Abstract
Data prefetching is a technique allowing to retrieve data which will be most likely needed in a near future, before the actual demand. Considerable research was devoted to this technique, however, it is typically based on short-term data access patterns. We propose to predict future accesses based on long-term periodic pattern mining. Human activity in many areas, and thus many real-world business processes appear to have natural periods: they may have day, week and/or month periods. Discovering of such periods in I/O (or higher level) activity logs should allow to build a prefetch predictor, which is aware of data accesses not only in a near future, but in a far future perspective as well, and thus able to make more reasonable prefetch decisions. In this work we investigate the algorithm for mining periodic long-term access patterns, and discuss issues involved in building a prefetch system, which integrates predictor based on discovering these patterns with a prefetch cost model.
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Vasilik, D. (2013). Data Prefetching Based on Long-Term Periodic Access Patterns. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_41
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DOI: https://doi.org/10.1007/978-3-642-32518-2_41
Publisher Name: Springer, Berlin, Heidelberg
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