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Journal articles on the topic 'ANALYZE BIG DATA'

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Published: 11 September 2023

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1

Dhar, Vasant. "Can Big Data Machines Analyze Stock Market Sentiment?" Big Data 2, no. 4 (December 2014): 177–81. http://dx.doi.org/10.1089/big.2014.1528.

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2

Venkateswara Reddy, R., and Dr D. Murali. "Analyzing Indian healthcare data with big data." International Journal of Engineering & Technology 7, no. 3.29 (August 24, 2018): 88. http://dx.doi.org/10.14419/ijet.v7i3.29.18467.

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Big Data is the enormous amounts of data, being generated at present times. Organizations are using this Big Data to analyze and predict the future to make profits and gain competitive edge in the market. Big Data analytics has been adopted into almost every field, retail, banking, governance and healthcare. Big Data can be used for analyzing healthcare data for better planning and better decision making which lead to improved healthcare standards. In this paper, Indian health data from 1950 to 2015 are analyzed using various queries. This healthcare generates the considerable amount of heterogeneous data. But without the right methods for data analysis, these data have become useless. The Big Data analysis with Hadoop plays an active role in performing significant real-time analyzes of the enormous amount of data and able to predict emergency situations before this happens.
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3

Li, Ruowang, Dokyoon Kim, and Marylyn D. Ritchie. "Methods to analyze big data in pharmacogenomics research." Pharmacogenomics 18, no. 8 (June 2017): 807–20. http://dx.doi.org/10.2217/pgs-2016-0152.

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4

Ahmed, Waseem, and Lisa Fan. "Analyze Physical Design Process Using Big Data Tool." International Journal of Software Science and Computational Intelligence 7, no. 2 (April 2015): 31–49. http://dx.doi.org/10.4018/ijssci.2015040102.

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Physical Design (PD) Data tool is designed mainly to help ASIC design engineers in achieving chip design process quality, optimization and performance measures. The tool uses data mining techniques to handle the existing unstructured data repository. It extracts the relevant data and loads it into a well-structured database. Data archive mechanism is enabled that initially creates and then keeps updating an archive repository on a daily basis. The logs information provide to PD tool is a completely unstructured format which parse by regular expression (regex) based data extraction methodology. It converts the input data into the structured tables. This undergoes the data cleansing process before being fed into the operational DB. PD tool also ensures data integrity and data validity. It helps the design engineers to compare, correlate and inter-relate the results of their existing work with the ones done in the past which gives them a clear picture of the progress made and deviations that occurred. Data analysis can be done using various features offered by the tool such as graphical and statistical representation.
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Zhang, Yucheng Eason, Siqi Liu, Shan Xu, Miles M. Yang, and Jian Zhang. "Integrating the Split/Analyze/Meta-Analyze (SAM) Approach and a Multilevel Framework to Advance Big Data Research in Psychology." Zeitschrift für Psychologie 226, no. 4 (October 2018): 274–83. http://dx.doi.org/10.1027/2151-2604/a000345.

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Abstract. Though big data research has undergone dramatic developments in recent decades, it has mainly been applied in disciplines such as computer science and business. Psychology research that applies big data to examine research issues in psychology is largely lacking. One of the major challenges regarding the use of big data in psychology is that many researchers in the field may not have sufficient knowledge of big data analytical techniques that are rooted in computer science. This paper integrates the split/analyze/meta-analyze (SAM) approach and a multilevel framework to illustrate how to use the SAM approach to address multilevel research questions with big data. Specifically, we first introduce the SAM approach and then illustrate how to implement this to integrate two big datasets at the firm level and country level. Finally, we discuss theoretical and practical implications, proposing future research directions for psychology scholars.
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Sp, Syedibrahim sp. "Big Data Analytics Framework to Analyze Student’s Performance." International Journal of Computational Complexity and Intelligent Algorithms 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijccia.2018.10021266.

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7

Gogołek, Włodzimierz. "Refining Big Data." Bulletin of Science, Technology & Society 37, no. 4 (December 2017): 212–17. http://dx.doi.org/10.1177/0270467619864012.

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Refining big data is a new multipurpose way to find, collect, and analyze information obtained from the web and off-line information sources about any research subject. It gives the opportunity to investigate (with an assumed level of statistical significance) the past and current status of information on a subject, and it can even predict the future. The refining of big data makes it possible to quantitatively investigate a wide spectrum of raw information on significant human issues—social, scientific, political, business, and others. Refining creates a space for new, rich sources of information and opens innovative ways for research. The article describes a procedure for refining big data and gives examples of its use.
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8

Liu, Xin Xing, Xing Wu, and Shu Ji Dai. "The Paradoxes of Big Data." Applied Mechanics and Materials 743 (March 2015): 603–6. http://dx.doi.org/10.4028/www.scientific.net/amm.743.603.

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The era of Big Data poses a big challenge to our way of living and thinking. Big Data refers to things which can do at a large scale but cannot be done at a smaller size. There are many paradoxes of Big Data: In this new world far more data can be analyzed, though using all the data can make the datum messy and lose some accuracy, sometimes reach better conclusions. As massive quantities of information produced by and about people and their interactions exposed on the Internet, will large scale search and analyze data help people create better services, goods and tools or it just lead to privacy incursions and invasive marketing? In this article, we offer three main provocations, based on our analysis we have constructed some models to help explain the amazing contradiction in Big Data.
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9

Valdez, Alicia, Griselda Cortes, Laura Vazquez, Adriana Martinez, and Gerardo Haces. "Big Data Analysis Proposal for Manufacturing Firm." European Journal of Electrical Engineering and Computer Science 5, no. 1 (February 15, 2021): 68–75. http://dx.doi.org/10.24018/ejece.2021.5.1.298.

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The analysis of large volumes of data is an important activity in manufacturing companies, since they allow improving the decision-making process. The data analysis has generated that the services and products are personalized, and how the consumption of the products has evolved, obtaining results that add value to the companies in real time. In this case study, developed in a large manufacturing company of electronic components as robots and AC motors; a strategy has been proposed to analyze large volumes of data and be able to analyze them to support the decision-making process; among the proposed activities of the strategy are: Analysis of the technological architecture, selection of the business processes to be analyzed, installation and configuration of Hadoop software, ETL activities, and data analysis and visualization of the results. With the proposed strategy, the data of nine production factors of the motor PCI boards were analyzed, which had a greater incidence in the rejection of the components; a solution was made based on the analysis, which has allowed a decrease of 28.2% in the percentage of rejection.
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10

Raich, Vivek, and Pankaj Maurya. "Analytical Study on Big Data." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 75. http://dx.doi.org/10.23956/ijarcsse.v8i5.668.

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in the time of the Information Technology, the big data store is going on. Due to which, Huge amounts of data are available for decision makers, and this has resulted in the progress of information technology and its wide growth in many areas of business, engineering, medical, and scientific studies. Big data means that the size which is bigger in size, but there are several types, which are not easy to handle, technology is required to handle it. Due to continuous increase in the data in this way, it is important to study and manage these datasets by adjusting the requirements so that the necessary information can be obtained.The aim of this paper is to analyze some of the analytic methods and tools. Which can be applied to large data. In addition, the application of Big Data has been analyzed, using the Decision Maker working on big data and using enlightened information for different applications.
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11

Liu, Xiaolun. "Local Government Governance Path Optimization Based on Multisource Big Data." Mathematical Problems in Engineering 2022 (June 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/1941558.

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With the development of Internet technology, multisource big data can collect and analyze information so as to provide people with a good vision. In the process of governance, local governments will have problems of incomplete information. With the development of big data, multisource and big data will have advanced nature. Therefore, based on multisource big data, this paper analyzes the multisource big data algorithm in detail and establishes a local government governance model based on multisource big data. Then, the proposed model is applied to the local government governance process of Beijing, Shanghai, Chongqing, and Tianjin, and the local governance situation of each city is compared and analyzed so as to provide some reference for the optimization of the local government governance path. The experimental results show that the local governance model based on multisource big data can optimize the local government governance path and point out the direction for the local government governance path.
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12

Bakare, Archana, and Rajesh Argiddi. "To Study, Analyze and predict the Diseases using Big Data." International Journal of Computer Applications 165, no. 7 (May 17, 2017): 17–19. http://dx.doi.org/10.5120/ijca2017913917.

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13

Ha, Ilkyu, Bonghyun Back, and Byoungchul Ahn. "MapReduce Functions to Analyze Sentiment Information from Social Big Data." International Journal of Distributed Sensor Networks 11, no. 6 (January 2015): 417502. http://dx.doi.org/10.1155/2015/417502.

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14

Zhou, Jiangping, Yuling Yang, and Chris Webster. "Using Big and Open Data to Analyze Transit-Oriented Development." Journal of the American Planning Association 86, no. 3 (April 13, 2020): 364–76. http://dx.doi.org/10.1080/01944363.2020.1737182.

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15

Courtney, Kyle, Rachael Samberg, and Timothy Vollmer. "Big data gets big help: Law and policy literacies for text data mining." College & Research Libraries News 81, no. 4 (April 9, 2020): 193. http://dx.doi.org/10.5860/crln.81.4.193.

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A wealth of digital texts and the proliferation of automated research methodologies enable researchers to analyze large sets of data at a speed that would be impossible to achieve through manual review. When researchers use these automated techniques and methods for identifying, extracting, and analyzing patterns, trends, and relationships across large volumes of un- or thinly structured digital content, they are applying a methodology called text data mining or TDM. TDM is also referred to, with slightly different emphases, as “computational text analysis” or “content mining.”
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16

Shan, Biaoan, Xiaoju Liu, Yang Gao, and Xifeng Lu. "Big Data in Entrepreneurship." Journal of Organizational and End User Computing 34, no. 8 (March 22, 2022): 1–19. http://dx.doi.org/10.4018/joeuc.310551.

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Entrepreneurship research is paying increasing attention to big data. However, there is only a fragmented understanding on how big data influences entrepreneurial activities. To review previous research systematically and quantitatively, the authors use bibliometrics method to analyze 164 research articles on big data in entrepreneurship. They visualize the landscape of these studies, such as publication year, country, and research area. They then use VOSviewer to conduct theme clustering analysis, finding four themes, namely the COVID-19 pandemic and small medium enterprise (SME) digitization, application of big data analytics to decision making, application of big data in platform, and the effects of big data on enterprises. In addition, they construct an integrated framework that integrates the antecedents of big data adoption and influence mechanism of big data on entrepreneurial activities.
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17

Bohár, Balázs, David Fazekas, Matthew Madgwick, Luca Csabai, Marton Olbei, Tamás Korcsmáros, and Mate Szalay-Beko. "Sherlock: an open-source data platform to store, analyze and integrate Big Data for biology." F1000Research 10 (May 21, 2021): 409. http://dx.doi.org/10.12688/f1000research.52791.1.

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In the era of Big Data, data collection underpins biological research more so than ever before. In many cases this can be as time-consuming as the analysis itself, requiring downloading multiple different public databases, with different data structures, and in general, spending days before answering any biological questions. To solve this problem, we introduce an open-source, cloud-based big data platform, called Sherlock (https://earlham-sherlock.github.io/). Sherlock provides a gap-filling way for biologists to store, convert, query, share and generate biology data, while ultimately streamlining bioinformatics data management. The Sherlock platform provides a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to analyse, process, query and extract the information from extremely complex and large data sets. Furthermore, Sherlock is capable of handling different structured data (interaction, localization, or genomic sequence) from several sources and converting them to a common optimized storage format, for example to the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and easily execute distributed analytical queries on extremely large data files as well as share datasets between teams. The Sherlock platform is freely available on Github, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users are able to easily and quickly create and work with the specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, data analytics, data integration and collaboration through modern big data technologies.
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18

Mouyassir, Kawtar, Mohamed Hanine, and Hassan Ouahmane. "Business Intelligence Model to analyze Social Media through Big Data analytics." SHS Web of Conferences 119 (2021): 07006. http://dx.doi.org/10.1051/shsconf/202111907006.

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Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.
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19

Mathias Kalema, Billy, and Motau Mokgadi. "Developing countries organizations’ readiness for Big Data analytics." Problems and Perspectives in Management 15, no. 1 (May 11, 2017): 260–70. http://dx.doi.org/10.21511/ppm.15(1-1).2017.13.

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Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations’ readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees’ e-skills, organization’s customers’ and vendors are significant factors for organizations’ readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.
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20

Hirve, Sumit Arun, and Pradeep Reddy C. H. "Improving Big Data Analytics With Interactive Augmented Reality." International Journal of Information System Modeling and Design 13, no. 7 (October 20, 2022): 1–11. http://dx.doi.org/10.4018/ijismd.315124.

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Since, data is generated every minute by everyone including consumers and/or business worldwide, there is an enormous worth for big data analytics. Big data analytics is a technique for extracting important information from large amounts of a data. Visualization is the best medium to analyze and share information. Visual images help to transmit bid data to the human brain within a few seconds. Visual interpretations help in visualizing data from different angles. Visualization helps to outline problems and understand current trends. Augmented reality enables the user to experience the real world, which is digitally augmented in a way. The main objective of this research work is to find the solution to visualize the analyzed data and show it to the users in a 3D view and to improve the angle of visualization with the help of augmented reality techniques.
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21

Jalgaonkar, Mrunmay. "INDUSTRIAL B2B BIG DATA." International Research Journal of Computer Science 9, no. 5 (May 31, 2022): 106–9. http://dx.doi.org/10.26562/irjcs.2022.v0905.002.

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Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software &for buying and selling of personal and consumer data. The use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from B2B firms. The study finds that the use of customer big data significantly fosters sales growth and enhances the customer relationship performance. However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.
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22

Sulaiman, S. M., P. Aruna Jeyanthy, and D. Devaraj. "Smart Meter Data Analysis Using Big Data Tools." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3629–36. http://dx.doi.org/10.1166/jctn.2019.8338.

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In recent years, the problem of electrical load forecasting gained attention due to the arrival of new measurement technologies that produce electrical energy consumption data at very short intervals of time. Such short term measurements become voluminous in very short time. The availability of big electrical consumption data allows machine learning techniques to be employed to analyze consumption behavior of every consumer on a greater detail. Predicting the consumption of a residential customer is crucial at this point of time because tailor-made consumer-specific tariffs will play a vital role in load balancing process of Utilities. This paper analyzes the electrical consumption of a single residential customer measured using a smart meter that is capable of measuring electrical consumption at circuit level. The issues and challenges in collecting the data and pre-processing required for making them suitable for data analytics are discussed in detail. A comparison of the performance of different machine learning algorithms implemented using Python’s Scikit-learn module gives an insight on the consumption pattern.
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23

Yu, Xiaomei. "Analysis of Innovation Paths Big Data Mining to Big Data Algorithm of Business Administration." South Asian Journal of Social Studies and Economics 18, no. 2 (April 4, 2023): 22–32. http://dx.doi.org/10.9734/sajsse/2023/v18i2653.

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In the process of enterprise management, there are some problems such as poor accuracy and long selection time of computer science innovation path, which seriously affect the effective selection of computer science path innovation. Based on a big data mining method, this paper analyzes the path innovation of computer science from three dimensions, constructs the path set of path innovation by least dichotomy, and obtains the optimal innovation path by derivation. Then, the maximum likelihood theory is used to calculate the innovation path and compared it with the previous path innovation methods, comparing the accuracy and calculation time of different innovation paths. MATLAB simulation results show that the big data mining method can improve the accuracy and comprehensiveness of innovation path selection, reaching more than 90%, and control the selection time of the innovation path within 25 seconds, and the overall result is better than the previous path innovation methods. Therefore, the big data mining method can improve the accuracy of computer science innovation path selection and meet the needs of computer science path innovation in business administration. However, in the research of big data mining methods, this paper ignores the analysis of multi-path innovation, which leads to insufficient research depth. In the future, it will further analyze multi-path innovation.
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Qu, Qingling, Meiling An, Jinqian Zhang, Ming Li, Kai Li, and Sukwon Kim. "Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data." Contrast Media & Molecular Imaging 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/3725295.

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Thinking of big data as a collection of huge and sophisticated data sets, it is hard to process it effectively with current data management tools and processing methods. Big data is reflected in that the scale of data exceeds the scope of traditional volume measurement, and it is difficult to collect, store, manage, and analyze through traditional methods. Analyzing the biomechanics of table tennis training through big data is conducive to improving the training effect of table tennis, so as to formulate corresponding neuromuscular control training. This paper mainly analyzes various indicators in biomechanics and kinematics in table tennis training under big data. Under these metrics, an improved decision tree method was then used to analyze the differences between athletes trained for neuromuscular control and those who did not. It analyzed the effect of neuromuscular control training on the human body through different experimental control groups. Experiments showed that after nonathletes undergo neuromuscular control training, the standard rate of table tennis hitting action increases by 10% to 20%, reaching 80%. The improvement of athletes is not very obvious.
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25

Lidong, Lidong, and Cheryl Ann Alexander. "Additive Manufacturing and Big Data." International Journal of Mathematical, Engineering and Management Sciences 1, no. 3 (December 1, 2016): 107–21. http://dx.doi.org/10.33889/ijmems.2016.1.3-012.

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Additive manufacturing (AM) can produce parts with complex geometric shapes and reduce material use and weight. However, there are limited materials available for AM processes; the speed of production is slower compared with traditional manufacturing processes. Big Data analytics helps analyze AM processes and facilitate AM in impacting supply chains. This paper introduces advantages, applications, and technology progress of AM. Cybersecurity in AM and barriers to broad adoption of AM are discussed. Big data in AM and Big Data analytics for AM are also presented.
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26

Sharma, Aryaman. "QUANTUM COMPUTING: A REVIEW ON BIG DATA ANALYTICS AND DATA SECURITY." International Research Journal of Computer Science 9, no. 4 (April 30, 2022): 96–100. http://dx.doi.org/10.26562/irjcs.2021.v0904.005.

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The world's most difficult problem during the preceding decade was the big data problem. The big data challenge refers to the fact that data is growing at a much faster rate than computational rates. People, as well as virtually all organizations and scientific institutions, are keeping a rising amount of data as the cost of storage falls every day. Large amounts of data are generated by social activities, scientific inquiries, biological discoveries, and sensor devices. Big data is beneficial to society and economy, but it also poses challenges to scientific communities. Traditional tools, machine learning algorithms, and methodologies cannot handle, manage, or analyze large amounts of data. Quantum computing, in the realm of Big Data, allows businesses to collect and analyze large volumes of data quickly using quantum algorithms. Separate data sets may be detected, analyzed, integrated, and diagnosed with far greater ease. To find patterns, all of the items of a large database can be studied at the same time. As a result, it may be years before quantum computing makes its way into most businesses or becomes a common data analytics tool. Quantum computing will still be a relatively new technology in 2021. Machine learning algorithms are currently improving thanks to breakthroughs in quantum computing technology. There's still a lot to learn about quantum computing's capabilities and the implications of such a strong technology.
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27

Li, Hongyan, Ziyi Cheng, and Haitong Wang. "Research of Agricultural Big data." E3S Web of Conferences 214 (2020): 01011. http://dx.doi.org/10.1051/e3sconf/202021401011.

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With the development of economy and the popularization of Internet, the development of agricultural big data is the inevitable trend of agricultural development, which is gradually changing people’ s lives. But there are many problems in the process of rural development. This paper will analyze the agricultural big data, from the meaning, significance, research status, problems and solutions of agricultural big data, in order to explore new ways for agricultural development, so as to promote the development of rural economy, improve the living standards of farmers and contribute to the construction of agricultural modernization.
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28

Aljumah, Ahmad Ibrahim, Mohammed T. Nuseir, and Md Mahmudul Alam. "Organizational performance and capabilities to analyze big data: do the ambidexterity and business value of big data analytics matter?" Business Process Management Journal 27, no. 4 (June 24, 2021): 1088–107. http://dx.doi.org/10.1108/bpmj-07-2020-0335.

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PurposeThe aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance.Design/methodology/approachThis study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM).FindingsThe big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm.Research limitations/implicationsThe study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics.Originality/valueThis is an original study based on primary data from UAE manufacturing firms.
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29

Bohar, Balazs, David Fazekas, Matthew Madgwick, Luca Csabai, Marton Olbei, Tamás Korcsmáros, and Mate Szalay-Beko. "Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists." F1000Research 10 (August 10, 2022): 409. http://dx.doi.org/10.12688/f1000research.52791.2.

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In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.
APA, Harvard, Vancouver, ISO, and other styles
30

Bohar, Balazs, David Fazekas, Matthew Madgwick, Luca Csabai, Marton Olbei, Tamás Korcsmáros, and Mate Szalay-Beko. "Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists." F1000Research 10 (January 12, 2023): 409. http://dx.doi.org/10.12688/f1000research.52791.3.

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In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.
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Gutmann, Myron P., Emily Klancher Merchant, and Evan Roberts. "“Big Data” in Economic History." Journal of Economic History 78, no. 1 (March 2018): 268–99. http://dx.doi.org/10.1017/s0022050718000177.

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Big data is an exciting prospect for the field of economic history, which has long depended on the acquisition, keying, and cleaning of scarce numerical information about the past. This article examines two areas in which economic historians are already using big data – population and environment – discussing ways in which increased frequency of observation, denser samples, and smaller geographic units allow us to analyze the past with greater precision and often to track individuals, places, and phenomena across time. We also explore promising new sources of big data: organically created economic data, high resolution images, and textual corpora.
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32

Yashkun, U., W. Akram, and I. A. Memon. "AN EFFICIENT AND COST-EFFECTIVE MATHEMATICAL MODEL TO ANALYZE BIG DATA." Journal of Mountain Area Research 2 (August 7, 2017): 23. http://dx.doi.org/10.53874/jmar.v2i0.31.

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An efficient and cost-effective piecewise mathematical model is presented to represent a descriptive huge data mathematically. The techniques of function lines as decision boundaries are applied to incorporate the big data of the organization into slope intercept form. Which may be very helpful for a better understanding of discrete data to obtain sustainable and accurate results. Based on the boundaries limitation results of the collected data of the Federal Board of Revenue, the income tax against the income is studied. And finally the reliability of piecewise function to optimize the role of strategic management in any organization is investigated. The results showed that, the slope rate measured in the boundaries of income in percentage or increased slope rate is in good agreement with that predicted by the organization in descriptive form.
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33

Wright, Erik,S. "Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R." R Journal 8, no. 1 (2016): 352. http://dx.doi.org/10.32614/rj-2016-025.

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34

Knight, Rob. "Integrated Approaches to Analyze Big Data in the Perinatal/Neonatal Space." Breastfeeding Medicine 13, S1 (April 2018): S—5—S—6. http://dx.doi.org/10.1089/bfm.2018.29072.rjk.

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35

Li, Weihua, and Chunyan Yang. "Combining Channel Theory, HowNet and Extension Model to Analyze Big Data." Procedia Computer Science 91 (2016): 452–59. http://dx.doi.org/10.1016/j.procs.2016.07.118.

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36

Jeong, Kwon-Hyuk, and Ik-Ki Jeon. "A Study On Analyze of ‘Taekwondowon’Using Big data Text-mining Analysis." Journal of Martial Arts 14, no. 2 (May 31, 2020): 313–33. http://dx.doi.org/10.51223/kosoma.2020.05.14.2.313.

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37

Z. H, Jesmeen M., J. Hossen, S. Sayeed, CK Ho, Tawsif K, Armanur Rahman, and E. M. H. Arif. "A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (June 1, 2018): 1234. http://dx.doi.org/10.11591/ijeecs.v10.i3.pp1234-1243.

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<span>Recently Big Data has become one of the important new factors in the business field. This needs to have strategies to manage large volumes of structured, unstructured and semi-structured data. It’s challenging to analyze such large scale of data to extract data meaning and handling uncertain outcomes. Almost all big data sets are dirty, i.e. the set may contain inaccuracies, missing data, miscoding and other issues that influence the strength of big data analytics. One of the biggest challenges in big data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics and unpredictable conclusions. Data cleaning is an essential part of managing and analyzing data. In this survey paper, data quality troubles which may occur in big data processing to understand clearly why an organization requires data cleaning are examined, followed by data quality criteria (dimensions used to indicate data quality). Then, cleaning tools available in market are summarized. Also challenges faced in cleaning big data due to nature of data are discussed. Machine learning algorithms can be used to analyze data and make predictions and finally clean data automatically.</span>
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Yusof, Mohd Kamir. "Efficiency of JSON for Data Retrieval in Big Data." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (July 1, 2017): 250. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp250-262.

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Big data is the latest industry buzzword to describe large volume of structured and unstructured data that can be difficult to process and analyze. Most of organization looking for the best approach to manage and analyze the large volume of data especially in making a decision. XML is chosen by many organization because of powerful approach during retrieval and storage processes. However, XML approach, the execution time for retrieving large volume of data are still considerably inefficient due to several factors. In this contribution, two databases approaches namely Extensible Markup Language (XML) and Java Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records of publication data. The results showed JSON is the best choice for query retrieving speed and CPU usage. These are essential to cope with the characteristics of publication’s data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to increase of data annually.
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Chen, Qinuo, Jingyao Guo, Bocheng Wei, Bangcheng Li, and Jack Michael Kelly. "When Big Data Meets NFT." International Journal of Information Systems and Social Change 14, no. 1 (January 1, 2023): 1–16. http://dx.doi.org/10.4018/ijissc.314570.

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Non-fungible tokens (NFTs) are unique digital assets that are based on smart contracts of blockchain technology and traded via cryptocurrencies. They became known to the public in 2021 and kept growing rapidly. Until the second quarter of 2022, the total volume of NFTs has reached $12.22 billion. However, since the NFT market is still in its early stage, there are limited studies on this topic. In this paper, the first purpose is to analyze the overall market structure and volatility, characteristics of top NFTs on famous marketplaces, and future trends of NFTs. The next focus is to summarize current research trends about the concept of NFT, investigate the challenges faced by researchers, and provide current data collection or feature extraction techniques that are frequently utilized to solve those challenges.
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Zhao, Liuqi, Xing Wen, Zhenlin Huang, Ning Wang, and Yuheng Zhang. "Power Big Data Analysis Platform Design Based on Hadoop." Journal of Physics: Conference Series 2476, no. 1 (April 1, 2023): 012082. http://dx.doi.org/10.1088/1742-6596/2476/1/012082.

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Abstract To better analyze and process power data to obtain effective information, a power big data analysis scheme based on Hadoop architecture is proposed. We analyze the cloud computing environment Hadoop distributed platform to obtain massive data of large-scale distributed power system. According to the characteristics of intelligent power consumption data, common data mining algorithm modules such as parallel classification algorithm and parallel real-time clustering algorithm are designed, and the implementation of clustering algorithm with different principles is further analyzed. Then, HBase is adopted to access data in a distributed way, and MapReduce is used to realize the visual management of power GIS data. The experimental results show that the parallel processing method of power big data based on Hadoop has high efficiency and good scalability, and the algorithm has good identification ability for massive data in the cluster mode.
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41

Sharma, Shalini, Naresh Kumar, and Kuldeep Singh Kaswan. "Big data reliability: A critical review." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 5501–16. http://dx.doi.org/10.3233/jifs-202503.

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Big data requires new technologies and tools to process, analyze and interpret the vast amount of high-speed heterogeneous information. A simple mistake in processing software, error in data, and malfunctioning in hardware results in inaccurate analysis, compromised results, and inadequate performance. Thus, measures concerning reliability play an important role in determining the quality of Big data. Literature related to Big data software reliability was critically examined in this paper to investigate: the type of mathematical model developed, the influence of external factors, the type of data sets used, and methods employed to evaluate model parameters while determining the system reliability or component reliability of the software. Since the environmental conditions and input variables differ for each model due to varied platforms it is difficult to analyze which method gives the better prediction using the same set of data. Thus, paper summarizes some of the Big data techniques and common reliability models and compared them based on interdependencies, estimation function, parameter evaluation method, mean value function, etc. Visualization is also included in the study to represent the Big data reliability distribution, classification, analysis, and technical comparison. This study helps in choosing and developing an appropriate model for the reliability prediction of Big data software.
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Falsarella, Orandi Mina, Maria Thereza Miranda de Camargo, Cibele Roberta Sugahara, Celeste Aída Sirotheau Corrêa Jannuzzi, and Duarcides Ferreira Mariosa. "Big Data and IoT applications." International Journal for Innovation Education and Research 8, no. 6 (June 1, 2020): 241–54. http://dx.doi.org/10.31686/ijier.vol8.iss6.2408.

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Currently, the search for a competitive advantage is a reality in all business sectors. While several reports in the literature that address this theme, only a few discuss the relationship between competitive advantages and the implementation of Information and Communication Technologies. Thus, in the present study, we sought to investigate how the application of emerging ICTs, such as the IoT and Big Data, can provide a competitive advantage to organizations. To achieve this goal, we conducted a qualitative bibliographic survey of the literature, to identify and analyze the presently available publications on the subject of ICTs and organizational management. Additionally, we defined the elements that corroborated the conceptual construction of the results. Based on the literature, we were able to demonstrate that organizations that utilize Big Data and IoT applications can gain a competitive advantage over their competitors.
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43

Zhu, Hai Ping, Yu Xu, Qin Liu, and Yun Qing Rao. "Cloud Service Platform for Big Data of Manufacturing." Applied Mechanics and Materials 456 (October 2013): 178–83. http://dx.doi.org/10.4028/www.scientific.net/amm.456.178.

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With the rapid growth of manufacturing data, it is becoming an important issue to collect, manage, analyze and utilize the big data of manufacturing generated in the cloud manufacturing services mode. In order to solve the above problems, the characteristics of the big data and the difference from traditional manufacturing data were analyzed first. Then, the core technologies of manufacturing big data cloud service platform were grouped into a roadmap which was the guidance for research hierarchy. The platform architecture were also discussed to give an overall solution for each part and based on this platform a kind of application scenario was proposed. Thereby novel construction thinking for basic application mode of cloud manufacturing service based on manufacturing big data was provided.
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44

Sathis Kumar, B., and S. Sountharrajan. "Improving Big Data Projects Requirement Understandability by Using Big Data Use Case Diagram." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3617–22. http://dx.doi.org/10.1166/jctn.2019.8335.

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In today’s Business world, big data projects play an important role in increasing the profit of the business. Success of the big data project lies on the complete understanding of the customer requirement. A big data project not only gives the solution for day to day functionality of the system but also gives a solution to improve the business. Hence understanding the requirement is very important. Most of the big data projects are based on the object oriented approach. Most of the software industry relies on UML use case model to specify, analyze and design the big data project. The difference between other project and big data project is data characteristics. The three V characters, volume, velocity and variety play major role in the design decision of big data project. The existing UML use case diagram does not have a facility to represent the big data V characteristics. In this paper we are proposing big data use case diagram, suitable for specifying big data characteristics. The research result shows that big data use case diagram improves the understandability for the requirements of the big data project.
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45

Yang, Qianyi. "Summary of the Researches on the Influence of Investor Sentiment on Stock Returns Under the Background of Big Data." SHS Web of Conferences 151 (2022): 01001. http://dx.doi.org/10.1051/shsconf/202215101001.

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In order to analyze the promoting effect of big data technology on research results, and to explore the impact of investor sentiment on stock returns, this paper combs and summarizes the research results of domestic and foreign scholars on the impact of investor sentiment on stock returns under the background of big data. This paper defines the concepts of big data, investor sentiment and stock returns, analyzes the measurement methods of investor sentiment, and deeply analyzes the overall effect and cross-sectional effect of investor sentiment on stock returns under the background of living alone. The results show that big data technology plays a strong role in promoting the research results, can comprehensively analyze various influencing factors, and investor sentiment has a great impact on stock returns.
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46

ORLOV, GRIGORY A., ANDREY V. KRASOV, and ARTEM M. GELFAND. "THE USE OF BIG DATA IN THE ANALYSIS OF BIG DATA IN COMPUTER NETWORKS." H&ES Research 12, no. 4 (2020): 76–84. http://dx.doi.org/10.36724/2409-5419-2020-12-4-76-84.

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The concept of Big Data includes the totality of all data sets, the total size of which is several times larger than the capabilities of conventional databases.it is also necessary to note the use of non-classical data processing methods. For example, in the management, analysis of information received, or simply storage. Big Data algorithms have emerged in parallel with the introduction of the first high-performance servers of their kind, such as the mainframe, which have sufficient resources required for operational information processing, as well as corresponding to computer calculations with subsequent analysis. The algorithms are based on performing series-parallel calculations, which significantly increases the speed of performing various tasks. Entrepreneurs and scientists are interested in Big Data, who are concerned with issues related to not only high-quality, but also up-to-date interpretation of data, as well as creating innovative tools for working with them. A huge amount of data is processed in order for the end user to get the results they need for their further effective use. Big Data enables companies to expand the number of their customers, attract new target audiences, and also helps them implement projects that will be in demand not only among current customers, but also attract new ones. Active implementation and subsequent use of Big Data correspond to the solution of these problems. In this paper, we compare the main types of databases and analyze intrusion detection using the example of distributed information system technologies for processing Big Data. Timely detection of intrusions into data processing systems is necessary to take measures to preserve the confidentiality and integrity of data, as well as to correctly correct errors and improve the protection of the data processing system.
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47

Liang, Ye. "Big Data Storage Method in Wireless Communication Environment." Advanced Materials Research 756-759 (September 2013): 899–904. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.899.

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Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive data sets. Big data service is very attractive in the field of wireless communication environment, especially when we face the spatial applications, which are typical applications of big data. Because of the complexity to ingest, store and analyze geographical information data, this paper reflects on a few of the technical problems presented by the exploration of big data, and puts forward an effective storage method in wireless communication environment, which is based on the measurement of moving regularity through proposing three key techniques: partition technique, index technique and prefetch technique. Experimental results show that the performance of big data storage method using these new techniques is better than the other storage methods on managing a great capacity of big data in wireless communication environment.
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48

Belle, Ashwin, Raghuram Thiagarajan, S. M. Reza Soroushmehr, Fatemeh Navidi, Daniel A. Beard, and Kayvan Najarian. "Big Data Analytics in Healthcare." BioMed Research International 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/370194.

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The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.
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Lv, Lihua. "RFID Data Analysis and Evaluation Based on Big Data and Data Clustering." Computational Intelligence and Neuroscience 2022 (March 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/3432688.

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The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.
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Babu, K. R. Remesh, and K. P. Madhu. "Intelligent Secure Storage Mechanism for Big Data." Webology 18, Special Issue 01 (April 29, 2021): 246–61. http://dx.doi.org/10.14704/web/v18si01/web18057.

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The management of big data became more important due to the wide spread adoption of internet of things in various fields. The developments in technology, science, human habits, etc., generates massive amount of data, so it is increasingly important to store and protect these data from attacks. Big data analytics is now a hot topic. The data storage facility provided by the cloud computing enabled business organizations to overcome the burden of huge data storage and maintenance. Also, several distributed cloud applications supports them to analyze this data for taking appropriate decisions. The dynamic growth of data and data intensive applications demands an efficient intelligent storage mechanism for big data. The proposed system analyzes IP packets for vulnerabilities and classifies data nodes as reliable and unreliable nodes for the efficient data storage. The proposed Apriori algorithm based method automatically classifies the nodes for intelligent secure storage mechanism for the distributed big data storage.
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