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

Write-limited sorts and joins for persistent memory

To mitigate the impact of the widening gap between the memory needs of CPUs and what standard memory technology can deliver, system architects have introduced a new class of memory technology termed persistent memory. Persistent memory is byteaddressable, but exhibits asymmetric I/O: writes are typically one order of magnitude more expensive than reads. Byte addressability combined with I/O asymmetry render the performance profile of persistent memory unique. Thus, it becomes imperative to find new ways to seamlessly incorporate it into database systems. We do so in the context of query processing. We focus on the fundamental operations of sort and join processing. We introduce the notion of write-limited algorithms that effectively minimize the I/O cost. We give a high-level API that enables the system to dynamically optimize the workflow of the algorithms; or, alternatively, allows the developer to tune the write profile of the algorithms. We present four different techniques to incorporate persistent memory into the database processing stack in light of this API. We have implemented and extensively evaluated all our proposals. Our results show that the algorithms deliver on their promise of I/O-minimality and tunable performance. We showcase the merits and deficiencies of each implementation technique, thus taking a solid first step towards incorporating persistent memory into query processing. 1