Abstract
In order to take into account the ever increasing volumes of data to be stored in computing systems and applications, the concept of cloud computing offers appropriate environments in which elastic architectures are provided under a pay-as-you-go cost model. Thus, when it comes to exploit these huge volumes of data, it is of paramount importance that optimization techniques can be used in order to reduce the computing costs. Query rewriting and caching is one of the most popular optimization techniques in this respect.
In this work, we propose a novel approach to deal with rewriting of aggregate queries, the most common queries in data warehousing applications. We propose a new strategy to generate possible rewritings for a new query, using the content of the cache, namely previously asked queries stored as cache entries. Our solution relies on a semantic matrix, and is generic enough to consider queries involving selection, projection and any aggregation functions.
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Perriot, R., d’Orazio, L., Laurent, D., Spyratos, N. (2016). A Semantic Matrix for Aggregate Query Rewriting. In: Grant, E., Kotzinos, D., Laurent, D., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personalization. ISIP 2015. Communications in Computer and Information Science, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-319-43862-7_3
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DOI: https://doi.org/10.1007/978-3-319-43862-7_3
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