Relational extensions for OLAP

2010

The aim of this study is to show that multidimensional modelling of existing data in organizations, depending on the topics of interest of managers and multidimensional view of data. It may also provide an effective informational support of managers in decision making, regardless of field of activity. To prove it, this study will design a data model and an OLAP multidimensional analysis of scientific research in education university.

Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems - PODS '97, 1997

During the 1980's there was a lot of activity in the area of Statistical Databases, focusing mostly on socio-economic type applications, such as census data, national production and consumption patterns, etc. In the 1990's the area of On-Line-Analytic Processing (OLAP) was introduced for the analysis of transaction based business data, such as retail stores transactions.

International Journal of Computer Applications, 2018

Nowadays automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories in business organizations. In this paper, a novel approach of developing a decision support system using on-line analytical processing (OLAP) is presented. The OLAP application is optimal for data queries that do not change data. The system database is designed to promote: heavy indexing to improve query performance, denormalisation of the database to satisfy common query requirements and improve query response times, and use of a star-snowflake schema to organize the data within the database. The existing partitioning strategy is used to partition the fact table by time into equal segments, differentsize segments and on different dimension of data warehouse, which provide the good query performance, optimize hardware performance, and simplify the management of the data warehouse by reducing the volume of data to satisfy a query. Entity-relation modeling is used to create a single complex model, which proven effective in creating efficient online transaction processing systems. In this system, cubes are used to organize and summarize data for efficient analytical querying. In addition, an enterprise information system model has also been presented in this paper in order to optimize the utilization of operational data for use strategically. The proposed system is evaluated on the cube data for the Rajshahi and Khulna regions for ten years from 1994 to 2003. Experimental results indicate the effectiveness of the proposed OLAP based Decision Support System.

Proc. ICEIS, 2002

The number of On-Line Analytical Processing (OLAP) applications in the market has dramatically increased in the last years. Most of these applications provide their own multidimensional (MD) models to represent main MD properties, thereby making the design totally dependent of the target commercial OLAP application. In this paper, we present a Computer-Aided Software-Engineering (CASE) operational environment to accomplish the design of an OLAP applications totally independent of the target commercial OLAP tool. The designer uses a Unified Modeling Language (UML) compliant approach to represent MD properties at the conceptual level. Once the conceptual design is finished, the CASE tool semi-automatically generates the corresponding implementation into the target commercial OLAP tool. Therefore, our approach frees conceptual design from implementation issues.

Proceedings of the fifteenth international workshop on Data warehousing and OLAP - DOLAP '12, 2012

One of the problems in analyzing large multidimensional databases through OLAP sessions is that decision makers can be overwhelmed by the size of query answers, while they need a concise summary of data. Intensional query answering can help by providing a concise description of extensional answers (i.e., the sets of retrieved facts), generally relying on knowledge like integrity constraints, taxonomies, or patterns discovered from data. This paper proposes a framework for computing an intensional answer to an OLAP query by leveraging on the previous queries in the current session. Such intensional answer is concise and semantically rich, and allows the size of the extensional answers returned to be reduced, so as to achieve an effective trade-off between conciseness and informational content. After describing the general framework, we propose a specific instantiation that relies on previous contributions in cube modeling and intensional query answering.

There are a set of noteworthy newfangled concepts and tools developed into a innovative technology that makes it conceivable to occurrence the problem of providing all the key people in the innovativeness with admittance to whatever level of information needed for the inventiveness to endure and flourish in an progressively modest world. The term that has come to characterize this new technology is "Data Warehousing" The problem of getting combined and generalized information fast from an active enterprise database becomes actual having its data been accumulated for some years. The classical reports even if optimized for particular purposes do not let one obtain fast the enterprise information with differently data-dependent views. The problem is proper to absolutely all the systems that accumulate large data volumes of information for further processing. To solve the problem is the destiny of the OLAP (On-Line Analytical Processing) technology. The technology nowadays acquiring more and more popularity is assigned to be active and operative handle for multidimensional data and Knowledge D.iscovery is defined as "the non-trivial extraction of implicit, unknown, and potentially useful information from data''. This paper shows the role of OLAP Technology in Data Warehousing for Knowledge Discovery.

Dagstuhl Reports, 2011

This report documents the outcomes of the Dagstuhl Seminar 11361 "Data Warehousing: from Occasional OLAP to Real-time Business Intelligence". In the past, data warehousing and analytical processing (OLAP) have produced fundamental and important technologies for the design, management, and use of information systems for decision support. Indeed, many industrial and administrative organizations have successfully used data warehouses to collect essential indicators that help them improve their business processes and their decision making efforts. Recent developments like column stores instead of row stores at the physical level, real-time data warehousing and Business Intelligence applications at the conceptual level, and decision support for new emerging applications have raised new research questions. This seminar has focused on the following five main topics: (i) Real-Time Data Warehouses and Business Intelligence, (ii) Spatio-Temporal Data Warehousing, (iii) Situational B...

Innovative Methods and Applications

ACM SIGMOD Record, 2008

HAL (Le Centre pour la Communication Scientifique Directe), 2019