Data-intensive Industry Research Articles

Capitalization Accounting of Data Factor: Theoretical Mechanism, Methodological Path, and Statistical Measurement

From the perspective of factor input in the production process, this paper puts forward the issue of accounting and capitalization of generalized data output, brings the output of data reprocessing into the scope of statistical accounting, and proposes a systematic evolution chain of data factor forms and the division method of data factor value composition and sources. Then, based on the GDP accounting platform, a three-in-one theoretical framework covering “output-investment-assets” is built for capitalization accounting of data factor, and an accounting path covering “cost → input → output → capital formation → data assets” is designed to improve and highlight the cost accounting method with “value appreciation” as the core. Taking China as an example, its data capital formation and asset size are measured by matching data-intensive industries with data professionals, and by synthesizing data from multiple sources. The rationality and self-consistency of the theoretical and methodological research is verified by the empirical results. This study can provide theoretical reference for bringing the value accounting of data factor into the basic framework of the Systems of National Accounts (SNA). Moreover, its empirical research paradigm can also provide reference for relevant countries to carry out capitalization accounting for data factor (CADF).

Sosialisasi dan Penerapan Digitalisasi Sistem Pengelolan Peternakan Ikan pada Nagari Bomas Koto Baru, Kecamatan Sungai Pagu, Kabupaten Solok Selatan

Modernization of animal husbandry requires technological know-how for efficient use of animal husbandry inputs. In the era of information technology, where information plays a key role in people's lives, animal husbandry is rapidly becoming a highly data-intensive industry where farmers need to collect and evaluate a large amount of information in their business processes to become more efficient in production and communicate appropriate information. in order to become more efficient in production and communicate information accordingly. This relates to a number of factors such as ecological footprint, product safety, labor welfare, nutritional responsibility, plant and animal health and welfare, economic responsibility and local market presence. However, this is often overlooked by farmers due to a lack of literacy towards technology, one of which is the Fish Breeder Group in Nagari Bomas Koto Baru, Sungai Pagu District, South Solok Regency. The main challenge in this village is that farmers have never recorded their fish farming activities. So there are often discrepancies and planning errors. This is very concerned that it can interfere with business operations and sustainability. For this reason, it is necessary for fish farmers in Nagari Bomas Koto Baru, Sungai Pagu District, South Solok Regency to be given technical knowledge and socialization regarding the application of digitization of fish farm management systems

Deep Learning-based Sequence Modeling for Advanced Process Control in Semiconductor Manufacturing

Semiconductor manufacturing is one of the most data-intensive industries in manufacturing. Many so-called Advanced Process Control (APC) approaches based on equipment or process data have been presented over the years to improve quality and efficiency, reduce waste and increase energy savings. While on the one hand, intensive production leads to large amount of data available for the development of Machine Learning-based technologies, on the other hand, the high-mix production, multiple processes and machines lead researchers and developers to deal with ’small data’ scenarios. In this context, domain adaptation approaches are highly relevant to enhance scalability. In this work, we are extending a state-of-the-art Deep Learning architecture called Domain Adversarial Neural Network based Alignment Model (DBAM) by considering new sequence learning layers. In the real-world case study, the proposed architecture is shown to achieve higher accuracy, allow for better management in the absence of large amounts of data and make previously trained models reusable in similar scenarios.

Study on the Digital Transformation Capability of Cost Consultation Enterprises Based on Maturity Model

The digital maturity model (DMM) can help enterprises scientifically evaluate their digital maturity level, identify their weaknesses, and then further formulate improvement strategies. Most of the existing relevant studies focus on the Internet industry, new retail industry, and manufacturing industry, while discussion on other industries is rare. The cost consultation industry is a typical data-intensive industry. The study on its digital transformation capacity is a response to the digital cost trend in the current engineering field. The purpose of this paper is to build a DMM to evaluate the digital maturity level of cost consultation enterprises. Based on capability maturity theory and industry characteristics, the key dimensions and specific evaluation index system of the digital transformation of cost consultation enterprises are determined by using literature analysis, expert interview, and other methods, and a DMM of cost consultation enterprises is built. The model covers six dimensions, namely, top-level design, infrastructure, cost consultation business process, professional management, comprehensive integration, and digital cost performance, which are divided into 18 categories and 61 domains. On this basis, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation are comprehensively used to evaluate the digital maturity. Finally, a benchmark central enterprise in China’s high-speed rail industry, the Institute of Industry and Economics, China Railway Siyuan Survey and Design Group Co., Ltd. is selected for the case study. Results show that this enterprise, as a whole, is at the digital pioneer level. However, the effects of professional management and comprehensive integration are general, which should be the future improvement direction of digital transformation. This paper can provide the theoretical reference for the digital transformation of the cost consultation industry.

Guest Editorial Process-Level Machine Learning Applications in Semiconductor Manufacturing

The constantly increasing availability of data, the rapid expansion in computational and storage capacities of information technology systems, and algorithmic advances in Machine Learning (ML) and Artificial Intelligence (AI) are making a huge impact in the manufacturing industry for improving efficiency, operations and throughput. The semiconductor industry, being one of the most data-intensive industries, has seen the diffusion of several ML-based technologies in recent years.

Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information.

The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users’ views, as well as experts’/users’ complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users’ views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model.

РИНОК ЗЛИТТІВ І ПОГЛИНАНЬ В УКРАЇНІ: ТЕНДЕНЦІЇ ТА ПРОБЛЕМИ

The purpose of the article is to study the current state, features and problems of the development of the market of mergers and acquisitions in Ukraine. The urgency of considering this topic is due to the importance for Ukrainian companies of participating in the processes of mergers and acquisitions, which gives the opportunity to expand the scope of activities, increase profitability, penetrate new markets, and reach a qualitatively new level of development. The essence of mergers and acquisitions as a form of integration processes has been specified. Motives for initiating the process of merger or acquisition are analyzed. The classification of types of mergers of companies is considered. An analysis of the state of development of the Ukrainian M&A market for the period 2017–2021 was carried out, positive dynamics of the number and value of deals were revealed. The distribution of M&A deals by industry was analyzed. The most attractive sectors of the Ukrainian economy from the point of view of conducting mergers and acquisitions were identified: IT and telecommunications, agriculture, food industry, mining industry. Attention was drawn to the significant growth of M&A deals in software, electronic and data-intensive industries due to the impact of the COVID-19 pandemic on increased Internet activity. It was concluded that the most active participants in the Ukrainian M&A market are European investors, investors from the Middle East and North America. Obstacles to the formation of investment attractiveness of the Ukrainian market are named, in particular: high tax pressure; lack of stimulating function of taxes; high currency risks and currency restrictions; ineffective system of protection of private property rights. Among the problems of the development of the domestic M&A market, the following are identified as the main ones: underdevelopment of the stock market; a significant share of unfriendly takeovers, shady deals and raiding of business assets; a high share of insiders among shareholders; low development of business culture and ethics; insufficient use of mechanisms for financing deals, such as IPO and LBO financing; lack of transparency and availability of information about concluded agreements. The main directions of activation of M&A processes on the Ukrainian market are outlined.

HUNTER: AI based holistic resource management for sustainable cloud computing

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

DBAM: Making Virtual Metrology/Soft sensing with time series data scalable through Deep Learning

Machine Learning (ML) based technologies, like Virtual Metrology (VM)/Soft Sensing, Predictive Maintenance and Fault Detection, have been successfully applied in the past recent years in data intensive manufacturing industries, like semiconductor manufacturing, to improve process monitoring and related operations. Standardization and alignment over multiple equipment is a key element to ensure industry-wide adoption and scalability for ML-based technologies in complex production environment. In this work we address the topic of VM/Soft Sensing – a particular ML-based technology for process control – in the context of equipment matching and scalability. We present a Deep Learning-based domain adaptation approach, called DANN-Based Model Alignment (DBAM), that provides a common VM model for two identical-in-design systems whose data are following different distributions. The proposed approach has the merit of (i) exploiting directly raw sensor data (that typically present themselves in the form of time series) and (ii) offering interpretability of the features. The proposed approach is compared against other approaches in the literature for VM/Soft Sensing on a real-world case study from semiconductor manufacturing.

Privacy Preserving Analytics in Outsourced Healthcare System

The most data intensive industry today is the healthcare system. The advancement in technology has revolutionized the traditional healthcare practices and led to enhanced E-Healthcare System. Modern healthcare systems generate voluminous amount of digital health data. These E-Health data are shared between patients and among groups of physicians and medical technicians for processing. Due to the demand for continuous availability and handling of these massive E-Health data, mostly these data are outsourced to cloud storage. Being cloud-based computing, the sensitive patient data is stored in a third-party server where data analytics are performed, hence more concern about security raises. This paper proposes a secure analytics system which preserves the privacy of patients’ data. In this system, before outsourcing, the data are encrypted using Paillier homomorphic encryption which allows computations to be performed over encrypted dataset. Then Decision Tree Machine Learning algorithm is used over this encrypted dataset to build the classifier model. This encrypted model is outsourced to cloud server and the predictions about patient’s health status is displayed to the user on request. In this system nowhere the data is decrypted throughout the process which ensures the privacy of patients’ sensitive data.

AI's Role in Sustainable Energy

This study overlook on the perspective role of AI in the energy sector, with main focus on improving efficiency and sustainability. The role is to establish a realistic benchmark for AI technology, facilitating global efforts, ambitions, novel applications, and industry obstacles. The study focuses three main areas: The application of AI in solar and hydrogen power creation. The utilisation of AI for contribution and demand management control of energy i. ii. iii. An overview of the latest techniques in AI technology This research explores how AI techniques outperform traditional models across various domains, including controllability, energy efficiency optimization, cyber-attack prevention, IoT (Internet of Things), big data handling, smart grid management, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as a pivotal tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within an increasingly competitive landscape. This research overlooks how AI techniques outperform traditional models across various domains, including Controllability, energy efficiency optimising, cyber-attack precaution, IoT (Internet of Things), big data handling, smart grid managing, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as an important tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within a rapid competitive landscap

The Future of Digital Agriculture: Technologies and Opportunities

This article presents key technological advances in the digital agriculture, which will have significant impact. Artificial intelligence-based techniques, together with big data analytics, address the challenges of agricultural production in terms of productivity and sustainability. Emerging new applications will transform agriculture from the traditional farm practices to a highly automated and data intensive industry.

Dissolving privacy, one merger at a time: Competition, data and third party tracking

Amid growing concern about the use and abuse of personal data over the last decade, there is an emerging suggestion that regulators may need to turn their attention towards the concentrations of power deriving from large-scale data accumulation. No longer the preserve of data protection or privacy law, personal data is receiving attention within competition and antitrust law. Recent mergers and acquisitions between large digital technology platforms have raised important questions about how these different areas intersect and how they can complement one another in order to protect consumer welfare while ensuring competitive markets.This paper draws attention to one particularly complicated kind of digital data-intensive industry: that of third party tracking, in which a firm does not (only or primarily) collect and process personal data of its own customers or users, but rather data from the users of other ‘first party’ services. Mergers and acquisitions between firms active in the third party tracking industry raise unique challenges for privacy and fundamental rights which are often missed in regulatory decisions and academic discussions of data and market concentration. In this paper, we combine empirical and normative insights to shed light on the role of competition regulators in addressing the specific challenges of mergers and acquisitions in the third party tracking industry. After critically assessing some of the US and EU case law in this area, we argue that a bolder approach is needed; one that engages in a pluralist analysis of economic and noneconomic concerns about concentrations of power and control over data.

Value creation through big data application process management: the case of the oil and gas industry

Purpose The purpose of this study is twofold: to investigate the role of big data in firms’ co-knowledge and value creation and to understand the underlying drivers behind value creation through big data in the oil and gas industry by underscoring the role of firms’ capabilities, trends and challenges. Design/methodology/approach Following an inductive approach, semi-structured interviews were conducted with senior managers and analysts working in oil and gas companies across eight countries. The data collected from these key informants were then analysed using the qualitative data analysis software ATLAS.ti. Findings Value creation through big data is an important factor for enhancing performance. It has a positive impact on both tangible (organisational performance) and intangible (societal) aspects depending on the context. Oil and gas companies understand the importance of big data to creating value in their operations. However, implementing and using big data has been problematic. In this study, a framework was developed to show that factors such as the shortage of data experts, poor data quality, the risk of cyber-attacks and unsupportive organisational cultures impede its implementation and utilisation. Research limitations/implications The findings from this study have implications for managers and executives implementing big data and creating value across various data-intensive industries. The research findings, are contextual, however, and should be applied cautiously. Originality/value This study contributes to the value creation literature in the big data context. The findings identify the key areas to be considered for the effective implementation and utilisation of big data in the oil and gas sector. This study addresses a broad but under-explored issue (i.e. knowledge creation from big data and its implementation) and strengthens the academic debate within this research stream.

Technology Focus: Wellbore Tubulars (July 2019)

Technology Focus The oil and gas industry recently has transformed itself into a data-intensive industry with the addition of artificial intelligence, intelligent or cognitive computing, machine learning, the Internet of things, and robotics. With such growth and expansion, various exponential technologies can carry the energy industry’s transformation forward in the new era of cyberautomation. Exponential technology and its adoption is based on my growth criterion, defined as Exponential Growth = (Exponential Technology)(Exponential Thinking) Exponential technologies in other industries drive the industry after the bifurcation point, depending on how the knowledge is being assimilated. Exponential thinking is called the “exponential surprise factor.” These underpinnings are observed on the tubular-mechanics side also through data analytics, machine learning, artificial intelligence, and cognitive processes. Even though we have made some inroads, we have to deal with a complex high-aspect-ratio system of length to diameter, connected from surface to bottom by way of drillstring, casing string, and tubing string that is also highly dynamic in nature. We are making advancements by changing the tubulars from dumb iron to intelligent tubulars. The complexity of tubular modeling is very specific to our industry; we deal with many uncertainties and coupled variables and not observed effects. Because of the inherently exquisite difficulties of turning modeling to action, we need a strong coupling between the data and physics-based engineering models. As we move to automation, we need a strong coupling with the tubular engineering and manufacturing through data analytics in the form of adaptive engineering summarization to control the bit from surface. This is a combination of white-box models, which are perfectly specified by previous knowledge and physical insight through laboratory tests, reliability studies, and black-box models, which are models in the absence of a priori physical knowledge or insight that will result in the downhole state. In this way, paradoxes of the tubular behavior are neutralized, speculations are suppressed, conundrums are counteracted, and engineering principles are honored so that we have empirically verifiable and practically dependable models. I have selected a few papers that are structured toward this hybrid approach that includes data mining, design improvement, and reliability. SPE 195203 Numerical Study on Casing Integrity During Hydraulic Fracturing Shale Formation by Xiaye Wu, University of Oklahoma, et al. SPE 194155 Frictional Heating of Casing Due to Drillstring Rotation—Finite Element and CFD Simulations by Wissam Assaad, Shell, et al.

Bridging the divide

PurposeThe purpose of this paper is to present an analysis of the development of a new graduate certificate course in Petroleum Data Management. The course was developed in response to an identified gap in skills and training in data management that was perceived to be a substantial risk in terms of: industry sustainability, efficiency and potentially wider implications of safety as assets are transferred between operators and for decommissioning. The aim of this paper is to critically reflect on how academia and industry can work together to support emerging professions in information management.Design/methodology/approachThe paper draws on observations and interviews from key stakeholders involved in the course development.FindingsThe course development process was ultimately successful but also challenging and lessons have been learned which will be of interest to the wider professional and academic body. These include: securing resources and industry engagement for course development, negotiating cultural differences between academic and industry and managing stakeholder relationships throughout the lifecycle of the course development.Originality/valueThe paper demonstrates the challenges and opportunities of developing a university course in collaboration with industry partners. Oil and gas exploration and production is a data-intensive industry but it was only relatively recently that attempts have been made to set industry standards and roles of “data manager” or “data analyst” have been created to manage these. This paper has wider implications for understanding the professionalisation of the nascent data management disciplines and contributes to the ongoing dialogue around the changing library and information science profession.

Understanding the scope and impact of the California Consumer Privacy Act of 2018

The California Consumer Privacy Act of 2018 (CCPA or the Act) marks a groundbreaking development in US privacy law. While the CCPA ostensibly regulates business practices involving the personal information of Californian consumers, the size, population and economic importance of California means the CCPA will be felt by businesses far beyond California’s borders. Just as Europe’s General Data Protection Regulation (GDPR) required many multinationals to conform their personal information processing to Europe’s high standards in data privacy, the CCPA is poised to have a similar effect in the USA when it goes into effect in 2020. Large companies and businesses that rely on the collection and dissemination of personal information from Californian consumers will need to begin evaluating their exposure now so as to move their operations into compliance. As a result, the CCPA will begin to have a noticeable effect on the personal information collection and disclosure practices of certain dataintensive industries. Moreover, the CCPA is likely to serve as a testing ground and catalyst for other laws pertaining to personal information and data privacy concerns. Thus, it is important to understand the impact of the CCPA in California, in the USA and globally. This paper discusses the scope and impact of the CCPA and critically evaluates the CCPA’s anticipated impact on businesses across select industries. Next, this paper contextualises the CCPA with the GDPR, identifying key similarities and differences between the two regimes. Finally, this paper looks ahead at how the CCPA may affect state and federal law on privacy and personal information protection.

Sacred Excess: Organizational Ignorance in an Age of Toxic Data

Actors in data-intensive industries at times deliberately induce and reproduce organizational ignorance by engaging in over-production of data. This observation leads the paper to make two claims. First, members of these industries fetishize data excess not in order to reduce, but in order to reproduce and stabilize organizational ignorance. Second, in this process of fetishization, organizational ignorance gives rise to forms of collective effervescence similar to that found in totemistic religions. This effervescence allows organizational actors to draw defining lines around that which is marked as awe-inspiring, dangerous and off-limits, namely the sacred. In reviewing organizational ignorance from the perspective of the sacred, this paper proposes that, paradoxically, contemporary forms of data creation allow companies and industries to organize themselves around ignorance as opposed to the promise of knowledge and insight. The paper uses this theoretical proposal in order to outline the contours of an alternative ontology of organizational ignorance, one that understands this phenomenon in terms of excessive presence of data and information.

In Search of Big Medical Data Integration Solutions - A Comprehensive Survey

In recent years, the radical advancement of technologies has given rise to an abundance of software applications, social media, and smart devices such as smartphone, sensors, and so on. More extensive use of these applications and tools in various industrial domains has led to data deluge, which has fostered enormous challenges and opportunities. However, it is not only the volume of the data but also the speed, variety, and uncertainty, which are promoting a massive challenge for traditional technologies such as data warehouse. These diverse and unprecedented characteristics have engendered the notion of “Big Data.” The data-intensive industries have been experiencing a wide variety of challenges in terms of processing, managing, and analysis of data. For instance, the healthcare sector is confronting difficulties in respect of integration or fusion of diverse medical data stemming from multiple heterogeneous sources. Data integration is critically important within the healthcare sector because it enriches data, enhances its value, and more importantly paves a solid foundation for highly efficient and effective healthcare analytics such as predicting diseases or an outbreak. Several data integration technologies and tools have been developed over the last two decades. This paper aims at studying data integration technologies, tools, and applications within the healthcare domain. Furthermore, this paper discusses future research directions in the integration of Big healthcare data.

Big Data analytics in oil and gas industry: An emerging trend

This paper reviews the utilization of Big Data analytics, as an emerging trend, in the upstream and downstream oil and gas industry. Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume, variety, velocity, veracity, value, and complexity. With the recent advent of data recording sensors in exploration, drilling, and production operations, oil and gas industry has become a massive data intensive industry. Analyzing seismic and micro-seismic data, improving reservoir characterization and simulation, reducing drilling time and increasing drilling safety, optimization of the performance of production pumps, improved petrochemical asset management, improved shipping and transportation, and improved occupational safety are among some of the applications of Big Data in oil and gas industry. Although the oil and gas industry has become more interested in utilizing Big Data analytics recently, but, there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry. Furthermore, quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.