TY - CHAP A1 - Zierenberg, Marcel ED - Traina, Agma Juci Machado ED - Traina, Caetano ED - Cordeiro, Robson Leonardo Ferreira T1 - Partial Refinement for Similarity Search with Multiple Features T2 - Similarity Search and Applications - 7th International Conference, SISAP 2014, Los Cabos, Mexico, October 29-31, 2014, Proceedings N2 - Filter refinement is an efficient and flexible indexing approach to similarity search with multiple features. However, the conventional refinement phase has one major drawback: when an object is refined, the partial distances to the query object are computed for all features. This frequently leads to more distance computations being executed than necessary to exclude an object. To address this problem, we introduce partial refinement, a simple, yet efficient improvement of the filter refinement approach. It incrementally replaces partial distance bounds with exact partial distances and updates the aggregated bounds accordingly each time. This enables us to exclude many objects before all of their partial distances have been computed exactly. Our experimental evaluation illustrates that partial refinement significantly reduces the number of required distance computations and the overall search time in comparison to conventional refinement and other state-of-the-art techniques. KW - metric indexing KW - filter refinement KW - multi-feature search KW - complex query KW - combination of features KW - nearest neighbor Y1 - 2014 UR - http://link.springer.com/chapter/10.1007%2F978-3-319-11988-5_2 SN - 978-3-319-11987-8 U6 - https://doi.org/10.1007/978-3-319-11988-5_2 SP - 13 EP - 24 PB - Springer International Publishing ER - TY - CHAP A1 - Zierenberg, Marcel A1 - Schmitt, Ingo ED - Amato, Giuseppe ED - Connor, Richard ED - Falchi, Fabrizio ED - Gennaro, Claudio T1 - Optimizing the Distance Computation Order of Multi-Feature Similarity Search Indexing T2 - Similarity Search and Applications - 8th International Conference, SISAP 2015, Glasgow, UK, October 12-14, 2015, Proceedings N2 - Multi-feature search is an effective approach to similarity search. Unfortunately, the search efficiency decreases with the number of features. Several indexing approaches aim to achieve efficiency by incrementally reducing the approximation error of aggregated distance bounds. They apply heuristics to determine the distance computations order and update the object's aggregated bounds after each computation. However, the existing indexing approaches suffer from several drawbacks. They use the same computation order for all objects, do not support important types of aggregation functions and do not take the varying CPU and I/O costs of different distance computations into account. To resolve these problems, we introduce a new heuristic to determine an efficient distance computation order for each individual object. Our heuristic supports various important aggregation functions and calculates cost-benefit-ratios to incorporate the varying computation costs of different distance functions. The experimental evaluation reveals that our heuristic outperforms state-of-the-art approaches in terms of the number of distance computations as well as search time. KW - combination of features KW - multi-feature search KW - metric indexing KW - nearest neighbor Y1 - 2015 UR - http://link.springer.com/chapter/10.1007/978-3-319-25087-8_8 SN - 978-3-319-25087-8 U6 - https://doi.org/10.1007/978-3-319-25087-8_8 SP - 90 EP - 96 ER -