Using parallelism and pipeline for the optimisation of join queries

1992 ◽  
pp. 277-294 ◽  
Author(s):  
Maria Spiliopoulou ◽  
Michalis Hatzopoulos ◽  
Costas Vassilakis
Keyword(s):  
Join Queries

About projection-selection-join queries addressed to possibilistic relational databases

2005 ◽  
Vol 13 (1) ◽  
pp. 124-139 ◽  
Author(s):  
P. Bosc ◽  
O. Pivert
Keyword(s):  
Relational Databases ◽  
Join Queries

Efficient integrity checks for join queries in the cloud1

2016 ◽  
Vol 24 (3) ◽  
pp. 347-378 ◽  
Author(s):  
Sabrina De Capitani di Vimercati ◽  
Sara Foresti ◽  
Sushil Jajodia ◽  
Stefano Paraboschi ◽  
Pierangela Samarati
Keyword(s):  
Join Queries

scholarly journals Processing top-k join queries

2010 ◽  
Vol 3 (1-2) ◽  
pp. 860-870 ◽  
Author(s):  
Minji Wu ◽  
Laure Berti-Équille ◽  
Amélie Marian ◽  
Cecilia M. Procopiuc ◽  
Divesh Srivastava
Keyword(s):  
Join Queries

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.

A Taxonomy for Distance-Based Spatial Join Queries

2017 ◽  
Vol 13 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Lingxiao Li ◽  
David Taniar
Keyword(s):  
Fixed Points ◽  
Spatial Data ◽  
Database Management ◽  
Spatial Databases ◽  
Family Type ◽  
Management Systems ◽  
Data Types ◽  
Full Spectrum ◽  
Spatial Join ◽  
Join Queries

Join operation is one of the most used operations in database management systems, including spatial databases. Hence, spatial join queries are very important in spatial database processing. There are many different kinds of spatial join queries, due to the richness in spatial data types and spatial operations. Therefore, it is important to understand the full spectrum of spatial join queries. The aim of this paper is to give a classification to one family type of spatial join, called the Distance-based Spatial Join. In the taxonomy, the authors divide this spatial join into three categories: (i) AllRange, (ii) All-kNN, and (iii) All-RNN. Each of these categories has its own variants. In this taxonomy, the authors confine the discussions to join queries on fixed points.

On resource scheduling of multi-join queries in parallel database systems

1993 ◽  
Vol 48 (4) ◽  
pp. 189-195 ◽  
Author(s):  
Kian-Lee Tan ◽  
Hongjun Lu
Keyword(s):  
Resource Scheduling ◽  
Database Systems ◽  
Parallel Database ◽  
Join Queries ◽  
Parallel Database Systems

Join queries with external text sources

1995 ◽  
Author(s):  
Surajit Chaudhuri ◽  
Umeshwar Dayal ◽  
Tak W. Yan
Keyword(s):  
Join Queries

A Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases

2000 ◽  
Vol 2 (3) ◽  
pp. 373-385 ◽  
Author(s):  
Cyrus Shahabi ◽  
Latifur Khan ◽  
Dennis McLeod
Keyword(s):  
Join Queries ◽  
Internet Databases
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