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Conference Paper

InZeit: Efficiently Identifying Insightful Time Points

MPS-Authors
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Setty,  Vinay
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Bedathur,  Srikanta
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Berberich,  Klaus
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Setty, V., Bedathur, S., Berberich, K., & Weikum, G. (2010). InZeit: Efficiently Identifying Insightful Time Points. In Proceedings of the 36th International Conference on Very Large Data Bases (pp. 1605-1608). New York, NY: ACM. Retrieved from http://portal.acm.org/citation.cfm?id=1920841.1921050.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-150A-D
Abstract
Web archives are useful resources to find out about the temporal evolution of persons, organizations, products, or other topics. However, even when advanced text search functionality is available, gaining insights into the temporal evolution of a topic can be a tedious task and often requires sifting through many documents. The demonstrated system named \textsc{InZeit}\footnote{{\bf Zeit} (German): n. Time, temporal, period (die Zeit).} (pronounced ``insight'') assists users by determining insightful time points for a given query. These are time points at which the top-$k$ time-travel query result changes substantially and for which the user should therefore inspect query results. \systemname determines the $m$ most insightful time points efficiently using an extended segment tree for in-memory bookkeeping.