scholarly journals Similarity Group-by Operators for Multi-Dimensional Relational Data

2016 ◽  
Vol 28 (2) ◽  
pp. 510-523 ◽  
Author(s):  
Mingjie Tang ◽  
Ruby Y. Tahboub ◽  
Walid G. Aref ◽  
Mikhail J. Atallah ◽  
Qutaibah M. Malluhi ◽  
...  
Keyword(s):  
Relational Data ◽  
Similarity Group

scholarly journals Similarity Group-By operators for multi-dimensional relational data

2016 ◽  
Author(s):  
Mingjie Tang ◽  
Ruby Y. Tahboub ◽  
Walid G. Aref ◽  
Mikhail J. Atallah ◽  
Qutaibah M. Malluhi ◽  
...  
Keyword(s):  
Relational Data ◽  
Similarity Group

Best Matching: Technical Details

2018 ◽  
Author(s):  
Flavio Mercati
Keyword(s):  
Point Particle ◽  
Euclidean Group ◽  
Scale Invariant ◽  
Similarity Group ◽  
N Body Problem ◽  
Technical Details ◽  
Matching Procedure ◽  
Principal Fibre ◽  
Principal Fibre Bundle ◽  
Gauge Connection

The best matching procedure described in Chapter 4 is equivalent to the introduction of a principal fibre bundle in configuration space. Essentially one introduces a one-dimensional gauge connection on the time axis, which is a representation of the Euclidean group of rotations and translations (or, possibly, the similarity group which includes dilatations). To accommodate temporal relationalism, the variational principle needs to be invariant under reparametrizations. The simplest way to realize this in point–particle mechanics is to use Jacobi’s reformulation of Mapertuis’ principle. The chapter concludes with the relational reformulation of the Newtonian N-body problem (and its scale-invariant variant).

scholarly journals A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data

2018 ◽  
Vol 14 (11) ◽  
pp. 1475-1487
Author(s):  
Carlos Roberto Valêncio ◽  
Guilherme Henrique Morais ◽  
Márcio Zamboti Fortes ◽  
Angelo Cesar Colombini ◽  
Leandro Alves Neves ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Relational Data ◽  
Rule Mining
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