Towards Collaborative Multidimensional Query Recommendation with Triadic Association Rules

Abstract

This paper describes a new personalization process for decisional queries through a new approach based on triadic association rules mining. This process exploits the decision query log files of end users and follows these five steps: (1) generation of a triadic context from the multidimensional query logs of OLAP1 query analysis server; (2) mapping the triadic context into the dyadic one; (3) computation of (conventional) dyadic association rules; (4) generation of triadic association rules through a factorization process of dyadic ones and convey a richer semantics. The aim of the personalization approach which is based on triadic rules is to recommend new decision queries to OLAP end users sharing some common properties. This paper aims at helping this class of users by recommending them personalized OLAP queries that they might use in their future OLAP sessions. To validate the approach, the authors developed a software prototype called P-TRIAR (Personalization based on TRIadic Association Rules) which extracts two types of triadic association rules from decision query log files. The first type of triadic rules will serve to the recommending queries by taking the collaborative aspect of OLAP users into account. The second type of triadic rules will enrich user queries. Preliminary experiments were conducted on both real and synthetic datasets to assess the quality of the recommendations in term of precision and recall measures, as well as the performance of their on-line computation.

OLAP4R: A Top-K Recommendation System for OLAP Sessions

“…L.Zhu et.al put forward a semantical pattern and preference-aware service mining method for personalized point of interest recommendation [6]. To acquire a better recommendation, a collaborative filtering approach is put forward to recommend the next query [7][8]. S. Rizzi et al designed a realistic OLAP workloads tool for similarity calculation which can be used as a benchmark for OLAP recommendation.…”

Redes de regras de associação filtradas e multialvo

“…Na fase de pós-processamento da mineração (CHERN-TONG; AZIZ, 2018; MARTÍNEZ-BALLESTEROS et al, 2017;LI et al, 2017;MINELGA et al, 2017;MEI;PAPER et al, 2015;SELMANE;BOUS-SAID;BENTAYEB, 2015), a estrutura em árvore foi utilizada a fim de facilitar a extração do conhecimento por meio da análise das relações entre as regras extraídas dos dados.…”

“…Muitas pesquisas utilizam estruturas de Rede para realizar o pós-processamento das Regras de Associação a fim de facilitar a extração do conhecimento por intermédio da análise das relações extraídas dos dados (CHERN-TONG; AZIZ, 2018;MARTÍNEZ-BALLESTEROS et al, 2017;LI et al, 2017;MINELGA et al, 2017;MEI;PAPER et al, 2015;SELMANE;BOUSSAID;BENTAYEB, 2015). Apenas os trabalhos de Pandey et al (2009) e Chawla, Davis e Pandey (2004) apresentam abordagens que possuem um atributo alvo (item objetivo), mas sem nenhum tipo de garantia de que as relações observadas geram hipóteses verdadeiras ou que o conhecimento formulado possa ser utilizado em aplicações de diversas áreas.…”