# Introduction connectors and protocols, Power BI Desktop is the component in Architecture where the data is analyzed and transformed through some procedure using tools and made to report on the web by means of several visuals, tools and publish feature. There are two types of visuals in power BI. 1) Microsoft Visuals, 2) Custom Visuals. # 1) Microsoft Visuals These are the official visuals from Microsoft, and some were built-in visuals in power BI installation package. They are secured in nature and following are the official Visuals of Microsoft such as Stacked bar chart, line chart, Waterfall chart, Area Chart, Clustered Bar chart, Slicer, table, and Matrix?etc. # 2) Custom Visuals These visuals contexts are developed by the third party or the end users such as developers and these are shared among the users through the portal like office store and git Hub. They are unsecured in nature and following are the Custom visuals provided by third party and Microsoft officials such as dot plot, route map, flow map, journey chart, and scroller?etc. e) Visualize: In this step, the analyzed and processed data is visualized through means of visuals such as Microsoft Power BI Visuals and Custom Visuals like plots, graphs, slicer, KPI...Etc. After this process, the report is published to power BI services. f) Editing: In this step, the published report on to web is finalized after rectifying errors for any changes such as if any filters or visuals need to solve and after completion of the editing process, the report is made to publish on the web. g) Web: As said above step, it is a state where the report is converted into the dashboard, and it can be share-able via URLs, websites. Etc. Details of Data Sources # Literature Reviews Marija Blagojevic et al. [1] studied on web-based intelligent report of e-learning system by using the technique of data mining and it deals about PDCA method such as (Plan, Do Check, Act) for improving the web-based intelligent reports of eLearning system by means of data mining techniques and concluded that their proposed system has an improvement since it predicts behavior patterns thus leading to the increase in count of participants and in there study ,it proved that their proposed system has improvements in terms of report system in the field of LMS (learning management system) or e-learning . Moreover, the development and implementation of new modules. Daniel J.Power [2]studied the data-driven decision support system and it deals with data-driven decision support system and its advantages at Business Intelligence and concluded that mainframe-based decision support systems would need to be updated or replaced by web-based or web-enabled systems the accessibility reach for data-driven decision support systems are open source software's, new hardware's, web technologies, etc. Zhijun Ren [3] studied the delivering of a comprehensive Business Intelligence solution using Microsoft Business Intelligence stack and it deals about features and advantages of business intelligence stack of Microsoft and concluded that by integrating several technologies such as database, connectors, SharePoint servers, and business intelligence tools will lead to Faster delivery of comprehensive business intelligence solution within an enterprise Guangzhi Zheng et al. [4] studied on business intelligence to healthcare informatics Curriculum and their paper deals with the preliminary analysis of integration of Business Intelligence with Healthcare Information Technology and concluded that Business Intelligence had been a neglected part in many healthcare information technology programs yet both the industry and academia have realized the importance of Business Intelligence Michelle Hoda Wilkerson et al. [5]done work on youth reasoning with interactive data visualization and it deals with the youth understanding of data by interactive data visualization, they concluded that supporting learners in the coordination of any resources they choose to leverage is more likely helpful than supporting a particular approach or sequence of resource use Yuri Vanessa Nieto et al. [6]done a work on academic decision-making model for higher education institutions with the help of learning analytics .it deals with modeling and construction of software architecture for creating and categorizing indicators and they concluded that proposed software architecture has benefit of providing integration of learning analytics indicators and supports decision making in universities. # VI. # Methodology In Power BI, the different types of data are fetched by means of getting data function from different data sources and the different data sources are Files, Database, Azure, Online Services and other, the detailed description of data sources as shown in details of data sources After selecting the Data sources, we have to get the data by means of queries (if the data source is other than files) or selecting files from folders. The data will be loaded in the Power BI tool and before making the report in the power BI tool, the uploaded data should be Analysed and Corrected for error freed data through edit queries function in data part of Power BI tool and we can have relations between different datasets by means of relationships part, As the option is seen at the left side of the power BI tool, if we required any conditional columns or to add new data in datasets by means of edit queries function we can proceed it and next step is to make report by means of clicking on report option and white empty sheet appears on the screen where we create our own report. The analyzed and corrected data is visualized by means of different Visuals such as Custom Visuals and Power BI Visuals such as stacked bar chart, stacked column chart and by means of Publish option the report is published on the Power BI Services. We have to log in to power bi services, if required we can do any editing operations otherwise the report is made into the dashboard by means of publish on the web # Global Journal of Computer Science and Technology Volume XVIII Issue IV Version I As shown in the Pseudo code Algorithms 1,2, and 3 for different modules, Firstly the tool Programme is started and required data such as P i , P ii , P iii in Algorithms 1,2 and 3 are captured into the tool from different data sources via getting data function and dataset is viewed if any corrections to be done for selected data and then data is initialized with multiple columns such as P at , P t1 , P t2 , P ha , P q , P ch , P ca , P Api , P am ,P id , P na , P up , P Qf , P br , P st , P cn , P lp as shown in Pseudo-Code 1,2,and 3 respectively where different datasets contain a different number of columns. If required Uploaded dataset is processed by means of string operations, otherwise by means of queries conditional columns are created in datasets using edit queries as shown in pseudo code 1 and 2 with mathematical and conditional operations and then the dataset is Visualized through Microsoft Power Bi Visuals, by visualization of datasets report process is completed and then it is published to power BI Services by means of Publish function in the tool. After Publishing the report into the services of Power BI, the report is made into the dashboard by means of clicking publish on to web function in the file tab and then generated link can be shareable to anyone, it can be share to individuals of organization by means of Uniform Resource Locator (URL) directly or indirectly by means of SharePoint, Website,..Etc., as shown in the Results and discussion section. # VII. # Results and Discussion After publishing the dashboard to the power BI services it appears as shown in below figure and we can have some editing operations if any filters or visuals are not properly accessible then they can be rectified here before publishing on to the web. Not only above discussed modules, we can have dashboards of different modules of every educational institution, it may be related to staffs, Infrastructure and other Amenities of institutions...Etc. following are the results of dashboards of discussed modules of educational Institutions. 1![Fig. 1: Architecture of Microsoft Power BI Power BI Apps are the crucial components at user side where viewing and accessing of dashboard through some applications such as Power Apps, Mobile Power BI...etc., Power BI connectors leads crucial role in getting data from the database and other sources using connector application such as database engines, Azure Consumption Insight Connector?etc. The general operations of Microsoft Power BI are as follows: 1) Get the Data from Required Data Source2) Analyse the data by means of connectors and gateways of organization3) Build the Report by means of Different Visuals and Filters4) Publish the Report into web through Power BI Desktop5) Edit the report if any changes are needed and make shareable by means of publishing on to web option for creating embed URL 6)Access the report data from different applications of Microsoft such as Power Apps, Mobile Power BI 7) Refresh the data using different gateways of Organization for updating the dashboard. In this paper, we discussed on process model and Visuals of the Power BI tool and interactive data visualization technique for analysis and design of educational institution data visualization using Microsoft Power BI tool. II.](image-2.png "Fig. 1 :") ![Get Data: In this step, the end user is going to get data from different sources such as files, databases, Microsoft Azure, Online services and other as shown in details of data sources. b) Fetch: In this step, the data which is selected through browsing data location or using queries by selecting types of data sources and connectors. c) Process: In this step, the data is truncated and edited using several operations while loading the data into the Power BI.d) Analyze: In this step, the data is analyzed using filters, conditional Queries and other operations such as adding columns conditionally, filtering the erroneous data.](image-3.png "a)") 2![Fig. 2: Process Model of the Power BI Desktop](image-4.png "Fig. 2 :") 345![Fig. 3: View of publishing to web option A report in the Power BI Services](image-5.png "Fig. 3 :Fig. 4 :Fig. 5 :") 68![Fig 6: View of Student Modules for viewing in the Power BI Services Purpose](image-6.png "Fig 6 :Fig. 8 :") 79![Fig. 7: View of Student Internal Analysis Dashboard in the web](image-7.png "Fig. 7 :Fig. 9 :") 1 P ape =P at /5*100Create new Column P t1peData FormatData SourcesP t1pe =P t1 /20*100 Create new Column P t2peFilesExcel, Text/CSV, XML, JSON, Folder, Share Point FolderP t2 pe=P t2 /20*100 Create new Column P qpePseudo Code Algorithm 3:P qpe =P q /5*100C ( ) Volume XVIII Issue IV Version I 4 Year 2018Database Azure Online Services Other Pseudo Code Algorithms: SQL SERVER, ACCESS, ORACLE, IBM DB2, IBM INFORMIX, IBM NETEZZA, MySQL, Postgre, Sysbase, Teradata, SAP, Google Bigquery, etc. Azure SQL database, Azure SQL Data Warehouse, Azure Blob Storage, Azure Table Storage, Azure HD Insight(HDFS), etc. Power Bi service, Share point online list, Dynamics 365, Microsoft Exchange Online, Salesforce, Google Analytics, Facebook, Github, etc. Web, SharePoint list, OData Feed, Active Directory, Microsoft Exchange, Hadoop File(HDFS), R Script, ODBC, OLE DB, etc. Pseudo Code Algorithm 1: Start Create new column P ap P ap =P ca /P ch *100; Create new column P am P am = If P ap ==100, 5 Else If P ap >=95, View 'P i ' Get 'P i ' Start Get 'P iii ' View 'P iii 'notations P i = Dataset of Students Internal marks from different data sources P at = Variable name of Assignment Test marks P t1 = Variable name of Test 1 marks P t2 = Variable name of Test 2 marks P ha = Variable name of Home Assignment marks P q = Variable name of Quiz exam P ca = Variable name of Classes Attended P ch = Variable name of classes held Publish the Report to power bi services Create new Column P hape P hape =P ha /5*100 Visualize the above-considered variables using different Visuals3 Year 2018 ( ) CGlobal Journal of Computer Science and TechnologyElse If P ap >=90, Else If P ap >=85, Else If P ap >=80, Else If P ap >=75, Create new column P ttm Else 0 P ttm =If P t1

=16, Promotion Else If P t <16, DetentionP ape =Variable name of assignment test performance P am =Variable name of Attendance marks P ap = Variable name of attendance percentage P t = Variable name of the total percentage P ttm =Variable name of test total marks P fs =Variable name of Final Status X t1pe = Variable name of Test 1 Performance P t2pe = Variable name of Test 2 Performance P qp = Variable name of quiz performance P hap = Variable name of Home Assignment Performance Pseudo Code Algorithm 2: Start Get 'P ii ' View 'P ii 'Else If P ap <65, DetentionInitialise P id ,P na ,P up ,P Qf ,P br ,P st ,P cn ,P lpElse If P ap >75&&P ap <=100, PromotionProcessing of P ii by means of string operationsElse Condonation Else ErrorIfP ap >=65&&P t >=16&&P ap <75,Visualize the above-considered variables using different Visuals Publish the Report to power bi services notationsCreate new column P apeP id = Variable name of id number© 2018 Global Journalsfunctionality © 2018 Global Journals * A web-based Intelligent report e-learning system using data mining technique MarijaBlagojevic ZivadinMicic Computers and Electrical Engineering 39 2013 Elsevier * Understanding Data-Driven Decision Support Systems DanielJPower Information Systems management 25 2 2008 * Delivering a Comprehensive BI solution with Microsoft Business Intelligence Stack ZhijunRen International Conference on Challenges in Environmental Science and Computer Engineering 183 2010 * GuangzhiZheng ChiZhang LeiLi Bringing Business Intelligence to HealthCare Informatics Curriculum: A Preliminary Investigation, SIGCSE '14 Proceedings of the 45 th ACM technical symposium on computer science education 2014 * Youth Reasoning with Interactive Data Visualizations: A preliminary study MichelleHoda Wilkerson VasilikiLaina 2017 * Academic Decision Making model for higher education institutions using learning analytics YuriVanesaNieto VicenteGarciaDiaz CarlosEnriqueMontenegro th International Symposium on computational and Business Intelligence 2016 * Analysis of data mining techniques applied to LMS for personalized education WVillegas-Ch SLujan-Mora 2017 * Identification of varying standard of student based on Moodle Pattern Identification Business Intelligence Tool JR K CJayakody WH PAllagalla Proceedings of the International Symposium on ICT for sustainable development the International Symposium on ICT for sustainable development * NoopurSyed Mohd Ali Gupta KrishnaGopala RakeshNayak Kumar Lenka Big data Visualization: Tools and Challenges, Contemporary Computing and Informatics (IC31) 2016 * A framework based approach to utility big data analytics JunZhu EricZhuang JianFu JohnBaranowski AndrewFord JamesShen 31 * MicrosoftPower BIOrganization MicrosoftPower BIWebsite September 2017 * How to display group information on node-link diagrams: an evaluation RJianu ARusu YHu DTaggart IEEE Transactions on Visualization & Computer Graphics 20 11 2014 * A multilevel algorithm for forcedirected graph drawing CWalshaw Proceedings of Graph Drawing Graph DrawingBerlin Heidelberg Springer 2000 * Time Sets: Timeline visualization with set relations. Information Visualization PHNguyen KXu RWalker BWWong =10.1177/1473871615605347 2015. Oct 2015