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Journal articles on the topic 'Fast search'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

S Sao, Sphurti, and Rahila Shiekh. "A Review: Fast nearest Neighbour Search with Keywords." International Journal of Scientific Engineering and Research 4, no. 1 (2016): 52–54. https://doi.org/10.70729/ijser15657.

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2

Jiang, Qixia, and Maosong Sun. "Fast Query Recommendation by Search." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1192–97. http://dx.doi.org/10.1609/aaai.v25i1.8077.

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Query recommendation can not only effectively facilitate users to obtain their desired information but alsoincrease ads’ click-through rates. This paper presentsa general and highly efficient method for query recommendation. Given query sessions, we automatically generate many similar and dissimilar query-pairs as the prior knowledge. Then we learn a transformation from the prior knowledge to move similar queries closer such that similar queries tend to have similar hash values.This is formulated as minimizing the empirical error on the prior knowledge while maximizing the gap between the data

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3

Bandyopadhyay, Sanghamitra, Garisha Chowdhary, and Debarka Sengupta. "FOCS: Fast Overlapped Community Search." IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (2015): 2974–85. http://dx.doi.org/10.1109/tkde.2015.2445775.

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Yan, Zhiqiang, Remco Dijkman, and Paul Grefen. "Fast business process similarity search." Distributed and Parallel Databases 30, no. 2 (2012): 105–44. http://dx.doi.org/10.1007/s10619-012-7089-z.

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5

Brunig, M., and W. Niehsen. "Fast full-search block matching." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 2 (2001): 241–47. http://dx.doi.org/10.1109/76.905989.

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6

Burns, Ethan, Matthew Hatem, Michael Leighton, and Wheeler Ruml. "Implementing Fast Heuristic Search Code." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (2021): 25–32. http://dx.doi.org/10.1609/socs.v3i1.18245.

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Published papers rarely disclose implementation details. In this paper we show how such details can account for speedups of up to a factor of 28 for different implementations of the same algorithm. We perform an in-depth analysis of the most popular benchmark in heuristic search: the 15-puzzle. We study implementation choices in C++ for both IDA* and A* using the Manhattan distance heuristic. Results suggest that several optimizations deemed critical in folklore provide only small improvements while seemingly innocuous choices can play a large role. These results are important for ensuring tha

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7

Murase, Hiroshi, and V. V. Vinod. "Fast visual search using focused color matching?active search." Systems and Computers in Japan 31, no. 9 (2000): 81–88. http://dx.doi.org/10.1002/1520-684x(200008)31:9<81::aid-scj9>3.0.co;2-v.

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8

Matsugu, Shohei, Hiroaki Shiokawa, and Hiroyuki Kitagawa. "Fast Algorithm for Attributed Community Search." Journal of Information Processing 29 (2021): 188–96. http://dx.doi.org/10.2197/ipsjjip.29.188.

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9

Wang, Jason T. L., Huiyuan Shan, Dennis Shasha, and William H. Piel. "Fast Structural Search in Phylogenetic Databases." Evolutionary Bioinformatics 1 (January 2005): 117693430500100. http://dx.doi.org/10.1177/117693430500100009.

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10

CHANG, MING-CHING, CHIOU-SHANN FUH, and HSIEN-YEI CHEN. "FAST SEARCH ALGORITHMS FOR INDUSTRIAL INSPECTION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 04 (2001): 675–90. http://dx.doi.org/10.1142/s0218001401001039.

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This paper presents an efficient general purpose search algorithm for alignment and an applied procedure for IC print mark quality inspection. The search algorithm is based on normalized cross-correlation and enhances it with a hierarchical resolution pyramid, dynamic programming, and pixel over-sampling to achieve subpixel accuracy on one or more targets. The general purpose search procedure is robust with respect to linear change of image intensity and thus can be applied to general industrial visual inspection. Accuracy, speed, reliability, and repeatability are all critical for the industr

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11

Chen, F. K., J. F. Yang, and Y. L. Yan. "Candidate scheme for fast ACELP search." IEE Proceedings - Vision, Image, and Signal Processing 149, no. 1 (2002): 10. http://dx.doi.org/10.1049/ip-vis:20020151.

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12

Kulis, B., P. Jain, and K. Grauman. "Fast Similarity Search for Learned Metrics." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 12 (2009): 2143–57. http://dx.doi.org/10.1109/tpami.2009.151.

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13

Lin, Y., and S. W. Foo. "Fast search algorithm for tolerance design." IEE Proceedings - Circuits, Devices and Systems 145, no. 1 (1998): 19. http://dx.doi.org/10.1049/ip-cds:19981592.

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14

Mihov, Stoyan, and Klaus U. Schulz. "Fast Approximate Search in Large Dictionaries." Computational Linguistics 30, no. 4 (2004): 451–77. http://dx.doi.org/10.1162/0891201042544938.

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The need to correct garbled strings arises in many areas of natural language processing. If a dictionary is available that covers all possible input tokens, a natural set of candidates for correcting an erroneous input P is the set of all words in the dictionary for which the Levenshtein distance to Pdoes not exceed a given (small) bound k. In this article we describe methods for efficiently selecting such candidate sets. After introducing as a starting point a basic correction method based on the concept of a “universal Levenshtein automaton,” we show how two filtering methods known from the

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15

Zhu, Xiaofeng, Zi Huang, Hong Cheng, Jiangtao Cui, and Heng Tao Shen. "Sparse hashing for fast multimedia search." ACM Transactions on Information Systems 31, no. 2 (2013): 1–24. http://dx.doi.org/10.1145/2457465.2457469.

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16

Wu, Ming-Te. "MapReduce Analytics Based Fast Search Algorithms." Journal of the Chinese Institute of Engineers 43, no. 8 (2020): 831–37. http://dx.doi.org/10.1080/02533839.2020.1794977.

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17

Analyti, Anastasia, and Sakti Pramanik. "Fast search in main memory databases." ACM SIGMOD Record 21, no. 2 (1992): 215–24. http://dx.doi.org/10.1145/141484.130317.

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18

Sunday, Daniel M. "A very fast substring search algorithm." Communications of the ACM 33, no. 8 (1990): 132–42. http://dx.doi.org/10.1145/79173.79184.

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19

Zhou, Y., Y. Liang, K. H. Lynch, J. J. Dennis, and D. S. Wishart. "PHAST: A Fast Phage Search Tool." Nucleic Acids Research 39, suppl (2011): W347—W352. http://dx.doi.org/10.1093/nar/gkr485.

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20

Davis and De-Lei Lee. "Fast Search Algorithms for Associative Memories." IEEE Transactions on Computers C-35, no. 5 (1986): 456–61. http://dx.doi.org/10.1109/tc.1986.1676788.

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21

Yufei Tao and Cheng Sheng. "Fast Nearest Neighbor Search with Keywords." IEEE Transactions on Knowledge and Data Engineering 26, no. 4 (2014): 878–88. http://dx.doi.org/10.1109/tkde.2013.66.

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22

Zou, Fuhao, Fan Yang, Wei Chen, et al. "Fast large scale deep face search." Pattern Recognition Letters 130 (February 2020): 83–90. http://dx.doi.org/10.1016/j.patrec.2019.01.012.

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23

Korepin, Vladimir E., and Jinfeng Liao. "Quest for Fast Partial Search Algorithm." Quantum Information Processing 5, no. 3 (2006): 209–26. http://dx.doi.org/10.1007/s11128-006-0024-3.

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24

Zhao, Wan-Lei, Chong-Wah Ngo, and Hanzi Wang. "Fast Covariant VLAD for Image Search." IEEE Transactions on Multimedia 18, no. 9 (2016): 1843–54. http://dx.doi.org/10.1109/tmm.2016.2585023.

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25

Wu, H. S. "Fast search algorithm for vector quantisation." Electronics Letters 28, no. 5 (1992): 457. http://dx.doi.org/10.1049/el:19920288.

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26

Adeney, K. M., and M. J. Korenberg. "Fast orthogonal search for direction finding." Electronics Letters 28, no. 25 (1992): 2268–69. http://dx.doi.org/10.1049/el:19921459.

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27

Kolbe, D. L., and S. R. Eddy. "Fast filtering for RNA homology search." Bioinformatics 27, no. 22 (2011): 3102–9. http://dx.doi.org/10.1093/bioinformatics/btr545.

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28

Cassidy, K., and J. Snell. "Fast wideband search for spurious responses." IEEE Aerospace and Electronic Systems Magazine 7, no. 2 (1992): 21–27. http://dx.doi.org/10.1109/62.127165.

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29

Uras, Tansel, and Sven Koenig. "Identifying Hierarchies for Fast Optimal Search." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (2021): 211–12. http://dx.doi.org/10.1609/socs.v5i1.18307.

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For some search problems, the graph is known beforehand and there is time to preprocess the graph to make the search faster. One such example is video games, where one can often preprocess maps before a game is released or while a map is loaded into memory. The data produced by preprocessing should use only a small amount of memory, and, in case they are generated during runtime, preprocessing should be fast. Search with Subgoal Graphs (Uras, Koenig, and Hernandez 2013) was a non-dominated optimal path-planning algorithm in the Grid-Based Path Planning Competitions 2012 and 2013. During a prep

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30

Leis, John, and Sridha Sridharan. "Fast Search Methods for Spectral Quantization." Digital Signal Processing 9, no. 2 (1999): 76–88. http://dx.doi.org/10.1006/dspr.1999.0334.

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31

Ramasubramanian, V., and Kuldip K. Paliwal. "Fast nearest-neighbor search algorithms based on approximation-elimination search." Pattern Recognition 33, no. 9 (2000): 1497–510. http://dx.doi.org/10.1016/s0031-3203(99)00134-x.

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32

Chang, Chin-Chen, and Yi-Pei Hsieh. "A fast VQ codebook search with initialization and search order." Information Sciences 183, no. 1 (2012): 132–39. http://dx.doi.org/10.1016/j.ins.2011.08.025.

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33

Pattillo, Gary. "Fast Facts." College & Research Libraries News 80, no. 10 (2019): 536. http://dx.doi.org/10.5860/crln.80.10.536.

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34

Pattillo, Gary. "Fast Facts." College & Research Libraries News 80, no. 9 (2019): 536. http://dx.doi.org/10.5860/crln.80.9.536.

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35

Helmert, M. "The Fast Downward Planning System." Journal of Artificial Intelligence Research 26 (July 12, 2006): 191–246. http://dx.doi.org/10.1613/jair.1705.

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Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the inpu

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36

Jing, Xuan, and Lap-Pui Chau. "Partial Distortion Search Algorithm Using Predictive Search Area for Fast Full-Search Motion Estimation." IEEE Signal Processing Letters 14, no. 11 (2007): 840–43. http://dx.doi.org/10.1109/lsp.2007.900035.

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37

Lim, Dong-Young, Sang-Jun Park, and Je-Chang Jeong. "Direction-Oriented Fast Full Search Algorithm at the Divided Search Range." Journal of Broadcast Engineering 12, no. 3 (2007): 278–88. http://dx.doi.org/10.5909/jbe.2007.12.3.278.

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38

Soongsathitanon, S., W. L. Woo, and S. S. Dlay. "Fast search algorithms for video coding using orthogonal logarithmic search algorithm." IEEE Transactions on Consumer Electronics 51, no. 2 (2005): 552–59. http://dx.doi.org/10.1109/tce.2005.1468001.

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39

Liaw, Yi-Ching, Maw-Lin Leou, and Chien-Min Wu. "Fast exact k nearest neighbors search using an orthogonal search tree." Pattern Recognition 43, no. 6 (2010): 2351–58. http://dx.doi.org/10.1016/j.patcog.2010.01.003.

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40

Liaw, Yi-Ching, Chien-Min Wu, and Maw-Lin Leou. "Fast k-nearest neighbors search using modified principal axis search tree." Digital Signal Processing 20, no. 5 (2010): 1494–501. http://dx.doi.org/10.1016/j.dsp.2010.01.009.

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41

Sirisha, U., R. Kalyan Kumar, and B. Mani Krishna. "An Efficient Fast Phrase Search with Nth-Gram For Encrypted Cloud Storage." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1339–41. http://dx.doi.org/10.31142/ijtsrd11358.

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42

Mukhtоrovich, Karshiev Aziz. "THEORETICAL AND LEGAL ANALYSIS OF THE CONDITIONS OF CONDUCTING FAST SEARCH EVENTS." International Journal of Law And Criminology 4, no. 4 (2024): 55–61. http://dx.doi.org/10.37547/ijlc/volume04issue04-11.

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In this state, the concept of the conditional implementation of the operational-rational management system, the principle of ego reliability and principles, the different opinions and shortcomings of the management system, and also the following concepts under the conditions of the operational-system management management, and the basis of the analysis of the theoretical conditions of the management system are presented. operativno-rozysknoy deyatelnosti. Conducted legal analysis, study of the opinions of students and etom napravlenii, a takje razrabotany predlozhenii i rekomendatsii po vlyuch

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43

Yang, Wenzhe, Sheng Wang, Yuan Sun, and Zhiyong Peng. "Fast dataset search with earth mover's distance." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2517–29. http://dx.doi.org/10.14778/3551793.3551811.

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The amount of spatial data in open data portals has increased rapidly, raising the demand for spatial dataset search in large data repositories. In this paper, we tackle spatial dataset search by using the Earth Mover's Distance (EMD) to measure the similarity between datasets. EMD is a robust similarity measure between two distributions and has been successfully applied to multiple domains such as image retrieval, document retrieval, multimedia, etc. However, the existing EMD-based studies typically depend on a common filtering framework with a single pruning strategy, which still has a high

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44

Ko, Dae-Young, Sung-June Baek, Jun-Kyu Park, Yu-Gyeong Seo, and Sung-Il Seo. "The Fast Search Algorithm for Raman Spectrum." Journal of the Korea Academia-Industrial cooperation Society 16, no. 5 (2015): 3378–84. http://dx.doi.org/10.5762/kais.2015.16.5.3378.

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45

WU Ben-tao, 吴本涛, 吴敏渊 WU Min-yuan, and 曾霖 ZENG Lin. "Fast fragment based tracking using adaptive search." Optics and Precision Engineering 19, no. 3 (2011): 703–8. http://dx.doi.org/10.3788/ope.20111903.0703.

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46

Pan, Naiqiao, Tian Chen, Houjun Sun, and Xiangdong Zhang. "Electric-Circuit Realization of Fast Quantum Search." Research 2021 (July 26, 2021): 1–8. http://dx.doi.org/10.34133/2021/9793071.

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Quantum search algorithm, which can search an unsorted database quadratically faster than any known classical algorithms, has become one of the most impressive showcases of quantum computation. It has been implemented using various quantum schemes. Here, we demonstrate both theoretically and experimentally that such a fast search algorithm can also be realized using classical electric circuits. The classical circuit networks to perform such a fast search have been designed. It has been shown that the evolution of electric signals in the circuit networks is analogies of quantum particles random

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47

Zhang, Rui, and Tian Chen. "Fast quantum search driven by environmental engineering." Communications in Theoretical Physics 74, no. 4 (2022): 045101. http://dx.doi.org/10.1088/1572-9494/ac539d.

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Abstract Studies have demonstrated that a joined complete graph is a typical mathematical model that can support a fast quantum search. In this paper, we study the implementation of joined complete graphs in atomic systems and realize a quantum search of runtime O ( N ) based on this implementation with a success probability of 50%. Even though the practical systems inevitably interact with the surrounding environment, we reveal that a successful quantum search can be realized through delicately engineering the environment itself. We consider that our study will bring about a feasible way to r

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48

Botea, Adi. "Ultra-Fast Optimal Pathfinding without Runtime Search." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (2011): 122–27. http://dx.doi.org/10.1609/aiide.v7i1.12443.

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Pathfinding is important in many applications, including games, robotics and GPS itinerary planning. In games, most pathfinding methods rely on runtime search. Despite numerous enhancements introduced in recent years, runtime search has the disadvantage that, in bad cases, most parts of a map need to be explored, causing a time performance degradation. In this work we explore a significantly different approach to pathfinding, eliminating the need for runtime search. Optimal paths between all pairs of locations are pre-computed. Since straightforward ways to store pre-computed paths are prohibi

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49

Junming Lu and Zhenghui Lin. "Deliberation with "fast full-search block matching"." IEEE Transactions on Circuits and Systems for Video Technology 13, no. 1 (2003): 97–99. http://dx.doi.org/10.1109/tcsvt.2002.808090.

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50

Crain, Tyler, Vincent Gramoli, and Michel Raynal. "A Fast Contention-Friendly Binary Search Tree." Parallel Processing Letters 26, no. 03 (2016): 1650015. http://dx.doi.org/10.1142/s0129626416500158.

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This paper presents a fast concurrent binary search tree algorithm. To achieve high performance under contention, the algorithm divides update operations within an eager abstract access that returns rapidly for efficiency reason and a lazy structural adaptation that may be postponed to diminish contention. To achieve high performance under read-only workloads, it features a rebalancing mechanism and guarantees that read-only operations searching for an element execute lock-free. We evaluate the contention-friendly binary search tree using Synchrobench, a benchmark suite to compare synchronizat

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