Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Integrating user and group preferences for top-k search from distributed web resources
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Considering Data-Mining Techniques in User Preference Learning
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Best-Effort Top-k Query Processing Under Budgetary Constraints
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Combining Various Methods of Automated User Decision and Preferences Modelling
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Learning User Preferences for 2CP-Regression for a Recommender System
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
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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.