ForCE: Is Estimation of Data Completeness Through Time Series Forecasts Feasible?

Endler G, Baumgärtel P, Wahl AM, Lenz R (2015)

Publication Type: Conference contribution, Original article

Publication year: 2015

Publisher: Springer

Series: Lecture Notes in Computer Science

City/Town: Switzerland

Pages Range: 261-274

Conference Proceedings Title: Advances in Databases and Information Systems

Event location: Futuroscope, Poitiers - France FR

Journal Issue: 9282

ISBN: 978-3-319-23134-1

URI: http://link.springer.com/chapter/10.1007/978-3-319-23135-8_18

DOI: 10.1007/978-3-319-23135-8_18

Abstract

Measuring the completeness of a data population often requires either expert knowledge or the presence of reference data. If neither is available, measuring population completeness becomes nontrivial. We present the ForCE approach (Forecasting for Completeness Estimation), a method to estimate the completeness of timestamped data using time series forecasting. We evaluate the method’s feasibility using a medical domain real-world dataset, which we provide for download. The method is compared to three baselines. ForCE manages to surpass all three.

Authors with CRIS profile

Gregor Endler Chair for Computer Science 6 (Data Management) Philipp Baumgärtel Chair for Computer Science 6 (Data Management) Andreas Maximilian Wahl Chair for Computer Science 6 (Data Management) Richard Lenz Professur für evolutionäres Datenmanagement

How to cite

APA:

Endler, G., Baumgärtel, P., Wahl, A.M., & Lenz, R. (2015). ForCE: Is Estimation of Data Completeness Through Time Series Forecasts Feasible? In Advances in Databases and Information Systems (pp. 261-274). Futuroscope, Poitiers - France, FR: Switzerland: Springer.

MLA:

Endler, Gregor, et al. "ForCE: Is Estimation of Data Completeness Through Time Series Forecasts Feasible?" Proceedings of the ADBIS 2015, Futuroscope, Poitiers - France Switzerland: Springer, 2015. 261-274.

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