Abstract
The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real-Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses.
Highlights
This Online analytical processing (OLAP) [1] is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user
OLAP functionality is characterized by dynamic multidimensional analysis of consolidated enterprise data supporting end user analytical and navigational activities including calculations and modelling applied across dimensions, through hierarchies and/or across members, trend analysis over sequential time periods, slicing subsets for on-screen viewing, drill-down to deeper levels of consolidation, rotation to new dimensional comparisons in the viewing area ...etc
OLAP works with data warehouses that have sizes of terabytes and petabytes which results in slower response time to answer queries
Summary
This Online analytical processing (OLAP) [1] is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. All the materialization solutions cannot meet the RealTime requirements which motivated other researchers to focus on different direction recently; how to optimally exploit processing facilities in order to function them to immediately answer queries. The new approach uses packed R-trees, round robin distributed disk striping and Hilbert curve based (previous approaches used XYZ data format – called lowX or nearest-X – which its response time deteriorate rapidly when relevant point are dispersed over the data set) for data ordering in order to achieve the minimum communication volume among p processors that process a balanced load and without a need for a shared disk in addition to maintain the scalability in terms of number of processors, data size and dimensions. Materialization Enhancements The last issue – memory space - has been addressed by a considerable number of researches that suggest two solutions: compression and view selection
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