Abstract
Uncertainty in data naturally arises in various applications, such as data integration and Web information extraction. A few examples are the following. When information from different sources is conflicting, inconsistent, or simply presented in incompatible forms the result of integrating these sources necessarily involves uncertainty as to which fact is correct or which is the best mapping to a global schema. Data uncertainty is often ignored, or modeled in a specific, per-application manner. This may be an unsatisfying solution in the long run, especially when the uncertainty needs to be retained throughout complex and potentially imprecise processing of the data. In this paper, we study the basic activities of web resources that are affected by uncertainty, more specifically, modeling, programming and evaluation. We propose a probabilistic approach that treats uncertainty in all these activities.
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References
Maleshkova, M., Pedrinaci, C., Domingue, J.: Investigating Web APIs on the World Wide Web. In: 8th IEEE European Conference on Web Services (ECOWS 2010), 1-3 December 2010, Ayia Napa, Cyprus, 2010, DBLP:conf/ecows/2010 (2017). https://doi.org/10.1109/ECOWS.2010.9
Halevy, A.Y., Rajaraman , A., Ordille, J.J.: Data integration: the teenage years. In: Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, September 12-15, 2006, DBLP:conf/vldb/2006, http://dl.acm.org/citation.cfm?id=1164130, dblp computer science bibliography, https://dblp.org
Abiteboul, S., Kanellakis, P.C., Grahne, G.: On the representation and querying of sets of possible worlds. Theor. Comput. Sci. 78, 159–187 (1991). https://dblp.org/rec/bib/journals/tcs/AbiteboulKG91. DBLP computer science bibliography
Parag, A., Omar, B., Das, S.A., Chris, H., Shubha, N., Tomoe, S., Jennifer, W.: Trio: a system for data, uncertainty, and lineage. In: Proceedings of the 32nd International Conference on Very Large Data Bases (2006)
Nierman, A., Jagadish, H.V.: ProTDB: probabilistic data in XML. In: Proceedings of the 28th VLDB Conference. Springer (2002)
Benslimane, D., Sheng, Q.Z., Barhamgi, M., Prade, H.: Concepts, Challenges, and Current Solutions, TOIT, The Uncertain Web (2016)
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Omri, A., Bensliamne, D., Omri, M.N. (2023). A Probabilistic Approach: Querying Web Resources in the Presence of Uncertainty. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_18
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DOI: https://doi.org/10.1007/978-3-031-48232-8_18
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