Earticle

Range-aggregate queries are popular in many applications having business relational data. In order to efficiently evaluate it, several works on data cubes (such as the aggregate cubetree) are proposed. In the aggregate cubetree, each entry in every node stores the aggregate values of its corresponding subtree. Therefore, range-aggregate queries can be processed without visiting the child nodes whose parent nodes are fully included in the query range. However, the aggregate cubetree does not take range queries using partial dimensions and range queries without aggregation operations into account. That is, 1) a great deal of information that is irrelevant to the queries also has to be read from the disk for partially-dimensional range queries and 2) while it improves the performance of range queries with aggregate operations, it degrades the performance of the range queries without aggregate operations. In this paper, we proposed a novel index structure, called Aggregate-Tree (denoted as Ag-Tree), which gets rid of the above-mentioned weaknesses of the aggregate cubetree without any side effects. The experiments and discussions presented in this paper indicate that the new proposal is significant for range queries in data warehouse environments.

Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. We proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies. Three approaches for discovering multi-level frequent itemsets under constraints were proposed and discussed: Basic approach, “Test and Generate” approach and Pruning based Approach.

Data warehouse is organized as collection of multidimensional cube, which represents data in the form of data values, called measures, associated with multiple dimensions and their multiple classification levels. In multidimensional cube, object oriented approach may be used for structuring the data. In this paper object oriented multidimensional data model is defined for description of data which include aggregation, generalization, multiple path hierarchies, multiplicity etc. Seven operators over the model are introduced, necessary to make a query and format results: intersection, difference, symmetric difference, restriction, union, join, projection. These operators are minimal means none can be expressed in terms of others nor can anyone be dropped without sacrificing functionality. This paper extends the work of Alexander [7] which includes only four operators: Restriction, union, join and projection.

Visualization is the process of transforming data, information, and knowledge into visual form, making use of humans’ natural visual capabilities. Different methodologies are available for analyzing large multidimensional data sets and providing insights with respect to scientific, economic, and engineering applications. This problem has traditionally been formulated as a non-linear mathematical programming. In this paper, we formulate the data visualization problem as a quadratic assignment problem. However, this formulation is computationally difficult to solve optimally using an exact approach. Consequently, we investigate the use of the genetic algorithm for the data visualization problem. To examine capabilities of proposed method, we use a demand database by electricity customers, and compare the results with results by Self Organizing Maps (SOMs). This can be concluded that this approach generates higher quality output.