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A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated social media and crowdsourcing data mining system for the synchronization of the police and law enforcement agencies for the prevention of criminal activities during and post a large crisis situation.

The paper realized qualitative research in the form of a review of the literature. This review focuses on the necessity of using social media and crowdsourcing data mining techniques in combination with advanced Web technologies for the purpose of providing solutions to problems related to criminal activities caused during and after a crisis. The paper presents the ATHENA crisis management system, which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention.

Conclusions are drawn on the significance of social media and crowdsourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.

The paper shows how the integrated use of social media and data mining algorithms can contribute in the resolution of problems that are developed during and after a large crisis.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

The purpose of this paper is to examine the important application value of extending the concept of classification rule, so that it can describe and measure the uncertainty of classification knowledge.

The rough concept lattice (RCL), which is an effective tool for uncertain data analysis and knowledge discovery, reflects a kind of unification of concept intent and upper/lower approximation extent, as well as the certain and uncertain relations between objects and attributes.

A classification rules extraction algorithm, extraction algorithm of classification rule (EACR), based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule. The algorithm EACR is experimentally validated by taking the star spectrum data as the decision context.

An efficient way for classification rule extraction is provided.

The algorithm EACR based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule.

The purpose of this paper is to establish a new approach for solving the expansion term problem.

This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions.

The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.

The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted.

Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.

The purpose of this paper is to demonstrate the critical role that context plays in measuring service quality.

This study replicated an experiment methodology to show that customers perceive an airline service drama as a sequence of scenes. A series of focus groups were then conducted to identify the context-specific set of service quality expectations that customers hold for each of these scenes. Finally, Formal Concept Analysis (FCA), a mathematical modeling technique, was applied to these findings to graphically illustrate how customer expectations for airline service quality vary by service scene.

Results from this study indicate that static measures of service quality are apparently inadequate in explaining customer expectations during more enduring service encounters. The FCA hierarchical model developed in this study revealed profound differences in customer service expectations across the six airline service scenes. These results suggest that more advanced methods for measuring service quality are necessary for service encounters that are longer in duration.

This research brings into question a broad spectrum of research which fails to recognize that customers use different reference points in time to evaluate service quality.

Researchers and practitioners need accurate and reliable measures of service quality but the findings suggests that measurement specificity and diagnostic capability should not be sacrificed in the pursuit of more robust instruments.

This is the first study to empirically demonstrate that customers perceive the airline service encounter as a sequence of scenes. It is also the first study to mathematically model service quality dimensions using FCA.

The purpose of this paper is to study how the concept “middle archive” is related to the Swedish archival theory and practice, by exploring its background and its practical and theoretical implications. The overarching aim is to increase the understanding of the interrelations between the archival theory and practice, with a special focus on changes occurring in the digital environment in Sweden.

The paper is based on a qualitative study of texts produced between 2010 and 2013, which were read and analyzed iteratively. The research design and analysis is inspired by Dryden (2014) who recommends taking the context into account. The three types of digital repository developed by Millar (2010) are used as a comparison to further extend the understanding of the concept of the “middle archive”.

The findings indicate that the “middle archive” was introduced as a solution to practical problems of managing digital records, and that there is a need to further discuss how the term relates to existing theoretical, legal and administrative contexts.

The study is limited to contemporary Sweden. The researcher did not follow the discussions and drafts which contributed to the development of the final definitions of the “middle archive”.

The study contributes to the research and development of archival concepts, with a special focus on recent developments in Sweden. The paper highlights some of the challenges relating to archives and records in the context of e- government development.

The purpose of this paper is to lay a theoretical foundation for identifying operational information units for library and information professional activities in the context of scholarly communication.

The study adopts a deduction-verification approach to formulate a typology of units for scholarly information. It first deduces possible units from an existing conceptualization of information, which defines information as the combined product of data and meaning, and then tests the usefulness of these units via two empirical investigations, one with a group of scholarly papers and the other with a sample of scholarly information users.

The results show that, on defining an information unit as a piece of information that is complete in both data and meaning, to such an extent that it remains meaningful to its target audience when retrieved and displayed independently in a database, it is then possible to formulate a hierarchical typology of units for scholarly information. The typology proposed in this study consists of three levels, which in turn, consists of 1, 5 and 44 units, respectively.

The result of this study has theoretical implications on both the philosophical and conceptual levels: on the philosophical level, it hinges on, and reinforces the objective view of information; on the conceptual level, it challenges the conceptualization of work by IFLA’s Functional Requirements for Bibliographic Records and Library Reference Model but endorses that by Library of Congress’s BIBFRAME 2.0 model.

It calls for reconsideration of existing operational units in a variety of library and information activities.

The study strengthens the conceptual foundation of operational information units and brings to light the primacy of “one work” as an information unit and the possibility for it to be supplemented by smaller units.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data.

Literature on dimensionality reduction (DR) witnesses the research efforts that combine random projections (RP) and singular value decomposition (SVD) so as to derive the benefit of both of these methods. However, SVD is well known for its computational complexity. Clustering under the notion of concept decomposition is proved to be less computationally complex than SVD and useful for DR. The method proposed in this paper combines RP and fuzzy k‐means clustering (FKM) for reducing dimensionality of the data.

The proposed RP‐FKM is computationally less complex than SVD, RP‐SVD. On the image data, the proposed RP‐FKM has produced less amount of distortion when compared with RP. The proposed RP‐FKM provides better text retrieval results when compared with conventional RP and performs similar to RP‐SVD. For the text retrieval task, superiority of SVD over other DR methods noted here is in good agreement with the analysis reported by Moravec.

The hybrid method proposed in this paper, combining RP and FKM, is new. Experimental results indicate that the proposed method is useful for reducing dimensionality of high‐dimensional data such as images, text, etc.

The purpose of this paper is to demonstrate how the semantic rules in conjunction with ontology can be applied for inferring new facts to dispatch news into corresponding departments.

Under a specific task domain, the proposed design comprises finding a glossary from electronic resources, gathering organization functions as controlled vocabularies, and linking relationships between the glossary and controlled vocabularies. Web ontology language is employed to represent this knowledge as ontology, and semantic web rule language is utilized to infer implicit facts among instances.

Document dispatching is highly domain dependent. Human perspectives being adopted as predefined knowledge in understanding document meanings are important. Knowledge‐intensive approaches such as ontology can model and represent expertise as reusable components. Ontology and rules together extend inference capabilities in semantic relationships between instances.

Empirical lessons reveal that ontology with semantic rules can be utilized to model human subjective judgement as knowledge bases. An example, including ontology and rules, based on news dispatching is provided.

An organization can classify and deliver documents to corresponding departments based on known facts by following the described procedure.