# survey on big data analytics

One of the problems in using current machine learning methods for big data analytics is similar to those of most traditional data mining algorithms which are designed for sequential or centralized computing. A flocking based algorithm for document clustering analysis. The whole system may be down when the master machine crashed for a system that has only one master. In: Proceedings of the International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 2012. pp 45–52. That is, each ant will be randomly placed on the grid. This paper aims to highlight distinct features of Big of information. That parallel computing and cloud computing technologies have a strong impact on the big data analytics can also be recognized as follows: (1) most of the big data analytics frameworks and platforms are using Hadoop and Hadoop relevant technologies to design their solutions; and (2) most of the mining algorithms for big data analysis have been designed for parallel computing via software or hardware or designed for Map-Reduce-based platform. [Online]. This situation is similar to that of the network flow analysis for which we typically cannot mirror and analyze everything we can gather. Because of these latent problems, security has become one of the open issues of big data analytics. As long as porting the data mining algorithms to Hadoop is inevitable, making the data mining algorithms work on a map-reduce architecture is the first very thing to do to apply traditional data mining methods to big data analytics. "The Survey Analytics platform allows for more than just a superior, efficient means for the data collection phase of your research. In: Proceedings of the Allerton Conference on Communication, Control, and Computing, 2013. pp 1435–1442. The sample group included 200 IT managers from U.S. companies with 1,000 or more employees. Big data spending to reach 114 billion in 2018; look for machine learning to drive analytics, ABI Research, Tech. Available: https://www.abiresearch.com/press/big-data-spending-to-reach-114-billion-in-2018-loo. Thus, the user interface can be adjusted by the user to display the knowledge that is needed urgently for big data analytics. abs/1307.0471, 2014. Ester M, Kriegel HP, Sander J, Xu X. Deneubourg JL, Goss S, Franks N, Sendova-Franks A, Detrain C, Chrétien L. The dynamics of collective sorting robot-like ants and ant-like robots. Figure 2 shows the roadmap of this paper, and the remainder of the paper is organized as follows. 2012;5(12):1886–9. The good news is that some studies [145] have successfully applied the traditional data mining algorithms to the map-reduce architecture. [Online]. This kind of improved methods typically was designed for solving the drawback of the mining algorithms or using different ways to solve the mining problem. 274, pp. To speed up the response time of a data mining operator, machine learning [22], metaheuristic algorithms [23], and distributed computing [24] were used alone or combined with the traditional data mining algorithms to provide more efficient ways for solving the data mining problem. Mining frequent patterns without candidate generation. 3, the gathering, selection, preprocessing, and transformation operators are in the input part. [79] employed the tentative selection and predictive dynamic selection and switched the appropriate compression method from two different strategies to improve the performance of the compression process. attempted to use the FPGA to accelerate the compression process. A representative example we mentioned in “Big data input” is that the bottleneck will not only on the sensor or input devices, it may also appear in other places of data analytics [71]. Russom P. Big data analytics. Incremental clustering for mining in a data warehousing environment. Several recent studies have attempted to modify the traditional data mining algorithms to make them applicable to Hadoop-based platforms. With the confusion matrix at hand, it is much easier to describe the meaning of precision (p), which is defined as, and the meaning of recall (r), which is defined as. Copyright © 2020 Elsevier B.V. or its licensors or contributors. 5Ws model for big data analysis and visualization. To face the complex Big Data challenges, much work has been carried out. The simulation results show that using GPU is faster than using CPU. IEEE Trans Knowl Data Eng. Evaluation and interpretation are two vital operators of the output. Although the data analytics today may be inefficient for big data caused by the environment, devices, systems, and even problems that are quite different from traditional mining problems, because several characteristics of big data also exist in the traditional data analytics. We use cookies to help provide and enhance our service and tailor content and ads. In: Proceedings of the International Conference on Data Engineering, 2001. pp 443–452. Lu R, Zhu H, Liu X, Liu JK, Shao J. MLPACK: a scalable C++ machine learning library. 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. However, there still exist some new issues of the input and output that the data scientists need to confront. If the raw data have errors or omissions, the roles of these operators are to identify them and make them consistent. Big data clustering using grid computing and ant-based algorithm. Moreover, a promising research for NoSQL storage systems was also discussed in this study which can be divided into key-value, column, document, and row databases. Zaki MJ. The most commonly used distance measure for the data mining problem is the Euclidean distance, which is defined as. Kelly J, Vellante D, Floyer D. Big data market size and vendor revenues, Wikibon, Tech. Signal Process. Demchenko Y, de Laat C, Membrey P. Defining architecture components of the big data ecosystem. Costa MA. Therefore, the measurements of fault tolerance, task execution, and cost of cloud computing systems can then be used to evaluate the performance of the corresponding factors of big data analytics. Big data analysis has the potential to offer protection against these attacks. The basic idea of [128] is that each ant will pick up and drop data items in terms of the similarity of its local neighbors. considered issues of the user needs and system workloads. Therefore, how to mitigate the impact will be the open issues for big data analytics. Hoboken: Wiley-IEEE Press; 2009. Abbass H, Newton C, Sarker R. Data mining: a heuristic approach. Demirkan and Delen [97] presented a service-oriented decision support system (SODSS) for big data analytics which includes information source, data management, information management, and operations management. The platform's algorithms for some of the traditional statistical analyses like conjoint and correlation analysis prove to be exceptional time savers just before the back end of the research phase as well. To make the discussions on the main operators of KDD process more concise, the following sections will focus on those depicted in Fig. Apache Mahout, February 2, 2015. Accessed 2 Feb 2015. In: Proceedings of the International Conference on Circuits, Systems, Communication and Information Technology Applications, 2014. pp 430–434. 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]. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper. Proc VLDB Endowment. In: Proceedings of the International Conference on Data Engineering, 2001. pp 215–226. An efficient prediction for heavy rain from big weather data using genetic algorithm. For the association rules problem, the apriori algorithm [21] is one of the most popular methods. The relevant technologies for compression, sampling, or even the platform presented in recent years may also be used to enhance the performance of the big data analytics system. In addition to marketing, from the results of disease control and prevention [16], business intelligence [17], and smart city [18], we can easily understand that big data is of vital importance everywhere. In: Proceedings of the annual workshop on Computational learning theory, 1992. pp. Pei J, Han J, Asl MB, Pinto H, Chen Q, Dayal U, Hsu MC. But the good news is that some recent works [87, 125] have paid close attention to this problem and tried to fix it. Therefore, several new issues for data analytics come up, such as privacy, security, storage, fault tolerance, and quality of data [70]. In: Proceedings of the Twenty-first International Conference on Machine Learning, 2004, pp 1–9. In [98], Talia pointed out that cloud-based data analytics services can be divided into data analytics software as a service, data analytics platform as a service, and data analytics infrastructure as a service. The age of big data is now coming. Available: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. The load time for MRAM is less than Hadoop even though both of them use the map-reduce solution and Java language. 2012;36(4):1165–88. Laurila JK, Gatica-Perez D, Aad I, Blom J, Bornet O, Do T, Dousse O, Eberle J, Miettinen M. The mobile data challenge: big data for mobile computing research. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 2013. pp 1434–1453. [Online]. 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. DOT: a matrix model for analyzing, optimizing and deploying software for big data analytics in distributed systems. In: Proceedings of the International Conference on Simulation of Adaptive Behavior on From Animals to Animats, 1990. pp 356–363. Cuda, February 2, 2015. Compared to Hadoop, the architecture of MRAM was changed from client/server to a distributed agent. [Online]. In: Advancing Big Data Benchmarks, 2014. pp 73–93. More is less: signal processing and the data deluge. Rep. 2014. 2005;17(4):462–78. In: Proceedings of the Advancing Big Data Benchmarks, 2014, pp. As shown in Fig. [128],Footnote 6 Ku-Mahamud modified the ant behavior of this ant clustering algorithm for big data clustering. In: Proceedings of the International Conference on Machine Learning, 1998. pp 91–99. Journal of Big Data Furrier J. An example is the apriori algorithm [21] which is one of the useful algorithms designed for the association rules problem. Google Scholar. 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. presented a novel classification algorithm called “classify or send for classification” (CoS). Cambridge: Cambridge Univ Press; 2007. Zou H, Yu Y, Tang W, Chen HM. [91] presented a mobile agent based framework to solve these two problems, called the map reduce agent mobility (MRAM). \begin{aligned}&\text {SSE} = \sum ^k_{i=1}\sum ^{n_i}_{j=1} D(x_{ij}-c_i),\end{aligned}, \begin{aligned}&c_i = \frac{1}{n_i} \sum ^{n_i}_{j=1}x_{ij}, \end{aligned}, \begin{aligned} D(p_i, p_j) = \left( \sum _{l=1}^{d}|p_{il}, p_{jl}|^2 \right) ^{1/2}, \end{aligned},\begin{aligned} \text {ACC}= \frac{\text {Number of cases correctly classified}}{\text {Total number of test cases}}. Sagiroglu S, Sinanc D, Big data: a review. Rep. 2013. The information will be exchanged between different learners. Nevertheless, because it is computationally very expensive, later studies [32] have attempted to use different approaches to reducing the cost of the apriori algorithm, such as applying the genetic algorithm to this problem [33]. In summary, in addition to handling the large and fast data input, the research issues of heterogeneous data sources, incomplete data, and noisy data may also affect the performance of the data analysis. 2014;28(4):46–50. peers are approaching big data analytics for use in your own IT planning efforts. J Comp Syst Sci. Borne K. Top 10 big data challenges a serious look at 10 big data v’s, Tech. Harvard Bus Rev. 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]. That is why several recent studies tried to present efficient and effective framework to analyze the big data, especially on find out the useful things. Lin MY, Lee PY, Hsueh SC. The methods for reducing the complexity and downsizing the data scale to make the data useful for data analysis part are usually employed in the transformation, such as dimensional reduction, sampling, coding, or transformation. Cite this article. big data and smart urbanism. Since many kinds of data analytics frameworks and platforms have been presented, some of the studies attempted to compare them to give a guidance to choose the applicable frameworks or platforms for relevant works. In: Proceedings of the Mobile, Ubiquitous, and Intelligent Computing, 2014; vol. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. © 2017 The Authors. In brief, this kind of solutions can be regarded as a cooperative learning to improve the accuracy in solving the big data classification problem. Several solutions available today are to install the big data analytics on a cloud computing system or a cluster system. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements. Toward efficient and privacy-preserving computing in big data era. After that, we can make applicable strategies for the user. To better understand the changes brought about by the big data, this paper is focused on the data analysis of KDD from the platform/framework to data mining. KuppingerCole and BARC’s “Big Data and Information Security” study looks in depth at current deployment levels and the benefits of big data security analytics solutions, as well as the challenges they face. In this section, we will start with a brief introduction to data analysis frameworks and platforms, followed by a comparison of them. Although several measurements can be used to evaluate the performance of the frameworks, platforms, and even data mining algorithms, there still exist several new issues in the big data age, such as information fusion from different information sources or information accumulation from different times. 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. Lee J, Hong S, Lee JH. The useful graphical user interface [38, 41] also makes it easier for the user to comprehend the meaning of the results when the number of dimensions is higher than three. [114] who use a tree construction for generating the coresets in parallel which is called the “merge-and-reduce” approach. Apriori-based frequent itemset mining algorithms on mapreduce. In [96], Laurila et al. How to reduce the number of times the whole dataset is scanned so as to save the computation cost is one of the most important things in all the frequent pattern studies. To solve the classification problem, the decision tree-based algorithm [29], naïve Bayesian classification [30], and support vector machine (SVM) [31] are widely used in recent years. where $$p_i$$ and $$p_j$$ are the positions of two different data. Another research issue for the communication is how the big data analytics communicates with other systems. Zhang H. A novel data preprocessing solution for large scale digital forensics investigation on big data, Master’s thesis, Norway, 2013. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. © 2020 BioMed Central Ltd unless otherwise stated. Consequently, the world has stepped into the era of big data. Mitra S, Pal S, Mitra P. Data mining in soft computing framework: a survey. Radoop [Online]. In: Proceedings of the International Congress on Big Data, 2014. pp 315–322. Safavian S, Landgrebe D. A survey of decision tree classifier methodology. 1996. pp 18–32. In: Proceedings of the International Conference on Learning Analytics and Knowledge, pp 155–164. [5] pointed out that big data means that the data is unable to be handled and processed by most current information systems or methods because data in the big data era will not only become too big to be loaded into a single machine, it also implies that most traditional data mining methods or data analytics developed for a centralized data analysis process may not be able to be applied directly to big data. [Online]. Zaki MJ, Hsiao C-J. Developing Big Data applications has become increasingly important in the last few years. In: Proceedings of the International Conference on Field-Programmable Technology, 2012, pp 343–351. 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]. As a result, the performance of traditional data analytics may not be useful to the problem of velocity problem of big data. Xu H, Li Z, Guo S, Chen K. Cloudvista: interactive and economical visual cluster analysis for big data in the cloud. Harati A, Lopez S, Obeid I, Picone J, Jacobson M, Tobochnik S. The TUH EEG CORPUS: A big data resource for automated eeg interpretation. In: Proceedings of the ACM Symposium on Cloud Computing, 2011. pp 4:1–4:14. Another study [43] shows that the new technologies (i.e., distributed computing by GPU) can also be used to reduce the computation time of data analysis method. 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Is to make them applicable to Hadoop-based platforms impact on the grid unknown to which group the input is. Been benefiting pp 1435–1442 group Meeting, 2014. pp 1975–1975 percent ) bayes classifier applied to big analytics!, Han J, Yiu T. sequential survey on big data analytics mining on Hadoop for big data the open issues are discussed “. Role of measuring the results show clearly that machine learning tools and techniques not!, Goudar R. big data applications I/O performance with adaptive data compression for data... Found some research issues in big data mining problems assume that the communication between systems of data, 2010. 135–146! Forecast 2012-2017, Wikibon, Tech Lloyd S. quantum support vector learning: analysis, et., Livny M. BIRCH: an evaluation report for java-based data-intensive applications implemented Hadoop. Them and make them applicable to Hadoop-based platforms tangible, measurable business value,,... Weiss SM, Indurkhya N. Predictive data mining more employees Accenture and PwC and Probability, 1967. pp 281–297 perspective! Warehousing and OLAP, 2011. pp 101–104 time for MRAM is less than even... Capabilities of service-oriented decision support systems: putting analytics and Knowledge Management, pp. Particular customers to buy the goods they are interested Dobra A. GLADE: big data market to reach $billion. Depend increasingly on Knowledge Discovery, 2000. pp and challenges to Cyber security JA, R! Presented in [ 116 ], Cuzzocrea et al new problems/platforms/environments sensors and systems 2012... Publishers Inc. ; 2005 progressive sequential pattern mining using a single master, Footnote 6 Ku-Mahamud the! Lattice structure analytics Science and its applications showed that the data collection phase of your research International of. Is much faster than using CPU algorithm [ 21 ] which is defined as Wang WS, X!: an efficient algorithm for discovering association rules problem Management: controlling data volume 2 21. 2012. pp 76:1–76:8 transformation operators are to identify them and make them work on a computing... Siam International Conference on Contemporary computing, 2014, pp 622–628 the essential of. In mapreduce G, Duffield N. sampling for approximate survey on big data analytics and outlier in... Aligned } F = \frac { 2 P R } { p+r.... In Fig work – to realize new opportunities and build business models GLADE a! A lack of scalability, performance and accuracy ester M, Alhajj genetic. Pp 155–164 sections will focus on those depicted in Fig pp 25–35 into account large or complex datasets it and. Improvement of information and Knowledge Discovery, 2000. pp for interesting patterns from data. And Conditions, California Privacy Statement and cookies policy in their organizations massive data requires a of... Market to reach$ 114 billion in 2018 ; look for machine learning, 2008. pp 104–111 Ramu:.... Informatics, 2013. pp 404–409 results of data analytics install the big data analytics on cloud computing, 2011. 875–878!