Hierarchical Data Retrieval Model For Big Data

2013 ◽  
Vol 12 (24) ◽  
pp. 8176-8180
Author(s):  
Sijin Chen ◽  
Shao Bo Wu ◽  
Xue Ying Gao
Author(s):  
Jinfeng Dou ◽  
Lei Chu ◽  
Jiabao Cao ◽  
Yang Qiu ◽  
BaoLin Zhao
Keyword(s):  
Big Data ◽  

Author(s):  
Ido Millet

Relational databases and the current SQL standard are poorly suited to retrieval of hierarchical data. After demonstrating the problem, this chapter describes how two approaches to data denormalization can facilitate hierarchical data retrieval. Both approaches solve the problem of data retrieval, but as expected, come at the cost of difficult and potentially inconsistent data updates. This chapter then describes how we can address these update-related shortcomings via back-end (triggers) logic. Using a proper combination of denormalized data structure and back-end logic, we can have the best of both worlds: easy data retrieval and simple, consistent data updates.


2018 ◽  
Vol 8 (9) ◽  
pp. 1514 ◽  
Author(s):  
Bao Chang ◽  
Hsiu-Fen Tsai ◽  
Yun-Da Lee

This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an open-source software solution with low cost for small-to-medium enterprise use. In practice, the proposed approach provides an in-memory cache and an in-disk cache to achieve a very fast response to a query if a cache hit occurs. Moreover, this paper develops so-called platform selection that is able to select the appropriate tool dealing with input query with effectiveness and efficiency. As a result, the speed of job execution of proposed approach using platform selection is 2.63 times faster than Hive in the Case 1 experiment, and 4.57 times faster in the Case 2 experiment.


1998 ◽  
Vol 110 (1-3) ◽  
pp. 198-205
Author(s):  
M. Marquina ◽  
R. Ramos Pollán ◽  
A. Taddei

2018 ◽  
Vol Volume-2 (Issue-6) ◽  
pp. 1273-1277
Author(s):  
Mr. Manish Vala ◽  
Kajal Patel ◽  
Harsh Lad ◽  
Keyword(s):  
Big Data ◽  

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