TY - GEN
T1 - Mining hidden mixture context with ADIOS-P to improve predictive pre-fetcher accuracy
AU - Choi, Jong Youl
AU - Abbasi, Hasan
AU - Pugmire, David
AU - Podhorszki, Norbert
AU - Klasky, Scott
AU - Capdevila, Cristian
AU - Parashar, Manish
AU - Wolf, Matthew
AU - Qiu, Judy
AU - Fox, Geoffrey
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Hidden context mining
KW - Prefetch
UR - http://www.scopus.com/inward/record.url?scp=84873644488&partnerID=8YFLogxK
U2 - 10.1109/eScience.2012.6404418
DO - 10.1109/eScience.2012.6404418
M3 - Conference contribution
AN - SCOPUS:84873644488
SN - 9781467344678
T3 - 2012 IEEE 8th International Conference on E-Science, e-Science 2012
BT - 2012 IEEE 8th International Conference on E-Science, e-Science 2012
T2 - 2012 IEEE 8th International Conference on E-Science, e-Science 2012
Y2 - 8 October 2012 through 12 October 2012
ER -