode networks.

Design/methodology/approach

This study proposes an approach to find three important substructures in social networks such as conservative access patterns, minimum behavior patterns and canonical access patterns. The present study approach considered as a case study a database containing the access logs of a cable internet service provider.

Findings

The result allows us to uncover access patterns, conservative access patterns and minimum access behavior patterns. Furthermore, through the use of implications sets, the relationships between event-type elements (websites) in two-mode networks are analyzed. This paper discusses, in a generic form, the adopted procedures that can be extended to other social networks.

Originality/value

A new approach is proposed for the identification of conservative behavior in two-mode networks. The proper implications needed to handle minimum behavior pattern in two-mode networks is also proposed to be analyzed. The one-item conclusion implications are easy to understand and can be more relevant to anyone looking for one particular website access pattern. Finally, a method for a canonical behavior representation in two-mode networks using a canonical set of implications (steam base), which present a minimal set of implications without loss of information, is proposed.

Details

International Journal of Web Information Systems, vol. 14 no. 3
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 6 July 2021

Shwe Sin Phyo

With the wealth of information available on the World Wide Web, it is difficult for anyone from a general user to the researcher to easily fulfill their information need…

91

Abstract

Purpose

With the wealth of information available on the World Wide Web, it is difficult for anyone from a general user to the researcher to easily fulfill their information need. The main challenge is to categorize the documents systematically and also take into account more valuable data such as semantic information. The purpose of this paper is to develop a concept-based search system that leverages the external knowledge resources as the background knowledge for getting the accurate and efficient meaningful search results.

Design/methodology/approach

The paper introduces the approach which is based on formal concept analysis (FCA) with the semantic information to support the document management in information retrieval (IR). To describe the semantic information of the documents, the system uses the popular knowledge resources WordNet and Wikipedia. By using FCA, the system creates the concept lattice as the concept hierarchy of the document and proposes the navigation algorithm for retrieving the hierarchy based on the user query.

Findings

The semantic information of the document is based on the two external popular knowledge resources; the authors find that it will be more efficient to deal with the semantic mismatch problems of user need.

Originality/value

The navigation algorithm proposed in this research is applied to the scientific articles of the National Science Foundation (NSF). The proposed system can enhance the integration and exploration of the scientific articles for the advancement of the Scientific and Engineering Research Community.

Details

Data Technologies and Applications, vol. 56 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 11 July 2019

M. Priya and Aswani Kumar Ch.

The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras…

Abstract

Purpose

The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

Design/methodology/approach

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

Findings

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

Practical implications

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

Originality/value

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

Details

Library Hi Tech, vol. 38 no. 2
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 16 April 2018

Paula Raissa, Sérgio Dias, Mark Song and Luis Zárate

Currently, social network (SN) analysis is focused on the discovery of activity and social relationship patterns. Usually, these relationships are not easily and…

Abstract

Purpose

Currently, social network (SN) analysis is focused on the discovery of activity and social relationship patterns. Usually, these relationships are not easily and completely observed. Therefore, it is relevant to discover substructures and potential behavior patterns in SN. Recently, formal concept analysis (FCA) has been applied for this purpose. FCA is a concept analysis theory that identifies concept structures within a data set. The representation of SN patterns through implication rules based on FCA enables the identification of relevant substructures that cannot be easily identified. The authors’ approach considers a minimum and irreducible set of implication rules (stem base) to represent the complete set of data (activity in the network). Applying this to an SN is of interest because it can represent all the relationships using a reduced form. So, the purpose of this paper is to represent social networks through the steam base.

Design/methodology/approach

The authors’ approach permits to analyze two-mode networks by transforming access activities of SN into a formal context. From this context, it can be extracted to a minimal set of implications applying the NextClosure algorithm, which is based on the closed sets theory that provides to extract a complete, minimal and non-redundant set of implications. Based on the minimal set, the authors analyzed the relationships between premises and their respective conclusions to find basic user behaviors.

Findings

The experiments pointed out that implications, represented as a complex network, enable the identification and visualization of minimal substructures, which could not be found in two-mode network representation. The results also indicated that relations among premises and conclusions represent navigation behavior of SN functionalities. This approach enables to analyze the following behaviors: conservative, transitive, main functionalities and access time. The results also demonstrated that the relations between premises and conclusions represented the navigation behavior based on the functionalities of SN. The authors applied their approach for an SN for a relationship to explore the minimal access patterns of navigation.

Originality/value

The authors present an FCA-based approach to obtain the minimal set of implications capable of representing the minimum structure of the users’ behavior in an SN. The paper defines and analyzes three types of rules that form the sets of implications. These types of rules define substructures of the network, the capacity of generation users’ behaviors, transitive behavior and conservative capacity when the temporal aspect is considered.

Details

International Journal of Web Information Systems, vol. 14 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 24 May 2011

Bokyoung Kang, Jae‐Yoon Jung, Nam Wook Cho and Suk‐Ho Kang

The purpose of this paper is to help industrial managers monitor and analyze critical performance indicators in real time during the execution of business processes by…

1729

Abstract

Purpose

The purpose of this paper is to help industrial managers monitor and analyze critical performance indicators in real time during the execution of business processes by proposing a visualization technique using an extended formal concept analysis (FCA). The proposed approach monitors the current progress of ongoing processes and periodically predicts their probable routes and performances.

Design/methodology/approach

FCA is utilized to analyze relations among patterns of events in historical process logs, and this method of data analysis visualizes the relations in a concept lattice. To apply FCA to real‐time business process monitoring, the authors extended the conventional concept lattice into a reachability lattice, which enables managers to recognize reachable patterns of events in specific instances of business processes.

Findings

By using a reachability lattice, expected values of a target key performance indicator are predicted and traced along with probable outcomes. Analysis is conducted periodically as the monitoring time elapses over the course of business processes.

Practical implications

The proposed approach focuses on the visualization of probable event occurrences on the basis of historical data. Such visualization can be utilized by industrial managers to evaluate the status of any given instance during business processes and to easily predict possible subsequent states for purposes of effective and efficient decision making. The proposed method was developed in a prototype system for proof of concept and has been illustrated using a simplified real‐world example of a business process in a telecommunications company.

Originality/value

The main contribution of this paper lies in the development of a real‐time monitoring approach of ongoing processes. The authors have provided a new data structure, namely a reachability lattice, which visualizes real‐time progress of ongoing business processes. As a result, current and probable next states can be predicted graphically using periodically conducted analysis during the processes.

Details

Industrial Management & Data Systems, vol. 111 no. 5
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 21 January 2020

Chemmalar Selvi G. and Lakshmi Priya G.G.

In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the…

Abstract

Purpose

In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information.

Design/methodology/approach

To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix.

Findings

The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score.

Social implications

The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products.

Originality/value

This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 7 January 2014

David Philip McArthur, Sylvia Encheva and Inge Thorsen

The aim of the paper is to propose a methodology that allows researchers and practitioners to structure a small amount of data in a way which aids understandings and…

Abstract

Purpose

The aim of the paper is to propose a methodology that allows researchers and practitioners to structure a small amount of data in a way which aids understandings and allows predictions to be made.

Design/methodology/approach

The paper explores how formal concept analysis can be combined with fuzzy reasoning to make predictions based on small datasets. A dataset of nine regions in Norway described by six attributes is used. The paper focuses on regional disparities in labour market outcomes such as unemployment and wages.

Findings

The paper finds that unemployment tends to be concentrated in the most prosperous parts of the study area. These regions have high incomes and experience population growth. More rural regions have virtually no unemployment. The methodology proposed allows these patterns to be seen. The authors made predictions with an accuracy rate of over 75 per cent.

Practical implications

A common response to high unemployment in urban areas is to stimulate employment growth. The findings suggest that this will simply increase migration towards the cities. The net result will be no change in unemployment but an accelerated depopulation of more rural regions.

Originality/value

To the authors' knowledge, this is the first application of fuzzy reasoning to the topic of regional disparities. The methodology aids in the interpretation of small datasets. The methodology should be of interested to practitioners at the local level, who are interested in analysing their own region, even when limited data are available.

Details

Journal of Economic Studies, vol. 41 no. 1
Type: Research Article
ISSN: 0144-3585

Keywords

Article
Publication date: 26 July 2011

Alon Friedman and Martin Thellefsen

The purpose of this paper is to explore the basics of semiotic analysis and concept theory that represent two dominant approaches to knowledge representation, and explore…

3721

Abstract

Purpose

The purpose of this paper is to explore the basics of semiotic analysis and concept theory that represent two dominant approaches to knowledge representation, and explore how these approaches are fruitful for knowledge organization.

Design/methodology/approach

In particular the semiotic theory formulated by the American philosopher C.S. Peirce and the concept theory formulated by Ingetraut Dahlberg are investigated. The paper compares the differences and similarities between these two theories of knowledge representation.

Findings

The semiotic model is a general and unrestricted model of signs and Dahlberg's model is thought from the perspective and demand of better knowledge organization system (KOS) development. It is found that Dahlberg's concept model provides a detailed method for analyzing and representing concepts in a KOS, where semiotics provides the philosophical context for representation.

Originality/value

This paper is the first to combine theories of knowledge representation, semiotic and concept theory, within the context of knowledge organization.

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