Self Adapting and prioritizing database algorithm for providing big data insight in domain knowledge and processing of volume based instructions based on scheduled and contextual shifting of data

Modern world is not only about software and technology as the world advances it is becoming more data oriented and mathematical in nature. The current size of information that is brought in and processed is large and complex in size. Data size does not only involve using every single point of data that is reported. This information needs to be sized down and understood according to the application at hand. Data size is one issue and the other issue is the knowledge or information that needs to be extracted from it in order to obtain and achieve the purposeful meaning from the data. In memory and column oriented databases have presented viable and efficient solutions to optimize query time and column compressions. In addition to storing and retrieving data the information world has stepped up into big data with millions and terabytes of data as influx every single second. With the increase in the influx of data and out flux of responses generated and required. The world is now in need of both systems and software’s that are efficient in storing huge data as well as application layer algorithms that are efficient enough to extract meaning from the layers or topologically dependent data. This paper is focused on analyzing in column store technique for managing mathematical and scientific big data involved in multiple markets; by using topological data meaning for analyzing and understanding the information from adaptive database systems. And for efficient storing in database the column oriented approach to big data analytics and query layers will be analyzed and optimized.