InterJoin: Exploiting Indexes and Materialized Views in XPath Evaluation

2006 ◽  
Author(s):  
D. Phillips ◽  
Ning Zhang ◽  
I.F. Ilyas ◽  
M.T. Ozsu
Keyword(s):  
Materialized Views

scholarly journals Materialized views and data warehouses

1998 ◽  
Vol 27 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Nick Roussopoulos
Keyword(s):  
Materialized Views ◽  
Data Warehouses

Universal Data Warehousing Based on a Meta-Data Modeling Approach

2003 ◽  
Vol 12 (03) ◽  
pp. 325-363 ◽  
Author(s):  
Joseph Fong ◽  
Qing Li ◽  
Shi-Ming Huang
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Object Oriented ◽  
Data Models ◽  
Heterogeneous Databases ◽  
Materialized Views ◽  
Prototype System ◽  
Relational View ◽  
Metadata Model ◽  
Star Schema

Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements based on the integrated schema, catalogued in the metadata, which stores the schema of relational database (RDB) and object-oriented database (OODB). Data materialization between RDB and OODB is achieved by unloading source database into sequential file and reloading into target database, through which an object relational view can be defined so as to allow the users to obtain the same warehouse view in different data models simultaneously. We describe our procedures of building the relational view of star schema by multidimensional SQL query, and the object oriented view of the data warehouse by Online Analytical Processing (OLAP) through method call, derived from the integrated schema. To validate our work, an application prototype system has been developed in a product sales data warehousing domain based on this approach.

Materialized View Selection in the Data Warehouse

2010 ◽  
Vol 29-32 ◽  
pp. 1133-1138 ◽  
Author(s):  
Li Juan Zhou ◽  
Hai Jun Geng ◽  
Ming Sheng Xu
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Materialized Views ◽  
Storage Space ◽  
View Selection ◽  
Materialized View ◽  
Query Response Time ◽  
Materialized View Selection ◽  
Optimal Efficiency ◽  
The Cost

A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. The goal is to select an appropriate set of views that minimizes sum of the query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space, etc. In this article, we present an improved PGA algorithm to accomplish the view selection problem; the experiments show that our proposed algorithm shows it’s superior.

On Object-Oriented Databases, Materialized Views, and Concurrent Engineering

1991 ◽  
Author(s):  
Martin Hardwick ◽  
Blair R. Downie
Keyword(s):  
Concurrent Engineering ◽  
Object Oriented ◽  
Materialized Views ◽  
Design Tools ◽  
Design Data ◽  
Multiple Inheritance ◽  
File Formats ◽  
Definition Of ◽  
Application Specific ◽  
Object Oriented Database

Abstract Concurrent engineering seeks to reduce the length of the design life cycle by allowing multiple engineers to work on a design concurrently using their different design tools. A major stumbling block in achieving this goal is that most design tools use different file formats. Emerging standards such as STEP/PDES/EXPRESS reduce this barrier, but conformance to standards is not enough. One reason design tools have different file formats is because each tool requires a different perspective or view of the design. Engineering databases must provide designers with the ability to define application specific views of design data, and the ability to propagate changes among those related views. In this paper, we examine how an object-oriented database system can support the definition of application views using a class hierarchy and multiple inheritance.

scholarly journals Adapting materialized views after redefinitions: techniques and a performance study

2001 ◽  
Vol 26 (5) ◽  
pp. 323-362 ◽  
Author(s):  
Ashish Gupta ◽  
Inderpal S. Mumick ◽  
Jun Rao ◽  
Kenneth A. Ross
Keyword(s):  
Materialized Views ◽  
Performance Study ◽  
A Performance

Proactive and intelligent evaluation of big data queries in edge clouds with materialized views

2021 ◽  
pp. 108664
Author(s):  
Qiufen Xia ◽  
Lizhen Zhou ◽  
Wenhao Ren ◽  
Yi Wang
Keyword(s):  
Big Data ◽  
Materialized Views

A Genetic Algorithm for Selecting Horizontal Fragments

2011 ◽  
pp. 920-925
Author(s):  
Ladjel Bellatreche
Keyword(s):  
Database Systems ◽  
Data Partitioning ◽  
Materialized Views ◽  
Access Path ◽  
Multiple Dimensions ◽  
Physical Database Design ◽  
Join Queries ◽  
Speed Up ◽  
Disjoint Sets ◽  
Horizontal Partitioning

Decision support applications require complex queries, e.g., multi way joins defining on huge warehouses usually modelled using star schemas, i.e., a fact table and a set of data dimensions (Papadomanolakis & Ailamaki, 2004). Star schemas have an important property in terms of join operations between dimensions tables and the fact table (i.e., the fact table contains foreign keys for each dimension). None join operations between dimension tables. Joins in data warehouses (called star join queries) are particularly expensive because the fact table (the largest table in the warehouse by far) participates in every join and multiple dimensions are likely to participate in each join. To speed up star join queries, many optimization structures were proposed: redundant structures (materialized views and advanced index schemes) and non redundant structures (data partitioning and parallel processing). Recently, data partitioning is known as an important aspect of physical database design (Sanjay, Narasayya & Yang, 2004; Papadomanolakis & Ailamaki, 2004). Two types of data partitioning are available (Özsu & Valduriez, 1999): vertical and horizontal partitioning. Vertical partitioning allows tables to be decomposed into disjoint sets of columns. Horizontal partitioning allows tables, materialized views and indexes to be partitioned into disjoint sets of rows that are physically stored and usually accessed separately. Contrary to redundant structures, data partitioning does not replicate data, thereby reducing storage requirement and minimizing maintenance overhead. In this paper, we concentrate only on horizontal data partitioning (HP). HP may affect positively (1) query performance, by performing partition elimination: if a query includes a partition key as a predicate in the WHERE clause, the query optimizer will automatically route the query to only relevant partitions and (2) database manageability: for instance, by allocating partitions in different machines or by splitting any access paths: tables, materialized views, indexes, etc. Most of database systems allow three methods to perform the HP using PARTITION statement: RANGE, HASH and LIST (Sanjay, Narasayya & Yang, 2004). In the range partitioning, an access path (table, view, and index) is split according to a range of values of a given set of columns. The hash mode decomposes the data according to a hash function (provided by the system) applied to the values of the partitioning columns. The list partitioning splits a table according to the listed values of a column. These methods can be combined to generate composite partitioning. Oracle currently supports range-hash and range-list composite partitioning using PARTITION - SUBPARTITION statement. The following SQL statement shows an example of fragmenting a table Student using range partitioning.

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