Automated Explanations of User-Expected Trends for Aggregate Queries

2018 ◽  
pp. 602-614
Author(s):  
Ibrahim A. Ibrahim ◽  
Xue Li ◽  
Xin Zhao ◽  
Sanad Al Maskari ◽  
Abdullah M. Albarrak ◽  
...  
Keyword(s):  
Aggregate Queries

Containment of aggregate queries

2005 ◽  
Vol 34 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Sara Cohen
Keyword(s):  
Aggregate Queries

FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

2015 ◽  
Vol 3 (2) ◽  
pp. 206-218 ◽  
Author(s):  
Xiaochun Yun ◽  
Guangjun Wu ◽  
Guangyan Zhang ◽  
Keqin Li ◽  
Shupeng Wang
Keyword(s):  
Big Data ◽  
Fast Approach ◽  
Aggregate Queries

Hybrid Query and Data Qodering for Fast and Progressive Range-Aggregate Query Answering

2008 ◽  
pp. 1250-1268
Author(s):  
Cyrus Shahabi ◽  
Mehrdad Jahangiri ◽  
Dimitris Sacharidis
Keyword(s):  
Experimental Studies ◽  
Query Answering ◽  
Common Belief ◽  
Multidimensional Datasets ◽  
Scientific Datasets ◽  
Aggregate Queries ◽  
Random Dataset ◽  
Aggregate Query ◽  
The Common ◽  
Superior Approach

Data analysis systems require range-aggregate query answering of large multidimensional datasets. We provide the necessary framework to build a retrieval system capable of providing fast answers with progressively increasing accuracy in support of range-aggregate queries. In addition, with error forecasting, we provide estimations on the accuracy of the generated approximate results. Our framework utilizes the wavelet transformation of query and data hypercubes. While prior work focused on the ordering of either the query or the data coefficients, we propose a class of hybrid ordering techniques that exploits both query and data wavelets in answering queries progressively. This work effectively subsumes and extends most of the current work where wavelets are used as a tool for approximate or progressive query evaluation. The results of our experimental studies show that independent of the characteristics of the dataset, the data coefficient ordering, contrary to the common belief, is the inferior approach. Hybrid ordering, on the other hand, performs best for scientific datasets that are inter-correlated. For an entirely random dataset with no inter-correlation, query ordering is the superior approach.

A Processing of Top-k Aggregate Queries on Distributed Data

2022 ◽  
pp. 815-826
Author(s):  
Trieu Minh Nhut Le ◽  
Muoi Le Thi Be ◽  
Dung Hoang Thi Ngoc
Keyword(s):  
Distributed Data ◽  
Aggregate Queries
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