Incremental Mining of Frequent Query Patterns from XML Queries for Caching

2006 ◽  
Author(s):  
Guoliang Li ◽  
Jianhua Feng ◽  
Jianyong Wang ◽  
Yong Zhang ◽  
Lizhu Zhou
Keyword(s):  
Incremental Mining ◽  
Query Patterns

IMIDB: An Algorithm for Indexed Mining of Incremental Databases

2017 ◽  
Vol 26 (1) ◽  
pp. 69-85
Author(s):  
Mohammed M. Fouad ◽  
Mostafa G.M. Mostafa ◽  
Abdulfattah S. Mashat ◽  
Tarek F. Gharib
Keyword(s):  
Data Structure ◽  
Association Rules ◽  
Efficient Algorithm ◽  
Performance Comparison ◽  
Incremental Mining ◽  
Itemset Mining ◽  
Large Databases ◽  
Transactional Databases ◽  
Dynamic Databases ◽  
Database Size

AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.

Finding hot query patterns over an XQuery stream

2004 ◽  
Vol 13 (4) ◽  
pp. 318-332 ◽  
Author(s):  
Liang Huai Yang ◽  
Mong Li Lee ◽  
Wynne Hsu
Keyword(s):  
Query Patterns

Incremental mining of closed inter-transaction itemsets over data stream sliding windows

2011 ◽  
Vol 37 (2) ◽  
pp. 208-220 ◽  
Author(s):  
Shih-Chuan Chiu ◽  
Hua-Fu Li ◽  
Jiun-Long Huang ◽  
Hsin-Han You
Keyword(s):  
Data Stream ◽  
Incremental Mining ◽  
Sliding Windows

Migrating a complex classification scheme to the semantic web: expressing the Integrative Levels Classification using SKOS RDF

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ceri Binding ◽  
Claudio Gnoli ◽  
Douglas Tudhope
Keyword(s):  
Knowledge Organization ◽  
Content Type ◽  
Classification Schemes ◽  
Facet Structure ◽  
Simple Knowledge Organization System ◽  
Trade Offs ◽  
Knowledge Organization System ◽  
Faceted Classification ◽  
Integrative Levels ◽  
Query Patterns

PurposeThe Integrative Levels Classification (ILC) is a comprehensive “freely faceted” knowledge organization system not previously expressed as SKOS (Simple Knowledge Organization System). This paper reports and reflects on work converting the ILC to SKOS representation.Design/methodology/approachThe design of the ILC representation and the various steps in the conversion to SKOS are described and located within the context of previous work considering the representation of complex classification schemes in SKOS. Various issues and trade-offs emerging from the conversion are discussed. The conversion implementation employed the STELETO transformation tool.FindingsThe ILC conversion captures some of the ILC facet structure by a limited extension beyond the SKOS standard. SPARQL examples illustrate how this extension could be used to create faceted, compound descriptors when indexing or cataloguing. Basic query patterns are provided that might underpin search systems. Possible routes for reducing complexity are discussed.Originality/valueComplex classification schemes, such as the ILC, have features which are not straight forward to represent in SKOS and which extend beyond the functionality of the SKOS standard. The ILC's facet indicators are modelled as rdf:Property sub-hierarchies that accompany the SKOS RDF statements. The ILC's top-level fundamental facet relationships are modelled by extensions of the associative relationship – specialised sub-properties of skos:related. An approach for representing faceted compound descriptions in ILC and other faceted classification schemes is proposed.

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