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Journal articles on the topic 'Big data and data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

W, Kiehn,. "From Big Data to Data Mining Von Big Data zu Data Mining." GIS Business 11, no. 6 (2016): 18–20. http://dx.doi.org/10.26643/gis.v11i6.5294.

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2

Fan, Wei, and Albert Bifet. "Mining big data." ACM SIGKDD Explorations Newsletter 14, no. 2 (2013): 1–5. http://dx.doi.org/10.1145/2481244.2481246.

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3

M, Rashmi Reddy, and Kavitha Juliet. "Data Mining for Big Data." IJARCCE 6, no. 3 (2017): 988–92. http://dx.doi.org/10.17148/ijarcce.2017.63230.

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4

Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. "Data mining with big data." IEEE Transactions on Knowledge and Data Engineering 26, no. 1 (2014): 97–107. http://dx.doi.org/10.1109/tkde.2013.109.

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5

Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.

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Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.

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7

Wang, Shuliang, and Hanning Yuan. "Spatial Data Mining." International Journal of Data Warehousing and Mining 10, no. 4 (2014): 50–70. http://dx.doi.org/10.4018/ijdwm.2014100103.

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Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.

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K, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.

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9

Wang, Shuliang, Ying Li, and Dakui Wang. "Data field for mining big data." Geo-spatial Information Science 19, no. 2 (2016): 106–18. http://dx.doi.org/10.1080/10095020.2016.1179896.

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10

LEE, JI HO. "Big Data, Data Mining and Temporary Reproduction." Journal of Intellectual Property 8, no. 4 (2013): 93–125. http://dx.doi.org/10.34122/jip.2013.12.8.4.93.

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Ding, Weiping, Gary G. Yen, Xinye Cai, and Zehong Cao. "Foreword: Evolutionary data mining for big data." Swarm and Evolutionary Computation 57 (September 2020): 100738. http://dx.doi.org/10.1016/j.swevo.2020.100738.

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12

Pal, Sankar K., Saroj K. Meher, and Andrzej Skowron. "Data science, big data and granular mining." Pattern Recognition Letters 67 (December 2015): 109–12. http://dx.doi.org/10.1016/j.patrec.2015.08.001.

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13

Elankavi, R., R. Kalaiprasath, and R. Udayakumar. "DATA MINING WITH BIG DATA REVOLUTION HYBRID." International Journal on Smart Sensing and Intelligent Systems 10, no. 4 (2017): 560–73. http://dx.doi.org/10.21307/ijssis-2017-270.

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14

A. Kandalkar, Ms Neha, and Prof Avinash Wadhe. "Extracting Large Data using Big Data Mining." International Journal of Engineering Trends and Technology 9, no. 11 (2014): 576–82. http://dx.doi.org/10.14445/22315381/ijett-v9p310.

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15

Samarkin, M. E., and V. N. Tarasov. "Telecommunication company big data classification by data mining technique." Infokommunikacionnye tehnologii 14, no. 3 (2016): 258–63. http://dx.doi.org/10.18469/ikt.2016.14.3.05.

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16

Gupta, Richa. "Journey from Data Mining to Web Mining to Big Data." International Journal of Computer Trends and Technology 10, no. 1 (2014): 18–20. http://dx.doi.org/10.14445/22312803/ijctt-v10p104.

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17

Lin, Jimmy, and Dmitriy Ryaboy. "Scaling big data mining infrastructure." ACM SIGKDD Explorations Newsletter 14, no. 2 (2013): 6–19. http://dx.doi.org/10.1145/2481244.2481247.

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18

Aghili, Maryamossadat, and Ruogu Fang. "Mining Big Neuron Morphological Data." Computational Intelligence and Neuroscience 2018 (June 24, 2018): 1–13. http://dx.doi.org/10.1155/2018/8234734.

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The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

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19

Iorio, Carmela, Giuseppe Pandolfo, Antonio D’Ambrosio, and Roberta Siciliano. "Mining big data in tourism." Quality & Quantity 54, no. 5-6 (2019): 1655–69. http://dx.doi.org/10.1007/s11135-019-00927-0.

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20

Gao, Fei, and Qilan Zhao. "Big Data Based Logistics Data Mining Platform: Architecture and Implementation." International Journal of Interdisciplinary Telecommunications and Networking 6, no. 4 (2014): 24–34. http://dx.doi.org/10.4018/ijitn.2014100103.

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With the development of intelligent logistics, enormous amount of logistics data are be-coming one of the sources of big data. Building the logistics information platform with big data mining and analysis capabilities to make full use of the huge logistics data is the inexorable trend for intelligent logistics. This paper studied the characteristics of the logistics big data, then, a big data based logistics data mining platform is designed and implemented by utilizing big data processing and storage techniques. The architecture and functions of the platform will be described in detail. This paper also studied the mining steps and requirements for logistics data mining, which is significant for practical applications.

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21

Yi, Wenquan, Fei Teng, and Jianfeng Xu. "Noval Stream Data Mining Framework under the Background of Big Data." Cybernetics and Information Technologies 16, no. 5 (2016): 69–77. http://dx.doi.org/10.1515/cait-2016-0053.

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Abstract Stream data mining has been a hot topic for research in the data mining research area in recent years, as it has an extensive application prospect in big data ages. Research on stream data mining mainly focuses on frequent item sets mining, clustering and classification. However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data. So, these traditional stream data mining methods need to be enhanced for big data applications. To resolve this issue, a hybrid framework is proposed for big steam data mining. In this framework, online and offline model are organized for different tasks, the interior of each model is rationally organized according to different mining tasks. This framework provides a new research idea and macro perspective for stream data mining under the background of big data.

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22

Chen, Weiru, Jared Oliverio, Jin Ho Kim, and Jiayue Shen. "The Modeling and Simulation of Data Clustering Algorithms in Data Mining with Big Data." Journal of Industrial Integration and Management 04, no. 01 (2019): 1850017. http://dx.doi.org/10.1142/s2424862218500173.

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Big Data is a popular cutting-edge technology nowadays. Techniques and algorithms are expanding in different areas including engineering, biomedical, and business. Due to the high-volume and complexity of Big Data, it is necessary to conduct data pre-processing methods when data mining. The pre-processing methods include data cleaning, data integration, data reduction, and data transformation. Data clustering is the most important step of data reduction. With data clustering, mining on the reduced data set should be more efficient yet produce quality analytical results. This paper presents the different data clustering methods and related algorithms for data mining with Big Data. Data clustering can increase the efficiency and accuracy of data mining.

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23

R, Padma Priya. "Review in Data Stream Mining in Big Data." International Journal for Research in Applied Science and Engineering Technology 8, no. 1 (2020): 405–8. http://dx.doi.org/10.22214/ijraset.2020.1075.

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24

Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

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25

Xue, Bing, and Mengjie Zhang. "Evolutionary feature manipulation in data mining/big data." ACM SIGEVOlution 10, no. 1 (2017): 4–11. http://dx.doi.org/10.1145/3089251.3089252.

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26

Wang, Lidong. "Data Mining, Machine Learning and Big Data Analytics." International Transaction of Electrical and Computer Engineers System 4, no. 2 (2017): 55–61. http://dx.doi.org/10.12691/iteces-4-2-2.

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27

Shastri, V. Harsha, and V. Sreeprada. "A Study of Data Mining with Big Data." International Journal of Computer Trends and Technology 38, no. 2 (2016): 99–103. http://dx.doi.org/10.14445/22312803/ijctt-v38p118.

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28

Song, Jihyun, and Kyeongjoo Kim. "A Big Data Analysis and Mining Approach for IoT Big Data." International Journal of Advances in Computer Science and Technology 7, no. 1 (2018): 1–3. http://dx.doi.org/10.30534/ijacst/2018/01712018.

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29

Ibrahim, Nadia, Alaa Hassan, and Marwah Nihad. "Big Data Analysis of Web Data Extraction." International Journal of Engineering & Technology 7, no. 4.37 (2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.37.24095.

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In this study, the large data extraction techniques; include detection of patterns and secret relationships between factors numbering and bring in the required information. Rapid analysis of massive data can lead to innovation and concepts of the theoretical value. Compared with results from mining between traditional data sets and the vast amount of large heterogeneous data interdependent it has the ability expand the knowledge and ideas about the target domain. We studied in this research data mining on the Internet. The various networks that are used to extract data onto different locations complex may appear sometimes and has been used to extract information on the web technology to extract and data analysis (Marwah et al., 2016). In this research, we extracted the information on large quantities of the web pages and examined the pages of the site using Java code, and we added the extracted information on a special database for the web page. We used the data network function to get accurate results of evaluating and categorizing the data pages found, which identifies the trusted web or risky web pages, and imported the data onto a CSV extension. Consequently, examine and categorize these data using WEKA to obtain accurate results. We concluded from the results that the applied data mining algorithms are better than other techniques in classification and extraction of data and high performance.

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30

Ruzgas, Tomas, Kristina Jakubėlienė, and Aistė Buivytė. "Big Data Mining and Knowledge Discovery." Journal of Communications Technology, Electronics and Computer Science 9 (December 27, 2016): 5. http://dx.doi.org/10.22385/jctecs.v9i0.134.

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The article dealt with exploration methods and tools for big data. It identifies the challenges encountered in the analysis of big data. Defined notion of big data. describe the technology for big data analysis. Article provides an overview of tools which are designed for big data analytics.

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31

Xin, Gang, and Hui Yan. "Study on the Optimization of Data Mining in Big Data." Advanced Materials Research 989-994 (July 2014): 1837–40. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1837.

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This paper proposes an analysis measure for Big Data by optimizing traditional data mining, base on Weka data analyzing platform ,K-means algorithm is employed through the interface programs in Weka system, so that optimized data mining techniques can be applied in cloud storage, cloud computing of Big Data by clustering analysis base on Big Data pre-processing and real-time monitoring of memory.

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32

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 (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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33

Hassani, Hossein, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, and Mohammad Reza Yeganegi. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1 (2020): 1. http://dx.doi.org/10.3390/bdcc4010001.

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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34

Hashmi, Adeel Shiraz, and Tanvir Ahmad. "Big Data Mining: Tools & Algorithms." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 4, no. 1 (2016): 36. http://dx.doi.org/10.3991/ijes.v4i1.5350.

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We are now in Big Data era, and there is a growing demand for tools which can process and analyze it. Big data analytics deals with extracting valuable information from that complex data which can’t be handled by traditional data mining tools. This paper surveys the available tools which can handle large volumes of data as well as evolving data streams. The data mining tools and algorithms which can handle big data have also been summarized, and one of the tools has been used for mining of large datasets using distributed algorithms.

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35

Raj, Anushree. "Survey on Big Data Mining Algorithms." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (2019): 1363–67. http://dx.doi.org/10.22214/ijraset.2019.6234.

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36

Hayhurst, Chris. "Mining for Answers from Big Data." Biomedical Instrumentation & Technology 49, no. 2 (2015): 84–92. http://dx.doi.org/10.2345/0899-8205-49.2.84.

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37

N, Sateesh. "Big Data Mining: Problems and Prospects." CVR Journal of Science & Technology 6, no. 1 (2014): 13–16. http://dx.doi.org/10.32377/cvrjst0603.

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38

Cen, Tao, Qianqian Chu, and Renke He. "Big Data Mining for Investor Sentiment." Journal of Physics: Conference Series 1187, no. 5 (2019): 052033. http://dx.doi.org/10.1088/1742-6596/1187/5/052033.

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39

Alkathiri, Mazin, Jhummarwala Abdul, and M. B. Potdar. "Geo-spatial Big Data Mining Techniques." International Journal of Computer Applications 135, no. 11 (2016): 28–36. http://dx.doi.org/10.5120/ijca2016908542.

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40

Tzanis, George. "Biological and Medical Big Data Mining." International Journal of Knowledge Discovery in Bioinformatics 4, no. 1 (2014): 42–56. http://dx.doi.org/10.4018/ijkdb.2014010104.

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This paper discusses the concept of big data mining in the domain of biology and medicine. Biological and medical data are increasing at very rapid rates, which in many cases outpace even Moore's law. This is the result of recent technological development, as well as the exploratory attitude of human beings, that prompts scientists to answer more questions by conducting more experiments. Representative examples are the advances in sequencing and medical imaging technologies. Challenges posed by this data deluge, and the emerging opportunities of their efficient management and analysis are also part of the discussion. The major emphasis is given to the most common biological and medical data mining applications.

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41

Lulli, Alessandro, Luca Oneto, and Davide Anguita. "Mining Big Data with Random Forests." Cognitive Computation 11, no. 2 (2019): 294–316. http://dx.doi.org/10.1007/s12559-018-9615-4.

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42

Zhu, Xiaofeng, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell. "Editorial: Deep Mining Big Social Data." World Wide Web 21, no. 6 (2018): 1449–52. http://dx.doi.org/10.1007/s11280-018-0635-5.

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43

Benkhaled, Hamid Naceur, Djamel Berrabah, and Faouzi Boufares. "Data Warehouses and Big Data." International Journal of Organizational and Collective Intelligence 10, no. 3 (2020): 1–13. http://dx.doi.org/10.4018/ijoci.2020070101.

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Before the arrival of the Big Data era, data warehouse (DW) systems were considered the best decision support systems (DSS). DW systems have always helped organizations around the world to analyse their stored data and use it in making decisive decisions. However, analyzing and mining data of poor quality can give the wrong conclusions. Several data quality (DQ) problems can appear during a data warehouse project like missing values, duplicates values, integrity constrains issues and more. As a result, organizations around the world are more aware of the importance of data quality and invest a lot of money in order to manage data quality in the DW systems. On the other hand, with the arrival of BD, new challenges have to be considered like the need for collecting the most recent data and the ability to make real-time decisions. This article provides a survey about the exiting techniques to control the quality of the stored data in the DW systems and the new solutions proposed in the literature to face the new Big Data requirements.

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Makhdoomi, Mudassir. "DATA MINING APPROACH FOR BIG DATA ANALYSIS:A THEORITICAL DISCOURSE." International Journal of Advanced Research in Computer Science 8, no. 7 (2017): 104–9. http://dx.doi.org/10.26483/ijarcs.v8i7.4032.

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kash, B. R. Pra, Dr M. Hanuman thappa, and Vasantha Kavitha. "Big Data in Educational Data Mining and Learning Analytics." International Journal of Innovative Research in Computer and Communication Engineering 02, no. 12 (2014): 7515–20. http://dx.doi.org/10.15680/ijircce.2014.0212044.

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Vashishtha, Sumit. "MINING BIG DATA FOR EFFICIENT DATA RETRIEVAL: A SURVEY." International Journal of Advanced Research in Computer Science 9, no. 1 (2018): 57–63. http://dx.doi.org/10.26483/ijarcs.v9i1.5221.

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47

Chen, Da Feng, and Bing Qing Han. "English Research Based on Big Data and Data Mining." Applied Mechanics and Materials 651-653 (September 2014): 2462–65. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2462.

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The arrival in big data ages makes English the research take place an all new change, English research is no longer single traditional unified mode, research target, research object, research resources, research contents, research form, research the method, research evaluation present a diversified complicated characteristics. the writher of the thesis studied a lot about Education Theory, Updated Curriculum Theory, Language Teaching Theory and some other relevant theories. With the aid of philology analysis method and the empirical studies, the writer combined the inquiry learning fashion with English teaching practice and pointed out creatively that Internet function can be fully used to support inquiry learning in English subject.Keywords: Big Data; Data Mining; English Research; Learning

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48

Namdev, Swati. "Data Mining with Big Data Its Issues and Challenges." International Journal of Computer Sciences and Engineering 7, no. 1 (2019): 862–64. http://dx.doi.org/10.26438/ijcse/v7i1.862864.

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49

Chongwen, Wang, and Daniel Scholten. "O2O E-Commerce Data Mining in Big Data Era." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 2A (2016): 396. http://dx.doi.org/10.12928/telkomnika.v14i2a.4375.

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Tseng, Shu Feng, Yu Ling Won, and Jiann Min Yang. "A bibliometric analysis on Data Mining and Big Data." International Journal of Electronic Business 13, no. 1 (2016): 38. http://dx.doi.org/10.1504/ijeb.2016.075333.

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