Big Data Query Engines

2017 ◽  
pp. 179-217
Author(s):  
Mohamed A. Soliman
Keyword(s):  
Big Data ◽  
Data Query

Mobile Big Data Query Based on Double R-Tree and Double Indexing

2013 ◽  
Vol 756-759 ◽  
pp. 916-921
Author(s):  
Ye Liang
Keyword(s):  
Big Data ◽  
Dynamic Condition ◽  
Index Structure ◽  
Limited Area ◽  
Universal Method ◽  
Mobile Objects ◽  
Data Query ◽  
Mobile Big Data ◽  
Structure Query ◽  
Window Query

The amount of data in our industry and the world is exploding. Data is being collected and stored at unprecedented rates. The challenge is not only to store and manage the vast volume of data, which is also called big data, but also to analyze and query from it. In order to put forward the universal method to response mobile big data query, queries are separated and grouped according to kinds of query for massive mobile objects in the space. The indexing method for grouping the mobile objects with Grid (GG TPR-tree) has great efficiency to manage a massive capacity of mobile objects within a limited area, but it only could meet a part of requirements for mobile big data query if the GG TPR-tree was used solely. This thesis offers solutions to simple immediate query, simple continuous query, active window query, and continuous window query, dynamic condition query and other query requests by employing DTDI index structure. The experiments prove that with the support of DTDI index structure, query of massive mobile objects has higher precision and better query performance.

Exploiting soft and hard correlations in big data query optimization

2016 ◽  
Vol 9 (12) ◽  
pp. 1005-1016 ◽  
Author(s):  
Hai Liu ◽  
Dongqing Xiao ◽  
Pankaj Didwania ◽  
Mohamed Y. Eltabakh
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Data Query

scholarly journals Business-oriented customized big data query system and its SQL parser design and implementation

2018 ◽  
Vol 232 ◽  
pp. 01004
Author(s):  
Wenshuai Ge ◽  
Gang He ◽  
Xinwen Liu
Keyword(s):  
Big Data ◽  
User Behavior ◽  
The Internet ◽  
Query Interface ◽  
User Behavior Analysis ◽  
Design And Implementation ◽  
Data Query ◽  
Query System ◽  
Internet User Behavior ◽  
Analysis Platform

This paper proposes a big data query system for customized queries based on specific business needs. This paper introduces the components and structure of the query system. ANTLR tools are used as language recognizer to design and implement a customized SQL dialect. The system builds a simpler and easier query interface on Spark SQL, which satisfies the query requirements of the Internet user behavior analysis platform.

Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
Nurfadhlina Mohd Sharef ◽  
◽  
Yasser M. Shafazand ◽  
Mohd Zakree Ahmad Nazri ◽  
Nor Azura Husin ◽  
...  
Keyword(s):  
Big Data ◽  
Ambiguity Resolution ◽  
Linked Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Query ◽  
Self Adaptive

Large-Scale Data Mining and Distributed Processing in Big Data Internet

2014 ◽  
Vol 989-994 ◽  
pp. 4594-4597
Author(s):  
Chun Zhi Xing
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Large Scale ◽  
Distributed Processing ◽  
Processing Method ◽  
Decision Tree Algorithm ◽  
Data Query ◽  
Large Scale Data ◽  
Scale Data

With the development of Internet, various Internet-based large-scale data are facing increasing competition. With the hope of satisfying the need of data query, it is necessary to use data mining and distributed processing. As a consequence, this paper proposes a large-scale data mining and distributed processing method based on decision tree algorithm.

scholarly journals Big Data Query Optimization -Literature Survey

2021 ◽  
Author(s):  
Anuja S. ◽  
Malathy C.
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Small Businesses ◽  
Large Data ◽  
Efficient Manner ◽  
Raw Data ◽  
Data Query ◽  
Big Data Applications ◽  
Large Databases ◽  
Private And Public

Abstract In today's world, most of the private and public sector organizations deal with massive amounts of raw data, which includes information and knowledge in their secret layer. In addition, the format, scale, variety, and velocity of generated data make it more difficult to use the algorithms in an efficient manner. This complexity necessitates the use of sophisticated methods, strategies, and algorithms to solve the challenges of managing raw data. Big data query optimization (BDQO) requires businesses to define, diagnose, forecast, prescribe, and cognize hidden growth opportunities and guiding them toward achieving market value. BDQO uses advanced analytical methods to extract information from an increasingly growing volume of data, resulting in a reduction in the difficulty of the decision-making process. Hadoop, Apache Hive, No SQL, Map Reduce, and HPCC are the technologies used in big data applications to manage large data. It is less costly to consume data for query processing because big data provides scalability. However, small businesses will never be able to query large databases. Joining tables with millions of tuples could take hours. Parallelism, which solves the problem by using more processors, may be a potential solution. Unfortunately, small businesses cannot afford to operate on a shoestring budget. There are many techniques to tackle the problem. The technologies used in the big data query optimization process are discussed in depth in this paper.

scholarly journals PoBery: Possibly-complete Big Data Queries with Probabilistic Data Placement and Scanning

2021 ◽  
Vol 2 (3) ◽  
pp. 1-28
Author(s):  
Jie Song ◽  
Qiang He ◽  
Feifei Chen ◽  
Ye Yuan ◽  
Ge Yu
Keyword(s):  
Big Data ◽  
Query Processing ◽  
State Of The Art ◽  
Data Placement ◽  
Probabilistic Data ◽  
Trade Off ◽  
Query Performance ◽  
Data Query ◽  
Query Efficiency ◽  
The Given

In big data query processing, there is a trade-off between query accuracy and query efficiency, for example, sampling query approaches trade-off query completeness for efficiency. In this article, we argue that query performance can be significantly improved by slightly losing the possibility of query completeness, that is, the chance that a query is complete. To quantify the possibility, we define a new concept, Probability of query Completeness (hereinafter referred to as PC). For example, If a query is executed 100 times, PC = 0.95 guarantees that there are no more than 5 incomplete results among 100 results. Leveraging the probabilistic data placement and scanning, we trade off PC for query performance. In the article, we propose PoBery (POssibly-complete Big data quERY), a method that supports neither complete queries nor incomplete queries, but possibly-complete queries. The experimental results conducted on HiBench prove that PoBery can significantly accelerate queries while ensuring the PC. Specifically, it is guaranteed that the percentage of complete queries is larger than the given PC confidence. Through comparison with state-of-the-art key-value stores, we show that while Drill-based PoBery performs as fast as Drill on complete queries, it is 1.7 ×, 1.1 ×, and 1.5 × faster on average than Drill, Impala, and Hive, respectively, on possibly-complete queries.

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