Data warehouses - models, techniques and applications

This paper discusses the basic concepts of modern data warehouses. It presents the multidimensional data model (logical model) and the physical model of a data warehouse, as well as selected design and implementation issues. The focus is on the practical aspects of the application of data warehousing in business enterprises and organizations

The architecture of modern database systems

The paper presents major trends in the modern architecture of large database systems. Two types of architecture are described in detail: the parallel architecture and the distributed architecture. It has been widely recognised that centralised, single processor computing systems and centralised database systems in particular are approaching their theoretical limits of performance. Hence we can observe a growing interest among researchers and developers in the design and implementation of highly efficient distributed architecture. The paper focuses on different types of client-server architecture, which nowadays is becoming very popular in data processing systems

Database systems for tomorrow: new challenges and research areas

Since the mid-80s, considerable progress has been achieved in relational database technology. The main achievements have been in high performance, high reliability and availability, scalability and development tools. However, the environment for database systems is rapidly changing. There are new challenges that originate from the present hardware technology achievements, as well as from new kinds of data resources that hardly conform to the well-established relational data model (e.g. data from the Web). In the paper, we present the new challenges and research areas, as well as motivations behind them

Ontology-Based Retrieval of Experts – The Issue of Efficiency and Scalability within the eXtraSpec System

Part 1: ConferenceInternational audienceIn the knowledge-based economy, organizations often use expert finding systems to identify new candidates or manage information about the current employees. In order to ensure the required level of precision of returned results, the expert finding systems often benefit from semantic technologies and use ontologies in order to represent gathered data. Usage of ontologies however, causes additional challenges connected with the efficiency, scalability as well as the ease of use of a semantic-based solution. Within this paper we present a reasoning scenario applied within the eXtraSpec project and discuss the underlying experiments that were conducted in order to identify the best approach to follow, given the required level of expressiveness of the knowledge representation technique, and other requirements towards the system

Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering

Context-aware Recommender Systems aim to provide users with better recommendations for their current situation. Although evaluations of recommender systems often focus on accuracy, it is not the only important aspect. Often recommendations are overspecialized, i.e. all of the same kind. To deal with this problem, other properties can be considered, such as serendipity. In this paper, we study how an ontology-based and context-aware pre-filtering technique which can be combined with existing recommendation algorithm performs in ranking tasks. We also investigate the impact of our method on the serendipity of the recommendations. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can improve the accuracy and serendipity