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1
Pender, Jocelyn. "Leveraging Industry Visualization Tools for Biodiversity Science." Biodiversity Information Science and Standards 2 (May 22, 2018): e25842. https://doi.org/10.3897/biss.2.25842.
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Widespread technology usage has resulted in a deluge of data that is not limited to scientific domains. For example, technology companies accumulate vast amounts of data on their users to support their applications and platforms. The participation of many domains in big data collection, data analysis and visualization, and the need for fast data exploration has provided a stellar market opportunity for high quality data visualization software to emerge. In this talk, leading industry visualization software (Tableau) will be used to explore a biodiversity dataset (<i>Carex</i> spp. distribution and morphology). The advantages and disadvantages of using Tableau for scientific exploration will be discussed, as well as how to integrate data visualization tools early into the data pipeline. Lastly, the potential for developing a data visualization "stack" (i.e., a combination of software products and programming languages) using available tools will be discussed, as well as what the future might look like for scientists looking to capitalize on the growth of industry tools.
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Larrucea, Xabier, Micha Moffie, and Dan Mor. "Enhancing GDPR compliance through data sensitivity and data hiding tools." JUCS - Journal of Universal Computer Science 27, no. (7) (2021): 650–66. https://doi.org/10.3897/jucs.70369.
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Since the emergence of GDPR, several industries and sectors are setting informatics solutions for fulfilling these rules. The Health sector is considered a critical sector within the Industry 4.0 because it manages sensitive data, and National Health Services are responsible for managing patients’ data. European NHS are converging to a connected system allowing the exchange of sensitive information cross different countries. This paper defines and implements a set of tools for extending the reference architectural model industry 4.0 for the healthcare sector, which are used for enhancing GDPR compliance. These tools are dealing with data sensitivity and data hiding tools A case study illustrates the use of these tools and how they are integrated with the reference architectural model.
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Larrucea, Xabier, Micha Moffie, and Dan Mor. "Enhancing GDPR compliance through data sensitivity and data hiding tools." JUCS - Journal of Universal Computer Science 27, no. 7 (2021): 650–66. http://dx.doi.org/10.3897/jucs.70369.
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Since the emergence of GDPR, several industries and sectors are setting informatics solutions for fulfilling these rules. The Health sector is considered a critical sector within the Industry 4.0 because it manages sensitive data, and National Health Services are responsible for managing patients&rsquo; data. European NHS are converging to a connected system allowing the exchange of sensitive information cross different countries. This paper defines and implements a set of tools for extending the reference architectural model industry 4.0 for the healthcare sector, which are used for enhancing GDPR compliance. These tools are dealing with data sensitivity and data hiding tools A case study illustrates the use of these tools and how they are integrated with the reference architectural model.
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Eldridge, Jeanette. "Data visualisation tools—a perspective from the pharmaceutical industry." World Patent Information 28, no. 1 (2006): 43–49. http://dx.doi.org/10.1016/j.wpi.2005.10.007.
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Zazdravnykh, Aleksey V., and Elena Yu Boitsova. "Big Data as a Factor of Industry Market Entry." Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika, no. 56 (2021): 50–66. http://dx.doi.org/10.17223/19988648/56/4.
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Over the past few years, the Russian expert and scientific community has registered a significant increase in professional interest in the problem of using the capabilities of big data and algorithms for processing them as effective tools for shaping the market advantages of firms, as well as tools for limiting competition in commodity markets. At the same time, in theoretical and practical terms, this urgent issue is still very poorly studied. This article examines certain aspects of the influence of big data control on the dynamics of markets. The authors aim to develop the theory of the issue of the modem typology of entry barriers and to expand scientific ideas about new tools for limiting competition in commodity markets. The authors state that the potential of big data control as a factor that restricts the entry of new operators is manifested in the restriction of access to big data sources and the technological infrastructure of their processing, in the effects of the “positive feedback loop”, and in new opportunities for a cooperative behavior of firms. The authors are convinced that the ability of firms to qualitatively structure data and work in real time with relevant data sets is of fundamental importance for the stability of firms’ market positions today. In the development of the discussion, alternative opinions on the issue are also given. Separately, the authors discuss the effects of using big data on consumer welfare, as well as the associated privacy concerns. The authors note that the ability of firms to guarantee confidentiality in the consumption process creates new points of growth in competitiveness and new types of entry barriers.
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Pender, Jocelyn. "Leveraging Industry Visualization Tools for Biodiversity Science." Biodiversity Information Science and Standards 2 (May 22, 2018): e25842. http://dx.doi.org/10.3897/biss.2.25842.
Full textAbstract:
Widespread technology usage has resulted in a deluge of data that is not limited to scientific domains. For example, technology companies accumulate vast amounts of data on their users to support their applications and platforms. The participation of many domains in big data collection, data analysis and visualization, and the need for fast data exploration has provided a stellar market opportunity for high quality data visualization software to emerge. In this talk, leading industry visualization software (Tableau) will be used to explore a biodiversity dataset (Carex spp. distribution and morphology). The advantages and disadvantages of using Tableau for scientific exploration will be discussed, as well as how to integrate data visualization tools early into the data pipeline. Lastly, the potential for developing a data visualization "stack" (i.e., a combination of software products and programming languages) using available tools will be discussed, as well as what the future might look like for scientists looking to capitalize on the growth of industry tools.
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Nisreen Nizar Raouf and Mohammad A. Taha Aldabbagh. "Cloud Data Integration Tools As Services." Jurnal Kendali Teknik dan Sains 2, no. 3 (2024): 08–19. http://dx.doi.org/10.59581/jkts-widyakarya.v2i3.3223.
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In this paper, a variety of data integration, cloud computing, and web services-related topics were discussed. The conversation covered the advantages of using cloud-based methods for cloud-based data integration, such as enhanced data accuracy and completeness, as well as the challenges and considerations that must be addressed. In addition, the significance of document integrity and the use of auto-enhance document tools to ensure data accuracy were emphasized. The paper also provided a broad overview of the subject, touching on a variety of aspects and providing insights into the potential of cloud-based data integration methods in the cloud industry.
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Kumar, Sunil, and Maninder Singh. "Big data analytics for healthcare industry: impact, applications, and tools." Big Data Mining and Analytics 2, no. 1 (2019): 48–57. http://dx.doi.org/10.26599/bdma.2018.9020031.
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Malviya, Shreekant. "AI-Powered Data Governance for Insurance: A Comparative Tool Evaluation." International journal of data science and machine learning 05, no. 01 (2025): 280–99. https://doi.org/10.55640/ijdsml-05-01-24.
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As insurers are increasingly utilizing artificial intelligence for underwriting, pricing, and claims processing in an automated manner, end-to-end, open, and industry-level data governance solutions became the top priority. Although numerous AI-driven governance technologies are available, they are mostly purpose-built for generic corporate requirements and do not entirely meet the decision-making-oriented, ethics-conscious, and regulation-compliant insurance industry requirements. This paper presents a comparative evaluation of six top governance platforms—Collibra, Informatica CLAIRE, BigID, Immuta, IBM Watson Knowledge Catalog, and Alation—on eight dimensions, such as explainability, consent management, and insurance-specific flexibility. The research also illustrates the industry specific adoption of AI driven data governance in finance, health care and insurance along with a comparative insights amongst the three most data centric industry. The study reviews insurance governance practices to assess capability gaps in the existing available commercial tools and strategic recommendations to insurers and tech vendors. This paper provides the basis for building AI governance systems that are compatible, scalable, fair, transparent, and flexible to the specific working context of the insurance data universe by overcoming technical limitations and moral dilemmas.
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Ifrim, Ana Maria, Cătălin Ionuț Silvestru, Mihai-Alexandru Stoica, Cristina Vasilica Icociu, Ionica Oncioiu, and Marian Ernuț Lupescu. "Quality Tools and Their Applications in Industry." International Journal of Innovation in the Digital Economy 14, no. 1 (2023): 1–11. http://dx.doi.org/10.4018/ijide.325068.
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Almost all quality improvement methods require data collection and analysis to solve quality problems. The combination of six sigma and agile creates a six sigma agile methodology that aims to reach quality levels according to the Six Sigma requirements of 3.4 defects per million measurements. In order to achieve these objectives, it is necessary to know the industry well and implicitly the product or the analysed process. Thus, the correctness of these analyses depends on the collection of the data that will be analysed. The use of data analysis methods at each stage, especially in the measurement and analysis stages, is critically important for making strong decisions. The purpose of this article is to present the added value of the integration of six sigma and agile methodologies for IT projects. Thus, the integration of the two methodologies will lead to faster decision-making without the risk of an increase in the number of failures.
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Alkadi, Ihssan. "Data Mining." Review of Business Information Systems (RBIS) 12, no. 1 (2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.
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Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has led to the development of relational database systems, data modeling tools, and indexing and data organization techniques. In relational database systems data are stored in relational tables. In addition, users can get convenient and flexible access to data through query languages, optimized query processing, user interfaces and transaction management and optimized methods for On-Line Transaction Processing (OLTP). The abundant data, which needs powerful data analysis tools, has been described as a data rich but information poor situation. The fast-growing, tremendous amount of data, collected and stored in large and numerous databases. Humans can not analyze these large amounts of data. So we need powerful tools to analyze this large amount of data. As a result, data collected in large databases become data tombs. These are data archives that are seldom visited. So, important decisions are often not made based on the information-rich data stored in databases rather based on a decision maker's intuition. This is because the decision maker does not have the tools to extract the valuable knowledge embedded in the vast amounts of data. Data mining tools which perform data analysis may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research. So data mining tools will turn data tombs into golden nuggets of knowledge.
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G., Mahendran. "Business Intelligence." Shanlax International Journal of Commerce 7, S1 (2019): 261–62. https://doi.org/10.5281/zenodo.3451722.
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Business Intelligence systems combine operational data with analytic tools to present complex and competitive information to planners and decision-makers. Although business intelligence systems are widely used in industry, research about them is limited. Effective business intelligence systems must account for goal-oriented behavior and decision-makers.
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Vijay, Laxmi Kalyani, Jha Pooja, and Ameta Vaishali. "The Importance of Data Science in Technical Industry with Special Reference to Hardware/Software Industry." Journal of Management Engineering and Information Technology (JMEIT) 4, no. 2 (2017): 8. https://doi.org/10.5281/zenodo.570019.
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The data is converted into information from long time ago. Information is the key for any business to excel and overcome to competitions. Earlier the data was not so large and it was computed and converted into information. As the industry grows and with the growing need of demand and supply in comparison to increasing population of the world. Companies are strategically focusing on the information so that they can make the best strategy to win. The new flavor of the old win named as “Data Science” which is dedicated to computing data and extract the valuable information for formulating strategies. In this paper first we discuss about data science and how it is beneficial for technical industry with special reference to hardware/software industry.
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Biard, Gabrielle, and Georges Abdul Nour. "Industry 4.0 Contribution to Asset Management in the Electrical Industry." Sustainability 13, no. 18 (2021): 10369. http://dx.doi.org/10.3390/su131810369.
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Industry 4.0 has revolutionized paradigms by leading to major technological developments in several sectors, including the energy sector. Aging equipment fleets and changing demand are challenges facing electricity companies. Forced to limit resources, these organizations must question their method and the current model of asset management (AM). The objective of this article is to detail how industry 4.0 can improve the AM of electrical networks from a global point of view. To do so, the industry 4.0 tools will be presented, as well as a review of the literature on their application and benefits in this area. From the literature review conducted, we observe that once properly structured and managed, big data forms the basis for the implementation of advanced tools and technologies in electrical networks. The data generated by smart grids and data compiled for several years in electrical networks have the characteristics of big data. Therefore, it leaves room for a multitude of possibilities for comprehensive analysis and highly relevant information. Several tools and technologies, such as modeling, simulation as well as the use of algorithms and IoT, combined with big data analysis, leads to innovations that serve a common goal. They facilitate the control of reliability-related risks, maximize the performance of assets, and optimize the intervention frequency. Consequently, they minimize the use of resources by helping decision-making processes.
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Chen, Ling Prin Laksitamas. "Critical Interview Sales Data For Furniture Industry: Sales People Perspective." Multicultural Education 8, no. 8 (2022): 87. https://doi.org/10.5281/zenodo.7017130.
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<em>This study aims to identify critical sales data for furniture products from perspective of salespeople in Bangkok Metropolis – capital of Thailand. The qualitative analysis and semi-structured interview were selected as the research method for this study. After relevant literature review, a set of questions about sales data created and later a face to face and online semi-structured interview were conducted to twelve salespeople, including eleven sales staff and one sales manager who are currently working in a furniture company in Bangkok Metropolis in order to identify the critical sales data that contribute to success of a sales team. These critical sales data’s storage, analysis, management tools, application and other relevant issues discussed in the interview process were also mentioned in the paper. The interview to eleven sales staff conducted by online format through Google Forms and through sales manager interview used face-to-face semi-structured interview research method. An extensive literature review has identified extant knowledge, theories and methodologies adopted by prior researchers to gain greater understanding of sales data.</em>
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Kazakova, Nataliya. "Predictive Financial Security Analytics Using Industry Paradigm and Big Data Technology." Auditor 8, no. 12 (2023): 37–43. http://dx.doi.org/10.12737/1998-0701-2022-8-12-37-43.
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An approach to the development of an algorithm and methodological tools for predicting the financial security of companies is considered, based on the risk-based concept of international financial reporting and auditing standards, the Harvard paradigm of industry analysis, which has been developed in modern studies in the fi eld of sustainable development and risk-based methods, as well as on the use of mathematical and statistical tools and modern big data technologies in economic research. Th e practical novelty of the proposed methodological tools lies in the complexity of assessing the risks of financial solvency (risk factors of the probability of bankruptcy), which may be in demand in audit and arbitration practice.
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Dic, Michal, Miriam Pekarčíková, Jozef Trojan, and Ján Kopec. "DATA PROCESSING FOR CREATING SIMULATION MODELS." Acta Simulatio 7, no. 2 (2021): 7–11. http://dx.doi.org/10.22306/asim.v7i2.60.
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This article is devoted to the main element of the Industry 4.0 concept, which is vertical integration software that connects the necessary parts of manufacturing companies. Software that meets these criteria is required to ensure error-free bi-directional communication and data transfer between IT and OT networks. Better competitiveness technological progress is pushing the possibilities of MES. As a result, MES is becoming an integral element that takes businesses to the next level. If we imagine all activities as operations connected by computer networks and analytical tools, the result is even greater efficiency in the industry and business. Analytical tools provide useful support for customer service.
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Al Jabri, Himyar Ali, Ali H. Al-Badi, and Oualid Ali. "Exploring the Usage of Big Data Analytical Tools in Telecommunication Industry in Oman." Information Resources Management Journal 30, no. 1 (2017): 1–14. http://dx.doi.org/10.4018/irmj.2017010101.
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Big Data has recently become a very hot topic in the field of Information Technology and Data Management. Data generated by the company's daily operations through different resources such as social media, etc. is very important because it can bring a value that will lead to a competitive advantage. The objectives of this research are to: 1) Explore the analytical tools used to manipulate Big Data in Omani telecom industry, 2) Present the benefits of using these tools, the extent of use, and the features specifically promoted these tools, and 3) Highlight the challenges/obstacles that the telecom industry in Oman facing in adopting/using Big Data analytical tools. To achieve the research objectives two case studies were conducted among the main telecom operators in Oman. This research concluded that both studied telecom operators in Oman are not ready for the DBAs. Both operators need to invest in developing the capabilities that enable them to use these tools. Once that is satisfied, then other components like the infrastructure, tools, and data can be managed very well.
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Seetharaman, Arumugam, Indu Niranjan, Varun Tandon, and A. S. Saravanan. "Impact of big data on the retail industry." Corporate Ownership and Control 14, no. 1 (2016): 506–18. http://dx.doi.org/10.22495/cocv14i1c3p11.
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With the recent emergence of Big Data with its Volume, Variety and Velocity (3V’s), data analysis has emerged as a crucial area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be resolved in business organizations, including the retail industry. This study has methodically identified and analysed four factors, namely, data source, data analysis tools, financial and economic outcomes and data security and data privacy, to gauge their influence on the impact of Big Data in the retail industry. This research analyses the impact of big data analysis on retail firms that use data and business analytics to make decisions, termed a data-driven decision-making (DDD) approach. The new finding is arrived that financial and economic outcome showed a strong support and have direct relationship with data analysis tools of retail industry. Data for the study were collected using a survey of various business practices and investments in information technology by retail organizations. The data analysis showed that retail organizations which use DDD have higher output and productivity. Using SMART PLS data analysis methods with solid support of review from ISI Journals, the relationship between DDD and performance is also evident in aspects of organization such as the utilization of inventory, customer engagement and market value in the retail industry.
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Geetha, Poornima K., and Prasad K. krishna. "Data Analytics Solutions for Transforming Healthcare Information to Quantifiable Knowledge – an Industry Study with Specific Reference to ScienceSoft." International Journal of Case Studies in Business, IT, and Education (IJCSBE) 4, no. 1 (2020): 51–63. https://doi.org/10.5281/zenodo.3766907.
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Big Data Analytics (BDA) has brought revolutionary changes in many fields. The areas of application such as banking, education, manufacturing, farming, government, transport, media, and entertainment are the ones that make extensive application of BDA. Healthcare is the one that has experienced drastic changes because of BDA. Due to the positive effects of big data, risky jobs like diagnosis, reporting, CRM, predicting the deceases, tracking medical records has become much easier these days. ScienceSoft is an IT company providing information technology services in emerging areas such as CRM, Data Analytics, Collaboration, Knowledge Management, Information Security, etc. ScienceSoft's headquarters is located in McKinney, USA. Organizations such as IBM, Microsoft, Oracle, etc. are collaborating with ScienceSoft due to the reliable and high-quality services provided. This paper attempts to give a broader outlook of Big Data, analyzes the company's business models for handling Big Data Analytics related projects, particularly in the healthcare sector. This paper also contains information related to the challenges of analyzing Big Data, the Company's technologies and tools that are needed in the development of BDA projects and how Big Data is converted into useful knowledge to deliver better results in healthcare allied sectors.
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Sciortino, Giacomo P. "INDUSTRY PERSPECTIVE." International Journal of Space Technology Management and Innovation 1, no. 2 (2011): 41–46. http://dx.doi.org/10.4018/ijstmi.2011070103.
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The “Distretto Virtuale” portal is an interactive area, featuring several virtual tools, of the Italian Space Agency’s (ASI) website (http://www.asi.it)1. Its community is steadily growing and includes all of the main Italian sector’s stakeholders: business, entrepreneurial associations, research entities, central and local public authorities, financial institutions, investors, consultants, etc. Some tools have been designed to promote membership, as well as “guest” access, abroad. At a macroeconomic level the portal provides, a periodical evaluation of the sector’s trends based on direct business data and a strategic report on the “integrated finance” initiatives, which are the main ASI’s activities supported by the portal. At a microeconomic level, the portal offers its virtual tools to promote the sector’s interactions with ASI and within itself, mainly (but not only) following an “integrated finance” approach: a platform where every commercial registered subject can be located by its size, capacities, and other factors, and where its technological proposals, both as prototypes and new products, can be considered and operationally supported. Special care is given to the promotion of the portal to international users and emerging countries in particular.
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Jiang, Weiwei, and Jiayun Luo. "Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools." Applied System Innovation 5, no. 1 (2022): 23. http://dx.doi.org/10.3390/asi5010023.
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Big data have been used widely in many areas, including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted to improve the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized, and off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.
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Page, Tom. "An Analysis of the Value of Data Ecosystem Tools for Industry 4.0." International Journal of Innovation in the Digital Economy 10, no. 4 (2019): 18–32. http://dx.doi.org/10.4018/ijide.2019100102.
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Product design is a process that involves many methods and practices to be able to create “good design.” From user studies to experimentation, the designer has many tools at his disposal to understand the market and the requirements of the product they wish to produce. Big data has been a disruptor in user analysis for many organizations wishing to get the bigger picture. It has proven to have many positive implications while also being restrictive to those willing to use it. Therefore, connected open systems where all stratums are able to access similar applications have been made available. With the 4th industrial revolution underway, and the ability to utilise a plethora of sensors and electronic data provided by internet connected devices, is it in the designer's interest to adopt modern data practices? During this research, the suitability of big data practices to designers was assessed to gain an understanding of the environment that would allow designers to utilise this new platform including the practice of open data and the systems required to manage it. This article will address emerging and current technologies in the use of data within the 4th industrial revolution. Big data and open data were critically examined of their processes and downfalls compared to how the designer would use the practices.
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Viacenza, Arraz Naoval, and Andri D. Setiawan. "DEVELOPMENT OF DATA ANALYTIC TOOLS IN MANUFACTURING SYSTEMS USING DESIGN THINKING METHOD." Sustainable Environmental and Optimizing Industry Journal 4, no. 2 (2022): 95–107. http://dx.doi.org/10.36441/seoi.v4i2.1183.
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Currently, it is rare for Data analytic Tools to take a user-centric approach to their users. An approach to the user is needed by collaborating between domain experts and business managers in modeling problems, finding insights, and designing models before entering the implementation phase. The objectives of this study include identifying the needs of manufacturing industry players for the application of Data Analytic Tools in the manufacturing system and providing recommendations for the application of the Design Thinking approach in developing Data Analytic models in the Manufacturing Industry. The results of this research include the manufacturing industry has a need for Data Analytic Tools to monitor and document machine performance in real time and accurately, and can help identify errors or other abnormal situations then Design Thinking can make it easier for us to design products from various user perspectives. We can understand user needs, user motivation, and user behavior towards the products we develop. Design Thinking also offers various model frameworks that can help us understand the user.
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Syamsu, Muhajir, and Widodo Widodo. "Peran Data Science dan Data Scientist Untuk Mentransformasi Data Dalam Industri 4.0." Jurnal Teknologi Informasi (JUTECH) 2, no. 1 (2021): 27–36. http://dx.doi.org/10.32546/jutech.v2i1.1540.
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Technological developments in the era of the industrial revolution 4.0, which places the role of Data Science and Data Scientist as tools to transform data into very important data, plus the need for Data Science and Data Scientist, which is very much needed in the midst of Industry 4.0 and is able to accompany increasingly large digital data in its management. This research aims to find out how big the role of Data Science and Data Scientist in transforming data in industry 4.0. This study uses the Research Area Coverage method because this research has shifted from various fields of science, with a method design with the SISP (Strategic Information System Planning) approach using steps such as data collection, condition analysis and interpretation. The data collection technique in this research is a survey and case study approach which is conditioned by the facts or facts that occur in the field. The results of the study show that the role of Data Science and Data Scientist is very much needed in industry 4.0 to perform very large data processing, with the capabilities and skills possessed by Data Science and Data Scientist being able to accompany digital technology that develops in industry 4.0.
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Bilad, Assia, Mounia Zaim, and Faical Zaim. "Towards a Systematic Review on Industry 4.0: Big data & Internet of things." ITM Web of Conferences 46 (2022): 03004. http://dx.doi.org/10.1051/itmconf/20224603004.
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Digital technologies are occupying more and more a very important place in the industry, and more precisely with the 4th industrial revolution or what is called industry 4.0. In addition, digital transformation requires the implementation of two tools: Big data and the Internet of Things as the two starting tools, which continue to evolve gradually. Intending to explore on this area, this paper studies the literature to get a detailed understanding of Industry 4.0, as well as an overview of the two digitization tools namely big data and the Internet of Things used to improve the quality of processes in different areas. Through a systematic literature review (SLR), the study is an effort to provide an overview of existing big data and the Internet of Things in the literature and to study the existing studies to classify them by application domain and according to a developed architectural framework. The search identified 81 relevant articles. Analyses of the distribution of articles by publication year, domain, country, type, tool, and source are presented and discussed. A research agenda for future research are provided.
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Wang, Ao. "Crawler Data Visualization in the Tourism Industry Based on Python." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 74–79. http://dx.doi.org/10.54097/6xwchs92.
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Data visualization is being widely used in various fields, such as medicine, biology, economics, and other disciplines. In the tourism industry, whether the display of tourism resources, the planning of tourism routes, the feedback of tourists' experience, and so on, all of them become clearer and more intuitive through data visualization. In particular, Python-based data visualization has many advantages in the tourism industry. This paper mainly analyzes the current situation and advantages of Python-based data visualization in the tourism industry, and puts forward corresponding improvement measures and suggestions, aiming to help tourism practitioners and tourists use data visualization more conveniently and efficiently. It is found that data visualization plays an important role in displaying tourist attractions, planning tourist routes, and enhancing the experience of tourism services. However, the problems of data visualization in tourism, such as insufficient interactivity and single visualization style, require improvement measures. For example, methods such as optimizing algorithms and introducing interactive visualization tools can solve these problems. The purpose of this study is to promote a wider and more effective application of data visualization in the tourism industry so that tourism-related industries can be more efficient and intuitive when using data visualization tools.
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Habib, Qasim, Hafiz Muhammad Khurram Ali, and Syed Muhammad Owais. "Sustainability Assessment of Quality 4.0 Tools in Telecom Industry." International Journal of Advanced Natural Sciences and Engineering Researches 7, no. 5 (2023): 100–104. http://dx.doi.org/10.59287/ijanser.909.
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The fourth industrial revolution, also commonly known as Industry 4.0, has brought about significant advancements in areas such as connection, mobility, analytics, scalability, and data, which have resulted in a complete transformation of the service and manufacturing industry. Quality 4.0 is an approach that integrates technology and data into quality management systems while also retaining traditional methods. This approach enhances value by facilitating modifications through culture, leadership, and collaboration. This paper aims to evaluate the sustainability and effectiveness of Quality 4.0 tools in the telecom industry by utilizing advanced Multi-Criteria Decision-Making (MCDM) techniques. The study seeks to analyze the impact of Quality 4.0 tools on both the quality management system and the overall sustainability of a telecom company through a combination of quantitative and qualitative data collection methods. The study has identified four crucial parameters of sustainability that significantly impact the sustainability evaluation of Quality 4.0 tools. It is focused on the telecom industry, specifically within the quality department, and identifies three potential tools for Quality 4.0 that are considered alternatives within the MCDM method. To effectively evaluate the effectiveness of these tools, the study utilizes the Analytic Hierarchy Process (AHP) techniques of MCDM. The results of this study provide a comprehensive overall ranking of the alternative tools, while a sensitivity analysis based on sustainability criteria demonstrates that the ranking of the alternatives can change based on specific sustainability factors. This groundbreaking research sheds invaluable light on the sustainability and effectiveness of Quality 4.0 hybrid tools in the telecom industry, providing valuable insights and practical recommendations for organizations considering their adoption.
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Ashraf, Waqar Muhammad, Ghulam Moeen Uddin, Muhammad Farooq, et al. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics." Energies 14, no. 5 (2021): 1227. http://dx.doi.org/10.3390/en14051227.
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Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator’s power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management.
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TIEN NGUYEN, Van Thanh, and Nhut Thi MINH VO. "Industrial Engineering and Management Applications: Evaluation of Data Integration Tools for Smart Manufacturing." Eurasia Proceedings of Science Technology Engineering and Mathematics 29 (December 15, 2024): 245–53. https://doi.org/10.55549/epstem.1566612.
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This study comprehensively evaluates leading data integration tools for smart manufacturing environments using a hybrid Analytic Hierarchy Process (AHP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology. As Industry 4.0 drives increased automation and data exchange in manufacturing processes, selecting appropriate data integration tools has become critical yet complex. We assessed seven prominent data integration tools across 24 criteria grouped into six main categories: functionality, vendor-related factors, user experience, cost, reliability, and flexibility. Our data collection was informed by expert interviews, vendor documentation analysis, user reviews, and benchmark testing. The AHP analysis revealed functionality and data integration features as the most crucial criteria (weight: 0.2493), followed by user-related factors (0.1814). The VIKOR method then ranked the tools, with Oracle Data Integrator emerging as the top performer (Q=0.0000), followed by Informatica PowerCenter (Q=0.22391). Our findings highlight the importance of cloud-native solutions and user experience in industrial data integration. This research contributes a robust framework for evaluating data integration tools in imaginative manufacturing contexts and offers insights to guide decision-making in Industry 4.0 initiatives.
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Langen, T., K. Falk, and M. Mansouri. "A Systems Thinking Approach to Data-Driven Product Development." Proceedings of the Design Society 2 (May 2022): 1915–24. http://dx.doi.org/10.1017/pds.2022.194.
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AbstractThe amount of information in our society and its opportunities have given rise to Big Data research. The systems supplier industry needs suitable tools and methods to ensure the harvest and utilization of Big Data in their product development. This paper used Systems Thinking to analyze the current state in the industry and suggested leverage points for further research direction. The findings suggest that the research project should emphasize the industry cases, the collaboration between the companies and academia, develop a Big Data systems architecture, and maintain a socio-technical view.
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Sazu, Mesbaul, and Sakila Jahan. "How Big Data Analytics is transforming the finance industry." Bankarstvo 51, no. 2 (2022): 147–72. http://dx.doi.org/10.5937/bankarstvo2202147h.
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The data revolution happening throughout the world has brought transformation in the financial services sector. This vast information has opened doors to understanding the needs of the customers, finding insights, and lowering risks. In addition, it helps the financial services industry to take actions to improve clients' satisfaction at a faster rate than it was previously possible. Financial firms can develop new insights using BD because they can now collect a large volume of data about their customers, their spending pattern, and provide services that are beneficial, convenient, and quick for the customers. They can expand the use of those insights not only for their consumers but also for their internal process optimization, benefiting everyone in the process. While the impact of BDA on financial service companies is ubiquitous, not so many studies have been published to understand which aspects of the financial services industry could greatly benefit from the rise of technology and BDA. Few published studies address the challenges faced by banks in this technology era if they do not have BD tools implemented. This research covers data from banks from January 2019 to January 2022 to address that gap of a several banks from America and Europe that faced declining customer satisfaction. It uncovers the best methods used by financial firms globally to implement BDA to improve the services. This paper will also look at how BDA has been successfully used in the banking industry, regarding the following elements: consumer behavior, channels use, consumer spending pattern and profile creation, product cross-selling based upon user-profiling, analysis of feedback and sentiment, management of secure transactions, and fraud etc. This study helps find out and makes contributions on how the financial services industry, such as banks, could leverage BDA and provide superior services. Further research could be conducted across other players in the finance industry to learn about how they are impacted by the BDA.
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Tiwari, Pradeep Kumar, Sunil Kr Pandey, W. Thamba Meshach, et al. "Improved Data Security in Cloud Environment for Test Automation Framework and Access Control for Industry 4.0." Wireless Communications and Mobile Computing 2022 (May 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/3242092.
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In analyzing project regressions, automation has emerged as a major agenda in managing changes in software which requires minimum manual intervention. For rapid testing environment, software development processes such as Agile, Scrum, and XP processes depend on continuous integration tools. There is no single tool to handle the project automation, and the main challenge is dependency on multiple tools. The proposed automation tool should support configuration, execution, and debugging facility. Integrating the project automation works such as software configuration management tools Mercurial and Git, job scheduling tools like Jenkins and Apache Continuum, test management tools like TestNG and Selenium need tight integration which is a challenge. The existing PKI infrastructure for access control does not share data among the software tools and processes increasing the complexity when an organization needs to leverage the existing cloud services. The proposed approach optimizes the execution time by taking single CSV with input test case and metadata information and efficiently group and executes the tests automatically. The proposed method includes implementation of security access control mechanism for the jobs execution platform in cloud environment.
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Dr., Rupesh Rastogi, and Kumar Singh Vivek. "The Consumer perception of Information Technology Tools in Organized Retail Industry: A study." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 06 (2018): 435–40. https://doi.org/10.5281/zenodo.1293983.
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Retail industry in general is one of the fastest growing sectors of the Indian Economy. Initially in India retailing was mostly unorganized. Indian Government was reluctant for opening the Indian retail sector for FDI. However in the year 1997 Indian Government allowed foreign direct investment in cash and carry wholesale and as such it paved the way for emergence of modern organized retail in India. Presently organized retail accounts for approx 5-6 % of the total retail revenue. Present retail industry operates on very thin margin and it is very much desirable that the resources of retail are optimized. Globalization of point of supply and point of sale generates tremendous amount of data, the manual processing of which is very difficult. Real time information is needed by almost all partners of retail like line managers, sales personnel and store managers and it can only be achieved by the use of IT tools. Information technology solutions are presently being used in various aspects of retail like finance and accounting, product display; data processing and analysis, radio trolleys, electronic labels, Radio Frequency Identification and customer relationship management etc. Further IT tools are also required to give customers a better experience. The present paper is an attempt to know the perception of organized retail customers of Indian State, Uttar Pradesh, regarding the application of IT tools in retail. For this purpose IT paraphernalia which are most frequently encountered by the customers have been selected for the study. The study was conducted in Kanpur, Allahabad, Varanasi and Agra (KAVAL) town of Uttar Pradesh. The data generated has been analyzed by statistical tools. The result of paper provides an insight of customers liking and satisfaction regarding various IT tools in retail. The result of the paper is useful for retail professionals, academicians, common man and in designing further IT assistance in retail.
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MORIMOTO, Junki, and Hideho TANAKA. "0817 Strength and Weakness Analysis of Machine Tools Industry : Evidence from patent data." Proceedings of Conference of Hokuriku-Shinetsu Branch 2012.49 (2012): 081701–2. http://dx.doi.org/10.1299/jsmehs.2012.49.081701.
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Sheeraz, Muhammad, Muhammad Sajid, Amjad Shahzad Gondal, and Yasir Mehmood. "Brand Awareness and Digital Marketing: Measurement Tools and Data Analytics for Agricultural Industry." Journal of Arable Crops and Marketing 5, no. 1 (2023): 35–48. http://dx.doi.org/10.33687/jacm.005.01.5047.
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The current era has been going through significant transformations owing to the innovative technologies, digitalization, social media, and online business trends worldwide. In the rapid and dynamic environment, marketers are continuously focused on exploring new and digital ways to reach and engage customers in the agricultural industry. Therefore, digital marketing has been recognized as a critical tool for marketers, researchers, and customers to develop brand awareness, interact and build customer engagement over longer periods. Consequently, generating positive customer outcomes i.e., brand awareness, persuasion, customer satisfaction, brand loyalty, and positive word of mouth. The study aimed to evaluate the impact of digital marketing tools on the brand awareness of consumers in agricultural industry. By examining diverse social media channels, the study aimed to provide an analysis of quantitative techniques used to assess the effectiveness of digital marketing. The study furnishes insights for the practitioners to select effective social media tools according to their goal and objectives. The study enables social media marketers to optimize their digital marketing strategies by utilizing minimum organizational resources.
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Langner, Christopher, Yevgeni Paliyenko, Benedikt Müller, Daniel Roth, Matthias R. Guertler, and Matthias Kreimeyer. "Challenges for capturing data within data-driven design processes." Proceedings of the Design Society 4 (May 2024): 2099–108. http://dx.doi.org/10.1017/pds.2024.212.
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AbstractCyber-Physical-Systems provide extensive data gathering opportunities along the lifecycle, enabling data-driven design to improve the design process. However, its implementation faces challenges, particularly in the initial data capturing stage. To identify those, a comprehensive approach combining a systematic literature review and an industry survey was applied. Four groups of interrelated challenges were identified as most relevant to practitioners: data selection, data availability in systems, knowledge about data science processes and tools, and guiding users in targeted data capturing.
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Castro, Hélio, Filipe Costa, Tânia Ferreira, et al. "Data Science for Industry 4.0 and Sustainability: A Survey and Analysis Based on Open Data." Machines 11, no. 4 (2023): 452. http://dx.doi.org/10.3390/machines11040452.
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In the last few years, the industrial, scientific, and technological fields have been subject to a revolutionary process of digitalization and automation called Industry 4.0. Its implementation has been successful mainly in the economic field of sustainability, while the environmental field has been gaining more attention from researchers recently. However, the social scope of Industry 4.0 is still somewhat neglected by researchers and organizations. This research aimed to study Industry 4.0 and sustainability themes using data science, by incorporating open data and open-source tools to achieve sustainable Industry 4.0. To that end, a quantitative analysis based on open data was developed using open-source software in order to study Industry 4.0 and sustainability trends. The main results show that manufacturing is a relevant value-added activity in the worldwide economy; that, foreseeing the importance of Industry 4.0, countries in America, Asia, Europe, and Oceania are incorporating technological principles of Industry 4.0 in their cities, creating so-called smart cities; and that the industries that invest most in technology are computers and electronics, pharmaceuticals, transport equipment, and IT (information technology) services. Furthermore, the G7 countries have a prevalent positive trend for the migration of technological and social skills toward sustainability, as it relates to the social pillar, and to Industry 4.0. Finally, on the global scale, a positive correlation between data openness and happiness was found.
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Usmani, Raja Sher Afgun, Ibrahim Abaker Targio Hashem, Thulasyammal Ramiah Pillai, Anum Saeed, and Akibu Mahmoud Abdullahi. "Geographic Information System and Big Spatial Data." International Journal of Enterprise Information Systems 16, no. 4 (2020): 101–45. http://dx.doi.org/10.4018/ijeis.2020100106.
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Geographic information system (GIS) is designed to generate maps, manage spatial datasets, perform sophisticated “what if” spatial analyses, visualize multiple spatial datasets simultaneously, and solve location-based queries. The impact of big data is in every industry, including the GIS. The location-based big data also known as big spatial data has significant implications as it forces the industry to contemplate how to acquire and leverage spatial information. In this study, a comprehensive taxonomy is created to provide a better understanding of the uses of GIS and big spatial data. The taxonomy is made up of big data technologies, GIS data sources, tools, analytics, and applications. The authors look into the importance of big spatial data and its implications, review the data sources, and GIS analytics, applications, and GIS tools. Furthermore, in order to guide researchers interested in GIS, the challenges that require substantial research efforts are taken into account. Lastly, open issues in GIS that require further observation are summarized.
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Saraswathi, Dr M., and S. Hruthik Kasyap. "Data Visualization: From Raw Data to Actionable Insight." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39340.
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Information age data is the backbone of industry decision-making. Raw data, however, lacks accessibility without effective visualization techniques to make it accessible and actionable. Data visualization takes complicated data sets and converts them into intuitive, visual formats, and using this, businesses can discover trends, correlations, and insights that guide informed decisions. This paper explores the process from raw data to actionable insights through data visualization, in terms of significance, core techniques, tools, and applications across multiple sectors. It further mentions best practices and challenges to ensure that the visual representation is accurate and meaningful.
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Abu-Abed, Fares. "Simulation Tools for Transport Monitoring Systems in the Mining Industry." E3S Web of Conferences 278 (2021): 01017. http://dx.doi.org/10.1051/e3sconf/202127801017.
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This paper presents a computer modeling approach for monitoring a fleet of mining machines based on a software solution for traffic modeling. Computer simulations can help reduce prototyping costs and reduce the risk of initial launch failure by analyzing and tuning a prototype to test the most appropriate options. Using a computer modeling approach, we show in the first part of the article that the resulting vehicle monitoring metrics can be tested during the modeling process, instead of adding equipment to vehicles during the prototyping phase. Using real equipment in the prototype phase increases fleet downtime and decreases productivity. Using modern solutions for storing time series, we show how easy it is to analyze the data obtained as a result of modeling. In the second part of the article, we propose a workflow for integrating SUMO with a time series data warehouse through a software interface (API) called TraCI, which allows you to aggregate and visualize vehicle fleet data over time. At the end of this work, we discuss the measurement methodology and propose a potential solution for efficient data transmission.
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Castañeda-Hipólito, M., K. Cruzado-Yesquén, S. Gastiaburú-Morales, et al. "The fourth industrial revolution in South America: a bibliometric study with data mining tools." Journal of Physics: Conference Series 2726, no. 1 (2024): 012010. http://dx.doi.org/10.1088/1742-6596/2726/1/012010.
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Abstract The fourth industrial revolution has boosted theory and technology up to the point of replicating human intelligence. Humankind generates enormous amounts of data that need to be stored, systematized, and applied to solve problems that improve daily life. This industrial revolution, also known as Industry 4.0, meets said need through practical applications in numerous fields such as medicine, commerce, robotics, transportation, tourism, and others. This study used the methodology of bibliometric analysis by using the Scopus database, aiming to evaluate the use of Industry 4.0 in South American countries in the last decade and its contribution to physics through the application of data mining tools. RStudio’s Biblioshiny software and VOSviewer were used to categorize and evaluate the contributions of some authors and countries. As a result, the use of remote sensing and machine learning technologies was found to be the most relevant. This bibliometric study provides a recent vision of Industry 4.0 to encourage its use for future research in South American countries.
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Dolzhikov, Dmitry, Rodriguez Romero Sol Alejandra, Martinez Terano Angel Francisco, Sandoval Ramos by Andrea Alexandra, and Zhou Xiaopeng. "Applications of the digital economics tools in the spatial industry." E3S Web of Conferences 389 (2023): 09009. http://dx.doi.org/10.1051/e3sconf/202338909009.
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The subject of the research is the digital economics tools applied in spatial industry and enterprises in the context of integration into the digital economy. The process of digitalization of economy and society, the increase of information volumes, data complexity and diversity of data sources with the parallel increase in the value of information create the need for essentially new methods, and tools of managing the spatial industry. The aim of the research was to study the tools of the digital economy in terms of their contribution to the efficient functioning of the space industry in the Russian Federation. The research is supported by the need to introduce new economic tools that ensure the digitization process in the space industry. The introduction of digital technologies and tools of the digital economy in companies generates the need to take into account the specific characteristics of production processes, the existing technical infrastructure, the composition and structure of production facilities, as well as the training of personnel. The paper concludes that the introduction of digital economy tools in the space industry is essential for the optimization of production processes and adaptation to the new digital era in this industry, as well as the development of it.
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Deepika, Rikhi. "Optimizing workload management: Human Services Perspective." Journal of Scientific and Engineering Research 8, no. 3 (2021): 275–77. https://doi.org/10.5281/zenodo.13757918.
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This paper delves into the crucial topic of optimizing workload management systems in the human services industry. It addresses key challenges such as the lack of an integrated system, backlog management, complexity, and task prioritization. The paper offers practical recommendations, including prioritizing backlogged work, leveraging unified data platforms, harnessing business intelligence tools for informed decision-making, and utilizing business process intelligence tools to streamline operations. By implementing these recommendations, social agencies can significantly enhance efficiency, improve decision-making processes, and ultimately achieve better outcomes in workload management.
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Esteban-Santos, Laura, Irene García Medina, Lindsey Carey, and Elena Bellido-Pérez. "Fashion bloggers: communication tools for the fashion industry." Journal of Fashion Marketing and Management: An International Journal 22, no. 3 (2018): 420–37. http://dx.doi.org/10.1108/jfmm-10-2017-0101.
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Purpose The purpose of this paper is to investigate fashion blogs’ influence on Spanish Millennials’ buying behaviour. Design/methodology/approach This research is quantitative in nature, utilising a mono method consisting of structured self-administered questionnaires. Data were exported to IBM SPSS Statistics, where different types of analyses were combined – such as frequencies, means, hypothesis testing analyses, principal components analysis or K-means cluster. Findings Findings show that the most important motivations to follow a fashion blog are entertainment and information seeking. Besides, consumers’ attitudes seem to be influenced by how consumers assess credibility, which is determined by trustworthiness, para-social interaction (PSI), expertise and message credibility. Finally, after showing covert and overt marketing posts, both trustworthiness and PSI were lower than before, identifying PSI as a possible moderator in these cases. Research limitations/implications The main limitation of this study is the sample size, which does not make it possible to generalise conclusions. Practical implications From this research, it can be said that, due to the importance of establishing a strong relationship with the public, bloggers should try to connect with readers on an emotional level, and brands need to select bloggers very carefully. Originality/value This paper reveals Millennials’ attitudes whilst they are visiting a fashion blog and the influence that these attitudes can exercise on their purchase intention.
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Gelfond, Daniil V. "Data Security in Digital Ports: Challenges and Possible Solutions." Общество: политика, экономика, право, no. 12 (December 20, 2023): 124–31. http://dx.doi.org/10.24158/pep.2023.12.15.
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The article presents the results of analyzing the dynamics of growth of cyberattacks on the maritime logistics industry, which allowed to identify the key aspects of digital vulnerability of this industry. The relevance is due to the annual increase in the number of cyberattacks on the activities of digital seaports and the growing volume of economic losses from the occurrence of such events. The classification of threats to the digital environment was performed, the causes and potential economic consequences of these threats for the main participants of the logistics chain, including port operators and carriers, were clarified. The main digital tools aimed at reducing cyber vulnerability of digital ports and reducing economic losses are systematized and analyzed. It is conclud-ed that it is necessary and economically feasible to further develop and implement digital tools in the field of maritime logistics to improve its security and efficiency.
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Arruda, Helder Moreira, Rodrigo Simon Bavaresco, Rafael Kunst, Elvis Fernandes Bugs, Giovani Cheuiche Pesenti, and Jorge Luis Victória Barbosa. "Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy." Sensors 23, no. 11 (2023): 5010. http://dx.doi.org/10.3390/s23115010.
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The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field.
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Samaskani, Samsudeen S. "The Shrewd Provoke of DATA Analytics in Automotive Industry." ECS Transactions 107, no. 1 (2022): 7299–313. http://dx.doi.org/10.1149/10701.7299ecst.
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Big Data is taking the world by storm. With the huge amounts of data emanating from various digital sources the importance of analytics has tremendously grown making the companies to tap the dark data that was considered useless all these years (2). Big Data tools and Technologies help the companies to interpret the huge amount of data very faster which helps to boost production. So, Big data applications are creating a new era in every industry (3). Transportation is a vital part of society and our everyday lives, so ensuring that our drives are safe is a top priority. Advancement in technology such as phone apps, black boxes, smart cars and so on, are all designed to take the human error factor out of driving and should help to enhance ultimate road safety. The Proposed system focuses on novel approach to provide enhanced road safety mechanism using Data Analytics.
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Zia, Amjad, Muzzamil Aziz, Ioana Popa, Sabih Ahmed Khan, Amirreza Fazely Hamedani, and Abdul R. Asif. "Artificial Intelligence-Based Medical Data Mining." Journal of Personalized Medicine 12, no. 9 (2022): 1359. http://dx.doi.org/10.3390/jpm12091359.
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Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.
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Noor, Ahmed. "Big Data." Mechanical Engineering 135, no. 10 (2013): 32–37. http://dx.doi.org/10.1115/1.2013-oct-1.
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This article reviews the benefits of Big Data in the manufacturing industry as more sophisticated and automated data analytics technologies are being developed. The challenge of Big Data is that it requires management tools to make sense of large sets of heterogeneous information. A new wave of inexpensive electronic sensors, microprocessors, and other components enables more automation in factories, and vast amounts of data to be collected along the way. In automated manufacturing, Big Data can help reduce defects and control costs of products. Smart manufacturing is expected to evolve into the new paradigm of cognitive manufacturing, in which machining and measurements are merged to form more flexible and controlled environments. The article also suggests that the emerging tools being developed to process and manage the Big Data generated by myriads of sensors and other devices can lead to the next scientific, technological, and management revolutions. The revolutions will enable an interconnected, efficient global industrial ecosystem that will fundamentally change how products are invented, manufactured, shipped, and serviced.