915 research outputs found
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
Clustering-Based Materialized View Selection in Data Warehouses
Materialized view selection is a non-trivial task. Hence, its complexity must
be reduced. A judicious choice of views must be cost-driven and influenced by
the workload experienced by the system. In this paper, we propose a framework
for materialized view selection that exploits a data mining technique
(clustering), in order to determine clusters of similar queries. We also
propose a view merging algorithm that builds a set of candidate views, as well
as a greedy process for selecting a set of views to materialize. This selection
is based on cost models that evaluate the cost of accessing data using views
and the cost of storing these views. To validate our strategy, we executed a
workload of decision-support queries on a test data warehouse, with and without
using our strategy. Our experimental results demonstrate its efficiency, even
when storage space is limited
Optimized Generation and Maintenance of Materialized View using Adaptive Mechanism
Data Warehouse is storage of enormous amount of data gathered from multiple data sources, which is mainly used by managers for analysis purpose. Hence to make this data available in less amount of time is essential. Using Materialize view we can have result of query in less amount of time compared to access the same from base tables. To materialize all of the views is not possible since it requires storage space and maintenance cost. So it is required to select materialized view which minimizes response time of query and cost of maintenance. In this paper, effective approach is suggested for selection and maintenance of materialize view.
DOI: 10.17762/ijritcc2321-8169.15050
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
Maintenance Modification Algorithms and its Implementation on object oriented data warehouse
A data warehouse (DW) is a database used for reporting Paper describes Modification Algorithm and implementation on Object Oriented Data Warehousing. A Data Warehouse collects information and data from source data bases to support analytical processing of decision support functions and acts as an information provider. In initial research data warehouses focused on relational data model. In this paper concept of object oriented data warehouse is introduced modification maintenance algorithms and its implementation to maintained consistency between data warehouse and source data base
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