Algorithms for Range-Aggregate Query Problems Involving Geometric Aggregation Operations

2005 ◽  
pp. 892-901 ◽  
Author(s):  
Prosenjit Gupta
Keyword(s):  
Aggregate Query ◽  
Aggregation Operations

Best-Worst method and Hamacher aggregation operations for intuitionistic 2-tuple linguistic sets

2021 ◽  
pp. 115088
Author(s):  
Shahzad Faizi ◽  
Wojciech Sałabun ◽  
Shoaib Nawaz ◽  
Atiq-ur-Rehman ◽  
Jarosław W. atróbski
Keyword(s):  
Best Worst Method ◽  
Aggregation Operations

Query Games in Databases

2021 ◽  
Vol 50 (1) ◽  
pp. 78-85
Author(s):  
Ester Livshits ◽  
Leopoldo Bertossi ◽  
Benny Kimelfeld ◽  
Moshe Sebag
Keyword(s):  
Game Theory ◽  
Cooperative Game ◽  
Shapley Value ◽  
Cooperative Game Theory ◽  
The Shapley Value ◽  
Computational Aspects ◽  
Aggregate Query ◽  
Boolean Query

Database tuples can be seen as players in the game of jointly realizing the answer to a query. Some tuples may contribute more than others to the outcome, which can be a binary value in the case of a Boolean query, a number for a numerical aggregate query, and so on. To quantify the contributions of tuples, we use the Shapley value that was introduced in cooperative game theory and has found applications in a plethora of domains. Specifically, the Shapley value of an individual tuple quantifies its contribution to the query. We investigate the applicability of the Shapley value in this setting, as well as the computational aspects of its calculation in terms of complexity, algorithms, and approximation.

scholarly journals Hybrid Query and Data Ordering for Fast and Progressive Range-Aggregate Query Answering

2005 ◽  
Vol 1 (2) ◽  
pp. 49-69 ◽  
Author(s):  
Cyrus Shahabi ◽  
Mehrdad Jahangiri ◽  
Dimitri Sacharidis
Keyword(s):  
Query Answering ◽  
Aggregate Query

A Semantic Matrix for Aggregate Query Rewriting

2016 ◽  
pp. 46-66
Author(s):  
Romain Perriot ◽  
Laurent d’Orazio ◽  
Dominique Laurent ◽  
Nicolas Spyratos
Keyword(s):  
Query Rewriting ◽  
Aggregate Query

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.

Impact of Applying Aggregate Query Processing in Mobile Commerce

2012 ◽  
Vol 8 (2) ◽  
pp. 1-17
Author(s):  
Say Ying Lim ◽  
Siew Fan Wong
Keyword(s):  
Query Processing ◽  
Mobile Devices ◽  
Communication Cost ◽  
Single Server ◽  
Distributed Environments ◽  
Mobile Environments ◽  
Memory Consumption ◽  
Screen Size ◽  
Aggregate Query ◽  
Small Screen

With the increased usage of mobile devices, society is seeing more and more users doing transactions wirelessly. Often, data from a single server may not be sufficient. Rather, data may need to be manipulated and to be gathered from multiple remote servers before useful information can be formed. Mobile transactions are constrained by small screen size of mobile devices, high communication cost, and high memory consumption. Existing techniques from traditional query processing in distributed environments cannot be directly applied to mobile environments. In this paper, the authors propose techniques for processing mobile queries that address the issue of high memory consumption. A set of walkthrough examples was provided and performances of various techniques were examined. The results show that the technique of first downloading primary keys only from one server and then sending a query to the second server using these primary keys before processing for qualified match in the second server gives the best performance.

Optimizing Aggregate Query Processing in Cloud Data Warehouses

2014 ◽  
pp. 1-12 ◽  
Author(s):  
Swathi Kurunji ◽  
Tingjian Ge ◽  
Xinwen Fu ◽  
Benyuan Liu ◽  
Amrith Kumar ◽  
...  
Keyword(s):  
Query Processing ◽  
Data Warehouses ◽  
Cloud Data ◽  
Aggregate Query
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