Optimal Data Cube Computation In Serial Processing
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Keywords
Cuboid computation, data cube, multidimensional model, OLAP
Abstract
In the business intelligence area there is a need for multidimensional data analysis which makes it fast and interactive. Data warehousing and online analytical processing policies have come in existence for this purpose, in which the data may see as a Multi-dimensional data cube, which allows interactive and different ways of analysis of data at different levels and displayed in image form so that calculating the data cube efficiently is great and significance greatly reduces the response time of the entire system. There are various basis to do this but widely used map-reduction algorithms, are effective data cube calculation procedures that utilize parallel systems for faster computation but their basic drawback is they are tied to the system's hardware environment and fail to be efficient on a single thread system, so we introduce the lowest first method, which is a sequential algorithm, which works efficiently in a single thread environment and performs data cube calculations in linear time this is possible for each query, without the need for complex systems such as distributed clusters by focusing on intermediate cuboids and better calculating their values as opposed to
Result: MR-cube policies based on dividing search space into batch areas may fail to lead to the proper division of work and sub-optimal results when executed sequentially. It took only 168.23 milliseconds to generate the total cuboid presented in this research work enabled by the non-heuristic nature of the lowest-first approach making it outperform other optimizers.
Conclusion: The preprocessing time complexity of the algorithm is O(nlogn) and query time complexity is O(n) which gives the best path of computing a cuboid starting from base cuboid representing data cube and for the preprocessing it may extended its use cases to bulk path finding queries over the prescribes data warehouse where q >> log(n).
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