Optimization and analysis of information using business intelligence techniques and reporting using dashboards


  • Ishwarappa Kalbandi
  • P. P. Hakarnika
  • Mohana .
  • H. P. Khandagale






Big Data Analysis, BIS, Data Warehouse, OLAP, Optimization.


In Today’s world data is collected at an unpredictable scale from various application areas. Prior to the arrival of Big Data, all the data that was generated was handled manually. With data being produced in the range of terabytes today, that is impossible. To make the situation worse, almost 80% of the data generated by organizations is unstructured. This means that it cannot be understood in its avail- able format. It is very difficult and risky to make decisions just based on such crude data. In order to make quick, yet correct decisions, the generated data has to be optimized. This Paper discusses to create an end-to-end system to optimize approximately 6 million records of unstructured data provided as .txt files, which is in the form of strings and numbers into understandable or structured data. The next step is to analyse the structured data in order to make calculations on the given dataset. Finally, the analysed data will be represented in the form of dashboards, which are tabular reports or charts. In this Paper, unstructured data in the form of .txt files will be transformed into structured data in the form of tables through the SQL stored procedures in SQL Server Management Studio (SSMS). Along with the data, four other tables called dimensions will be created and then all five tables will then be integrated using SQL Server Integrated Ser- vices. Then an Online Analytical Processing (OLAP) cube is built over this data with product, customer, currency and time as its dimen- sions using the SQL Server Analysis Services (SSAS). At last this analysed data is then reported through dashboards through SQL Server Reporting Services (SSRS).The results of the analysed data is viewed in the form of reports and charts. These reports are customizable and a variety of operations can be performed on them as required by an organization. Since these reports are short and informative, they will be easy to understand and will provide for easier and correct decision making.




[1] Agung W. Setiawan, NedyaUtami, Tati R. Mengko and AdiIn- drayanto, “Implementation of Electronic Medical Record in Com- munity Health Center Towards Medical Big Data Analytics Appli- cationâ€, 2014 IEEE International Conference on Electrical Engi- neering and Computer Science, 24-25 November 2014, Bali, Indo- nesia.

[2] Sara B. Elagib, Aisha-Hassan A. Hashim and R. F. Olanrewaju, “CDR Analysis using Big Data Technologyâ€, International Confe- rence on Computing, Control, Networking, Electronics and Embed- ded Systems Engineering, 2015.

[3] Kun Wang, Yun Shao, Lei Shu, Chunsheng Zhu, and Yan Zhang, “Mobile Big Data Fault-Tolerant Processing for eHealth Networksâ€, IEEE Network, January/February 2016.

[4] Alfred Daniel, Anand Paul and Awais Ahmad, “Near Real-Time Big Data Analysis on Vehicular Networksâ€, 2015 International Conference on Soft-Computing and Network Security (ICSNS - 2015), Feb. 25 – 27, 2015, Coimbatore, INDIA.

[5] Ishwarappa, Anuradha J, “ A Brief Introduction on Big data 5Vs Characterstics and Hadoop Technologyâ€,2015 International Confe- rence on Intelligent Computing, Communication & Convergence (ICCC-2015),procedia computer science pp319-324.

[6] Aravindkumar D Gumtaj, H. V. Ravish Aradhya, Mohana, Gouri S Katageri “GPS and GSM Based Database Systems for User Access†International Association of Scientific Innovation and Re- search-IASIR, International Journal of Software and Web Sciences (IJSWS), pp.24-28, March-May 2015.

[7] Tong Wu, “ETL Function Realization of Data Warehouse SystemBased on SSIS Platformâ€, IEEE 2010.

[8] Microsoft SQLCAT Team, “SQLCAT’s Guide to: BI and Ana- lyticsâ€, e-book, 2013 Edition.

View Full Article: