Mining hidden mixture context with ADIOS-P to improve predictive pre-fetcher accuracy

Abstract

Predictive pre-fetcher, which predicts future data access events and loads the data before users requests, has been widely studied, especially in file systems or web contents servers, to reduce data load latency. Especially in scientific data visualization, pre-fetching can reduce the IO waiting time. In order to increase the accuracy, we apply a data mining technique to extract hidden information. More specifically, we apply a data mining technique for discovering the hidden contexts in data access patterns and make prediction based on the inferred context to boost the accuracy. In particular, we performed Probabilistic Latent Semantic Analysis (PLSA), a mixture model based algorithm popular in the text mining area, to mine hidden contexts from the collected user access patterns and, then, we run a predictor within the discovered context. We further improve PLSA by applying the Deterministic Annealing (DA) method to overcome the local optimum problem. In this paper we demonstrate how we can apply PLSA and DA optimization to mine hidden contexts from users data access patterns and improve predictive pre-fetcher performance.