Efficient Relational Joins with Arithmetic Constraints on Multiple Attributes

2006 ◽  
Author(s):  
Chuang Liu ◽  
Lingyun Yang ◽  
I. Foster
Keyword(s):  
Multiple Attributes ◽  
Relational Joins

scholarly journals Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

2020 ◽  
pp. 1-1
Author(s):  
Xinyi Huang ◽  
Suphanut Jamonnak ◽  
Ye Zhao ◽  
Boyu Wang ◽  
Minh Hoai ◽  
...  
Keyword(s):  
Learning Model ◽  
X Ray ◽  
Visual Study ◽  
X Ray Scattering ◽  
Multiple Attributes ◽  
Ray Scattering

scholarly journals Depth-based classification for relational data with multiple attributes

2021 ◽  
Vol 184 ◽  
pp. 104732
Author(s):  
Xu Zhang ◽  
Yahui Tian ◽  
Guoyu Guan ◽  
Yulia R. Gel
Keyword(s):  
Relational Data ◽  
Multiple Attributes

Distributed numerical and machine learning computations via two-phase execution of aggregated join trees

2021 ◽  
Vol 14 (7) ◽  
pp. 1228-1240
Author(s):  
Dimitrije Jankov ◽  
Binhang Yuan ◽  
Shangyu Luo ◽  
Chris Jermaine
Keyword(s):  
Machine Learning ◽  
Query Execution ◽  
Relational System ◽  
Numerical Computations ◽  
Two Phase ◽  
Execution Strategy ◽  
Relational Joins

When numerical and machine learning (ML) computations are expressed relationally, classical query execution strategies (hash-based joins and aggregations) can do a poor job distributing the computation. In this paper, we propose a two-phase execution strategy for numerical computations that are expressed relationally, as aggregated join trees (that is, expressed as a series of relational joins followed by an aggregation). In a pilot run, lineage information is collected; this lineage is used to optimally plan the computation at the level of individual records. Then, the computation is actually executed. We show experimentally that a relational system making use of this two-phase strategy can be an excellent platform for distributed ML computations.

scholarly journals Empowering Natural Language Interfaces to Databases with Aggregations

2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Alexandre F. Novello ◽  
Marco A. Casanova
Keyword(s):  
Natural Language ◽  
Relational Database ◽  
Natural Language Interfaces ◽  
Aggregation Functions ◽  
Natural Language Interface ◽  
Measurement Units ◽  
Database Interface ◽  
Multiple Attributes ◽  
Generic Module ◽  
Query Aggregation

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.

scholarly journals Some Dynamic Graphics for Spatial Data (with Multiple Attributes) in a GIS

Compstat ◽  
1994 ◽  
pp. 105-119 ◽  
Author(s):  
Dianne Cook ◽  
Noel Cressie ◽  
James Majure ◽  
Jürgen Symanzik
Keyword(s):  
Spatial Data ◽  
Dynamic Graphics ◽  
Multiple Attributes
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