big data analytics journal

Disputes over the tectonic setting of the volcanic rocks of the Carboniferous Dahalajunshan Formation in the Western Tianshan Mountains mainly focus on "island arcs" or "continental rifts." In recent years, analyzing geochemical data based on machine learning method and inferring the tectonic background of basalt is one of the important development directions in the application of . In addition to considering the relationships between the input data, if we also consider the sequence or time series of the input data, then it will be referred to as the sequential pattern mining problem [34]. how to design an appropriate mining algorithm to find the useful things from big data. It covers a wide range of fields including statistics, biostatistics, big data, artificial intelligence, business, economics and finance, biological. Different from the concern of the security, the privacy issue is about if it is possible for the system to restore or infer personal information from the results of big data analytics, even though the input data are anonymous. In: Proceedings of the International Parallel and Distributed Processing Symposium Workshops, 2014. pp 12281237. 3, the gathering, selection, preprocessing, and transformation operators are in the input part. 629636. For instance, a user may have multiple accounts, or an account may be used by multiple users, which may degrade the accuracy of the mining results [69]. Cui X, Charles JS, Potok T. GPU enhanced parallel computing for large scale data clustering. Article The cloud computing technologies are widely used on these platforms and frameworks to satisfy the large demands of computing power and storage. attempted to use the FPGA to accelerate the compression process. Big data analytical tools are helpful in handling unstructured data. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. A later study [75] considered that the computation cost of preprocessing will be quite high for massive logs, sensor, or marketing data analysis. 2009;2(2):16269. https://cwiki.apache.org/confluence/display/PIG/PigMix. Refining initial points for k-means clustering. Witten IH, Frank E. Data mining: practical machine learning tools and techniques. In: Proceedings of the International Conference on Very Large Data Bases, 1998. pp 323333. Feldman D, Schmidt M, Sohler C. Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering. . IEEE Access. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. The open issues are discussed in The open issues while the conclusions and future trends are drawn in Conclusions. Systematic review," Journal of Medical Internet Research, vol. But the traditional data Compared to Hadoop, the architecture of MRAM was changed from client/server to a distributed agent. Berlin, Heidelberg: Springer-Verlag; 2007. dAquin M, Jay N. Interpreting data mining results with linked data for learning analytics: motivation, case study and directions. The learner typically represented the classification function which will create the classifier to help us classify the unknown input data. Trends Plant Sci. In [17], Chen et al. 2014;2:65287. Google Scholar. Decision Analytics Journal is a forum for exchange of research findings, analysis, information, and knowledge in areas that include but are not limited to: . As a result, new analytical tools are being taught in the Management Information Systems (MIS) or business analytics (BA) programs to foster students' development of this critical competency. Since big data has the unique features of massive, high dimensional, heterogeneous, complex, unstructured, incomplete, noisy, and erroneous, which may change the statistical and data analysis approaches [68]. But when we enter the age of big data, most of the current computer systems will not be able to handle the whole dataset all at once; thus, how to design a good data analytics framework or platformFootnote 3 and how to design analysis methods are both important things for the data analysis process. 5. Xue Z, Shen G, Li J, Xu Q, Zhang Y, Shao J. Compression-aware I/O performance analysis for big data clustering. This paper conducts a systematic and extensive review on 186 journal publications about big data from 2011 to 2015 in the Science Citation Index (SCI) and the Social Science . Since the data analysis (as shown in Fig. Int J Innov Res Comp Commun Eng 2014; 2(8): 54235432. Its potential is great; however there remain challenges to overcome. Djouadi A, Bouktache E. A fast algorithm for the nearest-neighbor classifier. Fortunately, some of the machine learning algorithms (e.g., population-based algorithms) can essentially be used for parallel computing, which have been demonstrated for several years, such as parallel computing version of genetic algorithm [122]. IEEE Netw. Mitra S, Pal S, Mitra P. Data mining in soft computing framework: a survey. Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G. Pregel: A system for large-scale graph processing. Topic. Advanced Search. The fact is that assuming we have infinite computing resources for big data analytics is a thoroughly impracticable plan, the input and output ratio (e.g., return on investment) will need to be taken into account before an organization constructs the big data analytics center. The performance of these methods by using map-reduce model for big data analysis is, no doubt, better than the traditional frequent pattern mining algorithms running on a single machine. Last but not least, to help the audience of the paper find solutions to welcome the new age of big data, the possible high impact research trends are given below: For the computation time, there is no doubt at all that parallel computing is one of the important future trends to make the data analytics work for big data, and consequently the technologies of cloud computing, Hadoop, and map-reduce will play the important roles for the big data analytics. Chen H, Chiang RHL, Storey VC. Mary Ann Liebert, Inc., in partnership with the Rosalind Franklin Society has launched a prestigious annual award to recognize outstanding published peer-reviewed research by women and underrepresented minorities in science in each of the publisher's peer-reviewed journals. Using GPU to enhance the performance of a clustering algorithm is another promising solution for big data mining. 1999;31(3):264323. Of course, these methods are constantly used to improve the performance of the operators of data analytics process.Footnote 1 The results of these methods illustrate that with the efficient methods at hand, we may be able to analyze the large-scale data in a reasonable time. This means that traditional reduction solutions can also be used in the big data age because the complexity and memory space needed for the process of data analysis will be decreased by using sampling and dimension reduction methods. In: Proceedings of the International Conference on Computational Science and Engineering, 2013. pp 10211028. Liu B. \end{aligned}$$, $$\begin{aligned} F = \frac{2 p r}{p+r}. Chiang M-C, Tsai C-W, Yang C-S. A time-efficient pattern reduction algorithm for k-means clustering. Rebentrost P, Mohseni M, Lloyd S. Quantum support vector machine for big feature and big data classification. This situation is just like the torrent of water (i.e., data deluge) rushed down the mountain (i.e., data analytics), how to split it and how to avoid it flowing into a narrow place (e.g., the operator is not able to handle the input data) will be the most important things to avoid the bottlenecks in data analytics system. The data scientists nowadays can pay more attention to finding out the useful information from the data even thought this task is typically like looking for a needle in a haystack. Baraniuk RG. 2014;79(1):114. These situations can be found in most association rules and sequential patterns problems because the original assumption of these problems is for the analysis of large-scale dataset. 2011;181(4):71631. The problem of handling a vast quantity of data that the system is unable to process is not a brand-new research issue; in fact, it appeared in several early approaches [2, 21, 72], e.g., marketing analysis, network flow monitor, gene expression analysis, weather forecast, and even astronomy analysis. AI Mag. Big Data Mining and Analytics. The goal of the journal is to showcase the latest methodological advances and applications of big data methods in transportation and appropriate implications for policy making. The data mining methods [20] are not limited to data problem specific methods. How to display the results of data mining will affect the users perspective to make the decision. They presented a self-tuning analytics system built on Hadoop for big data analysis. [128],Footnote 6 Ku-Mahamud modified the ant behavior of this ant clustering algorithm for big data clustering. Article Talia D. Clouds for scalable big data analytics. Therefore, the traditional data mining algorithms may not be able to deal with the problem that the formats of different input data may be different and some of the data may be incomplete. Mining big data: current status, and forecast to the future. For communication with other system, the security problem is on the communications between big data analytics and other external systems. The data summarization is generally expected to be one of the simple ways to provide a concise piece of information to the user because human has trouble of understanding vast amounts of complicated information. By using the map-reduce model for frequent pattern mining algorithm, it can be easily expected that its application to cloud platform [120, 121] will definitely become a popular trend in the forthcoming future. 3099067 In addition, compared to some early data mining algorithms, the performance of metaheuristic is no doubt superior in terms of the computation time and the quality of end result. Available: http://mahout.apache.org/. (1) Computational intelligence in the trust, security, and privacy of social big data. J Comp Syst Sci. Abstract-Big data analytics in security involves the ability to gather massive amounts of digital information to analyze, visualize and draw insights that can make it possible to predict and stop cyber attacks. For example, several studies [114, 145] used k-means as an example to analyze the big data, but not many studies applied the state-of-the-art data mining algorithms and machine learning algorithms to the analysis the big data. Similar to the input, the data mining algorithms also face the same situation that we mentioned in the previous section , how to make them work on parallel computing environment will be a very important research trend because there are abundant research results on traditional data mining algorithms. 1997;19(3):27782. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Big data market to reach $46.34 billion by 2018, EWEEK, Tech. Over the past few decades, with the development of automatic identification, data capture and storage technologies, people generate data much faster and collect data much bigger than ever before in business, science, engineering, education and other areas. To advance this position, we provide a conceptual framework based on structured/unstructured data and problem-driven/exploratory analysis. Hasan et al. For this reason, Zou et al. The Big Data Analytics in Manufacturing Industry market size is estimated to grow from USD 1.17 Billion in 2020 to USD 7.34 Billion by 2027, growing at a CAGR of 30% during the forecast year from . But for the big data analytics, most researches improve the performance of the system by adding more similar computer systems to make it possible for a system to handle all the tasks that cannot be loaded or computed in a single computer system (called scale out), as shown in Fig. Correspondence to In: Proceedings of the International Conference on Machine Learning, 1998. pp 9199. Similar situations also exist in the output part. Rep. 2001. In: Proceedings of the International Conference on Computing and Informatics, 2013. pp 614. The big data may be created by handheld device, social network, internet of things, multimedia, and many other new applications that all have the characteristics of volume, velocity, and variety. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. Ester M, Kriegel HP, Sander J, Wimmer M, Xu X. As shown in Fig. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, 2012. pp 697700. Abstract. To make it possible for the compression method to efficiently compress the data, a promising solution is to apply the clustering method to the input data to divide them into several different groups and then compress these input data according to the clustering information. For example, genetic algorithm, one of the machine learning algorithms, can not only be used to solve the clustering problem [25], it can also be used to solve the frequent pattern mining problem [33]. Diebold FX. presented a quantum-based support vector machine for big data classification and argued that the classification algorithm they proposed can be implemented with a time complexity \(O(\log NM)\) where N is the number of dimensions and M is the number of training data. ACM SIGKDD Explor Newslett. Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP, and Dell have spent more than $15 billion on software firms specializing in data management and analytics. In: Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, 1967. pp 281297. 2 Department of Biomedical Processes and Systems, Institute of Health and Nutrition Sciences, Czstochowa University of Technology, Czstochowa, Poland. 1991;21(3):66074. Wonner J, Grosjean J, Capobianco A, Bechmann D Starfish: a selection technique for dense virtual environments. ISSN 21961115 Coverage 2014-2021 Information Homepage How to publish in this journal Scope The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. Kopanakis I, Pelekis N, Karanikas H, Mavroudkis T. Visual techniques for the interpretation of data mining outcomes. Mani I, Bloedorn E. Multi-document summarization by graph search and matching. In: Proceedings of the SIAM International Conference on Data Mining, 2003. pp 166177. 1996. pp 1832. One of them is the synchronization issue because different mining procedures will finish their jobs at different times even though they use the same mining algorithm to work on the same amount of data. This research includes market size (production and. From these observations, the application of metaheuristic algorithms to big data analytics will also be an important research topic. It requires "data scientists" with deep knowledge of managing the six Vs of big data: volume, velocity, variety, volatility, veracity, and value. Kaya M, Alhajj R. Genetic algorithm based framework for mining fuzzy association rules. A training algorithm for optimal margin classifiers. Cloud Computing is the delivery of computing services such as servers, storage, databases, networking, software, analytics etc., over the Internet ("the cloud") with the aim of providing flexible resources, faster innovation and economies of scale [ 13 ]. Fan W, Bifet A. It has huge impacts on data-related problems. Register a free Taylor & Francis Online account today to boost your research and gain these benefits: Big data analytics and business analytics, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA, /doi/full/10.1080/23270012.2015.1020891?needAccess=true. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002. pp 462468. big data analytics. Journal Home Current Issue Archive. The purpose of this paper is to focus on how the HR function takes advantage of human resource analytics (HRA), including big data (BD), and discuss factors hindering HRA and data utilization. Deep learning algorithms and all applications of big data are welcomed. 2022 Springer Nature Switzerland AG. 4148. As a result, the design of big data analytics needs to consider how to make these tasks (e.g., data clean, data sampling, data compression) work well. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. Rep. 2013. 6119, 2010, pp 2734. In this paper, we identify the key issues related to big data analytics and then investigate its applications specifically related to business problems. Lyman P, Varian H. How much information 2003? Classification [20] is the opposite of clustering because it relies on a set of labeled input data to construct a set of classifiers (i.e., groups) which will then be used to classify the unlabeled input data to the groups to which they belong. As a result of this trend, new analytical tools are being taught in business analytics (BA) programs to foster . Parallel k-means clustering based on mapreduce. Project Office Journal; Data & Analytics Journal; Technology. In: Proceedings of the International Conference on Collaboration Technologies and Systems, 2013. pp 4247. [94] presented an architecture of the services platform which integrates R to provide better data analysis services, called cloud-based big data mining and analyzing services platform (CBDMASP). The data is virtually present in a real-time environment (Internet logs) (Sivarajah, Kamal, Irani, & Weerakkody, 2017 ). Cui X, Gao J, Potok TE. Advanced Analytics 6 Action Items to Face the Big Data 'Governance' Challenge. Available: https://www.mapr.com/blog/top-10-big-data-challenges-look-10-big-data-v. Press G. $16.1 billion big data market: 2014 predictions from IDC and IIA, Forbes, Tech. " Data analytics " begins with a brief introduction to the data analytics, and then " Big data analytics " will turn to the discussion of big data analytics as well as state-of-the-art data analytics algorithms and frameworks. In: Proceedings of the Advancing Big Data Benchmarks, 2014, pp. The system performance can be easily enhanced by adding more DOT blocks to the system. Because the communication will appear more frequently between systems of big data analytics, how to reduce the cost of communication and how to make the communication between these systems as reliable as possible will be the two important open issues for big data analytics. By using this website, you agree to our California Privacy Statement, The bottlenecks will be appeared in different places of the data analytics for big data because the environments, systems, and input data have changed which are different from the traditional data analytics. The mission of the International Journal of Big Data and Analytics in Healthcare (IJBDAH) is to provide timely and innovative research on the ways in which big data is revolutionizing the medical and healthcare fields. A useful graphical user interface is another way to provide the meaningful information to an user. [93], cluster services, Hadoop related services, data analytics tools, databases, servers, and massively parallel processing databases are typically the required applications and services in big data analytics infrastructure. In: Proceedings of the International Conference on Field-Programmable Technology, 2012, pp 343351. Thus, veracity, validity, value, variability, venue, vocabulary, and vagueness were added to make some complement explanation of big data [8]. Journal of Biometrics & Biostatistics. For example, in [116], Rebentrost et al. Chandarana P, Vijayalakshmi M. Big data analytics frameworks. Currently, enormous publications of big data analytics make it difficult for practitioners and researchers to find topics they are interested in and track up to date. According to our observations, a flexible user interface is needed because although the big data analytics can help us to find some hidden information, the information found usually is not knowledge. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. In: Proceedings of the International Conference on Extending Database Technology: Advances in Database Technology, 1996. pp 317. 2005;152(3):587601. [Online]. Beckmann M, Ebecken NFF, deLima BSLP, Available: http://wikibon.org/wiki/v/Big_Data_Market_Size_and_Vendor_Revenues. Conclusions. Data mining: concepts and techniques. Cheptsov A. Hpc in big data age: An evaluation report for java-based data-intensive applications implemented with hadoop and openmpi. The design of this platform is composed of four layers: the infrastructure services layer, the virtualization layer, the dataset processing layer, and the services layer. This usually plays vital roles in big data analytics system, one of which is to simplify the explanation of the needed knowledge to the users while the other is to make it easier for the users to handle the data analytics system to work with their opinions. Zaki MJ, Hsiao C-J. For instance, a business intelligence system can use the analysis results to encourage particular customers to buy the goods they are interested. Rep. 2012. Among them, the map-reduce solution was used for the studies [117119] to enhance the performance of the frequent pattern mining algorithm. Since big data analysis is generally regarded as a high computation cost work, the high performance computing cluster system (HPCC) is also a possible solution in early stage of big data analytics. Different from the traditional GA, as shown in Fig. The Strategic CIO Journal; CAI Media Group; Managing IT. In: Proceedings of the ACM International Conference on Information and Knowledge Management, 2012. pp 8594. Intelligent sampling for big data using bootstrap sampling and chebyshev inequality. To avoid the application-level slow-down caused by the compression process, in [78], Jun et al. pointed out that by using this solution for clustering, the update time per datum and memory of the traditional clustering algorithms can be significantly reduced. MathSciNet Science. TPC, transaction processing performance council [Online]. Hasan S, Shamsuddin S, Lopes N. Soft computing methods for big data problems. In fact, the problems of analyzing the large scale data were not suddenly occurred but have been there for several years because the creation of data is usually much easier than finding useful things from the data. The user interface for cloud system [142, 143] is the recent trend for big data analytics. The data deluge of big data will fill up the input system of data analytics, and it will also increase the computation load of the data analysis system. BigBench: Towards an industry standard benchmark for big data analytics. The journal is also interested in the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. SpringerOpen will continue to host an archive of all articles previously published in the journal. [Online]. Although the problem [64] of analyzing large-scale and high-dimensional dataset has attracted many researchers from various disciplines in the last century, and several solutions [2, 109] have been presented presented in recent years, the characteristics of big data still brought up several new challenges for the data clustering issues. CloudVista [111] is a representative solution for clustering big data which used cloud computing to perform the clustering process in parallel. McQueen JB. For example, the classifiers are usually fixed which cannot be automatically changed. However, there still exist some new issues of the input and output that the data scientists need to confront. Essa YM, Attiya G, El-Sayed A. The traditional data preprocessing methods [73] (e.g., compression, sampling, feature selection, and so on) are expected to be able to operate effectively in the big data age. Note that yellow, red, and blue of different colored box represent the order of appearance of reference in this paper for particular year. Some important open issues and further research directions will also be presented for the next step of big data analytics. Hoboken: Wiley-IEEE Press; 2009. The comparison between basic idea of traditional GA (TGA) and parallel genetic algorithm (PGA). Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R. Hive: a warehousing solution over a map-reduce framework. His-work has been published in high impact journals such as International Journal of Production Economics , International Journal of Production Research . Kollios G, Gunopulos D, Koudas N, Berchtold S. Efficient biased sampling for approximate clustering and outlier detection in large data sets. This paper aims to present . In Fig. Journal Alert. For this reason, information fusion will also be a future trend for improving the end results of big data analytics. [Online]. Design/methodology/approach One of the important security issues on the input part of big data analytics is to make sure that the sensors will not be compromised by the attacks. Efficient algorithms for mining closed itemsets and their lattice structure. To make the discussions on the main operators of KDD process more concise, the following sections will focus on those depicted in Fig. Since most big data analytics systems will be designed for parallel computing, and they typically will work on other systems (e.g., cloud platform) or work with other systems (e.g., search engine or knowledge base), the communication between the big data analytics and other systems will strongly impact the performance of the whole process of KDD. To build a scalable and fault-tolerant manager for big data analysis, Huai et al. Ye F, Wang ZJ, Zhou FC, Wang YP, Zhou YC. To date, we can easily find tools and platforms presented by well-known organizations. Journal of Big Data The Journal of Big Data: Theory and Practice (JBDTP) (ISSN 2692-7977) is an open access peer-reviewed journal devoted to the publication of high-quality papers on theoretical and practical aspects of big data, AI and machine learning. It means that the open issues of data analysis from the literature [2, 64] usually can help us easily find the possible solutions. The most commonly used technologies (i.e., very frequent use) were statistical analysis (47.6 percent), data mining (39 percent), data visualization (34.1 percent), Structured Query Language (28.0 percent), and Java (26.8 percent). Modern Information Retrieval. Budget Transparency A Benefactor for Data Regulation, How End-to-End Analytics Are Becoming Useful for Engineers, How to Upscale IT Departments and Data Science in Banking. The age of big data is now coming. Another research issue for the communication is how the big data analytics communicates with other systems. For this reason, any sensitive information needs to be carefully protected and used. Han J. Google Scholar. Cambridge: Cambridge Univ Press; 2007. What is big data exactly? Big Data Analytics ceased to be published by SpringerOpen as of 31st of December 2020. An interesting solution uses the quantum computing to reduce the memory space and computing cost of a classification algorithm. Keywords: Big data, Analytics, Hadoop, Healthcare, Framework, Methodology. The open issues are discussed in " The open issues " while the conclusions and future trends are drawn in " Conclusions ". From the perspective of data mining problem, this paper gives a brief introduction to the data and big data mining algorithms which consist of clustering, classification, and frequent patterns mining technologies. This research will help you understand the key market and consumer trends that are driving the Hadoop Big Data Analytics Solution industry. System which consists of two different ways in a distributed agent E. fast Make the input part article identifies the role of making them workable analysis And big data analytics journal lattice structure format will be one of the ACM International Conference on Languages! 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