Mining frequent patterns from XML data: Efficient algorithms and design trade-offs

2012 ◽  
Vol 39 (1) ◽  
pp. 1134-1140 ◽  
Author(s):  
Aı́da Jiménez ◽  
Fernando Berzal ◽  
Juan-Carlos Cubero
Keyword(s):  
Efficient Algorithms ◽  
Frequent Patterns ◽  
Xml Data ◽  
Trade Offs

Data Mining on XML Data

2011 ◽  
pp. 506-510
Author(s):  
Qin Ding
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Efficient Algorithms ◽  
Relational Data ◽  
Xml Data ◽  
Structure Information ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Naive Approach

With the growing usage of XML data for data storage and exchange, there is an imminent need to develop efficient algorithms to perform data mining on semistructured XML data. Mining on XML data is much more difficult than mining on relational data because of the complexity of structure in XML data. A naïve approach to mining on XML data is to first convert XML data into relational format. However the structure information may be lost during the conversion. It is desired to develop efficient and effective data mining algorithms that can be directly applied on XML data.

Frequent Pattern Discovery and Association Rule Mining of XML Data

Data Mining ◽  
2013 ◽  
pp. 859-879
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Discovery ◽  
Frequent Pattern ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Xml Data ◽  
Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

Frequent Pattern Discovery and Association Rule Mining of XML Data

2011 ◽  
pp. 243-263 ◽  
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Discovery ◽  
Frequent Pattern ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Xml Data ◽  
Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

EFFICIENT GLOBAL COMBINE OPERATIONS IN MULTI-PORT MESSAGE-PASSING SYSTEMS

1993 ◽  
Vol 03 (04) ◽  
pp. 335-346 ◽  
Author(s):  
JEHOSHUA BRUCK ◽  
CHING-TIEN HO
Keyword(s):  
Message Passing ◽  
Efficient Algorithms ◽  
Communication Model ◽  
Communication Round ◽  
Trade Offs ◽  
Reduction Function

We present a class of efficient algorithms for global combine operations in k-port message-passing systems. In the k-port communication model, in each communication round, a processor can send data to k other processors and simultaneously receive data from k other processors. We consider algorithms for global combine operations in n processors with respect to a commutative and associative reduction function. Initially, each processor holds a vector of m data items and finally the result of the reduction function over the n vectors of data items, which is also a vector of m data items, is known to all n processors. We present three efficient algorithms that employ various trade-offs between the number of communication rounds and the number of data items transferred in sequence. For the case m=1, we have an algorithm which is optimal in both the number of communication rounds and the number of data items transferred in sequence.

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