Materializing views in data warehouse: an efficient approach to OLAP.

Gou Gang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 83-87).Abstracts in English and Chinese.Acknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Warehouse and OLAP --- p.4Chapter 1.2 --- Computational Model: Dependent Lattice --- p.10Chapter 1.3 --- Materialized View Selection --- p.12Chapter 1.3.1 --- Materialized View Selection under a Disk-Space Constraint --- p.13Chapter 1.3.2 --- Materialized View Selection under a Maintenance-Time Con- straint --- p.16Chapter 1.4 --- Main Contributions --- p.21Chapter 2 --- A* Search: View Selection under a Disk-Space Constraint --- p.24Chapter 2.1 --- The Weakness of Greedy Algorithms --- p.25Chapter 2.2 --- A*-algorithm --- p.29Chapter 2.2.1 --- An Estimation Function --- p.36Chapter 2.2.2 --- Pruning Feasible Subtrees --- p.38Chapter 2.2.3 --- Approaching the Optimal Solution from Two Directions --- p.41Chapter 2.2.4 --- NIBS Order: Accelerating Convergence --- p.43Chapter 2.2.5 --- Sliding Techniques: Eliminating Redundant H-Computation --- p.45Chapter 2.2.6 --- Examples --- p.50Chapter 2.3 --- Experiment Results --- p.54Chapter 2.3.1 --- Analysis of Experiment Results --- p.55Chapter 2.3.2 --- Computing for a Series of S Constraints --- p.60Chapter 2.4 --- Conclusions --- p.62Chapter 3 --- Randomized Search: View Selection under a Maintenance-Time Constraint --- p.64Chapter 3.1 --- Non-monotonic Property --- p.65Chapter 3.2 --- A Stochastic-Ranking-Based Evolutionary Algorithm --- p.67Chapter 3.2.1 --- A Basic Evolutionary Algorithm --- p.68Chapter 3.2.2 --- The Weakness of the rg-Method --- p.69Chapter 3.2.3 --- Stochastic Ranking: a Novel Constraint Handling Technique --- p.70Chapter 3.2.4 --- View Selection Using the Stochastic-Ranking-Based Evolu- tionary Algorithm --- p.72Chapter 3.3 --- Conclusions --- p.74Chapter 4 --- Conclusions --- p.75Chapter 4.1 --- Thesis Review --- p.76Chapter 4.2 --- Future Work --- p.78Chapter A --- My Publications for This Thesis --- p.81Bibliography --- p.8

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

XML Reconstruction View Selection in XML Databases: Complexity Analysis and Approximation Scheme

Query evaluation in an XML database requires reconstructing XML subtrees rooted at nodes found by an XML query. Since XML subtree reconstruction can be expensive, one approach to improve query response time is to use reconstruction views - materialized XML subtrees of an XML document, whose nodes are frequently accessed by XML queries. For this approach to be efficient, the principal requirement is a framework for view selection. In this work, we are the first to formalize and study the problem of XML reconstruction view selection. The input is a tree $T$, in which every node $i$ has a size $c_i$ and profit $p_i$, and the size limitation $C$. The target is to find a subset of subtrees rooted at nodes $i_1,\cdots, i_k$ respectively such that $c_{i_1}+\cdots +c_{i_k}\le C$, and $p_{i_1}+\cdots +p_{i_k}$ is maximal. Furthermore, there is no overlap between any two subtrees selected in the solution. We prove that this problem is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) as a solution

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

Automatic physical database design : recommending materialized views

This work discusses physical database design while focusing on the problem of selecting materialized views for improving the performance of a database system. We first address the satisfiability and implication problems for mixed arithmetic constraints. The results are used to support the construction of a search space for view selection problems. We proposed an approach for constructing a search space based on identifying maximum commonalities among queries and on rewriting queries using views. These commonalities are used to define candidate views for materialization from which an optimal or near-optimal set can be chosen as a solution to the view selection problem. Using a search space constructed this way, we address a specific instance of the view selection problem that aims at minimizing the view maintenance cost of multiple materialized views using multi-query optimization techniques. Further, we study this same problem in the context of a commercial database management system in the presence of memory and time restrictions. We also suggest a heuristic approach for maintaining the views while guaranteeing that the restrictions are satisfied. Finally, we consider a dynamic version of the view selection problem where the workload is a sequence of query and update statements. In this case, the views can be created (materialized) and dropped during the execution of the workload. We have implemented our approaches to the dynamic view selection problem and performed extensive experimental testing. Our experiments show that our approaches perform in most cases better than previous ones in terms of effectiveness and efficiency