Automatic text processing
On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reducing the Braking Distance of an SQL Query Engine
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating Top-k Queries over Web-Accessible Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Neural Computation
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Joining ranked inputs in practice
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Preference SQL: design, implementation, experiences
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
On Top-k Search with No Random Access Using Small Memory
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
RV-SVM: An Efficient Method for Learning Ranking SVM
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Probabilistic Ranking Support Vector Machine
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Efficient feature weighting methods for ranking
Proceedings of the 18th ACM conference on Information and knowledge management
RankSVR: can preference data help regression?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Selective sampling techniques for feedback-based data retrieval
Data Mining and Knowledge Discovery
Exact indexing for support vector machines
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
iKernel: Exact indexing for support vector machines
Information Sciences: an International Journal
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Data retrieval finding relevant data from large databases - has become a serious problem as myriad databases have been brought online in the Web. For instance, querying the for-sale houses in Chicago from realtor.com returns thousands of matching houses. Similarly, querying ''digital camera'' in froogle.com returns hundreds of thousand of results. This data retrieval is essentially an online ranking problem, i.e., ranking data results according to the user's preference effectively and efficiently. This paper proposes a new rank query framework, for effectively incorporating ''user-friendly'' rank-query formulation into ''data base (DB)-friendly'' rank-query processing, in order to enable ''soft'' queries on databases. Our framework assumes, as the ''back-end,'' the score-based ranking model for expressive and efficient query processing. On top of the score-based model, as the ''front-end,'' we adopt an SVM-ranking mechanism for providing intuitive and exploratory query formulation. In essence, our framework enables users to formulate queries simply by ordering some sample objects, while learning the ''DB-friendly'' ranking function F from the partial orders. Such learned functions can then be processed and optimized by existing database systems. We demonstrate the efficiency and effectiveness of our framework using real-life user queries and datasets: our results show that the system effectively learns quantitative ranking functions from qualitative feedback from users with efficient online processing.