Business Intelligence as a Service in Analysis of Academic Courses

Transcription

1 Business Intelligence as a Service in Analysis of Academic Courses V.S. Akshaya Associate Professor, Department of Computer Science and Engineering, SNS College of Engineering, Coimbatore, Tamil Nadu , India. Dr. T. Purusothaman Associate Professor, Department of Computer Science and Engineering & Information Technology, Government College of Technology, Coimbatore, Tamil Nadu , India. Abstract Business intelligence (BI) has become a very reliable means in the decision making process. Business intelligence performs the operations to gather, transform, analyze and present the raw data to aid in the decision making. The implementation of service oriented architecture (SOA) for BI provides flexible services that can be accessed on demand. In this paper the BI architecture based on the service oriented concept is used for analyzing the current situation of educational courses and predicting their near future inorder to support decision making. The raw unstructured data can be extracted from the social media and transformed into an acceptable format for storing in data warehouse. The data can be analyzed and the demand for the courses is computed. The reasons enabling the selection of the courses like course features, student nature, etc are utilized for analyzing the defining factors. The profilerizarion factors related to the student s background and lifestyle of them are analyzed separately. Analyzing all these factors provides better awareness about the current situation of the educational courses and aid in better decision making. Finally keeping the future in mind the risk connected to these courses in future can be predicted and suggestions are made to prevent or tackle them. Thus the proposed SOA for BI considerably improves the decision making process. Experimental results also show that the SOA for BI applications provides better decision making options for the users. Keywords: Business intelligence, Service oriented architecture, Machine learning algorithms Introduction Business intelligence (BI) is a management term in business normally used in describing the applications and the technologies that are used in gathering, transforming and analyzing the data about an enterprise or concern inorder to provide better decision making process. BI consists of functions and required technologies to support a vendor based service or software as a service. A business intelligence system allows the organizations to access analyze and share the knowledge among its users. The BI system generally has a data warehouse as the backend layer to perform the storage. BI generally does not require separate deployment or maintenance and can be accessed over the Internet. The advantage of using BI system [1] as a service can be deployed easily and do not require on premises solutions like IT premises. BI utilizes the data into insights which in turn becomes decisions and actions. BI is of major importance in the financial and economic concerns in an organization and the similar approach could be enabled in any field in the name of organization. Service oriented architecture is efficient software as a service approach and can be co-performed with BI to enable a service vendor [2]. SOA combined with BI has been presented with better options and includes query based technologies which provides solutions to both intended and unintended queries by the users. Software as a service [3] is a technique that provides license to certain users to utilize the software on a service basis. Service oriented architecture with BI falls under this category with little assessment required. The system has two main concepts, BI applications and the software platform as a service. The platform as a service enables the system with those services that supports the on demand BI and user service requirements. SOA with BI requires the additional components for BI development and data integration services. This paper focus on the implementation of SOA based BI for the educational applications particularly in helping the newer students in selecting a course after analyzing the current and future scope of it. The paper includes the process for extraction of data from the social media and transforming it into an acceptable form. By analyzing these data using suitable algorithms the queries regarding the status of a course can be determined and the decision making process can be improved. The remainder of the paper is organized as follows: Section II describes the previous researches that were performed in the field if service oriented BI applications. Section III describes the methodologies used in this paper and the detailed explanation of the proposed techniques. Section IV represents the experimental results conducted to evaluate the performance of the techniques. Section V represents the conclusion of the research. Related Work Service oriented architecture for enabling BI applications has been the discussion point for various business analysts and computer intellectuals. Many researchers have come up with their ideas to support this efficient architecture. Some of the best ideas are discussed in this section: 2458

2 Business intelligence is an important approach in decision making process which requires suitable architecture for integrating the technologies for enabling best results. Liya Wu et al [4] discussed about service oriented architecture for BI applications inorder to adapt technologies to provide data delivery in a simplified way and with low latency. The data warehousing technologies are required for BI applications for the purposes like reporting, ad-hoc querying, online analytical processing (OLAP). The main problems addressed in OLAP are the inflexibility and the complex closed loop work process. The solution initializes with breaking down the legacy components into reusable service oriented components. The SOA based system can be implemented using the agile development principle. It came up with many phases that include the data extracting, reporting, predicting scenarios and finally providing performance report required to take decisions. Umar Ruhi [5] discussed the possibility of using the social media analytics for the betterment of business intelligence process. The social media is a fast growing media which could improve the performance of the business intelligence. Two online expert panels are made to collect the qualitative data. The expert panels are facilitated through the hosted solution providing group support system (GSS) functionality. The group system is used in structured question and answer sessions to gather data regarding issues, challenges and current state of social media analytics. The collected expert data are reviewed and rated to aid better decision making. Service oriented Business intelligence applications are widely presented for various purposes. Irya Wisnubhadra [6] applied web based business intelligence for monitoring the academic quality of the educational institutions. The system contains database which has application like academic information system, web based E-learning system, alumni information, etc that can be accessed through online transaction system which is maintained by the selected faculty members. The SOA displays and monitor the vital informations of both the students and the employees to store them in data warehouses. The stored data are used to analyze current scenario and deliver suggestions to the authority for further actions. The SOA for BI applications are more efficient in decision making process of various organizations but there are several technical factors other than the management perspectives. The technical factors are large in number including the flexibility, reusability, scalability and security as discussed by Lee-Kwun Chan et al [7]. These technical factors are presented by different persona in their researches. The technical factors can be achieved by consistent approach of openness and single open API factors. Another factor is the efficient framework for gathering essential data irrespective of the data structure. A framework for efficient data collection is presented in [8] for retrieving important data from the unstructured data apart from the structured data. A better framework for the implementation of efficient BI applications can be achieved by modifying the process of data extraction. Cristian Bucur [9] modified the data sources by including the external data from the persona unrelated to the enterprise. Such data may seem unnecessary but it might contain some rare important information for the development of the enterprise. Hence such data are mined to generate reports, dashboards and also to build the forecasting systems. BI provides better results based on two aspects: the effectiveness and the timing of the decisions. The best solutions can be determined by employing optimization algorithms. Mary Jeyanthi Prem et al [10] adapted the optimization concept in the implementation of the best result web oriented BI by applying the genetic algorithms and the ant colony optimization. The optimized web approach provided excellent results by analyzing the customer profiling, survival features and some of the most important parameters. William Yeoh et al [11] presented a real time business intelligence system based on success factors for critical management. The critical factors that assure the better BI solutions for the absolute determination of the resources for the BI market decisions are considered. Sonal Tiwari et al [12] suggested the use of business intelligence for natural computation from the web page mining concepts. The web page mining is utilized for the effective decision making related to the business perspective. Raul Valverde et al [13] presented business intelligence system for the effective risk management in the real estate industry. The risk management approach improves the detection of risks in the specified real estate industry inorder the decision making without indulging in the risks. Omar El- Gayar et al [14] presented an approach to support the evidence based medicine for efficient decision making in regards with the suffering patients. This decision making approach leverages the business intelligence concepts in the evidence based medicine to reduce the errors in deciding the actions for individual patients during the time of emergency. Michael Seibold et al [15] also suggested a similar method but for the processing of mixed workloads in the IT industry by providing effective decision making model. Josef Schiefer et al [16] suggested rule management in the complex event processing for sense and response infrastructure using the business intelligence concepts. The rule management approach presented service oriented architecture for providing the platform for business intelligence by employing the suggestions and the expertise for decision making. Maryam Marefati et al [17] presented service oriented architecture for BI systems in the perspective of determining the success factors of an organization or the factors that diverse the growth of the organization from other organizations that meant for banking facilities for effective long time and short time decision management. Utilizing BI as a service is a challenging but effective mechanism for deciding the resource allocation in the cloud environment [18]. The approach aims at developing the business of cloud resource allocation by using BI as a combination of software, hardware, the communication infrastructure and services regarding data preparation, integration and delivery to the system. SOA-driven BI architecture by network design [19] considering the previous academic literatures to improve the enterprises to deploy a more agile, flexible, cheaper, reusable, reliable and responsive BI applications in supporting process of decision. Extract, Load, Transform and Analyze (ELTA) [20] has been presented to include the BI solutions for the betterment of the service oriented concept in the big data applications. 2459

3 The problem with most of the existing techniques discussed in this section is that the methods focus only on the final decision making but the consideration of factors influencing the decision making does not include vital reasons. The decision influencing factors differs in each field of study which means the educational field has many factors that are not suited sometimes unknown to the other fields. Thus the existing methods fail in gathering the basic and vital factors that reduces the range of choices and excellent suggestions for decision making in educational field. Hence in this paper, a new Service Oriented Architecture for BI applications has been proposed for efficient decision making by complete analyses of the educational courses. Proposed System The proposed method is for implementing an efficient web based service oriented Business intelligence application for the purpose of helping the students in deciding the courses. The social media is the source selected for extracting training data. The data of selected individuals regarding the educational details are gathered from the social media and converted to suitable form for storing it in the data warehouse. Then the data is analyzed and grouped based on its information content. The current scenario of the selected courses can be determined by using the grouped data while the future scope of the courses can also be predicted. The results of such analysis and the forecasting could be taken into consideration for choosing a course or college. Thus the decision making process can be improved by the provided knowledge bases. The method provides efficient results that could guide the students in understanding the current best and efficient course which could excel in the future. The architecture of the system is developed to provide web services that include the following: Trend Recognition Brand Monitoring Customer Profilerizarion Risk Identification & Prevention These services are web based services that should provide the required details to the queries that being asked related to the course and the institution. These details along with the feedback responses of the users are utilized to review the status of the architecture at the end of every performance. The performances of the services are evaluated using two novel tests. Trend Recognition Trend Recognition is the first service provided in the web based BI application that is implemented by integrating some of the best machine learning algorithms. The function that is accomplished in trend recognition is the extraction and transformation of the input data for training. The data are gathered from the social media that may be of different format which might not be able to store in the data warehouses. So the data are collected using the ETL (Extract, Transform and Load) algorithm. ETL algorithm is efficient in transforming the raw data into storable content. The method is feasible in aggregating large amount of data that includes the publically posted personal details that reveal their education and details related to the trend without affecting their privacy. There are different types of social media that provide data in different formats which makes the life of ETL difficult. To assist the transforming phase, Scatter search algorithm is introduced. This algorithm utilizes the split and analyze concept that groups the data into two sections: convertible and non convertible data. Then the search operation is initiated to gather more informative data in the non convertible section which are stored by a distinct approach in the data warehouse. After gathering the data, the storage process is in random manner which needs to be regularized. The genetic algorithm is introduced to rank the stored data in a specialized form based on the course or degree which would be easier in analyzing the data. The data can be arranged in sections denoting the particular field of study. By using the integrated approach the method arranges the courses in best order based on the user feedback. Brand Monitoring Brand Monitoring service helps in finding the best course and the best institution that provides it. The students are provided better solutions by intimating the best institution that has the better performance for the particular course. The best courses provided by the trend recognition in order are referred and the intensive search approaches are used to brand the data. The Support Vector Machine (SVM) algorithm is used to classify and analyze the data to search for particular courses that are arranged in best order. SVM approach generalizes the search operation by focusing on the particular data. The data from the training samples are analyzed for categorizing the data based on courses. In the processing method, the data are aligned and then the best institution providing it. The arrangement in such an order makes room for broader selection options. SVM provides efficient processing but it becomes tedious when a certain course lies in two categories. In order to process the categorized data, Linear Discriminant analysis (LDA) can be implemented. The LDA approach utilizes the search operation for tracing the courses with same name which are provided in more than one category, i.e. education degrees. For instances, the course computer science is provided in engineering field in some institutions while also provided in science and research field in some other institutions. Such cases are dealt on the basis considering both the course specification and the field of study. LDA approach further categorizes these data into sub groups based on the field of study. The processing of data analysis and categorizing for efficient decision making is effectively done by LDA. It can be further enhanced to simplify the decision process using a Random forest approach. The Random forest approach is a group of uncorrelated trees which can be utilized to classify the data as per the best parameters that are set. The approach constructs a decision tree to align the data and integrates the decision making process. It is worth using the Random forest as the accuracy is improved considerably. 2460

4 Customer Profilerizarion The reason for the selection of particular courses depends mainly on the factors like socio-economy and personal background. Such factors influence the decision making process. The data from the training set are analyzed to retrieve the informations of students choosing a particular course based on the mentioned parameters. The Simulated Annealing algorithm is used to collect these informations. The algorithm gathers data related to the nature of study, class, background and the residence location. All such factors have greater influence in the decision process. The algorithm efficiently groups the data and optimizes in terms to confront the profilerizarion. The grouping of the data on the basis of classes requires high diversity to achieve accurate informations. In order to provide high accuracy the Perceptron algorithm is implemented. Perceptron algorithm provides diverse classification on specific features that are found to be essential. If class is taken as a specific factor then the data related to it are gathered together for processing. Perceptron not only analyzes the data on courses but also based on the college providing it thus improving the accuracy. The profilerizarion is indefinite and can be improved by adding several features when required. For the purpose of enhancing profilerizarion by suiting the flexible changes, Krill based algorithm is implemented which is nature inspired swarm algorithm. Krill algorithm adapts quickly to the features selected or classes included and provide information analysis based on the new class. Thus the accuracy of profilerizarion is maintained. Risk Identification & Prevention Any service provided in BI must consider the risks associated with them as the decisions taken are not limited to the current situations but also to the future scenarios. Thus risk identification becomes an important aspect to the decision making process. The risk identification and prevention is initialized as a service. The scenario based risk identification is utilized in the service to analyze the risks that would be generated in the future regarding the changes in the current scenario. A course regarded as best in current scene might reach low esteems in future. The scenario based risks may be due to social, economical or political changes that influences the current course status. Such risks can be identified using the scenario based risk identification. Some risks may occur due to changes in motives or objectives of the people including students which in turn may affect the course. Such risks are called object oriented risks and hence object oriented risk analysis is implemented. Risk analysis approach focuses on the predicting the changes in motives of people on certain courses over a period of time. The prediction takes place by collecting details about the listed courses along with their profilerizarion that forms a decision table. By using the table the future status of the courses can be estimated. Similar approaches provide a set of various risks related with the courses in the future and the risks are listed using a Risk charting algorithm. The algorithm analyzes the risk details to arrange them in a priority order. For each course with the high priority risks are analyzed and simple practical suggestions are made for their prevention. Risk prevention models are suggested to employ optimal solutions to the analyzed risks. With the use of this service the BI application can provide efficient options for the students for better decision making process. Algorithm: Service Oriented Architecture for BI applications Input: Data D from social media Output: Course options with its features for decision making Begin Collect data D of courses from the social media Initialize NN // NN is the list of conflicts and problems in D such as duplication, non-match structure, etc Initialize T={Normalize, De-normalize, reformat, re-calculate, summarize, Merging, delete} // T is the list of actions to transform or clean the NN problems in D Convert D into acceptable form Apply ETL algorithm If (NN exists in D) Perform T Load D into buffer Buf Split D into Dc and Dnc // Dc=convertible data and Dnc=Non-convertible data Search Dnc If (Dnc NN) Store Dnc separately in Buf //Apply Genetic algorithm on D Initialize population and chromosomes from D Fitness f(x)=feedback Crossover & Mutation If (chromosomes=positive feedback) Best Chromosome=Cbest Place Cbest in List L Repeat process Rank Cbest in descending order in L Classify L using SVM Initialize E={Civil, ECE, EEE, Mechanical} SR={Accounts, Commerce, Chemistry, Physics, Mathematics, Psychology, others} P={Medical, Law, Other professional courses} Class 1=Engineering Class 2=Science & Research Class 3=Medical, Law, Other professional courses If (L E) L=Class 1 Else if (L SR) L=Class 2 Else Class 3 U=List of Unconstraint class {Computer Science, Information Technology, etc} If (L U) Apply LDA analysis Initialize Threshold T Class A={B.Sc., M.Sc., MS} 2461

5 Class B={B.E., B.Tech., M.E., M.Tech} Estimate Co-variance of L // and are the random data in the list L If L=Class A Else L=Class B Use Random forest approach Estimate generalization error // h(x)=hypothesis prediction and L becomes true prediction Calculate permutation of courses in list End //Collect customer data d Apply Simulated Annealing algorithm k=0 to kmax CL=list of customer profiles Assign Threshold Tt Select data d > Tt Apply Perceptron algorithm Initialize weights and threshold for customer data d Assign inputs I and expected outputs O If (Y=O) Add data d to CL Else Reject data d d=d+1 Apply krill based algorithm Select a neighbor n If (d ~n) Add n to CL Else n=n+1 Risk Analysis Predict Risks R for each course Chart the risks R End The significance level of the data can be determined by this test. Kolmogorov Smirnov test is similar to the Wald- Wolfowitz test but it is more powerful considering the fact Wald-Wolfowitz test performs only on one-dimensional functions while Kolmogorov Smirnov tests are multidimensional performs multi-dimensional functions more efficiently. p-value and Significance Level The p-value is the function for testing the statistical hypothesis and significance level is the threshold chosen before the test. The p-value is defined as the probability of obtaining a result almost equal to the actually observed result when considering the given hypothesis as true. The p-value of the machine learning algorithms is compared to determine the better algorithm for each of the web service. In this section, the Wald-Wolfowitz test and Kolmogorov Smirnov tests are conducted on Scatter search algorithm and genetic algorithm in trend recognition, Support vector machine (SVM), Linear Discriminant analysis (LDA) and Random forest approach in brand monitoring, Simulated Annealing algorithm, Perceptron algorithm and Krill based algorithm in Customer Profilerization. The machine learning algorithms and the risk analysis models are also evaluated in terms of precision, recall and F-measure. Experimental Results The data containing the educational details, comments providing feedback about courses and colleges, customer data related to the selection of the courses are collected from the social media using the interface applications. The data are filtered and transformed in order to utilize for the construction of the proposed service architecture. The performance of the proposed service oriented architecture for BI applications can be evaluated by the Wald-Wolfowitz test and Kolmogorov Smirnov tests. The Wald-Wolfowitz test is used to test the randomness of a distribution. It fixes a median above which the data are positive and below the median the data are negative. The approach takes the data in the given order and marks it as positive or negative which determines whether the given data function fits into a dataset. Figure 1: Trend Recognition The Genetic algorithm and the Scatter Search algorithm are utilized for effective trend recognition. The significance level of the two algorithms in the trend recognition is needed to be determined for calculating the p-value. Figure.1 shows the p- value comparison of the two algorithms used in trend recognition. The scatter search algorithm has a value of 0.7 while the genetic algorithm has a value 0.9. Hence the genetic algorithm can provide better trend recognition. 2462

6 based algorithm is better in providing Customer Profilerization service. Precision Precision is defined as the measurement of deviation of the true value and its deviation. Figure 2: Brand Monitoring Figure.2 shows the p-value comparison of the Support vector machine (SVM), Linear Discriminant analysis (LDA) and Random forest approach in the brand monitoring service. The SVM has a value of 0.75, LDA has 0.85 and Random forest approach has The graph shows that the Random forest approach is better than the other methods in providing brand monitoring service. Figure 4: Precision in Trend Recognition In figure.4 the Scatter search algorithm and the Genetic algorithm are compared in terms of precision. When the number of data is 30, the Scatter search algorithm has precision of 0.63 and the Genetic algorithm has precision of The comparison graphs indicate that the Genetic algorithm is better suited for trend recognition in terms of precision. Figure 3: Customer Profilerization Figure.2 shows the p-value comparison of the comparison of the Simulated Annealing algorithm, Perceptron algorithm and Krill based algorithm in Customer Profilerization service. The Simulated Annealing algorithm has a value of 0.77, Perceptron algorithm has value of 0.87 and Krill based algorithm has a value of The graph shows that the Krill Figure 5: Precision in Brand Monitoring 2463

7 In figure.5 the Support vector machine (SVM), Linear Discriminant analysis (LDA) and Random forest approach are compared in terms of precision. When the number of data is 30, the SVM algorithm has precision of 0.52, LDA has precision of 0.65 and the Random forest approach has precision of The comparison graphs indicate that the Random Forest approach has better precision. In figure.7 the Scenario based risk analysis and object oriented risk analysis are compared in terms of precision. When the number of data is 30, the Scenario based risk analysis has precision of 0.66 and the object oriented risk analysis has precision of The comparison graphs indicate that the Object oriented risk analysis has better precision value. Recall Recall is defined as the measure of exact identification of the true positives and the true negatives. Figure 6: Precision in Customer Profilerization In figure.6 the Simulated Annealing algorithm, Perceptron algorithm and Krill based algorithm are compared in terms of precision. When the number of data is 30, the Simulated Annealing algorithm has precision of 0.54, Perceptron algorithm has precision of 0.62 and the Krill based algorithm has precision of The comparison graphs indicate that the Krill based algorithm has better precision. Figure 8: Recall in Trend Recognition In figure.8 the Scatter search algorithm and the Genetic algorithm are compared in terms of recall. When the number of data is 30, the Scatter search algorithm has recall of 0.58 and the Genetic algorithm has recall of The comparison graphs indicate that the Genetic algorithm is recall rate. Figure 7: Precision in Risk Analysis Figure 9: Recall in Brand Monitoring 2464

8 In figure.9 the Support vector machine (SVM), Linear Discriminant analysis (LDA) and Random forest approach are compared in terms of recall. When the number of data is 30, the SVM algorithm has recall of 0.53, LDA has recall of 0.67 and the Random forest approach has recall of The comparison graphs indicate that the Random Forest approach has better recall value. In figure.11 the Scenario based risk analysis and object oriented risk analysis are compared in terms of precision. When the number of data is 30, the Scenario based risk analysis has recall of 0.58 and the object oriented risk analysis has recall of The comparison graphs indicate that the Object oriented risk analysis has better recall value. F-measure F-measure is the measure of accuracy of the proposed approach by considering both the precision and recall value. Figure 10: Recall in Customer Profilerization In figure.10 the Simulated Annealing algorithm, Perceptron algorithm and Krill based algorithm are compared in terms of recall. When the number of data is 30, the Simulated Annealing algorithm has recall of 0.56, Perceptron algorithm has recall of 0.68 and the Krill based algorithm has recall of The comparison graphs indicate that the Krill based algorithm has better recall value. Figure 12: F-measure in Trend Recognition In figure.12 the Scatter search algorithm and the Genetic algorithm are compared in terms of F-measure value. When the number of data is 30, the Scatter search algorithm has F- measure of 0.60 and the Genetic algorithm has F-measure of The comparison graphs indicate that the Genetic algorithm is F-measure value. Figure 11: Recall in Risk Analysis Figure 13: F-measure in Brand Monitoring 2465

9 In figure.13 the Support vector machine (SVM), Linear Discriminant analysis (LDA) and Random forest approach are compared in terms of F-measure. When the number of data is 30, the SVM algorithm has F-measure of 0.50, LDA has F- measure of 0.60 and the Random forest approach has F- measure of The comparison graphs indicate that the Random Forest approach has better F-measure value. In figure.15 the Scenario based risk analysis and object oriented risk analysis are compared in terms of F-measure value. When the number of data is 30, the Scenario based risk analysis has F-measure of 0.57 and the object oriented risk analysis has F-measure of The comparison graphs indicate that the Object oriented risk analysis has better F- measure value. Figure 14: F-measure in Customer Profilerization In figure.14 the Simulated Annealing algorithm, Perceptron algorithm and Krill based algorithm are compared in terms of F-measure. When the number of data is 30, the Simulated Annealing algorithm has F-measure of 0.50, Perceptron algorithm has F-measure of 0.61 and the Krill based algorithm has F-measure of The comparison graphs indicate that the Krill based algorithm has better F-measure value. Figure 15: F-measure in Risk Analysis Conclusion Study on the Selection of educational courses is a very important research for the effective decision making. The inclusion of Business intelligence (BI) creates very promising approaches in the decision making model for the students. This paper focused on developing Service oriented architecture (SOA) for BI applications in the educational field. The proposed SOA has been developed for providing a query based service to the users to understand the current situation of educational courses and predicting their near future inorder to support decision making. The source of the research has been the social media from which the student data are gathered and the comments are analyzed to evaluate the trend of education being followed and the reasons for the evolution of such trends. The research also aims at providing suggestions to the users regarding the educational courses based on the predictions and risk analysis carried out. Analyzing the developments of the educational factors helps in choosing a more suitable course based on the future scope for it. Thus the proposed SOA provides efficient service for better decision making. In future, in proposed SOA for BI applications can be extended to be deployed for more flexible and reliable BI applications to a greater number of business stack-holder in improving decision support. References [1] Chan, L.K., Sim, Y.W., and Yeoh. W., "A SOAdriven business intelligence architecture." Communications of the IBIMA, vol no , pp: 1-8, [2] Abelló, A., and Romero, O., "Service-oriented business intelligence." In Springer Berlin Heidelberg on Business Intelligence, pp , [3] Marco, M. D., Rossignoli, C., Ferrari, A., Mola, L., & Zardini, A., BI as a service: an attempt to understand the leading adoption factors In Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI2010), Atlantis Press [4] Wu, L., Barash, G., and Bartolini, C, "A serviceoriented architecture for business intelligence." In IEEE International Conference on Service-Oriented Computing and Applications, pp , [5] Ruhi, U., "Social Media Analytics as a Business Intelligence Practice: Current Landscape & Future Prospects." In Journal of Internet Social Networking & Virtual Communities, vol.2014, pp.1-12, [6] Wisnubhadra, I., "Service Oriented Business Intelligence for Monitoring Academic Quality." In 2466

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