The research on the query optimization on the distributed heterogeneous database based on the response time

2011 ◽  
Author(s):  
Zhang Zhenyou ◽  
Luo Bin ◽  
Cao Zhi
Keyword(s):  
Response Time ◽  
Query Optimization ◽  
Heterogeneous Database

scholarly journals Pengujian Optimization dan Non-Optimization Query Metode Topsis untuk Menentukan Tingkat Kerusakan Sektor Bencana Alam

2022 ◽  
Vol 6 (1) ◽  
pp. 89-99
Author(s):  
Annisa Heparyanti Safitri ◽  
Agung Teguh Wibowo Almais ◽  
A'la Syauqi ◽  
Roro Inda Melani
Keyword(s):  
Response Time ◽  
Query Optimization ◽  
Average Response ◽  
Volume Data ◽  
Average Response Time

Volume data yang sangat besar dari tim surveyor Perencanaan dan Pengendalian Penanganan Bencana(P3B) menciptakan masalah yang luas dan beragam sehingga dapat menghabiskan sumber daya sistem dan waktu pemrosesan yang terbilang lama. Oleh karena itu penelitian ini mengusulkan solusi dengan melakukan Optimasi query pada metode TOPSIS yang diimplementasikan pada sistem pendukung kepeutusan untuk menentukan tingkat kerusakan pasca bencana. Berdasarkan 3 kali uji coba dengan jumlah data yang berbeda-beda yaitu ujicoba ke-1 menggunakan 114 data, ujicoba ke-2 sebanyak 228 data dan ujicoba ke-3 menggunakan 334 data. Selain itu, setiap ujicoba dilakukan lagi pengukuran re-spons time sebanyak 3 kali maka didapatkan hasil rata-rata (average) response time dari masing-masing langkah metode TOPSIS. Didapati bahwa hasil dari tahapan perangkingan menggunakan query optimiza-tion lebih cepat 0.00076 dibandingakan dengan qury non-optimization. Sehingga dapat di simpulkan bahwa response time yang didapat query optimization pada setiap langkah metode TOPSIS pada sistem pendukung keputusan kerusakan sektor pasca bencana alam lebih kecil dibandingkan dengan response time pada query non-optimization.

Query Optimization Using Fuzzy Logic in Integrated Database

2016 ◽  
Vol 4 (3) ◽  
pp. 637
Author(s):  
Susana Susana ◽  
Suharjito Suharjito
Keyword(s):  
Fuzzy Logic ◽  
Response Time ◽  
Query Optimization ◽  
Membership Functions ◽  
Inference System ◽  
Query Response Time ◽  
Data Processing Method ◽  
Significant Difference ◽  
Inference Systems ◽  
Logic Inference

<p>Query optimization in integrated database can’t be separated from data processing method.  In order to have faster query response time, a method to optimize queries is required.  One of many methods that can be used for query optimization is using fuzzy logic with Tsukamoto inference system.  Value set on each variable is defined membership functions and Tsukamoto inference system used in determining these rules or the terms of query results, then apply it into query method or query line structure.  The application of fuzzy logic inference systems with Tsukamoto can accelerate query response time, and will have more significant difference when the amount of selected data is greater.</p>

scholarly journals Extended Adaptive Join Operator with Bind-Bloom Join for Federated SPARQL Queries

2017 ◽  
Vol 13 (3) ◽  
pp. 47-72
Author(s):  
Damla Oguz ◽  
Shaoyi Yin ◽  
Belgin Ergenç ◽  
Abdelkader Hameurlain ◽  
Oguz Dikenelli
Keyword(s):  
Response Time ◽  
Query Optimization ◽  
Linked Data ◽  
Completion Time ◽  
Optimal Response ◽  
Time Performance ◽  
Communication Time ◽  
Extended Operator ◽  
Adaptation Ability ◽  
Hash Join

The goal of query optimization in query federation over linked data is to minimize the response time and the completion time. Communication time has the highest impact on them both. Static query optimization can end up with inefficient execution plans due to unpredictable data arrival rates and missing statistics. This study is an extension of adaptive join operator which always begins with symmetric hash join to minimize the response time, and can change the join method to bind join to minimize the completion time. The authors extend adaptive join operator with bind-bloom join to further reduce the communication time and, consequently, to minimize the completion time. They compare the new operator with symmetric hash join, bind join, bind-bloom join, and adaptive join operator with respect to the response time and the completion time. Performance evaluation shows that the extended operator provides optimal response time and further reduces the completion time. Moreover, it has the adaptation ability to different data arrival rates.

Efficient Materialized View Selection for Multi-Dimensional Data Cube Models

2016 ◽  
Vol 6 (3) ◽  
pp. 52-74 ◽  
Author(s):  
Naveen Dahiya ◽  
Vishal Bhatnagar ◽  
Manjeet Singh
Keyword(s):  
Decision Support ◽  
Response Time ◽  
Decision Support Systems ◽  
Query Optimization ◽  
Support Systems ◽  
Optimization Techniques ◽  
View Selection ◽  
Materialized View ◽  
Selection Approach ◽  
Materialized View Selection

Decision Support Systems help managers to make intelligent decisions by throwing complex queries on large databases. The response time to queries is a very crucial factor in governing the quality of decision support systems. The response time can be greatly improved by using query optimization techniques. A powerful query optimization technique selects only some of the views and not all views for materialization. The authors in this paper present a refined greedy selection approach using forward references to give better materialized view selection. The approach works on lattice framework of data that is capable enough to show inter dependencies of data. The choice of materialized views using the proposed approach gives a better trade off in terms of space/benefits, which is proved from the experimental results. The refined greedy selection approach is independent of space constraint and depends on number of passes entered by the user. The view selection is further enhanced by including space constraints to the results of greedy and refined greedy approach using knapsack implementation.

scholarly journals Learning response time for WebSources using query feedback and application in query optimization

2000 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Jean-Robert Gruser ◽  
Louiqa Raschid ◽  
Vladimir Zadorozhny ◽  
Tao Zhan
Keyword(s):  
Response Time ◽  
Query Optimization

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

A Response-Time Investigation of Vehicle Stereotypes

2000 ◽  
Author(s):  
Michael Anthony ◽  
Robert W. Fuhrman
Keyword(s):  
Response Time
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