A Unified Storage and Query Optimization Framework for Sensor Data

2015 ◽  
Author(s):  
Ting Lu ◽  
Jun Fang ◽  
Cong Liu
Keyword(s):  
Query Optimization ◽  
Sensor Data ◽  
Optimization Framework

scholarly journals SQOPI: Semantic Query Optimization Framework

2014 ◽  
Vol 96 (6) ◽  
pp. 27-32 ◽  
Author(s):  
Mohamed MounirHassan ◽  
Ahmed Mohammed Sultan
Keyword(s):  
Query Optimization ◽  
Semantic Query ◽  
Optimization Framework ◽  
Semantic Query Optimization

scholarly journals A Multi-Staged Blackboard Query Optimization Framework for World-Spanning Distributed Database Resources

2011 ◽  
Vol 4 ◽  
pp. 156-165 ◽  
Author(s):  
Peter Paul Beran ◽  
Werner Mach ◽  
Erich Schikuta ◽  
Ralph Vigne
Keyword(s):  
Query Optimization ◽  
Distributed Database ◽  
Optimization Framework

A Big Data Query Optimization Framework for Telecom Customer Churn Analysis

2021 ◽  
pp. 475-484
Author(s):  
Aarti Chugh ◽  
Vivek Kumar Sharma ◽  
Manjot Kaur Bhatia ◽  
Charu Jain
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Customer Churn ◽  
Optimization Framework ◽  
Data Query ◽  
Churn Analysis

Demand-based sensor data gathering with multi-query optimization

2020 ◽  
Vol 13 (12) ◽  
pp. 2801-2804
Author(s):  
Julius Hülsmann ◽  
Jonas Traub ◽  
Volker Markl
Keyword(s):  
Query Optimization ◽  
Data Gathering ◽  
Sensor Data

Query grouping-based multi-query optimization framework for interactive SQL query engines on Hadoop

2018 ◽  
Vol 30 (19) ◽  
pp. e4676 ◽  
Author(s):  
Ling Chen ◽  
Yan Lin ◽  
Jingchang Wang ◽  
Heqing Huang ◽  
Donghui Chen ◽  
...  
Keyword(s):  
Query Optimization ◽  
Optimization Framework ◽  
Sql Query

Semantic Query Optimization: Correctness and Control

2006 ◽  
Vol 10 (3) ◽  
pp. 270-280
Author(s):  
Pongtawat Chippimolchai ◽  
◽  
Kiyoshi Akama ◽  
Vilas Wuwongse ◽  
Keyword(s):  
Query Optimization ◽  
Control Strategy ◽  
Semantic Knowledge ◽  
Integrity Constraints ◽  
Equivalent Transformation ◽  
Two Phase ◽  
Semantic Query ◽  
Optimization Framework ◽  
Semantic Query Optimization ◽  
And Control

We developed a semantic query optimization framework for deductive databases based on equivalent transformation (ET) rules. ET rules, prepared from the semantic knowledge of databases, such as integrity constraints, transform given queries into syntactically different but semantically equivalent and more efficient forms. We formally prove the correctness of query transformations by ET rules. For efficiency, we propose a two-phase heuristic-based strategy to guide query transformations and introduce a condition-based control strategy to prevent unwanted, unnecessary transformations. We give examples demonstrating the possible optimization.

scholarly journals FQ-BF: An Efficient Forwarding and Query Optimization Framework based on Bloom Filters in NDN

2019 ◽  
Vol 8 (4) ◽  
pp. 1001-1006
Keyword(s):  
Query Optimization ◽  
Search Algorithm ◽  
Bloom Filters ◽  
Communication Model ◽  
Data Generation ◽  
Average Delay ◽  
Data Intensive ◽  
Optimization Framework ◽  
Internet Users ◽  
Content Search

With the increase in internet users and data generation resources; Named Data Networking (NDN), a content-driven communication model that has come into the limelight. Most of the applications in today’s world are data-intensive, prefer a data-based network over the address-based network for communication. Continuous efforts by researchers try to optimize the NDN performance in various areas like routing, forwarding, caching, security, etc. NDN basically works on searching for the required data packet, so the content search is one of the promising areas in NDN that still needs improvement. This paper proposes a novel content search algorithm based on Bloom Filters (BF) named FQ-BF; which focuses on efficient and smart forwarding of I_pkt and reduces the number of queries to search a packet in the network. The important metrics where the proposed approach has shown significant improvement as compared to standard NDN approach; include the average delay of packets, improving the packet serving capacity of the network.

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