A Method to Improve the Fresh Data Query Efficiency of Blockchain

2020 ◽  
Author(s):  
Xinhua Liu ◽  
Xirui Yu ◽  
Xiaolin Ma ◽  
Hailan Kuang
Keyword(s):  
Data Query ◽  
Query Efficiency

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.

scholarly journals Patient-Oriented Cancer Information on the Internet: A Comparison of Wikipedia and a Professionally Maintained Database

2011 ◽  
Vol 7 (5) ◽  
pp. 319-323 ◽  
Author(s):  
Malolan S. Rajagopalan ◽  
Vineet K. Khanna ◽  
Yaacov Leiter ◽  
Meghan Stott ◽  
Timothy N. Showalter ◽  
...  
Keyword(s):  
Cancer Information ◽  
The Internet ◽  
Data Query ◽  
Coverage Accuracy

The coverage, accuracy, and readability of cancer information on Wikipedia are compared with the patient-orientated National Cancer Institute's Physician Data Query comprehensive cancer database.

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.

Research on Query Optimization Technology in Distributed Database

2012 ◽  
Vol 433-440 ◽  
pp. 3335-3339
Author(s):  
Bo Zhu Wu
Keyword(s):  
Query Processing ◽  
Query Optimization ◽  
Distributed Database ◽  
Query Time ◽  
Processing Technology ◽  
Database Query ◽  
Processing Program ◽  
Depth Study ◽  
Database Query Processing ◽  
Query Efficiency

Through the in-depth study of the existing distributed database query processing technology, this paper proposes a distributed database query processing program. This program optimizes the existing query processing, stores the commonly used query results according to the query frequency, to be directly used by the subsequent queries or used as intermediate query results, thus avoiding possible transmission of a large number of data, thereby reducing the query time and improving query efficiency.

Computer-assisted instruction in cancer for third-year medical students using the physician data query (PDQ) system

1989 ◽  
Vol 4 (1) ◽  
pp. 11-15 ◽  
Author(s):  
Q. Scott Ringenberg ◽  
E. Diane Johnson ◽  
Donald Doll ◽  
Sharon Anderson ◽  
John Yarbro
Keyword(s):  
Medical Students ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Data Query ◽  
Assisted Instruction

Optimization Method of Massive Data Query

2014 ◽  
Vol 602-605 ◽  
pp. 3247-3250
Author(s):  
Yu Ming Chen
Keyword(s):  
Optimization Method ◽  
Experimental Results ◽  
Massive Data ◽  
Information Complexity ◽  
Apriori Algorithm ◽  
Amount Of Information ◽  
Data Query ◽  
Target Data

Optimization method ofmassive dataquery is researched in this paper.In the modernInternet environment,the datahas the characteristics oflarge amount of information, complexity, disorder, andchaosassociation. Using traditionalqueried methodsoftenrequirea lot oflimitedconditions, witha lot of drawbacks such as time-consuming data query, moreineffective queryand low efficiency.To this end, anoptimizationmethod of massive data query based onparallel Apriori algorithm is proposed in this paper.The massive dataare made simplification processing andredundant data are deleted to providedata foundation for fast and accuratedataquery.Effectiveassociation rulesof the massive data are calculated, in order to obtain the relevantof the target data. Based onAprioriparallel algorithm,massivedata are processedto achieveaccurate query. Experimental results show thatthe use ofoptimization algorithm for massive dataquerycan improvethe query speedof target data and it has a strong superiority.

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