Special Issue "Challenges in Business Intelligence"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editor

Prof. Dr. Francisco Guijarro
SciProfiles
Guest Editor
Faculty of Business Administration and Management, Universitat Politècnica de València, València, Spain
Interests: financial markets; trading strategies; valuation; sustainability
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Business intelligence (BI) includes a bundle of mathematical models mainly related to statistics and operations research that exploit large amounts of data to extract useful knowledge for complex decision-making processes in firms and organizations. Such data play a key role in the development of BI platforms, and likewise, both researchers and practitioners have to face situations where data come from different and heterogeneous sources: the internet, emails, unstructured texts, web images, financial and administrative data, stock markets, cashier operations from supermarkets, etc.

This Special Issue will contribute to bringing original research in the area of business intelligence, thus enabling the publication of research related to new methodological approaches to deal with new challenges in the topic, or making publicly available large databases that allow the application and comparison of competitive BI techniques.

Prof. Dr. Francisco Guijarro
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. Data is an international peer-reviewed open access quarterly 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 1000 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.

Keywords

  • data analytics
  • big data
  • data cleaning
  • metadata
  • management information system
  • data warehouse
  • unstructured data
  • benchmarking
  • data mining

Published Papers (4 papers)

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Research

Open AccessArticle
The Role of Administrative and Secondary Data in Estimating the Costs and Effects of School and Workplace Closures due to the COVID-19 Pandemic
Data 2020, 5(4), 98; https://doi.org/10.3390/data5040098 - 18 Oct 2020
Abstract
As a part of mitigation strategies during a COVID-19 pandemic, the WHO currently recommends social distancing measures through school closures (SC) and work closures (WC) to control the infection spread and reduce the illness attack rate. Focusing on the use of administrative and [...] Read more.
As a part of mitigation strategies during a COVID-19 pandemic, the WHO currently recommends social distancing measures through school closures (SC) and work closures (WC) to control the infection spread and reduce the illness attack rate. Focusing on the use of administrative and secondary data, this study aimed to estimate the costs and effects of alternative strategies for mitigating the COVID-19 pandemic in Jakarta, Indonesia, by comparing the baseline (no intervention) with SC + WC for 2, 4, and 8 weeks as respective scenarios. A modified Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model accounting for the spread of infection during the latent period was applied by taking into account a 1-year time horizon. To estimate the total pandemic cost of all scenarios, we took into account the cost of healthcare, SC, and productivity loss due to WC and illness. Next to costs, averted deaths were considered as the effect measure. In comparison with the baseline, the result showed that total savings in scenarios of SC + WC for 2, 4, and 8 weeks would be approximately $24 billion, $25 billion, and $34 billion, respectively. In addition, increasing the duration of SC and WC would increase the number of averted deaths. Scenarios of SC + WC for 2, 4, and 8 weeks would result in approximately 159,075, 173,963, and 250,842 averted deaths, respectively. A sensitivity analysis showed that the wage per day, infectious period, basic reproduction number, incubation period, and case fatality rate were found to be the most influential parameters affecting the savings and number of averted deaths. It can be concluded that all the mitigation scenarios were considered to be cost-saving, and increasing the duration of SC and WC would increase both the savings and the number of averted deaths. Full article
(This article belongs to the Special Issue Challenges in Business Intelligence)
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Open AccessArticle
Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log
Data 2020, 5(3), 82; https://doi.org/10.3390/data5030082 - 09 Sep 2020
Abstract
In recent years, process mining has been attracting attention as an effective method for improving business operations by analyzing event logs that record what is done in business processes. The event log may contain missing data due to technical or human error, and [...] Read more.
In recent years, process mining has been attracting attention as an effective method for improving business operations by analyzing event logs that record what is done in business processes. The event log may contain missing data due to technical or human error, and if the data are missing, the analysis results will be inadequate. Traditional methods mainly use prediction completion when there are missing values, but accurate completion is not always possible. In this paper, we propose a method for understanding the tendency of missing values in the event log using decision tree learning without supplementing the missing values. We conducted experiments using data from the incident management system and confirmed the effectiveness of our method. Full article
(This article belongs to the Special Issue Challenges in Business Intelligence)
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Open AccessArticle
Data Wrangling in Database Systems: Purging of Dirty Data
Data 2020, 5(2), 50; https://doi.org/10.3390/data5020050 - 05 Jun 2020
Abstract
Researchers need to be able to integrate ever-increasing amounts of data into their institutional databases, regardless of the source, format, or size of the data. It is then necessary to use the increasing diversity of data to derive greater value from data for [...] Read more.
Researchers need to be able to integrate ever-increasing amounts of data into their institutional databases, regardless of the source, format, or size of the data. It is then necessary to use the increasing diversity of data to derive greater value from data for their organization. The processing of electronic data plays a central role in modern society. Data constitute a fundamental part of operational processes in companies and scientific organizations. In addition, they form the basis for decisions. Bad data quality can negatively affect decisions and have a negative impact on results. The quality of the data is crucial. This includes the new theme of data wrangling, sometimes referred to as data munging or data crunching, to find the dirty data and to transform and clean them. The aim of data wrangling is to prepare a lot of raw data in their original state so that they can be used for further analysis steps. Only then can knowledge be obtained that may bring added value. This paper shows how the data wrangling process works and how it can be used in database systems to clean up data from heterogeneous data sources during their acquisition and integration. Full article
(This article belongs to the Special Issue Challenges in Business Intelligence)
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Open AccessArticle
Big Data Usage in European Countries: Cluster Analysis Approach
Data 2020, 5(1), 25; https://doi.org/10.3390/data5010025 - 12 Mar 2020
Cited by 2
Abstract
The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data [...] Read more.
The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage. Full article
(This article belongs to the Special Issue Challenges in Business Intelligence)
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