Abstract
Skyline queries are a well-known technique for explorative retrieval, multi-objective optimization problems, and personalization tasks in databases. They are widely acclaimed for their intuitive query formulation mechanisms. However, when operating on incomplete datasets, skyline query processing is severely hampered and often has to resort to error-prone heuristics. Unfortunately, incomplete datasets are a frequent phenomenon due to widespread use of automated information extraction and aggregation. In this paper, we evaluate and compare various established heuristics for adapting skylines to incomplete datasets, focusing specifically on the error they impose on the skyline result. Building upon these results, we argue for improving the skyline result quality by employing crowd-enabled databases. This allows dynamic outsourcing of some database operators to human workers, therefore enabling the elicitation of missing values during runtime. Unfortunately, each crowd-sourcing operation will result in monetary and query runtime costs. Therefore, our main contribution is introducing a sophisticated error model, allowing us to specifically concentrate on those tuples that are highly likely to be error-prone, while relying on established heuristics for safer tuples. This technique of focused crowd-sourcing allows us to strike a perfect balance between costs and result’s quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Franklin, M., Kossmann, D., Kraska, T., Ramesh, S., Xin, R.: CrowdDB: Answering queries with crowdsourcing. In: ACM SIGMOD Int. Conf. on Management of Data, Athens, Greece (2011)
Khalefa, M.E., Mokbel, M.F., Levandoski, J.J.: Skyline Query Processing for Incomplete Data. In: Int. Conf. on Data Engineering (ICDE), Cancun, Mexico (2008)
Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline Operator. In: Int. Conf. on Data Engineering (ICDE), Heidelberg, Germany (2001)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: Symposium on Principles of Database Systems (PODS), Santa-Barbara, California, USA (2001)
Godfrey, P., Shipley, R., Gryz, J.: Algorithms and analyses for maximal vector computation. The VLDB Journal 16, 5–28 (2007)
Bartolini, I., Ciaccia, P., Patella, M.: Efficient sort-based skyline evaluation. ACM Transactions on Database Systems 33 (2008)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30, 41–82 (2005)
Selke, J., Lofi, C., Balke, W.-T.: Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 246–260. Springer, Heidelberg (2010)
Torlone, R., Ciaccia, P.: Finding the best when it‘s a matter of preference. In: 10th Italian Symposium on Advanced Database Systems (SEBD), Portoferraio, Italy (2002)
Boldi, P., Chierichetti, F., Vigna, S.: Pictures from Mongolia: Extracting the top elements from a partially ordered set. Theory of Computing Systems 44, 269–288 (2009)
Park, S., Kim, T., Park, J., Kim, J., Im, H.: Parallel skyline computation on multicore architectures. In: Int.Conf. on Data Engineering (ICDE), Shanghai, China (2009)
Heath, T., Hepp, M., Bizer, C.: Special Issue on Linked Data. International Journal on Semantic Web and Information Systems (IJSWIS) 5 (2009)
Lofi, C., El Maarry, K., Balke, W.-T.: Skyline Queries in Crowd-Enabled Databases. In: Int. Conf. on Extending Database Technology (EDBT), Genoa, Italy (2013)
Acu, E.: The treatment of missing values and its effect in the classifier accuracy. In: Classification Clustering and Data Mining Applications, pp. 1–9 (2004)
Balke, W.-T., Güntzer, U., Siberski, W.: Exploiting Indifference for Customization of Partial Order Skylines. In: Int. DB Engineering & Applications Symposium (IDEAS), Delhi, India (2006)
Balke, W.T., Güntzer, U., Siberski, W.: Restricting skyline sizes using weak Pareto dominance. Informatik - Forschung und Entwicklung 21, 165–178 (2007)
Balke, W.-T., Zheng, J.X., Güntzer, U.: Approaching the Efficient Frontier: Cooperative Database Retrieval Using High-Dimensional Skylines. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 410–421. Springer, Heidelberg (2005)
Godfrey, P.: Skyline cardinality for relational processing. In: Seipel, D., Turull-Torres, J.M. (eds.) FoIKS 2004. LNCS, vol. 2942, pp. 78–97. Springer, Heidelberg (2004)
Powers, D.M.W.: Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Flinders University Adelaide SIE07001 (2007)
Lofi, C., Selke, J., Balke, W.-T.: Information Extraction Meets Crowdsourcing: A Promising Couple. Datenbank-Spektrum 12 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lofi, C., El Maarry, K., Balke, WT. (2013). Skyline Queries over Incomplete Data - Error Models for Focused Crowd-Sourcing. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds) Conceptual Modeling. ER 2013. Lecture Notes in Computer Science, vol 8217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41924-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-41924-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41923-2
Online ISBN: 978-3-642-41924-9
eBook Packages: Computer ScienceComputer Science (R0)