scholarly journals Physical Datarepresentation in a Multiprocessor Database Machine

1983 ◽  
Vol 12 (168) ◽  
Author(s):  
Jørgen Staunstrup ◽  
Jens Ove Jespersen ◽  
Ole V. Johansen
Keyword(s):  
Relational Database ◽  
Physical Data ◽  
Data Representations ◽  
Database Operations ◽  
Database Machine

<p>By using a multiprocessor to implement the lowest level of a relational database we want to achieve fast execution of database operations such as join, find, and update.</p><p>But the potential speed improvements provided by a multiprocessor can only be achieved if one can construct algorithms and corresponding physical data representations that can utilize the potential.</p><p>By choosing a particular representation, the grid file, and analyzing its behaviour, we want to point out the difficulties encountered in trying to achieve speed improvements from a multiprocessor.</p>

scholarly journals Multi-objective database queries in combined knapsack and set covering problem domains

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sean A. Mochocki ◽  
Gary B. Lamont ◽  
Robert C. Leishman ◽  
Kyle J. Kauffman
Keyword(s):  
Relational Database ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Database Queries ◽  
Multi Objective ◽  
Data Representations ◽  
Np Hard Problem ◽  
Institute Of Technology ◽  
The One

AbstractDatabase queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.

DBMAC: A Parallel Relational Database Machine

1986 ◽  
pp. 85-126 ◽  
Author(s):  
M. Missikoff ◽  
S. Salza ◽  
M. Terranova
Keyword(s):  
Relational Database ◽  
Database Machine

Search operation methods in the relational database machine RINDA

1993 ◽  
Vol 24 (9) ◽  
pp. 1-13
Author(s):  
Haruo Hayami ◽  
Tetsuji Satoh ◽  
Toshio Nakamura ◽  
Junichi Kuroiwa ◽  
Hideaki Takeda
Keyword(s):  
Relational Database ◽  
Search Operation ◽  
Database Machine

A Tree-structured Database Machine for large relational database systems

1987 ◽  
Vol 2 (4) ◽  
pp. 265-275 ◽  
Author(s):  
Liming Meng ◽  
Xiaofei Xu ◽  
Huiyou Chang ◽  
Guangxi Chen ◽  
Mingzeng Hu ◽  
...  
Keyword(s):  
Relational Database ◽  
Database Systems ◽  
Relational Database Systems ◽  
Database Machine

scholarly journals Multi-Objective Database Queries in Combined Knapsack and Set Covering Problem Domains

2020 ◽  
Author(s):  
Sean Mochocki ◽  
Gary Lamont ◽  
Robert Leishman ◽  
Kyle Kauffman
Keyword(s):  
Relational Database ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Database Queries ◽  
Multi Objective ◽  
Data Representations ◽  
Np Hard Problem ◽  
Institute Of Technology ◽  
The One

Abstract Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomialtime queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack-/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.

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