Special Issue "Big Data Integration and Intelligent Information Integration"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2023 | Viewed by 5685

Special Issue Editor

Prof. Domenico Beneventano
E-Mail Website
Guest Editor
Dipartimento di Ingegneria "Enzo Ferrari" – DIEF, Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10 - int. 1 - 41125 Modena, Italy
Interests: database; data integration; data fusion; linked open data; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent information integration has continued to challenge research for over 30 years. While data integration processes are now well understood and widely used, a great deal of interest in Big Data Integration requires much higher levels of automation to limit the need for skilled human labor. In recent years, much of the big data work has focused on volume and speed to consider the size of the dataset. Indeed, the problems of variety, speed, and truthfulness are equally important in addressing the heterogeneity, diversity, and complexity of data in the big data integration process, where intelligent information integration and semantic technologies can be explored to address these problems.

Therefore, the purpose of this Special Issue is to publish high-quality research, from academic and industrial stakeholders, for disseminating innovative solutions that explore how big data can leverage data integration, i.e., what the challenges and opportunities are arising from adapting and transferring data integration methodologies and technologies to the big data context. 

Original, high-quality contributions that have not yet been published, submitted, or are not currently under review by other journals or peer-reviewed conferences are sought. 

Topics of interest include but are not limited to the following topics:

  • Automating data cleaning and pre-processing for big data;
  • Intelligent information integration from big data on the web;
  • Data quality issues in big data integration;
  • Scalability issues: intelligent information integration for big data;
  • Metadata integration and management;
  • Entity resolution and data fusion for big data;
  • Semantic for big data extraction, transformation, and integration.

Prof. Domenico Beneventano
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

Article
A Domain-Adaptable Heterogeneous Information Integration Platform: Tourism and Biomedicine Domains
Information 2021, 12(11), 435; https://doi.org/10.3390/info12110435 - 20 Oct 2021
Viewed by 717
Abstract
In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge [...] Read more.
In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge is the integration and implementation of processing pipelines using different technologies promoting the emergence of advanced architectures capable of processing such a number of diverse sources. This paper describes a semantic domain-adaptable platform to integrate those sources and provide high-level functionalities, such as recommendations, shallow and deep natural language processing, text enrichment, and ontology standardization. Our proposed intelligent domain-adaptable platform (IDAP) has been implemented and tested in the tourism and biomedicine domains to demonstrate the adaptability, flexibility, modularity, and utility of the platform. Questionnaires, performance metrics, and A/B control groups’ evaluations have shown improvements when using IDAP in learning environments. Full article
(This article belongs to the Special Issue Big Data Integration and Intelligent Information Integration)
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Article
Anticipation Next: System-Sensitive Technology Development and Integration in Work Contexts
Information 2021, 12(7), 269; https://doi.org/10.3390/info12070269 - 29 Jun 2021
Viewed by 703
Abstract
When discussing future concerns within socio-technical systems in work contexts, we often find descriptions of missed technology development and integration. The experience of technology that fails whilst being integrated is often rooted in dysfunctional epistemological approaches within the research and development process. Thus, [...] Read more.
When discussing future concerns within socio-technical systems in work contexts, we often find descriptions of missed technology development and integration. The experience of technology that fails whilst being integrated is often rooted in dysfunctional epistemological approaches within the research and development process. Thus, ultimately leading to sustainable technology-distrust in work contexts. This is true for organizations that integrate new technologies and for organizations that invent them. Organizations in which we find failed technology development and integrations are, in their very nature, social systems. Nowadays, those complex social systems act within an even more complex environment. This urges the development of new anticipation methods for technology development and integration. Gathering of and dealing with complex information in the described context is what we call Anticipation Next. This explorative work uses existing literature from the adjoining research fields of system theory, organizational theory, and socio-technical research to combine various concepts. We deliberately aim at a networked way of thinking in scientific contexts and thus combine multidisciplinary subject areas in one paper to present an innovative way to deal with multi-faceted problems in a human-centred way. We end with suggesting a conceptual framework that should be used in the very early stages of technology development and integration in work contexts. Full article
(This article belongs to the Special Issue Big Data Integration and Intelligent Information Integration)
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Article
Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology
Information 2021, 12(7), 259; https://doi.org/10.3390/info12070259 - 24 Jun 2021
Cited by 2 | Viewed by 1460
Abstract
While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties [...] Read more.
While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services. Full article
(This article belongs to the Special Issue Big Data Integration and Intelligent Information Integration)
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Article
Integrating Land-Cover Products Based on Ontologies and Local Accuracy
Information 2021, 12(6), 236; https://doi.org/10.3390/info12060236 - 31 May 2021
Viewed by 927
Abstract
Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet [...] Read more.
Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest. Full article
(This article belongs to the Special Issue Big Data Integration and Intelligent Information Integration)
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Article
Counterintelligence Technologies: An Exploratory Case Study of Preliminary Credibility Assessment Screening System in the Afghan National Defense and Security Forces
Information 2021, 12(3), 122; https://doi.org/10.3390/info12030122 - 12 Mar 2021
Cited by 2 | Viewed by 1002
Abstract
The preliminary credibility assessment screening system (PCASS) is a US-based program, which is currently being implemented by intelligence units of the North Atlantic Treaty Organization (NATO) to make the initial screening of individuals suspected of infiltrating the Afghan National Defense and Security Forces [...] Read more.
The preliminary credibility assessment screening system (PCASS) is a US-based program, which is currently being implemented by intelligence units of the North Atlantic Treaty Organization (NATO) to make the initial screening of individuals suspected of infiltrating the Afghan National Defense and Security Forces (ANDSF). Sensors have been instrumental in the PCASS, leading to organizational change. The aim of this research is to describe how the ANDSF adapted to the implementation of PCASS, as well as implemented changes since the beginning of the program. To do so, we have conducted a qualitative, exploratory, and descriptive case study that allows one to understand, through the use of a series of data collection sources, a real-life phenomenon of which little is known. The results suggest that the sensors used in PCASS empower security forces with reliable technologies to identify and neutralize internal threats. It then becomes evident that the technological leadership that PCASS provides allows the developing of a relatively stable and consistent organizational change, fulfilling the objectives of the NATO and the ANDSF. Full article
(This article belongs to the Special Issue Big Data Integration and Intelligent Information Integration)
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