A novel algorithm with IM-LSI index for incremental maintenance of materialized view

The ability to afford decision makers with both accurate and timely consolidated information as well as rapid query response times is the fundamental requirement for the success of a Data Warehouse. To provide fast access, a data warehouse stores materialized views of the sources of its data. As a result, a data warehouse needs to be maintained to keep its contents consistent with the contents of its data sources. Incremental maintenance is generally regarded as a more efficient way to maintain materialized views in a data warehouse The view has to be maintained to reflect the updates done against the base relations stored at the various distributed data sources. The proposed approach contains two modules namely, materialized view selection(MVS) and maintenance of materialized view. (MMV). In recent times, several algorithms have been proposed for keeping the views up-to-date in response to the changes in the source data. Therefore, we present an improved algorithm for MVS and MMV using IM-LSI(Itemset Mining using Latent Semantic Index) algorithm. selection of views to materialize using the IM(Itemset Mining) algorithm method to overcome the problem resulting from conventional view selection algorithms and then we consider the maintenance of materialized views using LSI. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs better than conventional algorithms.Facultad de Informátic

A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses

Furniture models learned from the WWW: using web catalogs to locate and categorize unknown furniture pieces in 3D laser scans

In this article, we investigate how autonomous robots can exploit the high quality information already available from the WWW concerning 3-D models of office furniture. Apart from the hobbyist effort in Google 3-D Warehouse, many companies providing office furnishings already have the models for considerable portions of the objects found in our workplaces and homes. In particular, we present an approach that allows a robot to learn generic models of typical office furniture using examples found in the Web. These generic models are then used by the robot to locate and categorize unknown furniture in real indoor environments