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- W1510089531 abstract "When addressing the problem of “big” data volume, preparation costs are one of the key challenges: the high costs for loading, aggregating and indexing data leads to a long data-to-insight time. In addition to being a nuisance to the end-user, this latency prevents real-time analytics on “big” data. Fortunately, data often comes in semantic chunks such as files that contain data items that share some characteristics such as acquisition time or location. A data management system that exploits this trait can significantly lower the data preparation costs and the associated data-to-insight time by only investing in the preparation of the relevant chunks. In this paper, we develop such a system as an extension of an existing relational DBMS (MonetDB). To this end, we develop a query processing paradigm and data storage model that are partial-loading aware. The result is a system that can make a 1.2 TB dataset (consisting of 4000 chunks) ready for querying in less than 3 minutes on a single server-class machine while maintaining good query processing performance." @default.
- W1510089531 created "2016-06-24" @default.
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- W1510089531 date "2015-04-01" @default.
- W1510089531 modified "2023-10-18" @default.
- W1510089531 title "The DBMS - your big data sommelier" @default.
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- W1510089531 doi "https://doi.org/10.1109/icde.2015.7113361" @default.
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