scholarly journals Geospatial stream query processing using Microsoft SQL Server StreamInsight

2010 ◽  
Vol 3 (1-2) ◽  
pp. 1537-1540 ◽  
Author(s):  
Seyed Jalal Kazemitabar ◽  
Ugur Demiryurek ◽  
Mohamed Ali ◽  
Afsin Akdogan ◽  
Cyrus Shahabi
Keyword(s):  
Query Processing ◽  
Sql Server ◽  
Stream Query Processing

XML Stream Query Processing

2010 ◽  
pp. 89-107
Author(s):  
Mingzhu Wei ◽  
Ming Li ◽  
Elke A. Rundensteiner ◽  
Murali Mani ◽  
Hong Su
Keyword(s):  
Query Processing ◽  
Input Data ◽  
Stream Processing ◽  
Query Execution ◽  
Data Semantics ◽  
Stream Query Processing ◽  
Xml Stream ◽  
And Algebra ◽  
Xml Streams ◽  
Compare And Contrast

Stream applications bring the challenge of efficiently processing queries on sequentially accessible XML data streams. In this chapter, the authors study the current techniques and open challenges of XML stream processing. Firstly, they examine the input data semantics in XML streams and introduce the state-of-the-art of XML stream processing. Secondly, they compare and contrast the automatonbased and algebra-based techniques used in XML stream query execution. Thirdly, they study different optimization strategies that have been investigated for XML stream processing – in particular, they discuss cost-based optimization as well as schema-based optimization strategies. Lastly but not least, the authors list several key open challenges in XML stream processing.

Distributed/Heterogeneous Query Processing in Microsoft SQL Server

2005 ◽  
Author(s):  
J.A. Blakeley ◽  
C. Cunningham ◽  
N. Ellis ◽  
B. Rathakrishnan ◽  
Ming-Chuan Wu
Keyword(s):  
Query Processing ◽  
Sql Server

scholarly journals Analisis Alat Bantu Tuning Fisikal Basis Data pada Sql Server 2008

2014 ◽  
Vol 5 (1) ◽  
pp. 334
Author(s):  
Marlene Martani ◽  
Hanny Juwitasary ◽  
Arya Nata Gani Putra
Keyword(s):  
Information Technology ◽  
Query Processing ◽  
Query Optimization ◽  
Business Processes ◽  
Database System ◽  
Sql Server ◽  
Process Data ◽  
Analysis Of Technique ◽  
Tuning Methods ◽  
Application Tuning

Nowadays every company has been faced with a business competition that requires the company to survive and be superior to its competitors. One strategy used by many companies is to use information technology to run their business processes. The use of information technology would require a storage which commonly referred to as a database to store and process data into useful information for the company. However, it was found that the greater the amount of data in the database, then the speed of the resulting process will decrease because the time needed to access the data will be much longer. The long process of data can cause a decrease in the company’s performance and the length of time needed to make decisions so that this can be a challenge to achieve the company’s competitive advantage. In this study performed an analysis of technique to improve the performance of the database system used by the company to perform tuning on SQL Server 2008 database physically. The purpose of this study is to improve the performance of the database used by speeding up the time it takes when doing query processing. The research methodology used was the method of analysis such as literature studies, analysis of the process and the workings of tuning tools that already exist in SQL Server 2008, and evaluation of applications that have been created, and also tuning methods that include query optimization and create index. The results obtained from this study is an evaluation of the physical application tuning tools that can integrate database functionality of other tuning tools such as SQL Profiler and Database Tuning Advisor.

scholarly journals In-Memory Based Incremental Processing Method for Stream Query Processing in Big Data Environments

2016 ◽  
Vol 16 (2) ◽  
pp. 163-173
Author(s):  
Kyoungsoo Bok ◽  
Misun Yook ◽  
Yeonwoo Noh ◽  
Jieun Han ◽  
Yeonwoo Kim ◽  
...  
Keyword(s):  
Big Data ◽  
Query Processing ◽  
Processing Method ◽  
Incremental Processing ◽  
Stream Query Processing
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