With the increasing data of the application system, the fast and efficient access to the information of support decision-making analysis has become more and more difficult. At the same time, analysis of the data is no longer on a single server or a single enterprise data, but on multiple servers, multiple departments or multiple enterprise data. So, the original OLAP technologies have also reve...

OLAP systems remain difficult to develop for two primary reasons. The first reason is that the REQ analysis step is typically overlooked [1]. The second reason is that, even when attention is accorded to this step, REQ are described from a too technical perspective for decision makers to understand and validate [2]. However, the lack of user involvement in the specification can easily lead to t...

The paper presents the original approach to the exploratory OLAP of the heterogeneous resources based on the integral analytical model. Applying the multidimensional data model as a technological fundament allows to analyze the structure of the joining sources according to unified notation, allows to present information in form of native multidimensional view and provides efficient integration ...

Decision makers query enterprise information stored in DataWarehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In prev...

We address issues related to the protection of private information in Online Analytical Processing (OLAP) systems, wherea major privacy concern is the adversarial inference of private information from OLAP query answers. Most previous work onprivacy-preserving OLAP focuses on a single aggregate function and/or addresses only exact disclosure, which eliminates fromconsideration an im...

This paper presents a framework for integrating OLAP and recommendations. We focus on the anticipatory recommendation process that assists the user during his OLAP analysis by proposing to him the forthcoming analysis step. We present a context-aware preference model that matches decisionmakers intuition, and we discuss a preference-based approach for generating personalized recommendations.

In this paper, we introduce ClustCube, an innovative OLAP-based framework for clustering and mining complex database objects extracted from distributed database settings. To this end, ClustCube puts together conventional clustering techniques and well-consolidated OLAP methodologies in order to achieve higher expressive power and mining effectiveness over traditional methodologies for mining tu...

The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation h...

On-line analytical processing (OLAP) systems deal with analytical tasks that support decision making. As these tasks do not depend on the latest updates by transactions, it is assumed that the data required by OLAP systems are kept in a data warehouse, which separates the input from operational databases from the outputs to OLAP. However, user requirements for OLAP systems change over time. Dat...

Web usage mining is the application of data mining techniques to discover usage patterns and behaviors from web data (clickstream, purchase information, customer information, etc.) in order to understand and serve e-commerce customers better and improve the online business. In this article, we present a general data warehouse/online analytic processing (OLAP) framework for web usage mining and ...