User Behavior Mining in Software as a Service Environment

User Behavior Mining in packet as a wait on environsAbstractSoftw ar as a dish (SaaS) provides packet industriousness vendors a ne bothrk found delivery mystify to serve large number of clients with multi-tenancy based infrastructure and application sharing architecture. With the growing of the SaaS business, selective information exploit in the environment becomes achallenging atomic number 18a. In this paper, we suggest a new careful on with a few existing prosody for client digest in a software program as a Service environment.Keywords bundle as a Service, SaaS, Customer Behavior abstract, Data mining in SaaS EnvironmentI. IntroductionWith the rapid development of Internet Technologarithmy and the application software utilisation, SaaS (Software as a Service) as a complete innovative model of software application delivery model is attracting much and more than customers to procedure SaaS for reducing the software purchase and maintenance costs as it net pro vide on-demand application software, and the users mass adjust the functions provided by helps to visit changes in demand. SaaS is gaining speed with the considerable increase in the number of vendors despicable into this space1. The SaaS model is different from a unceasing website model. In a stiff website model, users of the software localisely interact with the software application. But in the campaign of a SaaS model, users interact with the application through the service provider. The difference among a regular website model and a SaaS model toilet be shown in figure 1.Figure 1II. MotivationSoftware as a Service (SaaS) is being adopted by more and more software application vendors and enterprises 2.SaaS is beneficial for the customers in such a elbow room that, a customer can unsubscribe from the work whenever he wants which makes it a scrap to manage customer descents. One of the characteristics of the SaaS business model is that champion SaaS service needs to serve a large number of customers, among which considerable quite a little are customers for whom services are offered on trial basis. As at that place is competition in the market, both trial and paying customers may bunk their business to a nonher service provider based on their requirements. It is substantive for a service provider to retain the customers from migrating to an opposite service provider. preliminary studies show that a small increase in retention cast would lead to a considerable increment in the new show value of the customers. To withstand the competition in the market, a service provider should satisfy the customers by understanding their current sort and predicting their next print exchangeable if they are having any problems in exploitation the services, how much are the customers satisfied based on the seriousness and action mechanism of the customers.III. Related whole kit and boodleA lot of work has been done in the area of analyzing the custo mers bearing on website model. Various methodologies are stated by various authors on various processes in mining the web. In 3 Sindhu P Menon and Nagaratna P Hegde, analyze the views and methodologies stated by various authors on various processes in web mining.In 4 R. Suguna and D. Sharmila listed out work done by various authors in the web usage mining area.In 5 the authors Jiehui Ju. Et.al, gives a quick sight on SaaS. It covers key technologies in SaaS, difference between Application Software Provider and Software as a Service Provider, SaaS architecture and SaaS maturity date model and the advantages that SaaS offers to small businesses.In 6, the authors Espadas et. al, presents the analysis of the impart of a hardened of requirements and proposes guidelines to be applied for application deployment in Software as a Service (SaaS) Environment.In 7, the authors Ning Duan, et. al, proposed an algorithm and two rhythmic pattern which work with the coaction among the users of a customer in a Software as a Service environment.IV. Problem DefinitionIn a SaaS Environment, an effective relationship with the customer depends on how much the status of each customer is understood. In suppose to understand the status of a customer, it is necessary to study the fashion of ehte customer form time to time. It is necessary to predict the customers seriousness and readyness in using the service. This omen may help the service providers in improving their business strategies. In a business to customer website model, the mining is done based on selected metrics like visit frequency, average depth, average stay time etc. In the grapheme of SaaS model, thither is another level of users who actually use the service. So, regular user behavior metrics may not digest accurate results in the case of SaaS model. If individual customers users behavior is studied, indeed the difference between the customers may be identified.A lot of look for is done on user behavior an alysis in regular website model but those methods used for user behavior analysis may not guarantee accurate predictions. So an extra parameter or metric is to be considered. As in the SaaS model, a tenant is the direct customer of the service provider and the actual users of the service are the users of the customers, one way to study the behavior of the customers may be by summing up the individual users metrics of a customer to treasure the customers behavior. But this way ignore the individual differences of the behaviors of the users of a customer. In addition to these regular web usage mining metrics if collaboration among the users is also considered in the analysis of customer behavior, it may yield better results than just using the regular metrics. But previous workings done in user behavior analysis in SaaS uses single collaboration metrics in the analysis which ignores almost half of the analysis data.The experiment done aims at using collaboration metrics along with another metric which works with the data not considered in the collaboration metric calculation so that all the available data is considered in user behavior prediction.V. ExperimentThe experiment is done in two phases, namely Data Collection Phase and Data touch on Phase. In the Data collection phase, the necessary data (like server log files, dealings history, etc) are collected. In the second phase i.e. in Data Processing phase, the actual analysis takes place. This phase is save divided into individual modules like preprocessing, conception discovery, and pattern analysis.Preprocessing is a process of refining the sever log data and operation history removing noise in data (if any) and populating database for further use in next modules. It includes data cleaning, user identification, session identification, act identification.Pattern Discovery is the process of discovering the usage patterns from the cleaned raw log data. As in this experiment, it is not regular usage pa tterns that are to be considered, collaboration patterns are to be considered. Regular usage patterns are the sequences of activities that are performed by the users individually. But, collaboration patterns are those that are performed by users by interaction. Collaboration patterns are not the transaction patterns rather they are the patterns of users that collaborate to perform a transaction.Definition of Collaboration Collaboration is defined to happen when different users of a customer work on the same business object during a certain period of time. For example, in a Human Resource watchfulness SaaS service, the vacation request is submitted by a regular employee user of a customer and then is approved/rejected by manager user of the same customer. Here two users of a customer are come to in the process of granting a leave. This is called collaboration.After the raw data is cleansed, the data used for collaboration discovery may contain flesh out of the legal proceeding pe rformed by the users of any tenant with tenant id(tid), user id (uid), transaction id (transaction_id) (may also be called business object id), date, time, service id (sid). In this parry more than one user may be involved in the complete of a transaction.Algorithm Collaboration User Set Identification foreplay remit 1 that consists of the transaction detailsOutput Collaboration_Table with collaboration transaction detailsInitially Collaboration_Table is emptyGet first record from Table 1Insert details into Collaboration_TableWhile end of table 1 not reachedGet next record from table 1hunt for transaction_id in collaboration_TableIf found, update collaboration user set and no_of_usersElse Add details to collaboration_table as new recordTable 1 strain table showing the contents of Table 1Table 2 Sample Collaboration TablePattern analysis plays vital role in the experiment. This module deals with the behavior analysis based on the collaboration patterns extracted above.From 7, at that place are two type of collaboration. They are random collaboration and tell collaboration by certain group of users. The first type of collaboration can indicate the activeness of the customer no matter which users are involved in the collaboration process. It can be called as active Collaboration indication (ACI). The second type of collaboration can be described by the usage patterns among the users of a customer. It can be called imitate Collaboration Index (PCI). A racy ACI value tells that a customer is actively using the SaaS service and if such a customer is motionlessness a trial customer, it probably shall be the high priority quarry to get it converted into paying customer. A high PCI value tells that a tenant is seriously using the SaaS service with relatively strong loyalty, cross-selling or up-selling opportunity can be explored for such a customer. The formula to guide ACI and PCI are as followsThe AppCNorm is the normalizing factor indicating collaborati on characteristic of SaaS service. While roughly SaaS service are rich with collaborations and others may not be. In order to balance the difference among different SaaS services, this normalization factor is employed.Where Pni denotes the collaboration pattern i of customer n, N is the get number of customers, and m is the total number of patterns in customer n. supp(pni) is the support value of pattern Pni, and len(Pni) is the space of the pattern.These collaboration metrics works only with the collaboration data and neglects the rest data which is almost half of the data. Hence another metric can be added along with the above metrics which considers the non-collaboration transactions. As the new metric is for non-collaboration transactions of a tenant, it can be called Average Usage Index (AUI). This can be calculated using the formulaThis AUI increases the accuracy of prediction of activeness of the customer along with ACI.VI. RESULTSFor this experiment, the data created is for 100 customers of a Software as a Service provider who is providing 6 different components of an application as different services. Among these 100 customers, first 50 are interpreted as paid customers and the other 50 are taken as trial customers.Table 3 synopsis of transactionsTable 4 Sample pattern listTable 5 Sample measured MetricsFrom the above calculated values, we can observe that though T0 is a paid customer, less ACI and PCI values indicate that this customer is not using the services to the full and hence revenue generated from this particular customer is not appreciable. Rather, this customer may be planning to unsubscribe from the service and hence is an primary(prenominal) target for the service provider to retain the customer. In the case of T45, it has high ACI value, high AUI value indicating active usage of the services and high PCI indicating that this customer is completely migrating his business onto the SaaS service generating the service provider more re venue. Among the exemplification trial calculated values, customer T50 is active and serious and hence, there is a high probability for this customer to convert into paid customer. On the other hand, customer T89 is not very active and is not serious indicating that he may be facing technical difficulties in using the services and hence should be helped with or is thinking to unsubscribe from the services.Table 6 Summary of Calculated metricsFrom the above table, for any tenant to be considered active in using the services, minimum ACI and AUI values needed are 1 and 1 respectively and minimum PCI value needed is 2.VII. ConclusionThe metrics ACI and PCI are introduced in previous works done by Ning Daun, et. al in 7 which works with collaboration data and leaving the non collaboration data. In our work, a new metric is introduced AUI which considers the non collaboration data also in customer behavior analysis. Still further, frequent pattern analysis can be applied on this non col laboration data to get usage patterns and so the analysis can be further improved.VIII. References1 Wei Sun, Xing Zhang, Chang Jie Gou, Pei Sun, Hui Su, IBM China explore Lab, Beiing 100094, Software as a Service Configuration and Customization Perspective IEEE sexual intercourse on Services Part II, IEEE 2008.2 E. Knorr, Software as a Service The Next Big Thing, http//www. infoworld.com/article/06/03/20/76103_12FEsaas_1.html3 Sindhu P Menon, Nagaratna P Hegde, Requisite for network Usage Mining A Survey, surplus Issue of worldwide Journal of Computer Science Informatics 2231-5292, Vol-II, Issue-1, 2, pp. 209-215.4 R. Suguna, D. Sharmila An Overview of Web Usage Mining, International Conference of Computer Applications (0975 8887), Vol. 39, No, 13, February 2012, pp. 11 13.5 Jiehui, et. al, look for on Key Technologu=ies in SaaS, International Conference on clever Computing and Cognitive Informatics, 2010, pp. 384-387.6 Espadas et. al, Application Development over Softwar e-as-a-Service platforms, The Third International Conference on Software Engineering Advances, 2008, pp. 87-104.7 Ning Duan, et. al, Tenant Behavior analysis in Software as a Service Environment Service Operations, Logistics and Informatics (SOLI), 2011 IEEE International Conference, pp 132-137, July 2011.