Big Data Market Research Articles

Trading off data resource availability and privacy preservation in multi-layer network transaction

The big data market solves the problem of the effective utilization of data through treating data as the circulating commodity in the market. The existing body of research on the big data market suggests that either improving the availability of published data or protecting sensitive information when trading data s the current mainstream topic. To date, the balancing the availability and privacy of the released dataset is a gradually emerging challenge. Unfortunately, there are few studies that have concentrated on the combination of the two points, which is more in line with the actual trading demands and data interaction patterns. Our paper proposes a novel mechanism called Differential Privacy Data Trading (DPDT) mechanism by introducing the differential privacy into the data trading process. Our DPDT mechanism can meet the actual usage requirements of data consumers for the released dataset while ensuring privacy. In short, the DPDT mechanism balances availability and privacy by generating a private synthetic dataset whose accuracy is determined by the data consumer. It is customized for the big data market by improving appropriate synthetic dataset privacy releasing techniques. In addition, DPDT can calculate the corresponding security payment costs depending on the different accuracy of the released dataset by correlating the accuracy, privacy budget, and payment. We instantiate the DPDT with real-world data and the experimental results verify the proposed mechanism is feasible and robust. Our analysis and discussion results reveal that DPDT achieves the trade-off between availability and privacy of the released dataset during data trading.

DIFFUSION OF INNOVATIONS AS A KEY DRIVER OF THE DIGITAL ECONOMY DEVELOPMENT

The research objective includes detailing of the essence and structural changes of the digital economy (DE) at the national level; an explanation of the features of digital innovations; the role substantiation of diffusion of innovations (DI) as a key driver for DE development. The research methodology is based on: structural, institutional, historical, comparative, functional analysis, formalization method. The need to achieve all-encompassing digitalization nature and the systematic use of digital technologies, as well as the transition to digital models of organization through the diffusion of technologies, is taken into account. The research results are the detailing of the essence and structural features of DE. DE is considered as a new type of economy, characterized by the widespread adoption of digital technologies, cyber-physical systems, intelligent solutions, network technologies. The DE nature is revealed from the position of several approaches: technological; structural; component-resource; functional. The DE features (network nature, a new type of entrepreneurship, a new class of specialists, new institutions, etc.) are highlighted, as well as the negative consequences of the transition to such an economy; overcoming of these negative consequences requires smart policy with a focus on productivity growth, structural changes and growth incentives. The DE structure, including parts of the Internet economy (e-Commerce, e-Health, e-Education, e-Banking, etc.) and new forms of providing various services, is characterized. The IT sector growth is indicated as the DE “core”, where new jobs, infrastructure, cost are created, and the technology business is developed. The structural proportions of DE are largely determined by technological trends (on-demand economy, sharing economy, Internet of Things, etc.). Digital platforms as new intermediary mechanisms, as well as the Big Data market, are one of the key structural components of DE. The subject and features of digital innovations are highlighted. On this basis, the DI role in the DE development is substantiated, taking into account the provision of all-encompassing digitalization nature, stimulation of innovations, ensuring the benefits and economic growth, as well as the creation of economies of scale, chain and cumulative effects. Practical recommendations on the intensification of DI processes in the context of DE building include: targeted government support that concerns the IT and other sectors; establishment of technology transfer and commercialization centers, technology platforms and technology leadership programs; quality improvement of education and training; infrastructure role expanding of universities; development of research, innovation, entrepreneurial networks, professional communities, industry associations. In this context, it is necessary to strengthen support for small and medium-sized innovative businesses that should become a provider of new technologies. It is advisable to create portals for oriented R&D and adoption of technologies; knowledge management platforms during technology transfer process; industry knowledge bases; portals for experts work activity management for project evaluation. Value/originality. The article details the essence and reveals the structural features of DE, describes the specifics of digital innovations, as well as presents DI as a key driver for the development of such an economy.

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

The Antitrust Regulation on Abuse of Dominant Market Position in the Field of Big Data：From the Perspective of Methodological Improvement

The provision of prohibiting abuse of dominant market position in China’s antitrust law is limited to such types of behavior as “monopoly pricing” and “limited transaction”, which not only narrows the scope of regulation, but also blurs the criteria of regulation. A lot of anti-competitive behaviors in the field of big data are not stipulated by the antitrust law, which may lead to a lack of supervision. Furthermore, the current regulatory methods do not focus on the analysis of big data market behaviors, but use the deductive method from general to individual, tending to assume the type of monopoly behaviors on the basis of law. The double failure of legal provisions and interpretation methods contrasts strongly with the regulatory requirements in the field of big data. To tackle this problem, the traditional regulatory methodology needs to be adjusted in accordance with the characteristics of the big data market from the three aspects: market power identification, abuse behavior screening, and competition damage effect analysis.First of all, we should take the data value chain as the basis, and fully consider the network effect, cross-border competition, dynamic competition and many other characteristics of the big data market, to evaluate the market power. Secondly, with the help of the infringement identification of “exclusionary abuse” and “exploitative abuse”, we may quickly identify and summarize the behavior types of market dominant position abuse in the field of big data. Lastly, we should establish specific identification methods and competitive damage analysis to regulate those anti-competitive behaviors associated with big data such as “restricting interoperability” and “excessive collection and use of data”.As China’s anti-monopoly law is being comprehensively revised and improved, we should take this opportunity to modify the existing provisions and improve the regulatory methodology in view of the development of the digital economy. For one thing, the legislature should summarize special factors according to the characteristics of big data industry in the legal provisions defining relevant markets and evaluating market power. For another thing, China should refer to the legislative model of the EU competition law and determine “exclusionary abuse” and “exploitative abuse” as the basic types of the abuse of dominant market position, so as to expand the scope of application of antitrust laws. In addition, China should issue antitrust law enforcement guidelines, summarize the types of abuses common in the field of big data, and formulate corresponding antitrust law enforcement methods and regulatory measures.

Three-dimensional geological modeling of manganese deposits based on exploration big data and data market

Taking Lijiawan-Gaodi-Datuo super-large Datangpo manganese deposit in northeastern Guizhou as an example, this paper discusses the idea and method of establishing a data market of manganese mine exploration data through collecting, extracting and ETL operation of exploration data based on the analysis of the characteristics and structure of large geological data. Furthermore, the paper describes the way to establish a high-precision three-dimensional geological model of super-large manganese deposits in northeastern Guizhou based on the established data market. The three-dimensional geological model of the deposit condenses a large amount of spatial data and attribute data, and is an effective carrier for large data of heterogeneous exploration and development in manganese mines. Based on the fine and holographic three-dimensional geological model of the deposit, the model not only can directly reflect the characteristics of the deposit and orebody, but also can be used as a visual analysis tool for mine management and prospecting prediction in deep and peripheral areas.

A Stackelberg Game Pricing Through Balancing Trilateral Profits in Big Data Market

With the popularity of the Internet of Things (IoT) and large-scale deployment of sensors, data have exploded. Big data is utilized to extract useful knowledge and information and served as data services to consumers. While most of the current researches in the field of big data services focus on developing and improving algorithms for data mining and information extraction, and rarely studies “big data” from an economic perspective. This article thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then, we build an economic model of the data market, which consists of three parties: 1) data vendor; 2) service provider; and 3) service users. The data vendor gathers various raw data and sells them to the service provider. The raw data are further processed into data services by a service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of the data market can achieve the maximum profit through the proposed pricing mechanism and economic model.

The impact of big data market segmentation using data mining and clustering techniques

The impact of big data market segmentation using data mining and clustering techniques

РАЗВИТИЕ ЦИФРОВОГО МАРКЕТИНГА В РОССИИ ПРОБЛЕМЫ И ПЕРСПЕКТИВЫ

In modern conditions, the competitiveness of companies and the national economy is determined by their ability to apply digital technologies, including Big Data technologies. They offer great opportunities for increasing efficiency of marketing and other activities of the company. An analysis of the level of development of the Russian Big Data market, Russian cloud services and digital platforms made it possible to conclude that despite the fact that Russia has the prerequisites for creating digital products of “high redistribution”, only the fragmented development of digital platforms will greatly complicate this process. The solution is seen in the substantial participation of the state in the creation of digital platforms since business opportunities, both financial and information, are insufficient.

The damage done by big data-driven public relations

Over the last two decades it has been argued that public relations contributes to hegemony by corporate organizations over stakeholder groups who have less power and resources. In its original formulation, the concept of hegemony had two defining features: firstly, it was conceived of as a society-wide, macroscopic formation and second as self-reinforcing i.e. self-replicating. This article argues that the rise of big-data in public relations is a hegemonic development that further re-enforces the current institutional logics and power in the three main spheres of society: corporate, governance and civic. Using the economic notion of externalities, the authors argue that loss of agency over personal data is the unpriced, unrecognized externality that drives the big-data market. This externality is dependent on those who are the losers of agency not having the requisite information, power or resources to negotiate alternatives that might re-dress the balance. As users and proponents of the use of big data, the public relations profession has a number of key questions to answer if it is not to re-inforce arguments that it is a hegemonizing force in organizations and society. Normal economic remedies to address externalities are not adequate to a case that is both ethical as well as economic in nature. The article therefore concludes with five arguments that the public relations profession should debate to provide leadership within organisations and society.

Quantitative market analysis of the European Climate Services sector – The application of the kMatrix big data market analytical tool to provide robust market intelligence

Although activities within the Climate Services sector have been offered to public and private sector markets for a long time, the concept of “Climate Services” as a distinct sector is relatively new. Traditionally, these services have been considered an extension of Weather Services, but as the awareness of the Climate Services sector has spread, so has the need for in-depth, reliable and quantifiable market intelligence. The kMatrix big data analytical tool is a unique research methodology, providing market intelligence for “hard to measure” markets. The kMatrix method was applied to the Climate Services sector to define, measure and forecast the current and potential future market volume of the sector. The MARCO definition of Climate Services was developed by using an existing kMatrix Weather & Climate Services dataset, which was then scrutinized by the MARCO partners and a distinct and extended Climate Services taxonomy developed. Market intelligence is provided by country, industry, platform and service and includes value of sales (€m), import and export data, number of transactions, market growth rates, benchmarks of innovation and supplier and purchaser transactional data. The data has been mapped to NACE codes and NUTS 2 regions. The work provides a geographic baseline for Climate Services and allows for country and regional analysis. It provides a common definition of the sector and quantifies the market for those involved in the provision of climate services, policy makers and others, to have access to reliable market intelligence to monitor and assist in decision making at all levels.

Big Data Market Optimization Pricing Model Based on Data Quality

In recent years, data has become a special kind of information commodity and promoted the development of information commodity economy through distribution. With the development of big data, the data market emerged and provided convenience for data transactions. However, the issues of optimal pricing and data quality allocation in the big data market have not been fully studied yet. In this paper, we proposed a big data market pricing model based on data quality. We first analyzed the dimensional indicators that affect data quality, and a linear evaluation model was established. Then, from the perspective of data science, we analyzed the impact of quality level on big data analysis (i.e., machine learning algorithms) and defined the utility function of data quality. The experimental results in real data sets have shown the applicability of the proposed quality utility function. In addition, we formulated the profit maximization problem and gave theoretical analysis. Finally, the data market can maximize profits through the proposed model illustrated with numerical examples.

Features of Big Data approach and new opportunities of BI-systems in marketing activities

At the moment, the conditions of fierce competition in production and sales bring the role of marketing in business to the fore. Big data, which form the basis of BI-systems, and which arise as a result of digitization of economic and any other activity, are growing exponentially. To better understand the impact of Big Data on various marketing activities, it is necessary to consider the main aspects of Big Data approach. A description of the situation on the Big Data market was given, problems were posed, and ways to study them were described. The article describes the possible ways of classifying Big Data, the types of data that are used. Eventually, this article reveals features of Big Data approach and new opportunities of BI-systems in marketing activities. In conclusion, the problems of the study were reviewed, and the main guidelines for future research on the topic were given.

Machine learning based privacy-preserving fair data trading in big data market

In the era of big data, the produced and collected data explode due to the emerging technologies and applications that pervade everywhere in our daily lives, including internet of things applications such as smart home, smart city, smart grid, e-commerce applications and social network. Big data market can carry out efficient data trading, which provides a way to share data and further enhances the utility of data. However, to realize effective data trading in big data market, several challenges need to be resolved. The first one is to verify the data availability for a data consumer. The second is privacy of a data provider who is unwilling to reveal his real identity to the data consumer. The third is the payment fairness between a data provider and a data consumer with atomic exchange. In this paper, we address these challenges by proposing a new blockchain-based fair data trading protocol in big data market. The proposed protocol integrates ring signature, double-authentication-preventing signature and similarity learning to guarantee the availability of trading data, privacy of data providers and fairness between data providers and data consumers. We show the proposed protocol achieves the desirable security properties that a secure data trading protocol should have. The implementation results with Solidity smart contract demonstrate the validity of the proposed blockchain-based fair data trading protocol.

A Survey on Big Data Market: Pricing, Trading and Protection

Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.

Three Hierarchical Levels of Big-Data Market Model Over Multiple Data Sources for Internet of Things

This paper proposes three hierarchical levels of a competitive big-data market model. We consider that a service provider gathers data from multiple data sources and provides valuable information from refined data as a service to its customers. Under our approach, a service provider determines optimal data procurement from multiple data sources within its budget constraint. The multiple data sources follow the service provider’s action by independently submitting bidding prices to the service provider. Further, customers decide whether to subscribe or not based on the subscription fee, their willingness-to-pay, and the quality of the refined data. We study the economic benefits of such a market model by analyzing the hierarchical decision making procedures as a Stackelberg game. We show the existence and the uniqueness of the Nash equilibrium (NE), and the NE solution is given as a closed form. Finally, we reveal that the obtained unique equilibrium solution maximizes the payoff of all market participants.

Big data market analysis of e-health in medical neuroscience

Depression is associated with more than 100,000 patent applications for its diagnosis and prognosis, the highest number among mental disorders. This is followed by schizophrenia with 47,000, bipolar disorder with 32,110 and hypomania with 11,377. Among diagnostic tools, magnetic resonance imaging is associated with more than 31,000 patent applications. Among recent technologies, biomarkers are associated with more than 12,000 epigenetics with about 970 metabolomics with 515 genome-wide association study (GWAS) with 486 and bionics with 497 patent applications. The patent applications related to diagnosis and prognosis of psychiatric diseases peaked in 2008 and was overall decreasing until 2016, with a local peak in 2013. This trend has been observed despite the value addition of recent technologies like machine learning, big data and internet of things. However, more conservative diagnostic tools from the last decade like magnetic resonance imaging, epigenetics, bionics and neuro-psychological testing are improved by the recent technologies. For example, bionics is improved by sensors of internet of things to collect the data from patients around the world and use the big data analytics to efficiently diagnose the psychiatric diseases. The Regents of the University of California and Human Genome Sciences Incorporation are the respective academic and non-academic institutions leading the innovations related to diagnosis and prognosis of psychiatric diseases.Disclosure of interestThe authors declare that they have no competing interest.

A Study on Big Data New Technology Trends and Market Prospects

A Study on Big Data New Technology Trends and Market Prospects

Government big data opening and market utilization for smart city construction

Government big data opening and market utilization for smart city construction

Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

Big data and data science have experienced unprecedented growth in recent years. The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial. Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses.

An Empirical Study on the Influencing Factors for Big Data Intented Adoption: Focusing on the Strategic Value Recognition and TOE Framework

An Empirical Study on the Influencing Factors for Big Data Intented Adoption: Focusing on the Strategic Value Recognition and TOE Framework