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Advances in Business Intelligence: Theoretical and Empirical Issues

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 17273

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Special Issue Editor

Prof. Dr. Basilis Boutsinas

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Guest Editor

Department of Business Administration, University of Patras, 26504 Rio, Greece
Interests: data mining; intelligent information systems; machine learning; knowledge representation; business intelligence

Special Issue Information

Dear Colleagues,

Business intelligence (BI) supports strategic decision making. BI derives meaningful and actionable insights by analysing business data and processes, and by presenting findings in intuitive visual formats in order to drive decisions and actions. In light of the above, the term BI also includes business analytics and big data.

The rapid accumulation of business data arose as a result of today’s globally connected networked economies, characterized by the six Vs (volume, velocity, variety, veracity, value and validity), gaining much attention in recent years. The latter is also true for the assessment and improvement of business processes, horizontal in nature activities concerning major elements of company operations.

BI, as a data/process-centric approach, includes techniques, technologies and applications that analyse critical business data and processes. BI borrows from artificial intelligence, statistics, econometrics and distributed/cloud computing paradigms.

The main aim of this Special Issue is to explore active and novel research opportunities in BI by collecting original contributions, in the form of either research papers or comprehensive reviews, addressing and discussing where modern BI stands and what the future holds. Thus, this Special Issue invites submissions involving techniques, technologies and applications constituting the most recent and notable advancements in BI.

The topics of this Special Issue include, but are not limited to:

Integration of data/text/spatial/temporal/process mining into BI;
Integration of knowledge-based systems into BI;
Web analytics/intelligence;
Social network analysis;
Data visualization;
OLAP;
Business performance management using scorecards/dashboards;
Business process management;
Recommender systems;
Opinion mining—sentiment analysis;
Systems with location/context awareness;
Neuromarketing—social signal processing;
Applications of BI in accounting, portfolio/wealth management, logistics, human resource management, e-commerce and e-government.

Prof. Dr. Basilis Boutsinas
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

business intelligence
business analytics
big data
web analytics
business process
competitive intelligence

Published Papers (5 papers)

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Research

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29 pages, 3855 KiB

Open AccessArticle

Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

by Yan Fang, Jiayin Yu, Yumei Ding and Xiaohua Lin

Mathematics 2023, 11(22), 4709; https://doi.org/10.3390/math11224709 - 20 Nov 2023

Viewed by 795

Abstract

Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relationship. The methodology employed in our study unfolds through several stages. First, a knowledge graph refining entities and relationships from data was constructed. Second, we developed a two-stage knowledge representation learning method to better represent the structured and unstructured knowledge based on TransE and SBERT. Then, the relationship inferring problem was converted into a path reasoning problem under the Markov decision process environment by learning a dynamic policy network. We also applied a soft pruning strategy and a modified reward function to improve the effectiveness of the policy network training. We demonstrate the effectiveness of the proposed method on standard Amazon datasets, and it gives about 5–15% relative improvement over the state-of-the-art models in terms of NDCG@10, Recall@10, Precision @10, and HR@10. Full article

(This article belongs to the Special Issue Advances in Business Intelligence: Theoretical and Empirical Issues)

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19 pages, 7145 KiB

Open AccessArticle

A Point-of-Interest Recommender System for Tourist Groups Based on Cooperative Location Set Cover Problem

by George Telonis, Antiopi Panteli and Basilis Boutsinas

Mathematics 2023, 11(17), 3646; https://doi.org/10.3390/math11173646 - 23 Aug 2023

Cited by 1 | Viewed by 721

Abstract

Trip recommendation for groups of tourists (TRGT) is a challenging task in tourism since many tourists travel in groups, inducing social interaction and bringing various social benefits. However, TRGT must address various real-life constraints such as limited time for touring, cost, etc. TRGT aims to design personalized tours that meet the preferences of all group members by addressing a variety of tourists’ requirements that may sometimes result in conflicts and stress for the group members. TRGT should satisfy that both the preferences of group members need to be achieved as much as possible and the preferences of group members need to be achieved as evenly as possible. In this paper, we present a methodology for tackling the TRGT problem by reducing it to the Cooperative Location Set Cover Problem (CLSCP), formulated as an integer linear program. The CLSCP aims to select a group of facilities that can satisfy, in aggregate, all demand points. To tackle the CLSCP, we present a new method based on detecting frequent patterns. We also demonstrate the efficiency of the proposed methodology by presenting extensive experimental tests. Full article

(This article belongs to the Special Issue Advances in Business Intelligence: Theoretical and Empirical Issues)

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17 pages, 2586 KiB

Open AccessArticle

The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit

by Cheng-Hong Yang, Tshimologo Molefyane and Yu-Da Lin

Mathematics 2023, 11(14), 3085; https://doi.org/10.3390/math11143085 - 12 Jul 2023

Viewed by 1282

Abstract

Economic forecasting is crucial in determining a country’s economic growth or decline. Productivity and the labor force must be increased to achieve economic growth, which leads to the growth of gross domestic product (GDP) and income. Machine learning has been used to provide accurate economic forecasts, which are essential to sound economic policy. This study formulated a gated recurrent unit (GRU) neural network model to predict government expenditure, an essential component of gross domestic product. The GRU model was evaluated against autoregressive integrated moving average, support vector regression, exponential smoothing, extreme gradient boosting, convolutional neural network, and long short-term memory models using World Bank data regarding government expenditure from 1990 to 2020. The mean absolute error, root mean square error, and mean absolute percentage error were used as performance metrics. The GRU model demonstrates superior performance compared to all other models in terms of MAE, RMSE, and MAPE (with an average MAPE of 2.774%) when forecasting government spending using data from the world’s 15 largest economies from 1990 to 2020. The results indicate that the GRU can be used to provide accurate economic forecasts. Full article

(This article belongs to the Special Issue Advances in Business Intelligence: Theoretical and Empirical Issues)

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22 pages, 3442 KiB

Open AccessArticle

Object-Centric Process Mining: Unraveling the Fabric of Real Processes

by Wil M. P. van der Aalst

Mathematics 2023, 11(12), 2691; https://doi.org/10.3390/math11122691 - 13 Jun 2023

Cited by 6 | Viewed by 5093

Abstract

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a “three-dimensional” view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. Full article

(This article belongs to the Special Issue Advances in Business Intelligence: Theoretical and Empirical Issues)

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Other

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27 pages, 1205 KiB

Open AccessSystematic Review

A Systematic Review of Consensus Mechanisms in Blockchain

by Sisi Zhou, Kuanching Li, Lijun Xiao, Jiahong Cai, Wei Liang and Arcangelo Castiglione

Mathematics 2023, 11(10), 2248; https://doi.org/10.3390/math11102248 - 11 May 2023

Cited by 8 | Viewed by 8652

Abstract

Since the birth of Bitcoin, blockchain has shifted from a critical cryptocurrency technology to an enabling technology. Due to its immutability and trustworthiness, blockchain has revolutionized many fields requiring credibility and high-quality data for decision making. Particularly in business intelligence and business process management, users can use blockchain to build their blockchain-enabled collaboration and data-sharing ecosystem with their partners. In this paper, we present the development process of blockchain and consensus mechanisms, where important blockchain consensus mechanisms are introduced. The consensus mechanism is the kernel among various blockchain components to ensure security and performance. Again, we present a comparison of these consensus mechanisms from different perspectives. We take the blockchain-enabling business as an example and analyze the relationship between blockchain and business process characteristics and the ideas and principles for selecting consensus mechanisms. Finally, we describe the differences and connections among many consensus mechanisms while laying a foundation for selecting appropriate consensus mechanisms for different scenarios and fields of application. Full article

(This article belongs to the Special Issue Advances in Business Intelligence: Theoretical and Empirical Issues)

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