Query Matching for Report Recommendation

Veronika ThostVeronika Thost, Konrad VoigtKonrad Voigt, Daniel SchusterDaniel Schuster

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Veronika Thost, Konrad Voigt, Daniel Schuster
Query Matching for Report Recommendation
Proceedings of the 22Nd ACM International Conference on Conference on Information and Knowledge Management, CIKM '13, 1391-1400, 2013. ACM

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KurzfassungAbstract
Today, reporting is an essential part of everyday business life. But the preparation of complex Business Intelligence data by formulating relevant queries and presenting them in meaningful visualizations, so-called reports, is a challenging task for non-expert database users. To support these users with report creation, we leverage existing queries and present a system for query recommendation in a reporting environment, which is based on query matching. Targeting at large-scale, real-world reporting scenarios, we propose a scalable, index-based query matching approach. Moreover, schema matching is applied for a more fine-grained, structural comparison of the queries. In addition to interactively providing content-based query recommendations of good quality, the system works independent of particular data sources or query languages.We evaluate our system with an empirical data set and show that it achieves an F1-Measure of 0.56 and outperforms the approaches applied by state-of-the-art reporting tools (e.g., keyword search) by up to 30%.
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory

@inproceedings{ ThoSchVo-CIKM13,
  address = {San Francisco, California, USA},
  author = {Veronika {Thost} and Konrad {Voigt} and Daniel {Schuster}},
  booktitle = {Proceedings of the 22Nd ACM International Conference on Conference on Information and Knowledge Management},
  pages = {1391--1400},
  publisher = {ACM},
  series = {CIKM '13},
  title = {Query Matching for Report Recommendation},
  year = {2013},
}