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

Data Cube Approximation and Mining using Probabilistic Modeling

On-line Analytical Processing (OLAP) techniques commonly used in data warehouses allow the exploration of data cubes according to different analysis axes (dimensions) and under different abstraction levels in a dimension hierarchy. However, such techniques are not aimed at mining multidimensional data. Since data cubes are nothing but multi-way tables, we propose to analyze the potential of two probabilistic modeling techniques, namely non-negative multi-way array factorization and log-linear modeling, with the ultimate objective of compressing and mining aggregate and multidimensional values. With the first technique, we compute the set of components that best fit the initial data set and whose superposition coincides with the original data; with the second technique we identify a parsimonious model (i.e., one with a reduced set of parameters), highlight strong associations among dimensions and discover possible outliers in data cells. A real life example will be used to (i) discuss the potential benefits of the modeling output on cube exploration and mining, (ii) show how OLAP queries can be answered in an approximate way, and (iii) illustrate the strengths and limitations of these modeling approaches

A data cube model for analysis of high volumes of ambient data

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data

Diamond Dicing

In OLAP, analysts often select an interesting sample of the data. For example, an analyst might focus on products bringing revenues of at least 100 000 dollars, or on shops having sales greater than 400 000 dollars. However, current systems do not allow the application of both of these thresholds simultaneously, selecting products and shops satisfying both thresholds. For such purposes, we introduce the diamond cube operator, filling a gap among existing data warehouse operations. Because of the interaction between dimensions the computation of diamond cubes is challenging. We compare and test various algorithms on large data sets of more than 100 million facts. We find that while it is possible to implement diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a hundred times faster than popular database engines (including a row-store and a column-store).Comment: 29 page