the conception of Conceptual Information Systems and discusses the use of their visualization techniques for On-Line Analytical Processing (OLAP).

). This approach has been developed for the NLG system HYSSOP (HYpertext Summary System of On-line analyticalProcessing) whichsummarizes OLAP

describes the conception of Conceptual Information Systems and discusses the use of their visualization techniques for On-Line Analytical Processing (OLAP).

-oriented database or persistent objects — Object Oriented On-line Analytic Processing (O3LAP) — as a possible alternative, compares the O3LAP model with the current models, suggests possible extensions to the current OLAP models, defines the elements involved in the mapping of a logical model to the physical one

On-Line Analytical Processing (OLAP) has shown great success in many industry applications, including sales, marketing, management, financial data analysis, etc. In this paper, we propose Visual Cube and multi-dimensional OLAP of image collections, such as web images indexed in search engines (e

On-line analytical processing (OLAP) has gained its popularity in database industry. With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing

Decision support systems use On-Line Analytical Processing (OLAP) to analyze data by posing complex queries that require different views of data. Traditionally, a relational approach (ROLAP) has been taken to build such systems. More recently, multi-dimensional database techniques (MOLAP) have been

Multi-Dimensional Expressions (MDX) provide an interface for asking several related OLAP queries simultaneously. An interesting problem is how to optimize the execution of an MDX query, given that most data warehouses maintain a set of redundant materialized views to accelerate OLAP operations. A number of greedy and approximation algorithms have been proposed for different versions of the problem. In this paper we evaluate experimentally their performance using the APB and TPC-H benchmark concluding that they do not scale well for realistic workloads. Motivated by this fact, we develop two novel greedy algorithms. Our algorithms construct the execution plan in a top-down manner by identifying in each step the most beneficial view, instead of finding the most promising query. We show by extensive experimentation that our methods outperform the existing ones in most cases.

One of the problems with teaching ROLAP and Data Warehousing in database systems courses is the difficulty of providing practical exposure of the topics to the students. The approach taken in this work is intended to help students apply the knowledge learnt through lectures into a practical environment. This will provide the basis for analysis and evaluation at the level of critical analysis that would be expected on an advanced level course and at the highest level of learning taxonomies. In our work we have a large dataset reversed engineered from some Oracle based sample data. This dataset has a fact table with 918843 records and dimension tables. We have also produced a smaller sample dataset for simplification and understanding of complex queries involving Slicing, Dicing, Pivoting, Rollup and Cube operations. In our examples ROLAP operations and their alternative SQL approaches are performed to provide a comparison of the multi-dimensional results.

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