scholarly journals Query Optimization in OODBMS: Identifying Subquery for Complex Query Management

2014 ◽  
Author(s):  
Sheetal S. Dhande ◽  
Bamnote G.R
Keyword(s):  
Query Optimization ◽  
Complex Query ◽  
Query Management

scholarly journals A Novel IR for Relational Database using Optimize Query Building

2019 ◽  
pp. 251-257
Author(s):  
Sangeeta Vishwakarma ◽  
Avinash Dhole
Keyword(s):  
Natural Language ◽  
Search Engine ◽  
Query Optimization ◽  
Relational Database ◽  
Execution Time ◽  
Optimization Approach ◽  
Complex Query ◽  
Query Intent ◽  
Pos Tagger ◽  
Sql Query

The different type search engine like Google, binge, AltaVista is used to fetch the information from the database by easy language. The non-technical employee they don’t understand the database and query cannot access the database. The proposed system is performing work as a search engine where users can fetch the information from the database by natural human sounding language. The previous existing system doesn’t able to solve queries in one easy statement. The structured query approach, while expressive and powerful, is not easy for naive users. The keyword-based approach is very friendly to use, but cannot express complex query intent accurately. This paper emphasis on Natural Language based query processor. We have proposed the use of query optimization approach to convert complex NLP query to SQL query, SPAM word removal, POS tagger applied over NL query and concluded that execution time lesser when query size increases.

Multi-dimensional query optimization algorithm for bitmap index with binning

2010 ◽  
Vol 30 (8) ◽  
pp. 2013-2016 ◽  
Author(s):  
Li-ming WANG ◽  
Xiao CHENG ◽  
Yu-mei CHAI
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Bitmap Index

A Query Optimization Method Based on User Profiles Reasoning

2011 ◽  
Vol 30 (1) ◽  
pp. 33-37
Author(s):  
Xiang Mei ◽  
Xiang-wu Meng ◽  
Jun-Liang Chen ◽  
Meng Xu
Keyword(s):  
Query Optimization ◽  
Optimization Method ◽  
User Profiles

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 149
Author(s):  
Petros Zervoudakis ◽  
Haridimos Kondylakis ◽  
Nicolas Spyratos ◽  
Dimitris Plexousakis
Keyword(s):  
Query Optimization ◽  
Query Language ◽  
Computational Cost ◽  
Continuous Queries ◽  
Continuous Query ◽  
Query Rewriting ◽  
Query Evaluation ◽  
Clear Separation ◽  
Complete Dataset ◽  
High Level

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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