Detecting the Temporal Context of Queries

@inproceedings{Kennedy2014DetectingTT,
  title={Detecting the Temporal Context of Queries},
  author={Oliver Kennedy and Ying Yang and Jan Chomicki and Ronny Fehling and Zhen Hua Liu and Dieter Gawlick},
  booktitle={BIRTE},
  year={2014}
}
Business intelligence and reporting tools rely on a database that accurately mirrors the state of the world. Yet, even if the schema and queries are constructed in exacting detail, assumptions about the data made during extraction, transformation, and schema and query creation of the reporting database may be (accidentally) ignored by end users, or may change as the database evolves over time. As these assumptions are typically implicit (e.g., assuming that a sales record relation is appendonly… CONTINUE READING

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Showing 1-10 of 24 references

PIP: A database system for great and small expectations

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010) • 2010
View 2 Excerpts

SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases

2009 IEEE 25th International Conference on Data Engineering • 2009
View 1 Excerpt

Green , Grigoris Karvounarakis , and Val Tannen . Provenance semirings

W Keith Hastings
2007

Provenance in databases

SIGMOD Conference • 2007
View 2 Excerpts

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