Special Issue "Big Data Integration"

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

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editor

Dr. Kostas Stefanidis
E-Mail Website
Guest Editor
Faculty of Information Technology and Communication Sciences (ITC); Tampere University; Kalevantie 4, 33100 Tampere, Finland
Interests: large-scale entity resolution and information integration; personalization; recommender systems; query and data exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The MDPI Information Journal invites submissions to a Special Issue on “Big Data Integration”.
In the big data era, business, government, and scientific organizations’ operations increasingly rely on huge amounts of data collected from several data sources. Such data sources usually exhibit several quality issues, such as incompleteness, redundancy, inconsistency or simply incorrectness. A number of tasks for improving the various aspects of data quality and thus increase the reliability of the outcomes of data analytics are related to data integration. In this Special Issue, we pay special attention to how big data characteristics (such as volume, variety, velocity, and veracity) call for novel data integration frameworks that relax a number of assumptions underlying several methods and techniques proposed in the context of databases, machine learning, and semantic Web communities. Although individual characteristics of big data have been the focus of previous research work in data integration, such techniques are challenged when more than one of the big data characteristics have to be addressed simultaneously.
This Special Issue is concerned with groundbreaking topics at the interface of the data integration problem, with particular emphasis on big data.

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

  • Algorithms and techniques for data integration
  • End-to-end data integration systems
  • Scalable data integration
  • Integrating complex data
  • Incremental data integration
  • Real-time data integration
  • Query-based data integration
  • Diversity-aware data integration
  • Multilingual data integration
  • Crowd-based data integration
  • Human-in-the-loop data integration
  • Transparency in data integration
  • Explaining data integration
  • Fairness-aware data integration
  • Preserving Privacy in data integration
  • Semantics and ontologies in data integration
  • Entity resolution, record linkage, data matching and duplicate detection
  • Blocking for entity resolution
  • Distributed entity resolution
  • Entity resolution benchmarks
  • Data preparation for data integration
  • Big data cleaning
  • Schema matching
  • Schema evolution
  • Web content mining
  • Web structure mining
  • Web usage mining

Dr. Kostas Stefanidis
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 papers will be 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 (3 papers)

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Research

Article
The Effects of Major Selection Motivations on Dropout, Academic Achievement and Major Satisfactions of College Students Majoring in Foodservice and Culinary Arts
Information 2020, 11(9), 444; https://0-doi-org.brum.beds.ac.uk/10.3390/info11090444 - 14 Sep 2020
Viewed by 998
Abstract
This study is aimed at figuring out the effects of major selecting motivation on dropout, academic achievement, and major satisfactions of college students majoring in foodservice and culinary arts. To accomplish this, an empirical survey was conducted through a structural equation model. These [...] Read more.
This study is aimed at figuring out the effects of major selecting motivation on dropout, academic achievement, and major satisfactions of college students majoring in foodservice and culinary arts. To accomplish this, an empirical survey was conducted through a structural equation model. These findings showed that students are likely to drop out of college due to a career change or major maladjustment if they decide their major in consideration of college reputation or department recognition rather than their aptitude. Unlike existing studies, this study has practical implications concerning the importance of these factors in that their academic achievement is affected by their relationship and perception of their major satisfactions rather than their major selection motivations. Full article
(This article belongs to the Special Issue Big Data Integration)
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Article
A Sentiment-Statistical Approach for Identifying Problematic Mobile App Updates Based on User Reviews
Information 2020, 11(3), 152; https://0-doi-org.brum.beds.ac.uk/10.3390/info11030152 - 12 Mar 2020
Cited by 6 | Viewed by 1474
Abstract
Mobile applications (apps) on IOS and Android devices are mostly maintained and updated via Apple Appstore and Google Play, respectively, where the users are allowed to provide reviews regarding their satisfaction towards particular apps. Despite the importance of user reviews towards mobile app [...] Read more.
Mobile applications (apps) on IOS and Android devices are mostly maintained and updated via Apple Appstore and Google Play, respectively, where the users are allowed to provide reviews regarding their satisfaction towards particular apps. Despite the importance of user reviews towards mobile app maintenance and evolution, it is time-consuming and ineffective to dissect each individual negative review. In addition, due to the different app update strategies, it is uncertain that each update can be accepted well by the users. This study aims to provide an approach to detect the particular days during the mobile app maintenance phase when the negative reviews require developers’ attention. Furthermore, the method shall facilitate the mapping of the identified abnormal days towards the updates that result in such negativity in reviews. The method’s purpose is to enable app developers to respond swiftly to significant flaws reflected by user reviews in order to prevent user churns. Full article
(This article belongs to the Special Issue Big Data Integration)
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Article
A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point
Information 2020, 11(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/info11010016 - 25 Dec 2019
Cited by 1 | Viewed by 969
Abstract
Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the [...] Read more.
Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm. Full article
(This article belongs to the Special Issue Big Data Integration)
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