RESEARCH ARTICLE Intelligent Forecast of Product Purchase Based on User Behaviour and Purchase Strategies using big data

Transcription

1 International Journal of Advances in Engineering, 2015, 1(3), ISSN: (printed version); ISSN: (online version); url: RESEARCH ARTICLE Intelligent Forecast of Product Purchase Based on User Behaviour and Purchase Strategies using big data P.Rubini, S.Surya and S.Kumarasamy Department of Information Technology, S.K.P Engineering College, India. Received 23 February 2015 / Accepted 16 March 2015 Abstract: We all know Data Gathering Techniques are increased and Unstructured Data are plenty in availability, which could not be processed by Data Mining. Big Data Concept is utilised for Utility Mining of Purchase by the Users. User's interests of purchase of particular Products are monitored and Frequency Item set is extracted. Each node scan its local database and generates the frequent item sets using A-Priori algorithm then its corresponding gain value is computed. Based on this gain value, the high utility items sets are mined according to the user specified threshold send it to master node. Keywords: Data gathering techniques, Bigdata, Unstructured, A-Priori, Threshold. 1. INTRODUCTION By intelligently using the information in and around them, organizations are able to improve their decision-making and better realize their objectives [1,2]. Some authors even claim that organizations may lose competitiveness by not systematically analyzing the available information [3]. However, to obtain the desired insights, data need to be sourced, stored, and analyzed [4, 5]. During the past years, accessing and processing the collected, voluminous, and heterogeneous amounts of data has become increasingly time consuming and complex [6]. With a total of 1.8 zettabyte in 2011, the amount of generated data has not yet reached its climax: as expected by IDC, a global provider of IT market intelligence, the total amount of data collected until the end of 2012 is estimated to be 1.48 times the amount of data collected in previous years, with more than 90% of this data being unstructured [7]. Businesses increasingly use these data masses provided by millions of networked sensors in mobile phones, cashier systems, automobiles, or weather stations to learn more about their customers, suppliers, and operations [8]. For instance, in a recent survey, half of the respondents stressed the importance of analytics in their company and morethan 20% claimed to be under pressure to improve their business analytics [2]. This development raises the question of how companies manage to cope with the characteristics of the ever-increasing amount of data, referred to as Big Data. The aim of this paper is to provide a set of organizational contingency factors that influence different Big Data strategies organizations may implement. In order to do so, we reviewed existing literature to identify different Big Data strategies as well as contingency factors and synthesized both into a contingency matrix that may support practitioners in choosing a suitable Big Data strategy for their specific context. II. PROPOSED SYSTEM We all know Data Gathering Techniques are increased and Unstructured Data are plenty in availability, which could not be Processed by Data Mining.For each frequent itemset l, generate all nonempty subsets of l. For every nonempty subset s of l, output the rule s Æ (l-s) if support_count(l) / support_count(s) >= min_conf where min_conf is minimum confidence threshold. Big Data Concept is utilised for Utility Mining of Purchase by the Users. User's interests of purchase of particular Products are monitored and Frequency Item set is extracted. Each node scan its local database and generates the frequent item sets using A-Priori algorithm then its corresponding gain value is computed. Based on this gain value, the high utility item sets are mined according to the user specified threshold send it to master node Using Big data Concept we are Analysing follow up Purchas of the set of Products from the Date of Purchase of first Product. Purchase of the set of Products from the date of purchase of first product. Ex User 1 would have purchased Computer, then 2 to 3 months later same user would purchase Printer. Wed can also measure Expected purchase of the set of products from the first purchase. Data insertion and update In admin side we can add particular data and also update the data. Each item contains the profit and quantity. We can add and update the profit and quantity values for product list. All inserted values are added in the database. Also updated values are added in the database. We can also view the added and updated values. These values are will be display in the product list.

2 185 Int. J. Adv. Eng., 2015, 1(3), Figure.1 Proposed system Construction of up-tree The construction of UP-Tree can be performed with two scans of the original database. In First scan, TU of each transaction is computed.twu of each single item is also accumulated. Discarding global unpromising items. Unpromising items are removed from the transaction and utilities are eliminated from the TU of the transaction. The remaining promising items in the transaction are sorted in the descending order of TWU. In Second scan, Transactions are inserted into UP-Tree. Purchase the Item In client side user can enter all details. Then user can login using particular username and password. All the inserted also updated items are added into the product list. Then select user wanted items then add all items into cart products with count of the each item. A warning message will display in dialogue box when the customer type the quantity above the constraint value mentioned in the database. All selected items are displayed in the cart product list. Then purchase the required items. Discard unpromising items In FP growth mining, type the minimum utility. Based on the value of min utility we can find out the promising items and unpromising items. Transactional-weighted utility of an item set is the sum of the transaction utilites of all the transactions. If it s utility is less than a user specified minimum utility threshold. An item set is called a low utility item set. To select interesting rules from the set of all rules, constraints on several of significance and interest can be used. The best known constraints are minimum thresholds on support & confidence. Then the low utility item sets are discarded at end of the transaction. Table-I TID and transaction Figure.2 Work flow Finding the pattern from up tree

3 186 Int. J. Adv. Eng., 2015, 1(3), Searching process for high utility item set mining is difficult because a superset of a low utility item set may be a high utility item set. If transactional-weighted utility is no less than a user specified minimum utility threshold. An item set is called a high utility item set. Based on the TWU we can find out promising items and unpromising items. Based on the threshold value we discard the unpromising items. Then find out the promising items. Candidate item sets can be generated efficiently with only two scans of database. Mining high utility item sets from database refers to the discovery of item sets with high utility like profit. III. ANALYSIS OF CONSUMER BEHAVIOR Over the years Data mining (DM) can used to understand the consumer buying behavior using various techniques. Data mining has gradually increases many folds and today it is a giant 100 billion dollar industry. In data mining world every activity of a consumer in a supermarket is treated as a byte of data. How the consumer spends, which day what time normally he/she does the shopping, what they buy most often, how much they buy, in that locality etc. All this about which a consumer is not even aware and there is a big industry which is data which is gathered somewhere at the backend slicing & dicing this data & selling it at a premium price. Figure.3 Construction of FP Tree Big Data Analytics as a Service: The fourth strategy for dealing with Big Data is to buy in Big Data capabilities. The supply of BDAaaS solutions is rapidly increasing and the variety of vendors is large. Tresata, for instance, has specialized on analyzing. Another example is Cloudera, who offers a large variety of BDAaaS solutions for different industries. Therefore, organizations tackling the BDAaaS strategy are likely to find suitable solutions for their specific context. MapReduce and DFS: The second most often referenced strategy to approach Big Data analysis is the introduction of new systems that use distributedfile systems (DFS) and a MapReduce engine. A prominent exponent of such a system is Hadoop. Hadoop is an open source architecture composed of different engines such as a MapReduce engine and a DFS engine. The data to be analyzed is stored in the distributed file system and then processed using the MapReduce engine. The results are then again stored in the file system and directly streamed to a business intelligence application. In the MapReduce approach, unlike as in RDBMS, small programs are necessary to execute queries. To be more precise, users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. These programs are then injected into a distributed processing framework, that decides how many map and reduce instances have to be run on which nodes.

4 187 Int. J. Adv. Eng., 2015, 1(3), Figure.4 Map reduce and DFS Capturing the relevant customer information : Earlier customer information used to be distributed across the company through different departments. Then there was a need to harmonize them. This was essential from both technical and business point of view. A customer may interact through the web or through call centers. Thus all the data regarding the customer should stay updated for the employees to interact smoothly and capture new information. Thus the CRM solution integrates information from multiple sources to create consolidated customer view and then make this customer knowledge base available as source data for the numerous CRM analytical applications [14]. Thus every customer interaction can create new insights on customer behavior. IV. CUSTOMER BEHAVIOR MODEL (CBM) The model (Fig. 2.) describes the system where the customer historical data are taken and a suitable predictive model is applied at the scoring engine. There are a number of business predictive models like statistics which is an old discipline and then using regression analysis, clustering- method by which like records are grouped together, nearest neighbor techniques, neural networks, rule induction etc. Predictive Model Historical Data Scoring Engine Prediction Figure.5 Customer Behavior Model CONCLUSION In this paper, we aimed at providing guidance for companies on how to approach the phenomenon of Big Data. Based on a review of existing scientific as well as practitioner literature, we identified four Big Data strategies and discussed them regarding contingencies influencing strategy choice. The eight respective contingency factors can be grouped into three dimensions, namely strategy, resources, and operating environment. Although other authors have already discussed context factors that might influence Big Data strategy choice [e.g. 36, 44], a structured analysis of such contingency factors has not been performed so far. We therefore contribute to the still limited research on Big Data by providing a basis for future discussions on the adequacy and success of various Big Data strategies for differing corporate environments. As illustrated by the analysis of the opportunities and challenges of Big Data in this paper, organizational decision makers need to start thinking about whether and how to facilitate Big Data analytics. They therefore benefit from our research by gaining a better understanding of the different facets they should consider before deciding on Big Data solution investments.

5 188 Int. J. Adv. Eng., 2015, 1(3), REFERENCES [1] Park, Y.-T., "An Empirical Investigation of the Effects of Data Warehousing on Decision Performance", Information & Management, 43(1), 2006, pp [2] Lavalle, S., Lesser, E., Shockley, R., Hopkins, M.S., and Kruschwitz, N., "Big Data, Analytics and the Path from Insights to Value", Sloan Management Review, 52(2), 2011, pp [3] Argyris, C., and Schön, D.A., Organizational Learning: A Theory of Action Perspective, Addison-Wesley Pub. Co., [4] Laney, D., 3d Data Management: Controlling Data Volume, Velocity, and Variety, Stanford, [5] A lmeida, F., and Calistru, C., "The Main Challenges and Issues of Big Data Management", International Journal of Research Studies in Computing, 2(1), 2013, pp [6] Ang, J., and Teo, T.S.H., "Management Issues in Data Warehousing: Insights from the Housing and Development Board", Decision Support Systems, 29(1), 2000, pp [7] Shirer, M., and Murray, P., Idc Predicts: 2012 Will Be the Year of Mobile and Cloud Platform Wars as It Vendors Vie for Leadership While the Industry Redefines Itself, IDC Analyze, [8] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Hung Byers, A., "Big Data: The Next Frontier for Innovation, Competition, and Productivity", in (Editor, 'ed.''eds.'): Book Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, 2011 [9] Chen, H., Chiang, R.H.L., and Storey, V.C., "Business Intelligence and Analytics: From Big Data to Big Impact", MIS Quarterly, 36(4), 2012, pp [10] Dumbill, E., What Is Big Data? An Introduction to the Big Data Landscape., Strata, [11] Morgan, T., Ibm Global Technology Outl ook 2012, Warwick, 2012