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- W2994811235 abstract "Data is increasing so rapidly that new data warehousing approaches are required to process and analyze data. Aggregationof data incrementally is needed to fast access of data and compute aggregation functions. Multidimensional arrays are generally usedfor this purpose. But some disadvantages such as address space requirement is large and processing time is comparatively slow in caseof aggregation. For this purpose we use Extendible Karnaugh Array (EKA). EKA is an efficient scheme which has better performancethan other data structures that we have tested in our research. In this research work we use EKA as basic structure for implementingincremental aggregation of data and evaluate its performance over other approaches. We use Multidimensional Online AnalyticalProcessing (MOLAP) which stores data in optimized multi-dimensional array storage, rather than in a relational database. We create4 and 6 dimensional MOLAP data cube using Traditional Multidimensional Array (TMA) and EKA scheme and compare incrementalaggregation with Relational Online Analytical Processing (ROLAP). The effective outcome of EKA structure for incrementalaggregation on 4 and 6 dimensional MOLAP structure is shown by some experimental results and efficiency is proved for n higherdimensions." @default.
- W2994811235 created "2019-12-26" @default.
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- W2994811235 date "2017-04-01" @default.
- W2994811235 modified "2023-09-22" @default.
- W2994811235 title "Incremental aggregation on MOLAP cube based on n-dimensional extendible karnaugh arrays" @default.
- W2994811235 hasPublicationYear "2017" @default.
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