An approach for forecast prediction in data analytics field by tableau software
Автор: Bibhudutta Jena
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 1 vol.11, 2019 года.
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The current era is generally treated as the era of data, Users of computer are gradually increasing day by day and vast amount of data is generated from multiple domains such as healthcare- domain, Business related domains etc. The terminology Business Intelligence (BI) generally refers different technologies, applications and practices used for the collection, integration, analysis, and presentation of information of business related domain. The main motive for Business intelligence and analytics are to help in decision making process and to enhance the profit of the organisation. Various business related tools are used to analyze & visualize different types of data which are generated frequently. Tableau prepared its mark on the Field of BI by being one of the first companies to permit business customers the ability to achieve equitably arduous data visualization in a very interesting, drag and drop manner. Tableau will enhance decision making, add operational awareness, and increase performance throughout the organization The presented paper describes different tools used for business intelligence field and provides a depth knowledge regarding the tableau tool. It also describes why tableau is widely used for data visualization purpose in different organization day by day. The main aim of this paper is to describe how easily forecasting and analysis can be done by using this tool ,this paper has explained how easily prediction can be done through tableau by taking the dataset of a superstore and predict the forthcoming sales and profit for the next four quarters of the forthcoming year. In the collected dataset sales and profit details of different categories of goods are given and by using the forecasting method in tableau platform these two measures are calculated for the forthcoming year and represented in a fruitful way. Finally, the paper has compared all the framework used for business intelligence and analytics on the basis of various parameters such as complexity, speed etc.
Big Data, Business Intelligence, Data Visualization, Tableau, QlickVieew
Короткий адрес: https://sciup.org/15016160
IDR: 15016160 | DOI: 10.5815/ijieeb.2019.01.03
Список литературы An approach for forecast prediction in data analytics field by tableau software
- Raghupathi W: Data Mining in Health Care. In Healthcare Informatics: Improving Efficiency and Productivity. Edited by Kudyba S. Taylor & Francis; 2010:211–223.
- B. Jena, M. K. Gourisaria, S. S. Rautaray, and M. Pandey, A surveywork on optimization techniques utilizing map reduce framework in hadoop cluster," International Journal of Intelligent Systems and Appli- cations, vol. 9, no. 4, p. 61, 2017.
- B. Jena, M. K. Gourisaria, S. S. Rautaray and M. Pandey, "Name node performance enlarging by aggregator based HADOOP framework," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 112-116.
- B. Jena, M. K. Gourisaria, S. S. Rautaray and M. Pandey, "Improvising name node performance by aggregator aided HADOOP framework," 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2016, pp. 382-388.
- Nagesh HR, Guru Prasad “High Performance Computation of Big Data: Performance Optimization Approach towards a Parallel Frequent Item Set Mining Algorithm for Transaction Data based on Hadoop MapReduce Framework” International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.1, pp.75-84, 2017. DOI: 10.5815/ijisa.2017.01.08.
- Siddharth S Rautaray, and Manjusha Pandey, “Single and Multiple Hand Gesture Recognition Systems: A Comparative Analysis”, I.J. Intelligent Systems and Applications, 6 (11), 57-65, 2014.
- Rath Jairak, Prasong Praneetpolgrang, Nivet Chirawichitchai,"A Roadmap for Establishing Trust Management Strategy in E-Commerce Services Using Quality Based Assessment", IJIEEB, vol.6, no.5, pp.1-9, 2014. DOI: 10.5815/ijieeb.2014.05.01.
- Jeffrey Dean and Sanjay Ghemawat , " Map Reduce: Simplified Data Processing on Large Clusters " , IEEE Micro, 23(2):2228, April 2005.
- Esma Yildirim, Engin Arslan, Jangyoung Kim, Tevfik Kosar. "Application-Level Optimization of Big Data Transfers through Pipelining, Parallelism and Concurrency", IEEE Transactions on Cloud Computing, 2016
- J. Archenaa, E.A. Mary Anita,” A Survey of Big Data Analytics in Healthcare and Government”, Procedia Computer Science, Elsevier, Volume 50, 2015, Pages 408–413,Big Data, Cloud .
- Gunjan Varshney1, D. S. Chauhan2, M. P. Dave,” Evaluation of Power Quality Issues in Grid Connected PV Systems”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 6, No. 4, August 2016, pp. 1412~1420.
- Ziv J., Lempel A., “A Universal Algorithm for Sequential Data Compression,” IEEE Transactions on Information Theory, Vol. 23, No. 3, pp. 337-343.
- N.E. Ayat, M. Cheriet, C.Y. Suen ,“Automatic model selection for the optimization of SVM kernels,” Artificial intelligence in medicine, vol. 38, no.10, pp. 1733-1745, 2005.
- Hamid Bagheri , Abdusalam Abdullah Shaltooki., " Big Data: Challenges, Opportunities and Cloud Based Solutions", International Journal of Electrical and Computer Engineering (IJECE),2015.
- Sabyasachi Chakraborty, Kashyap Barua, Manjusha Pandey, Siddharth Rautaray," A Proposal for High Availability of HDFS Architecture based on Threshold Limit and Saturation Limit of the Namenode", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.9, No.6, pp. 27-34, 2017. DOI: 10.5815/ijieeb.2017.06.04.
- Mayank Bhushan , Monica Singh , Sumit K Yadav ," Big Data query optimization by using Locality Sensitive Bloom Filter ",IJCT, 2015.
- Liu, Yunxiang, and Jiongjun Du. "Parameter Optimization of the SVM for Big Data", 2015 8th International Symposium on Computational Intelligence and Design (ISCID), 2015.
- Lanchao Liu and Zhu Han , " Multi-Block ADMM for Big Data Optimization in Smart Grid " , IEEE, 2015.
- Al-Madi, Nailah, Ibrahim Aljarah, and Simone A. Ludwig. "Parallel glowworm swarm optimization clustering algorithm based on MapReduce", 2014 IEEE Symposium on Swarm Intelligence, 2014.
- A. Ramaprasath, K. Hariharan, A. Srinivasan, “Cache Coherency Algorithm to Optimize Bandwidth in Mobile Networks”, Springer Verlag, Lecture Notes in Electrical Engineering, Networks and Communications, Chapter 24, Volume 284, 2014, pp 297-305.
- Ziv J., Lempel A., “A Universal Algorithm for Sequential Data Compression,” IEEE Transactions on Information Theory, Vol. 23, No. 3, pp. 337-343
- E. Yildirim, J. Kim, and T. Kosar, “Optimizing the sample size for a cloud-hosted data scheduling service,” in Proc. 2nd Int. Workshop Cloud Computing. Sci. Appl., 2012.
- Anitha P, Malini M. Patil," A Review on Data Analytics for Supply Chain Management: A Case study", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.5, pp. 30-39, 2018. DOI: 10.5815/ijieeb.2018.05.05
- Abdus Satter, Nabil Ibtehaz," A Regression based Sensor Data Prediction Technique to Analyze Data Trustworthiness in Cyber-Physical System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.3, pp. 15-22, 2018. DOI: 10.5815/ijieeb.2018.03.03