3iCubing: An Interval Inverted Index Approach to Data Cubes

• Autor(a) principal: Domingues, Marco
• Data de Publicação: 2022
• Outros Autores: Silva, Rodrigo Rocha, Bernardino, Jorge
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/100598; https://doi.org/10.1109/ACCESS.2022.3142449
• topic: Big data; data cube; inverted index; OLAP

The increase in the amounts of information used to analyze data is problematic since the memory necessary to store and process it is getting quite big. The interval inverted index representation was developed to reduce the required memory to store data, and Frag-Cubing is one of the most popular algorithms. In this paper, we propose two new data cubing algorithms: 3iCubing and M3iCubing. 3iCubing is a Frag-Cubing-based algorithm that uses the interval inverted index representation, while M3iCubing uses both a normal and interval inverted index data representation. The algorithms were compared using synthetic and real data sets in indexation and querying operations, both runtime and memory-wise. The experimental evaluation shows that 3iCubing can considerably reduce the memory needed to index a data set, reducing around 25% of the memory used by Frag-Cubing. Moreover, the results show that the interval inverted index representation is dependent on the data skewness to reduce the memory consumption, having positive results with highly skewed and real-world data sets.