2 research outputs found
Building textual OLAP cubes using real-time intelligent heterogeneous approach
This article describes how the ever-growing amount of data entails introducing innovative solutions in
or-der to capture, process, and store the information. OLAP has been considered a powerful analytical
technology that enables analysts to gain insight into data and project information from diversified
points of view. Thereupon, OLAP has been utilized in a broad spectrum of sensitive applications
in the industry. The technology has occupied its place at the forefront of the vibrant information
technology landscape of research in order to meet the evolving needs. One of these needs that has
enticed the researchersโ attention is providing real-time answers which suggests, in particular cases,
processing billions of records in few seconds or less. The limited processing capacities have arisen
as a major hurdle in the way of achieving such an aim. Although numerous improvements have been
suggested, few have considered the heterogeneous computing approach, whereby quantum leap in
terms of the response time has been achieved, albeit in most cases, only numerical data have been
utilized. In this article, the authors introduce a novel heterogeneous OLAP approach targets textual
OLAP cubes aggregation and can be utilized efficiently in OLAP-based pattern recognition problems.
In this context, the approach (a) exploits the GPU along with the CPU in order to process textual data.
(b) Stores the queries aggregationsโ hash table in the global memory such that the higher aggregations
levels are being answered in a shorter time (c) Introduces an intelligent self-evaluating mechanism
(ISEM), that evaluates the resource efficiency on query-basis by deciding which resource (CPU or
GPU+CPU) is more reliable to process each query. The authorsโ empirical results have shown the
achieved gain is up to thirty-two folds over the parallel CPU-based counterpart solution. Furthermore,
their approach has demonstrated that adopting aggregation-memory optimization significantly
improves the performance of high-level textual aggregations
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
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