Database Systems - Present and Future

The database systems have nowadays an increasingly important role in the knowledge-based society, in which computers have penetrated all fields of activity and the Internet tends to develop worldwide. In the current informatics context, the development of the applications with databases is the work of the specialists. Using databases, reach a database from various applications, and also some of related concepts, have become accessible to all categories of IT users. This paper aims to summarize the curricular area regarding the fundamental database systems issues, which are necessary in order to train specialists in economic informatics higher education. The database systems integrate and interfere with several informatics technologies and therefore are more difficult to understand and use. Thus, students should know already a set of minimum, mandatory concepts and their practical implementation: computer systems, programming techniques, programming languages, data structures. The article also presents the actual trends in the evolution of the database systems, in the context of economic informatics.database systems - DBS, database management systems – DBMS, database – DB, programming languages, data models, database design, relational database, object-oriented systems, distributed systems, advanced database systems

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

Data mining techniques using decision tree model in materialised projection and selection view

With the availability of very large data storage today, redundant data structures are no longer a big issue. However, an intelligent way of managing materialised projection and selection views that can lead to fast access of data is the central issue dealt with in this paper. A set of implementation steps for the data warehouse administrators or decision makers to improve the response time of queries is also defined. The study concludes that both attributes and tuples, are important factors to be considered to improve the response time of a query. The adoption of data mining techniques in the physical design of data warehouses has been shown to be useful in practice

Analyze Large Multidimensional Datasets Using Algebraic Topology

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper also investigate the possibility of Hadoop integration and the challenges that come with the framework

Advance of the Access Methods

The goal of this paper is to outline the advance of the access methods in the last ten years as well as to make review of all available in the accessible bibliography methods

Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

International audienceThis paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis. Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier