Abstract
Our main motivation is the data access model and aggregation algorithm for middleware by R. Fagin, A. Lotem and M. Naor. They assume data attributes in a variety of repositories ordered by a grade of attribute values of objects. Moreover they assume the user has an aggregation function, which eventually qualifies an object to top-k answers.
In this paper we adopt a model of various users (there is no single ordering of objects in repositories and no single aggregation) with user preference learning algorithm on the middleware side. We present a new model of repository for simultaneous access by many users. The model is an extension of original model of Fagin, Lotem, Naor. Our solution is based on a model of fast learning of user preferences from his/her reactions. Experiments are focused on the performance of top-k algorithms (both TA and NRA) using data integration on an experimental prototype of our solution. Cache size, network latency and batch size were the features studied in experiments.
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Eckhardt, A., Horničák, E., Vojtáš, P. (2011). Evaluating Top-k Algorithms with Various Sources of Data and User Preferences. In: Christiansen, H., De Tré, G., Yazici, A., Zadrozny, S., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2011. Lecture Notes in Computer Science(), vol 7022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24764-4_23
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DOI: https://doi.org/10.1007/978-3-642-24764-4_23
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