Abstract
In this paper we extend STHoles, a very successful algorithm that uses query results to build and maintain multi-dimensional histograms of numerical data. Our contribution is the formal definition of extensions of all relevant concepts; such that they are independent of the domain of the data, but subsume STHoles concepts as their numerical specialization. At the same time, we also derive specializations for the string domain and implement these into a prototype that we use to empirically validate our approach. Our current implementation uses string prefixes as the machinery for describing string ranges. Although weaker than regular expressions, prefixes can be very efficiently applied and can capture interesting ranges in hierarchically structured string domains, such as those of filesystem pathnames and URIs. In fact, we base the empirical validation of the approach on existing, publicly available Semantic Web data where we demonstrate convergence to accurate and efficient histograms.
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Notes
- 1.
STRHist is available at https://bitbucket.org/acharal/strhist
For more details on Semagrow, please see http://www.semagrow.eu.
- 2.
Please see http://agris.fao.org for more details on AGRIS. The AGRIS site mentions 7 million distinct publications, but this includes recent additions that are not in end-2013 data dump used for these experiments.
- 3.
We use the canonical string representation of URIs as defined in Sect. 2, IETF RFC 7320 (http://tools.ietf.org/html/rfc7320).
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Acknowledgements
The work described here was partially carried out at the 2014 edition of the International Research-Centred Summer School, held at NCSR ‘Demokritos’, Athens, Greece, 3–30 July 2014. For more details please see http://irss.iit.demokritos.gr
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 318497. More details at http://www.semagrow.eu.
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Zoulis, N., Mavroudi, E., Lykoura, A., Charalambidis, A., Konstantopoulos, S. (2015). Workload-Aware Self-Tuning Histograms of String Data. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_20
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