The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Algorithms and analyses for maximal vector computation
The VLDB Journal — The International Journal on Very Large Data Bases
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Mining preferences from superior and inferior examples
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Continuous Top-k Dominating Queries in Subspaces
PCI '08 Proceedings of the 2008 Panhellenic Conference on Informatics
Personalized top-k skyline queries in high-dimensional space
Information Systems
Top-k dominating queries in uncertain databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
On Skylining with Flexible Dominance Relation
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Distance-Based Representative Skyline
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Multi-dimensional top-k dominating queries
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering relative importance of skyline attributes
Proceedings of the VLDB Endowment
Active learning for ranking through expected loss optimization
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Regret-minimizing representative databases
Proceedings of the VLDB Endowment
Representative skylines using threshold-based preference distributions
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank for Information Retrieval and Natural Language Processing
Top-k skyline: a unified approach
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Domination mining and querying
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
IPS: an interactive package configuration system for trip planning
Proceedings of the VLDB Endowment
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We study the notion of regret ratio proposed in [19] Nanongkai et al. [VLDB10] to deal with multi-criteria decision making in database systems. The regret minimization query proposed in [19] Nanongkai et al. was shown to have features of both skyline and top-k: it does not need information from the user but still controls the output size. While this approach is suitable for obtaining a reasonably small regret ratio, it is still open whether one can make the regret ratio arbitrarily small. Moreover, it remains open whether reasonable questions can be asked to the users in order to improve efficiency of the process. In this paper, we study the problem of minimizing regret ratio when the system is enhanced with interaction. We assume that when presented with a set of tuples the user can tell which tuple is most preferred. Under this assumption, we develop the problem of interactive regret minimization where we fix the number of questions and tuples per question that we can display, and aim at minimizing the regret ratio. We try to answer two questions in this paper: (1) How much does interaction help? That is, how much can we improve the regret ratio when there are interactions? (2) How efficient can interaction be? In particular, we measure how many questions we have to ask the user in order to make her regret ratio small enough. We answer both questions from both theoretical and practical standpoints. For the first question, we show that interaction can reduce the regret ratio almost exponentially. To do this, we prove a lower bound for the previous approach (thereby resolving an open problem from [19] Nanongkai et al.), and develop an almost-optimal upper bound that makes the regret ratio exponentially smaller. Our experiments also confirm that, in practice, interactions help in improving the regret ratio by many orders of magnitude. For the second question, we prove that when our algorithm shows a reasonable number of points per question, it only needs a few questions to make the regret ratio small. Thus, interactive regret minimization seems to be a necessary and sufficient way to deal with multi-criteria decision making in database systems.