Simple analytics in Retail Sales Data using Hadoop
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : R.R. Karthikeyan , Dr. B Raghu|
|DOI : 10.14445/22312803/IJCTT-V58P102|
R.R. Karthikeyan , Dr. B Raghu , "Simple analytics in Retail Sales Data using Hadoop". International Journal of Computer Trends and Technology (IJCTT) V58(1):14-19, April 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Data is collected from point of sale transactions, inventory status and pricing, competitive intelligence, social media, weather, and customers (scrubbed of personal identification) and then pulled together on the Hadoop Platform, allowing for a centralized analysis of correlations and patterns that are relevant to improving business. In-store and online purchases, Twitter trends, local sports events, and weather buying patterns are analyzed by big data algorithms to build innovative applications that personalize customer experience while increasing the efficiency of logistics. Point of sale transactions are analyzed to provide product recommendations or discounts, based on which products were bought together or before another product. Predictive analytics is used to know what products sell more on particular days in certain kinds of stores, to reduce overstock and to remain properly stocked on the most in-demand products, helping to optimize the supply chain.
 Berkovich, S., Liao, D.: On Clusterization of big data Streams. In: 3rd International Conference
 on Computing for Geospatial Research and Applications, article no. 26. ACM Press,New York (2012).
 Beyer, M.A., Laney, D.: The Importance of ?Big Data‘: A Definition. Gartner (2012)
 Madden, S.: From Databases to big data. IEEE Internet Computing 16(3), 4–6 (2012)Chen, H., Chiang, R., & Storey, V. (2012). Business Intelligence and Analytics: from Big Data to BigImpact. MIS Quarterly, 36 (4), 1165-1188.
 Cheng, Y., Qin, C., & Rusu, F. (2012). GLADE: Big Data Analytics Made Easy. SIGMOD (pp. 697-700). AR: ACM.Cuzzocrea, a., Song.
 K. Ashton, That ??Internet of Things‘‘ thing, RFiD Journal (2009).
 H. Sundmaeker, P. Guillemin, P. Friess, S. Woelfflé, Vision and challenges for realising the Internet of Things, Cluster of European Research Projects on the Internet of Things—CERP IoT, 2010.  J. Buckley (Ed.)
 The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems, Auerbach Publications, New York, 2006.
– Big Data Analytics, Hadoop,Retail analytics.