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

Data Warehouse Design and Management: Theory and Practice

The need to store data and information permanently, for their reuse in later stages, is a very relevant problem in the modern world and now affects a large number of people and economic agents. The storage and subsequent use of data can indeed be a valuable source for decision making or to increase commercial activity. The next step to data storage is the efficient and effective use of information, particularly through the Business Intelligence, at whose base is just the implementation of a Data Warehouse. In the present paper we will analyze Data Warehouses with their theoretical models, and illustrate a practical implementation in a specific case study on a pharmaceutical distribution companyData warehouse, database, data model.

Maintenance-cost view-selection in large data warehouse systems: algorithms, implementations and evaluations.

Choi Chi Hon.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 120-126).Abstracts in English and Chinese.Abstract --- p.iAbstract (Chinese) --- p.iiAcknowledgement --- p.iiiContents --- p.ivList of Figures --- p.viiiList of Tables --- p.xChapter 1 --- Introduction --- p.1Chapter 1.1 --- Maintenance Cost View Selection Problem --- p.2Chapter 1.2 --- Previous Research Works --- p.3Chapter 1.3 --- Major Contributions --- p.4Chapter 1.4 --- Thesis Organization --- p.6Chapter 2 --- Literature Review --- p.7Chapter 2.1 --- Data Warehouse and OLAP Systems --- p.8Chapter 2.1.1 --- What Is Data Warehouse? --- p.8Chapter 2.1.2 --- What Is OLAP? --- p.10Chapter 2.1.3 --- Difference Between Operational Database Systems and OLAP --- p.10Chapter 2.1.4 --- Data Warehouse Architecture --- p.12Chapter 2.1.5 --- Multidimensional Data Model --- p.13Chapter 2.1.6 --- Star Schema and Snowflake Schema --- p.15Chapter 2.1.7 --- Data Cube --- p.17Chapter 2.1.8 --- ROLAP and MOLAP --- p.19Chapter 2.1.9 --- Query Optimization --- p.20Chapter 2.2 --- Materialized View --- p.22Chapter 2.2.1 --- What Is A Materialized View --- p.23Chapter 2.2.2 --- The Role of Materialized View in OLAP --- p.23Chapter 2.2.3 --- The Challenges in Exploiting Materialized View --- p.24Chapter 2.2.4 --- What Is View Maintenance --- p.25Chapter 2.3 --- View Selection --- p.27Chapter 2.3.1 --- Selection Strategy --- p.27Chapter 2.4 --- Summary --- p.32Chapter 3 --- Problem Definition --- p.33Chapter 3.1 --- View Selection Under Constraint --- p.33Chapter 3.2 --- The Lattice Framework for Maintenance Cost View Selection Prob- lem --- p.35Chapter 3.3 --- The Difficulties of Maintenance Cost View Selection Problem --- p.39Chapter 3.4 --- Summary --- p.41Chapter 4 --- What Difference Heuristics Make --- p.43Chapter 4.1 --- Motivation --- p.44Chapter 4.2 --- Example --- p.46Chapter 4.3 --- Existing Algorithms --- p.49Chapter 4.3.1 --- A*-Heuristic --- p.51Chapter 4.3.2 --- Inverted-Tree Greedy --- p.52Chapter 4.3.3 --- Two-Phase Greedy --- p.54Chapter 4.3.4 --- Integrated Greedy --- p.57Chapter 4.4 --- A Performance Study --- p.60Chapter 4.5 --- Summary --- p.68Chapter 5 --- Materialized View Selection as Constrained Evolutionary Opti- mization --- p.71Chapter 5.1 --- Motivation --- p.72Chapter 5.2 --- Evolutionary Algorithms --- p.73Chapter 5.2.1 --- Constraint Handling: Penalty v.s. Stochastic Ranking --- p.74Chapter 5.2.2 --- The New Stochastic Ranking Evolutionary Algorithm --- p.78Chapter 5.3 --- Experimental Studies --- p.81Chapter 5.3.1 --- Experimental Setup --- p.82Chapter 5.3.2 --- Experimental Results --- p.82Chapter 5.4 --- Summary --- p.89Chapter 6 --- Dynamic Materialized View Management Based On Predicates --- p.90Chapter 6.1 --- Motivation --- p.91Chapter 6.2 --- Examples --- p.93Chapter 6.3 --- Related Work: Static Prepartitioning-Based Materialized View Management --- p.96Chapter 6.4 --- A New Dynamic Predicate-based Partitioning Approach --- p.99Chapter 6.4.1 --- System Overview --- p.102Chapter 6.4.2 --- Partition Advisor --- p.103Chapter 6.4.3 --- View Manager --- p.104Chapter 6.5 --- A Performance Study --- p.108Chapter 6.5.1 --- Performance Metrics --- p.110Chapter 6.5.2 --- Feasibility Studies --- p.110Chapter 6.5.3 --- Query Locality --- p.112Chapter 6.5.4 --- The Effectiveness of Disk Size --- p.115Chapter 6.5.5 --- Scalability --- p.115Chapter 6.6 --- Summary --- p.116Chapter 7 --- Conclusions and Future Work --- p.118Bibliography --- p.12

Data Mining-based Fragmentation of XML Data Warehouses

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority

A workload‑driven approach for view selection in large dimensional datasets

The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of read-intensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads

A solution to the materialized view selection problem in data warehousing

One of the most important decisions in the physical designing of a data warehouse is the selection of materialized views and indexes to be created. The problem is to select an appropriate set of views and indexes to storage that minimizes the total query response time, as long as the cost of maintaining them, given a constraint of some resource like storage space, is kept as low as possible.In this work, we have developed a new algorithm for the general problem of se-lection of views considering indexes, as an extension to a well-known algorithm. We present a heuristic for selection of views and indexes to optimize total que-ry response under a materialization time constraint. Finally, we present an ex-perimental comparison of our proposal with the considered state-of-art ap-proach.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI

A solution to the materialized view selection problem in data warehousing

One of the most important decisions in the physical designing of a data warehouse is the selection of materialized views and indexes to be created. The problem is to select an appropriate set of views and indexes to storage that minimizes the total query response time, as long as the cost of maintaining them, given a constraint of some resource like storage space, is kept as low as possible.In this work, we have developed a new algorithm for the general problem of se-lection of views considering indexes, as an extension to a well-known algorithm. We present a heuristic for selection of views and indexes to optimize total que-ry response under a materialization time constraint. Finally, we present an ex-perimental comparison of our proposal with the considered state-of-art ap-proach.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI

EFFICIENT APPROACH FOR VIEW SELECTION FOR DATA WAREHOUSE USING TREE MINING AND EVOLUTIONARY COMPUTATION

Selection of a proper set of views to materialize plays an important role indatabase performance. There are many methods of view selection which uses different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient, scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. Tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. Query Cost model achieves the objective of maximizing the performance benefits from the final view set which is derived from the frequent view set given by tree mining algorithm. Performance benefit of a query is defined as a function of queryfrequency, query creation cost, and query maintenance cost. The experimental results shows that the proposed method is successful in recommending a solution which is fairly close to optimal solution