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Expert Systems: Applications of Business Intelligence in Big Data Environments

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (20 October 2018) | Viewed by 106237

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Special Issue Editors

Dr. Mohamed Elhoseny

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Guest Editor

Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
Interests: intelligent systems; data security; networks; internet of things; big data analysis; machine learning algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaohui Yuan

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Guest Editor

Computer Science and Engineering, University of North Texas, Denton, TX, USA
Interests: artificial intelligence; computer vision; remote sensing; robotics; health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. M. Kabir Hassan

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Guest Editor

Department of Economics and Finance, University of New Orleans, New Orleans, LA, USA
Interests: financial institutions and markets; emerging markets and financial development; international finance; applied economics; corporate finance; Islamic economics, banking and finance
Special Issues, Collections and Topics in MDPI journals

Dr. Noura Metawa

E-Mail Website
Guest Editor

Collage of Business Administration, University of New Orleans, USA
Faculty of Commerce, Mansoura University, Egypt
Interests: corporate finance; banking and finance; financial management; statistics; banking; capital structure; financial market regulation; money and banking; expert systems

Special Issue Information

Dear Colleagues,

In order to reduce the risk of human mistakes in financial domains, expert systems have gained a great advantage in big data environments. In addition to their efficiency in quantitative analyses, such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues, including financial analysis, investments advisories, and knowledge-based decision support systems. Due to the increase in financial application sizes, complexity and the number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emerging of new engineering research areas is a clear evidence of the emergence of new demands and requirements of modern real-life applications to be more intelligent. Recently, expert systems with explanation for decision making can achieve a high accuracy rate to support financial institutions in a highly volatile climate. It is being promoted by the software engineering community to use such systems as the adequate solution to handle the current requirements of complex big data processing problems that demanding distribution, flexibility, and robustness.

The overall aim of this Special Issue is to collect state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of expert systems for business intelligence and big data analytics in financial applications. Proposed submissions should be original, unpublished, and have novel in-depth research that makes significant methodological or application contributions.

Dr. Mohamed Elhoseny
Dr. Xiaohui Yuan

Prof. Dr. M. Kabir Hassan

Dr. Noura Metawa
Guest Editors

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Keywords

Business Intelligence Applications
Intelligent Distributed Applications in E-Commerce, E-Health, E-Government
Big data analysis in real life business applications
Utility-Based Data Mining in Business Applications
Internet of Things (IoT) Application in Financial management
Intelligent Investment Advisory
Expert systems in banking management
Smart models of strategic financial planning
Decision Support Systems
Secure data processing in business applications
Smart applications of forint exchange trading
Big Data Economy, QoS and Business Models
Big Data analytics for customer value creation.
Evolutionary Computing Algorithms for Financial Applications
Swarm Intelligence for Business Applications
Genetic Algorithm for Business Applications
Big Data Quality and Management for Business Applications
Financial Analysis for Mobile and Cloud Applications
Business Intelligence Applications for Finance
Customer Segmentation or Profiling

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Published Papers (13 papers)

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Research

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17 pages, 554 KiB

Open AccessArticle

Predicting Corporate Financial Sustainability Using Novel Business Analytics

by Kyoung-jae Kim, Kichun Lee and Hyunchul Ahn

Sustainability 2019, 11(1), 64; https://doi.org/10.3390/su11010064 - 22 Dec 2018

Cited by 21 | Viewed by 4946

Abstract

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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12 pages, 1553 KiB

Open AccessArticle

An Empirical Study on the Relationship between Agricultural Science and Technology Input and Agricultural Economic Growth Based on E-Commerce Model

by Tianqi Wang and Lijun Huang

Sustainability 2018, 10(12), 4465; https://doi.org/10.3390/su10124465 - 28 Nov 2018

Cited by 12 | Viewed by 3808

Abstract

At present, e-commerce mode has been gradually applied to agricultural science and technology production, which has played an important role in agricultural economic growth and production efficiency. At the same time, the fundamental way out for sustainable and stable development of agriculture is science and technology. Generally speaking, part of the growth of the agricultural economy comes from agricultural production factors. The increase in input is partly due to the improvement of productivity of agricultural elements. Therefore, based on the background of the e-commerce environment, this paper chooses the entropy method to study the relationship between agricultural science and technology input and agricultural economic growth. Compared with the exponential method and the Bolat method, the entropy method can scientifically determine the specific weight of the indicators based on the variation of each quantitative index, so as to improve the accuracy and objectivity of the quantitative index analysis and avoid the adverse effects of human factors. The entropy method is used to evaluate and analyze the development level of agricultural e-commerce, which improves the accuracy and reliability of the evaluation results. Based on this, this paper makes an empirical study on the relationship between agricultural science and technology input and agricultural economic growth by using the method of entropy under the mode of e-commerce, constructs the index system of agricultural productivity, evaluates the situation of agricultural science and technology input and agricultural economic growth, and studies the relationship between them by using the method of regression analysis. Research shows that the application of agricultural science and technology investment and e-commerce mode can promote agricultural economic growth. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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21 pages, 3780 KiB

Open AccessArticle

Design and Application of a Multi-Variant Expert System Using Apache Hadoop Framework

by Muhammad Ibrahim and Imran Sarwar Bajwa

Sustainability 2018, 10(11), 4280; https://doi.org/10.3390/su10114280 - 19 Nov 2018

Cited by 12 | Viewed by 4189

Abstract

Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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12 pages, 1779 KiB

Open AccessArticle

Research on Sustainable Development of the Stock Market Based on VIX Index

by Lei Ruan

Sustainability 2018, 10(11), 4113; https://doi.org/10.3390/su10114113 - 9 Nov 2018

Cited by 16 | Viewed by 4861

Abstract

The frequent occurrence of financial crises has made the dynamic linkage between international financial markets an important research topic. In the past, scholars mostly studied the correlation between financial markets directly, however ignored the impact of exogenous financial variables on financial markets. The stock market is an important part of the financial market and plays an important role in the overall economy. Information asymmetry is common and has a certain degree of impact on investors’ returns. However, many scholars believe that the problem of information asymmetry in China has seriously negatively impacted investors, forming an unsustainable state. At present, there are still many problems in the Chinese stock market, especially the stock market fraud, which brings great challenges to the sustainable development of the stock market. Based on the idea of the STCC model, it is assumed that the Copula parameter is affected by the exogenous variables and the time-varying dynamic Copula model-ST-VCopula model is established. Based on the model, the influence of market volatility (VIX index) on the stock market is explored and then the stock index data of several countries are empirically analyzed. The empirical results show that the VIX index has a significant impact on the linkage between stock markets. The VIX index is easy and more intuitive to obtain, providing another way for the dynamic linkage research between the market, which can provide investors with some guidance and advice when conducting financial activities such as diversification. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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18 pages, 1543 KiB

Open AccessArticle

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

by Hyejung Chung and Kyung-shik Shin

Sustainability 2018, 10(10), 3765; https://doi.org/10.3390/su10103765 - 18 Oct 2018

Cited by 216 | Viewed by 13933

Abstract

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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20 pages, 4334 KiB

Open AccessArticle

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

by Umair Khan, Farhan Aadil, Mustansar Ali Ghazanfar, Salabat Khan, Noura Metawa, Khan Muhammad, Irfan Mehmood and Yunyoung Nam

Sustainability 2018, 10(10), 3702; https://doi.org/10.3390/su10103702 - 15 Oct 2018

Cited by 18 | Viewed by 5684

Abstract

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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15 pages, 2622 KiB

Open AccessArticle

Biometrics-Based RSA Cryptosystem for Securing Real-Time Communication

by Xiaolong Liu, Wei-Bin Lee, Quy-Anh Bui, Chia-Chen Lin and Hsiao-Ling Wu

Sustainability 2018, 10(10), 3588; https://doi.org/10.3390/su10103588 - 9 Oct 2018

Cited by 3 | Viewed by 3639

Abstract

Real-time online communication technology has become increasingly important in modern business applications. It allows people to easily connect with business partners over the Internet through the camera lens on digital devices. However, despite the fact that users can identify and confirm the identity of the person in front of the camera, they cannot verify the authenticity of messages between communication partners. It is because the tunnel for the video is not the same as the tunnel that delivers the messages. To protect confidential messages, it is essential to establish a secure communication channel between users. This paper proposes a biometrics-based RSA cryptosystem to secure real-time communication in business. The idea put forward is to generate a cryptographic public key based on a user’s biometric information without using Public Key Infrastructure (PKI) and establish a secured channel in a public network. In such a way, the key must be verified with the user’s biometrics online. Since the key is derived from the user’s biometrics, it is strongly user-dependent and works well to convince others of the authenticity of the owner. Additionally, the derived biometric key is self-certified with the user’s biometrics, which means the cost of certificate storage, delivery and revocation can be significantly reduced. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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24 pages, 3449 KiB

Open AccessArticle

Energy Efficient Deployment of a Service Function Chain for Sustainable Cloud Applications

by Jian Sun, Yue Chen, Miao Dai, Wanting Zhang, Arun Kumar Sangaiah, Gang Sun and Han Han

Sustainability 2018, 10(10), 3499; https://doi.org/10.3390/su10103499 - 29 Sep 2018

Cited by 6 | Viewed by 3146

Abstract

With the increasing popularity of the Internet, user requests for cloud applications have dramatically increased. The traditional model of relying on dedicated hardware to implement cloud applications has not kept pace with the rapid growth in demand. Network function virtualization (NFV) architecture emerged at a historic moment. By moving the implementation of functions to software, a separation of functions and hardware was achieved. Therefore, when user demand increases, cloud application providers only need to update the software; the underlying hardware does not change, which can improve network scalability. Although NFV solves the problem of network expansion, deploying service function chains into the underlying network to optimize indicators remains an important research problem that requires consideration of delay, reliability, and power consumption. In this paper, we consider the optimization of power consumption with the premise of guaranteeing a certain virtual function link yield. We propose an efficient algorithm that is based on first-fit and greedy algorithms to solve the problem. The simulation results show that the proposed algorithm substantially improves the path-finding efficiency, achieves a higher request acceptance ratio and reduces power consumption while provisioning the cloud applications. Compared with the baseline algorithm, the service function chain (SFC) acceptance ratio of our proposed algorithms improves by a maximum of approximately 15%, our proposed algorithm reduces the power consumption by a maximum of approximately 15%, the average link load ratio of our proposed algorithm reduces by a maximum of approximately 20%, and the average mapped path length of our proposed algorithm reduces by a maximum of approximately 1.5 hops. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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18 pages, 6698 KiB

Open AccessArticle

Risk Scenario Generation Based on Importance Measure Analysis

by Xing Pan, Lunhu Hu, Ziling Xin, Shenghan Zhou, Yanmei Lin and Yong Wu

Sustainability 2018, 10(9), 3207; https://doi.org/10.3390/su10093207 - 7 Sep 2018

Cited by 4 | Viewed by 3588

Abstract

A risk scenario is a combination of risk events that may result in system failure. Risk scenario analysis is an important part of system risk assessment and avoidance. In engineering activity-based systems, important risk scenarios are related to important events. Critical activities, meanwhile, mean risk events that may result in system failure. This article proposes these definitions of risk event and risk scenario based on the characteristics of risk in engineering activity-based systems. Under the proposed definitions, a risk scenario framework generated based on importance measure analysis is given, in which critical activities analysis, risk event identification, and risk scenario generation are the three main parts. Important risk events are identified according to activities’ uncertain importance measure and important risk scenarios are generated on the basis of a system’s critical activities analysis. In the risk scenario generation process based on importance analysis, the importance degrees of network activities are ranked to identify the subject of risk events, so that risk scenarios can be combined and generated by risk events and the importance of scenarios is analyzed. Critical activities are analyzed by Taguchi tolerance design, mathematical analysis, and Monte Carlo simulation methods. Then the degrees of uncertain importance measure of activities are solved by the three methods and these results are compared. The comparison results in the example show that the proposed method of uncertain importance measure is very effective for distinguishing the importance level of activities in systems. The calculation and simulation results also verify that the risk events composed of critical activities can generate risk scenarios. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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21 pages, 4309 KiB

Open AccessArticle

An Empirical Study on the Design of Digital Content Products from a Big Data Perspective

by Lin Feng, Baoping Sun, Kai Wang and Sang-Bing Tsai

Sustainability 2018, 10(9), 3092; https://doi.org/10.3390/su10093092 - 30 Aug 2018

Cited by 8 | Viewed by 3909

Abstract

The competition within the digital content market has become extremely fierce recently in China. With increasingly diversified product choices offered to customers, the focus on customer experience has been elevated to the highest level ever. It has become key to winning customers from within the intense competition and for companies to obtain differentiation advantages to create optimized customer experience from the customers’ viewpoint. The article analyzes the relationship between customer experience and the business model of digital content companies. Later, it comes up with an innovated business model based on the big data of customer experience to restructure the business process of digital content companies and to illustrate the designing process of new models in the product application design and customer care stages based on the model proposed, which can be used as references for the transformation and innovation of digital content companies. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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18 pages, 538 KiB

Open AccessArticle

SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

by Zhaoyi Pei, Songhao Piao, Mohammed El Habib Souidi, Muhammad Zuhair Qadir and Guo Li

Sustainability 2018, 10(8), 2946; https://doi.org/10.3390/su10082946 - 20 Aug 2018

Cited by 4 | Viewed by 4207

Abstract

The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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Review

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27 pages, 1222 KiB

Open AccessReview

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

by Mirjana Pejić Bach, Živko Krstić, Sanja Seljan and Lejla Turulja

Sustainability 2019, 11(5), 1277; https://doi.org/10.3390/su11051277 - 28 Feb 2019

Cited by 133 | Viewed by 27792

Abstract

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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Other

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15 pages, 2894 KiB

Open AccessCase Report

Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics

by Dong-Hui Jin and Hyun-Jung Kim

Sustainability 2018, 10(10), 3778; https://doi.org/10.3390/su10103778 - 19 Oct 2018

Cited by 44 | Viewed by 20354

Abstract

Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness. Full article

(This article belongs to the Special Issue Expert Systems: Applications of Business Intelligence in Big Data Environments)

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