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- W1810027194 abstract "Time-varying data are important in many applications. Starting from the 80’s, they have bee n widely studied in the field of temporal databases. More recently, data warehouses (DWs) have gained an increasing importance, strongly focusing on time-varying data. DWs support different temporal granularities (e.g., days, months, years), as well as several operators for aggregating data along them. In the past two decades, the importance of coping also with user-defined granularities has been clear ly identified. However, most current DWs only supports the treatment of limited sets of pre-defin ed granularities. In this paper, we ground our approa ch on some basic results in the temporal database literature, including the telicatelic distinction, and we propose a general and application-independent framework supporting aggregation of measures along user-defined granularities." @default.
- W1810027194 created "2016-06-24" @default.
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- W1810027194 date "2015-01-01" @default.
- W1810027194 modified "2023-09-24" @default.
- W1810027194 title "Supporting Aggregation in Data Warehousing Considering User-Defined Temporal Granularities" @default.
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