• Không có kết quả nào được tìm thấy

12.2% 169000 185M TOP 1% 154 6200

Nguyễn Gia Hào

Academic year: 2023

Chia sẻ "12.2% 169000 185M TOP 1% 154 6200"


Loading.... (view fulltext now)

Văn bản

The Benefit-Driving Model (BDM) was adopted to explain the reasons for the internationalization of China's e-commerce higher education. Wu and Yu [11] developed the Benefit-Driving Model (BDM) to illustrate the factors affecting the internationalization of China's higher education.

Table 5 shows the international pioneers in e-commerce education [20]. China’ s e-commerce educators could learn the experience of curriculum development from these international pioneers.
Table 5 shows the international pioneers in e-commerce education [20]. China’ s e-commerce educators could learn the experience of curriculum development from these international pioneers.

TOP 1%


Central and Eastern European countries are member states of the European Union that were once part of the former Eastern Bloc. In addition, it analyzes the business demographics of the ICT sector and provides an overview of companies' birth, death and churn rates.

Overview of digital entrepreneurship

The purpose of the study is to explore and present an overview of digital entrepreneurship in the Central and Eastern European countries and to examine how certain components of the DESI index affect GDP per population in the CEE countries and how modern information technologies affect their economies. The fourth part of the study provides digital economy and society index analysis and relates observed DESI components to countries' GDP per inhabitant, while the concluding remarks are presented at the end.

Research methodology

Descriptive analysis of ICT sector and its business demography Prior to the introduction of the DESI index (Digital Economy and Society

In the middle is the Republic of Croatia with a 4.40% share of the ICT industry in GDP. The average share of research and development in the ICT industry in the countries of Central and Eastern Europe in total research and development is 0.83%, and Lithuania has the largest share (2.49%).

DESI index analysis

  • Hypothesis
  • Data analysis

The null hypothesis assumes that the errors are not correlated with the regressors, which would indicate the use of the "Random" model. The intensity of the individual components of the DESI index is shown in Figure 1 below.


The discussion consists of (1) e-commerce: the main driver of Indonesia's digital economy; (2) Indonesia's digital regulatory framework and challenges; and (3) Paradoxes of Indonesia's digital economy. Due to the different sectors of the digital economy, the discussion focuses on the e-commerce sector.

Mainstream policy analysis

Policy controversies are common as they arise due to the multiple frames and perspectives of the government (ie, executive, legislature, judiciary), the general public, the community, or social groups in viewing a problematic situation. The documents are mainly statutes (ie, Presidential Decree, Ministerial Regulation, Government Regulation and other regulatory documents).

E-commerce: the main driver of Indonesian digital economy 1 The digital economy in global trend

  • Indonesian e-commerce highlights

Tapscott argues that the digital economy is the economy of "The Age of Networked Intelligence." He warns of the dark side of this era that includes (1) dislocations (many old jobs will have perished); (2) threat to privacy (the personal data breaches); (3) polarization of wealth (20% of the household worth 80% of the country's wealth); (4) digital divide between society; and also (5) digital slave (technology invades all parts of human time and space) [14]. The existing policies and regulations should not only ensure the growth of the digital economy industry but also to address these critical issues.

Indonesian digital regulatory framework and challenges 1 The Indonesian digital governance

  • The challenges of the Indonesian digital governance

In addition, the advancement of the digital economy may lead to job substitution, requiring more technology than human resources. The Department of Research and Human Resource Development of the Ministry of Communications and Informatics (the MCI) proposes the regulatory framework based on digital platforms, especially in the field of online transportation (including ride-hailing start-up).

The paradoxes of Indonesian digital economy

  • The regulative paradox
  • The paradox of IT market growth

PANDI, for example, as a non-governmental organization is authorized by the government to regulate the Indonesian top-level domain (.id) in addition to second-level military (.mil) and government domain (.gov). The fact that the government is trying to increase internet access as well as logistics infrastructure to support national digital economy may harm the growth of small business e-commerce market especially in Indonesia small business and medium business market in Indonesia. There are two paradoxes of the development of the Indonesian digital economy: the paradox of regulation and the paradox of productivity.


With the development of technologies and applications of various natural language processing methods, the understanding and definition of NLP has changed over time. Looking at the brief background discussed, it can be seen that there is a growing demand and scope for greater application of NLP in various industries.

Brief history of NLP

Considering the nature of this study, which focuses on investigating the development of NLP and its applications in business, literature review methodology is considered as it is identified as an effective methodological tool. Machine translation on multiple platforms is one of the classic representations of NLP implementation, the use of which is increasing in various industries and the market is projected to be $56 billion by 2021 [30].

Applications of NLP in business

  • NLP in commerce
  • NLP in “E-Governance”
  • NLP in healthcare
  • NLP in education
  • NLP in other sectors

In addition, in this context it can be used for increased security by preventing possible breaches of this. In addition, NLP can be used in the teaching of the use of language, that is, any subject that uses any language as a medium.

Future directions

Similar applications of NLP can be found in all sectors where services are provided to clients, such as streamlining programs based on clients' television viewing history (Media & Entertainment) [74]; or communicate with travel chat bots about the travel routes while driving like Google Maps(travel). Therefore, NLP can be applied in various industries that are not only service oriented, but also manufacturing industries.


In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Massachusetts Institute of Technology. Efficient identification of nationally reportable cancer cases using natural language processing and machine learning.

Global architecture: EAS-IT GRC

In this scientific work, we describe the match between the strategic goals of the organization and the processes of ITIL [8], PMBOK, ISO 27001 and ISO 27002 [9], based on a decision-making system for choosing the best framework by reporting the strategic goal. Update Layer: This layer ensures the adaptation of new practices in the EAS-IT GRC platform by defining change policies to integrate these new practices in relation to the information system.

Decision-making layer (DML)

  • Level 1: framework IT decision
  • Level 2: decision-making treatment

The following chart shows the order of the stages of generation of the categorized IT request. The second classification was made according to an evaluation strategy of the IT objectives related to each IT framework.

Graph of resolution of the algorithm “Categorization Decision 1.1.”
Graph of resolution of the algorithm “Categorization Decision 1.1.”

Contribution: framework IT decision 1 Method proposed in version 1

  • Method proposed in version 2
  • Method proposed in version 3
  • Case study

We apply the same treatment to the set of IT processes and obtain the matrix below (table 2) [12]. The second phase consists in estimating the connection between the IT goals (the COBIT processes) and the IT frameworks by basing themselves on aspects that each IT framework approaches (Table 3) [12].


Finally, concluding remarks on salient features of standard integer optimization solvers for business intelligence applications are offered, including perspectives for future research directions. Overview of Integer Optimization in Business Intelligence Applications Numerous business intelligence applications can be presented as a mathematical proposition.

Overview of integer optimization in business intelligence applications Numerous business intelligence applications can be posed as mathematical pro-

  • Computational performance of commercial integer optimization solvers The actual computational performance of a commercial optimizer (or opti-
  • A commercial success story: CPLEX integer optimization solver

The main solution methods and algorithms with certain enhanced features commonly available in standard integer optimization solvers are detailed in Section 3, including those designed to exploit model formulations. It represents an early commercial success story of an optimization suite with various acquisitions and a separate solver (called Gurobi) that is now a success story in its own right.

Solution methods and algorithms 1 Integer optimization algorithms

  • Branch-and-bound
  • Presolve and cutting planes
  • Heuristics
  • Combined local search and heuristics
  • Parallelization
  • Solution pools
  • Tuning tools

Branch-and-bound is a basic MIP solving algorithm that uses LP as a subroutine [10]. Within the branch-and-bound scheme, parallelization includes the solution of the root node and nodes and strongly parallel branching [20].

Use cases

  • Use case 1: energy optimization
  • Use case 2: financial optimization
  • Use case 3: manufacturing optimization

In the case of CPLEX, parallelization involves running multiple optimizers to solve the same problem—. Several computational options are invoked to accelerate solution convergence, including preferential branching within the branch-and-bound procedure and multiple processing with parallelization (i.e., the techniques presented in the previous section).


Stock market forecasting aims to determine the future movement of the stock value of a financial stock exchange. This paints a picture of the importance of text mining techniques to automatically extract meaningful information for stock market analysis.

A review of background concepts

  • Sentiment analysis
  • Textual data preprocessing

However, feature selection is a crucial step in the textual data preprocessing, and many other strategies can also be used for text analysis. Other feature representation methods can also be used successfully in text preprocessing, and we will discuss those in more detail in the following sections.

The relationship between stock market prediction and text mining Many papers study the relationship between stock price movements and the

The findings showed that a multiplex network approach incorporating information from both social media and financial data can be used to predict a causal relationship framework with high accuracy. The study illustrates that some of the proposed ad hoc forecasting models predict well the next day direction of the stock movements for some companies with 82% of success and there is no unified method to be used with all cases.

Machine learning for market prediction

  • Support vector machines
  • Deep learning

In the work of [31], the authors used a lexicon-based approach to predict the stock market based on Twitter user sentiment. In [13], the authors proposed a multi-source multiple-instance (M-MI) model to predict the stock market index movements.

Other machine learning methods

In [63], the authors forecasted the Argentine stock market using online message boards with topic discovery methods in addition to daily historical stock prices. The results indicate the excellent performance of using tweet message sentiment to predict the stock market movements 3 days later.

The reviewed work text source and period and number of collected items

28] 23 stocks of the Hang Seng Index10 (HSI) intraday prices and financial news from Caihua website, http://www.finet.hk/. Market predictive text mining could become much more advanced by concentrating on a specific text source, such as a specific social media site or the emerging news source from specialist financial news sites.

The reviewed work findings, limitations, and future work

Adding another source of information, such as financial news articles, can further improve prediction performance. Adding historical stock prices to the ranking model can improve prediction performance.


Machine learning in forecasting stock market indicators based on historical data and data from Twitter sentiment analysis. In this chapter, the importance of big data analytics, data mining, AI for building modern BI and improvement will be introduced and discussed.

Business intelligence (BI)

This was true for the advances made during the fourth Industrial Revolution (FIR) to the birth of the computer, and is still true for the era of AI. Therefore, many research works have been done to add modern features to improve the three-tier architecture, which will establish the next generation of BI.

Modern business intelligence (MBI)

  • Features of modern BI
  • Data architecture
  • Current BI systems
  • Data governance
  • Why BI needs AI?
  • Improving BI with AI

Modern BI systems that use different methods to store most or all of the data in main memory. Therefore, managing data across heterogeneous sources in the next generation BI system is very important.


Enabling efficient OS browsing for main memory oltp databases,” in Proceedings of the Ninth International Workshop on Data Management on New Hardware, ser. A hybrid OLTP and OLAP main-memory database system based on virtual memory snapshots,” in Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, ser.

Hình ảnh

Table 5 shows the international pioneers in e-commerce education [20]. China’ s e-commerce educators could learn the experience of curriculum development from these international pioneers.
Graph of resolution of the algorithm “Categorization Decision 1.1.”

Tài liệu tham khảo

Tài liệu liên quan