Algoritmos de junção por similaridade sobre fluxo de dados - Pacífico, Lucas Oliveira (2020)
In today's Big Data era, data is generated and collected at high speed, which imposes strict performance and memory requirements for processing this data. Also, the presence of heterogeneity data demands the use of similarity operations, which are computationally more expensive. In this context, the present work investigates the problem of performing similarity join over a continuous stream of data represented by sets. The concept of temporal similarity is employed, where the similarity between two data items decreases with the distance in their arrival time. The proposed algorithms directly incorporates this concept to reduce the comparison of space and memory consumption. Moreover, a new technique based on the partial frequency of the data elements is presented to substantially reduce processing cost. Results of the experimental evaluation performed demonstrate that the techniques presented provide substantial performance gains and good memory usage.