Pub Date : 2021-05-19DOI: 10.5772/INTECHOPEN.97374
A. A. Gad-Elrab
Currently, business intelligence (BI) systems are used extensively in many business areas that are based on making decisions to create a value. BI is the process on available data to extract, analyze and predict business-critical insights. Traditional BI focuses on collecting, extracting, and organizing data for enabling efficient and professional query processing to get insights from historical data. Due to the existing of big data, Internet of Things (IoT), artificial intelligence (AI), and cloud computing (CC), BI became more critical and important process and received more great interest in both industry and academia fields. The main problem is how to use these new technologies for creating data-driven value for modern BI. In this chapter, to meet this problem, the importance of big data analytics, data mining, AI for building and enhancing modern BI will be introduced and discussed. In addition, challenges and opportunities for creating value of data by establishing modern BI processes.
{"title":"Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value","authors":"A. A. Gad-Elrab","doi":"10.5772/INTECHOPEN.97374","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.97374","url":null,"abstract":"Currently, business intelligence (BI) systems are used extensively in many business areas that are based on making decisions to create a value. BI is the process on available data to extract, analyze and predict business-critical insights. Traditional BI focuses on collecting, extracting, and organizing data for enabling efficient and professional query processing to get insights from historical data. Due to the existing of big data, Internet of Things (IoT), artificial intelligence (AI), and cloud computing (CC), BI became more critical and important process and received more great interest in both industry and academia fields. The main problem is how to use these new technologies for creating data-driven value for modern BI. In this chapter, to meet this problem, the importance of big data analytics, data mining, AI for building and enhancing modern BI will be introduced and discussed. In addition, challenges and opportunities for creating value of data by establishing modern BI processes.","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134134451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-30DOI: 10.5772/INTECHOPEN.95961
Mladen Turuk
The aim of the study is to explore and present an overview of digital entrepreneurship in Central and Eastern European countries and to examine how certain components of the DESI index affect GDP per capita in CEE countries and in what way modern information technologies affect their economies. The paper uses secondary data sources, mostly scientific and professional journals from the studied area, DESI reports, Eurostat data, and other Internet sources. The first part of the paper presents a short introduction on digitization digital entrepreneurship and digital technologies. The second part provides a descriptive analysis of digital entrepreneurship indicators and explores business demography in the ICT sector while the third part refers to the analysis of the DESI index. The panel method on data from 2015 to 2019 was used to show the influence of the different DESI index components on the observed countries’ GDP per capita. The hypothesis that the components of the DESI index have a positive impact on GDP per capita has been partially confirmed. DESI rank, Connectivity and Human capital did not prove to be significant, while Use of internet services, Integration of digital technology, and Digital public services proved their significant positive effect.
{"title":"An Overview of Digital Entrepreneurship in Central and Eastern European Countries","authors":"Mladen Turuk","doi":"10.5772/INTECHOPEN.95961","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.95961","url":null,"abstract":"The aim of the study is to explore and present an overview of digital entrepreneurship in Central and Eastern European countries and to examine how certain components of the DESI index affect GDP per capita in CEE countries and in what way modern information technologies affect their economies. The paper uses secondary data sources, mostly scientific and professional journals from the studied area, DESI reports, Eurostat data, and other Internet sources. The first part of the paper presents a short introduction on digitization digital entrepreneurship and digital technologies. The second part provides a descriptive analysis of digital entrepreneurship indicators and explores business demography in the ICT sector while the third part refers to the analysis of the DESI index. The panel method on data from 2015 to 2019 was used to show the influence of the different DESI index components on the observed countries’ GDP per capita. The hypothesis that the components of the DESI index have a positive impact on GDP per capita has been partially confirmed. DESI rank, Connectivity and Human capital did not prove to be significant, while Use of internet services, Integration of digital technology, and Digital public services proved their significant positive effect.","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"993 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134550094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.5772/intechopen.92253
Faten Alzazah, Xiaochun Cheng
Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study.
{"title":"Recent Advances in Stock Market Prediction Using Text Mining: A Survey","authors":"Faten Alzazah, Xiaochun Cheng","doi":"10.5772/intechopen.92253","DOIUrl":"https://doi.org/10.5772/intechopen.92253","url":null,"abstract":"Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study.","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121507629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-27DOI: 10.5772/intechopen.92140
Vience Mutiara Rumata, A. S. Sastrosubroto
In line with the rapid growth of the global e-commerce industry today, Indonesia has enormous digital economic potential in the future. The Indonesian government is focusing on developing the digital economy by increasing the connectivity infrastructures as well as the local market. Nevertheless, there are some paradoxes caused by the existing regulations. This paper elaborates on the paradox of digital economy development in Indonesia. By using a mainstream-approach policy analysis method, this study describes the problematic situation of Indonesian digital economy governance. This is a qualitative study where the primary data derive from mostly statutes, government official documents, as well as reports. The discussion consists of (1) e-commerce: the main driver of Indonesian Digital Economy; (2) Indonesian Digital Regulatory Framework and Challenges; and (3) The Paradoxes of Indonesian Digital Economy. Due to various sectors of the digital economy, the discussion focuses on the e-commerce sector.
{"title":"The Paradox of Indonesian Digital Economy Development","authors":"Vience Mutiara Rumata, A. S. Sastrosubroto","doi":"10.5772/intechopen.92140","DOIUrl":"https://doi.org/10.5772/intechopen.92140","url":null,"abstract":"In line with the rapid growth of the global e-commerce industry today, Indonesia has enormous digital economic potential in the future. The Indonesian government is focusing on developing the digital economy by increasing the connectivity infrastructures as well as the local market. Nevertheless, there are some paradoxes caused by the existing regulations. This paper elaborates on the paradox of digital economy development in Indonesia. By using a mainstream-approach policy analysis method, this study describes the problematic situation of Indonesian digital economy governance. This is a qualitative study where the primary data derive from mostly statutes, government official documents, as well as reports. The discussion consists of (1) e-commerce: the main driver of Indonesian Digital Economy; (2) Indonesian Digital Regulatory Framework and Challenges; and (3) The Paradoxes of Indonesian Digital Economy. Due to various sectors of the digital economy, the discussion focuses on the e-commerce sector.","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127290834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-11DOI: 10.5772/INTECHOPEN.92203
Mohammed Bahja
Increasing dependency of humans on computer-assisted systems has led to researchers focusing on more effective communication technologies that can mimic human interactions as well as understand natural languages and human emotions. The problem of information overload in every sector, including business, healthcare, education etc., has led to an increase in unstructured data, which is considered not to be useful. Natural language processing (NLP) in this context is one of the effective technologies that can be integrated with advanced technologies, such as machine learning, artificial intelligence, and deep learning, to improve the process of understanding and processing the natural language. This can enable human-computer interaction in a more effective way as well as allow for the analysis and formatting of large volumes of unusable and unstructured data/text in various industries. This will deliver meaningful outcomes that can enhance decision-making and thus improve operational efficiency. Focusing on this aspect, this chapter explains the concept of NLP, its history and development, while also reviewing its application in various industrial sectors.
{"title":"Natural Language Processing Applications in Business","authors":"Mohammed Bahja","doi":"10.5772/INTECHOPEN.92203","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.92203","url":null,"abstract":"Increasing dependency of humans on computer-assisted systems has led to researchers focusing on more effective communication technologies that can mimic human interactions as well as understand natural languages and human emotions. The problem of information overload in every sector, including business, healthcare, education etc., has led to an increase in unstructured data, which is considered not to be useful. Natural language processing (NLP) in this context is one of the effective technologies that can be integrated with advanced technologies, such as machine learning, artificial intelligence, and deep learning, to improve the process of understanding and processing the natural language. This can enable human-computer interaction in a more effective way as well as allow for the analysis and formatting of large volumes of unusable and unstructured data/text in various industries. This will deliver meaningful outcomes that can enhance decision-making and thus improve operational efficiency. Focusing on this aspect, this chapter explains the concept of NLP, its history and development, while also reviewing its application in various industrial sectors.","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133095535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-17DOI: 10.5772/intechopen.91951
Wenying Huo, Mingxuan Wu, J. Soar
The purpose of this chapter is to review the development of China’s higher education in electronic commerce (e-commerce) and explore the requirements of the internationalization of China’s e-commerce higher education. The Benefit-Driving Model (BDM) was adopted to explain the reasons for the internationalization of China’s e-commerce higher education. The literature review spans 20 years from 2001 when the first 13 e-commerce programs were offered from China’s 597 universities. By 2019, 328 e-commerce programs were offered by 831 universities. There is a sustainable growth from 2001 (2.17%, 13 of 597) to 2019 (39.47%, 328 of 831). Currently, six universities offer two e-commerce programs with different majors. Eight universities established specialized e-commerce schools. There are also six jointly founded or cooperative e-commerce programs run in China with overseas universities. This research may be valuable for any international organization interested in collaboration with China’s e-commerce higher education. A limitation is that this research focuses only on bachelors of e-commerce programs. Further research will explore factors for success in jointly founded e-commerce programs with China’s e-commerce educators.
{"title":"Internationalization of China’s E-Commerce Higher Education: A Review between 2001 and 2019","authors":"Wenying Huo, Mingxuan Wu, J. Soar","doi":"10.5772/intechopen.91951","DOIUrl":"https://doi.org/10.5772/intechopen.91951","url":null,"abstract":"The purpose of this chapter is to review the development of China’s higher education in electronic commerce (e-commerce) and explore the requirements of the internationalization of China’s e-commerce higher education. The Benefit-Driving Model (BDM) was adopted to explain the reasons for the internationalization of China’s e-commerce higher education. The literature review spans 20 years from 2001 when the first 13 e-commerce programs were offered from China’s 597 universities. By 2019, 328 e-commerce programs were offered by 831 universities. There is a sustainable growth from 2001 (2.17%, 13 of 597) to 2019 (39.47%, 328 of 831). Currently, six universities offer two e-commerce programs with different majors. Eight universities established specialized e-commerce schools. There are also six jointly founded or cooperative e-commerce programs run in China with overseas universities. This research may be valuable for any international organization interested in collaboration with China’s e-commerce higher education. A limitation is that this research focuses only on bachelors of e-commerce programs. Further research will explore factors for success in jointly founded e-commerce programs with China’s e-commerce educators.","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116053363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-11DOI: 10.5772/intechopen.91440
Chakir Aziza, Chergui Meriyem
Many organizations spread and integrate the practices of the Information Technology Governance, Risk and Compliance (IT GRC). The problem that arises is how to choose the best practice to satisfy a precise need. This chapter concerns the study and the conception of decision-making architecture with the multi-agent system (MAS). So, the objective of this research is to build a decision-making model to satisfy a precise IT need. The proposed approach rests on four main stages to set up the decision-making model, which takes as input the strategic needs. The realized work has as objective to minimize the incoherence between the decisions taken by the stakeholders of an organization compared with the defined strategic objectives. The decision-making would contribute to legitimize the taken decision. This work is based on modeling a MAS, which rests on the idea that it is possible to represent directly the behavior and the interactions of a set of autonomous individuals evolving in a common environment. Finally, the proposed solution is part of a global platform for IT Governance, Risk and IT Compliance (EAS-IT GRC) (“EAS is the name of our team”).
{"title":"Using Multi-Agent System to Govern the IT Needs of Stakeholders","authors":"Chakir Aziza, Chergui Meriyem","doi":"10.5772/intechopen.91440","DOIUrl":"https://doi.org/10.5772/intechopen.91440","url":null,"abstract":"Many organizations spread and integrate the practices of the Information Technology Governance, Risk and Compliance (IT GRC). The problem that arises is how to choose the best practice to satisfy a precise need. This chapter concerns the study and the conception of decision-making architecture with the multi-agent system (MAS). So, the objective of this research is to build a decision-making model to satisfy a precise IT need. The proposed approach rests on four main stages to set up the decision-making model, which takes as input the strategic needs. The realized work has as objective to minimize the incoherence between the decisions taken by the stakeholders of an organization compared with the defined strategic objectives. The decision-making would contribute to legitimize the taken decision. This work is based on modeling a MAS, which rests on the idea that it is possible to represent directly the behavior and the interactions of a set of autonomous individuals evolving in a common environment. Finally, the proposed solution is part of a global platform for IT Governance, Risk and IT Compliance (EAS-IT GRC) (“EAS is the name of our team”).","PeriodicalId":170398,"journal":{"name":"E-Business - Higher Education and Intelligence Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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