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Journal articles on the topic 'Hierarchical search'

Author: Grafiati

Published: 23 June 2021

Last updated: 29 July 2025

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1

KOREPIN, VLADIMIR E., and YING XU. "HIERARCHICAL QUANTUM SEARCH." International Journal of Modern Physics B 21, no. 31 (2007): 5187–205. http://dx.doi.org/10.1142/s0217979207038344.

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Database search has wide applications and is used as a subroutine in many important algorithms. In this paper, we will consider a database with a single target item. Quantum algorithm (Grover) locates the target item faster than any classical algorithm. In addition to a full (Grover) search, it frequently occurs that one is looking for a group of items (a block) containing the target item, rather than the target item itself. This problem is known as partial search. As a generalization of the full search, partial search is of particular importance in practice. Partial search trades accuracy for

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2

de Buy Wenniger, Gideon Maillette, and Khalil Sima’an. "Visualization, Search and Analysis of Hierarchical Translation Equivalence in Machine Translation Data." Prague Bulletin of Mathematical Linguistics 101, no. 1 (2014): 43–54. http://dx.doi.org/10.2478/pralin-2014-0003.

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Abstract Translation equivalence constitutes the basis of all Machine Translation systems including the recent hierarchical and syntax-based systems. For hierarchical MT research it is important to have a tool that supports the qualitative and quantitative analysis of hierarchical translation equivalence relations extracted from word alignments in data. In this paper we present such a toolkit and exemplify some of its uses. The main challenges taken up in designing this tool are the efficient and compact, yet complete, representation of hierarchical translation equivalence coupled with an intu

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3

Zupan, Jure, and Morton E. Munk. "Feedback search of hierarchical trees." Analytical Chemistry 58, no. 14 (1986): 3219–25. http://dx.doi.org/10.1021/ac00127a065.

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4

Leighton, Michael, Wheeler Ruml, and Robert Holte. "Faster Optimal and Suboptimal Hierarchical Search." Proceedings of the International Symposium on Combinatorial Search 2, no. 1 (2021): 92–99. http://dx.doi.org/10.1609/socs.v2i1.18203.

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In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This paper makes two contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical searc

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5

Chen, Lin Chih. "Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods." Applied Mechanics and Materials 284-287 (January 2013): 3051–55. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3051.

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Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hier

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Liu, Guanting, Yujie Zhong, Sheng Guo, Matthew R. Scott, and Weilin Huang. "Unchain the Search Space with Hierarchical Differentiable Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8644–52. http://dx.doi.org/10.1609/aaai.v35i10.17048.

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Differentiable architecture search (DAS) has made great progress in searching for high-performance architectures with reduced computational cost. However, DAS-based methods mainly focus on searching for a repeatable cell structure, which is then stacked sequentially in multiple stages to form the networks. This configuration significantly reduces the search space, and ignores the importance of connections between the cells. To overcome this limitation, in this paper, we propose a Hierarchical Differentiable Architecture Search (H-DAS) that performs architecture search both at the cell level an

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7

del Campo, Jorge M., and Andreas M. Köster. "A hierarchical transition state search algorithm." Journal of Chemical Physics 129, no. 2 (2008): 024107. http://dx.doi.org/10.1063/1.2950083.

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8

Zhang, Qiong. "Hierarchical Route Representation, Indexing, and Search." IEEE Pervasive Computing 7, no. 2 (2008): 78–84. http://dx.doi.org/10.1109/mprv.2008.31.

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9

Pang, Bo, and Robert Holte. "Multimapping Abstractions and Hierarchical Heuristic Search." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (2021): 72–79. http://dx.doi.org/10.1609/socs.v3i1.18234.

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In this paper we introduce a broadly applicable method, called multimapping abstraction, that allows multiple heuristic values for a state to be extracted from one abstract state space. The key idea is to define an abstraction to be a multimapping, i.e., a function that maps a state in the original state space to a set of states in the abstract space. We performed a large-scale experiment on several benchmark state spaces to compare the memory requirements and runtime of Hierarchical IDA* (HIDA*) using multimapping domain abstractions to HIDA* with individual domain abstractions and to HIDA* w

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10

Proissl, Claudius. "Light Contraction Hierarchies: Hierarchical Search Without Shortcuts." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (2022): 234–38. http://dx.doi.org/10.1609/socs.v15i1.21773.

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Hierarchical search such as Contraction Hierarchies is a popular and successful branch of optimization techniques for shortest path computation. Existing hierarchical techniques have one component in common: they add edges to the graph, so called shortcuts. This component usually causes a considerable space overhead but is mandatory in order to preserve correctness. In this work we show a hierarchical method that requires to store only one additional number per node and no shortcuts at all. We prove the correctness of our method and experimentally show that it improves query times by one order

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11

Li, Yuxi, Fucai Zhou, Dong Ji, and Zifeng Xu. "A Hierarchical Searchable Encryption Scheme Using Blockchain-Based Indexing." Electronics 11, no. 22 (2022): 3832. http://dx.doi.org/10.3390/electronics11223832.

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Focusing on the fine-grained access control challenge of multi-user searchable encryption, we propose a hierarchical searchable encryption scheme using blockchain-based indexing (HSE-BI). First, we propose a hierarchical search index structure based on a DAG-type access policy and a stepwise hierarchical key derivation mechanism; which we outsourced to the blockchain network to achieve reliable hierarchical search. We design a dynamic append-only update protocol for the blockchain-based index to deal with adding and deleting files. Secondly, we propose a hierarchical authorization mechanism ba

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12

Li, Wei, Shaogang Gong, and Xiatian Zhu. "Hierarchical distillation learning for scalable person search." Pattern Recognition 114 (June 2021): 107862. http://dx.doi.org/10.1016/j.patcog.2021.107862.

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13

Chen, Di, Shanshan Zhang, Wanli Ouyang, Jian Yang, and Bernt Schiele. "Hierarchical Online Instance Matching for Person Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10518–25. http://dx.doi.org/10.1609/aaai.v34i07.6623.

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Person Search is a challenging task which requires to retrieve a person's image and the corresponding position from an image dataset. It consists of two sub-tasks: pedestrian detection and person re-identification (re-ID). One of the key challenges is to properly combine the two sub-tasks into a unified framework. Existing works usually adopt a straightforward strategy by concatenating a detector and a re-ID model directly, either into an integrated model or into separated models. We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical On

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14

NAGARAJ SHENOY, U., Y. N. SRIKANT, V. P. BHATKAR, and SANDEEP KOHLI. "AUTOMATIC DATA PARTITIONING BY HIERARCHICAL GENETIC SEARCH." Parallel Algorithms and Applications 14, no. 2 (1999): 119–47. http://dx.doi.org/10.1080/10637199808947382.

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15

Tedmori, S., and N. Al-Najdawi. "Hierarchical stochastic fast search motion estimation algorithm." IET Computer Vision 6, no. 1 (2012): 21. http://dx.doi.org/10.1049/iet-cvi.2010.0188.

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16

OISHI, Atsuya, Iori OKUNO, Shoji AMANO, and Shinobu YOSHIMURA. "1303 Hierarchical Contact Search for Isogeometric Analysis." Proceedings of The Computational Mechanics Conference 2013.26 (2013): _1303–1_—_1303–2_. http://dx.doi.org/10.1299/jsmecmd.2013.26._1303-1_.

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17

Gies, D. R., S. J. Williams, R. A. Matson, et al. "A SEARCH FOR HIERARCHICAL TRIPLES USINGKEPLERECLIPSE TIMING." Astronomical Journal 143, no. 6 (2012): 137. http://dx.doi.org/10.1088/0004-6256/143/6/137.

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18

Lim, Hyesook, Ha Chu, and Changhoon Yim. "Hierarchical Binary Search Tree for Packet Classification." IEEE Communications Letters 11, no. 8 (2007): 689–91. http://dx.doi.org/10.1109/lcomm.2007.070389.

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19

Zhen He. "Hierarchical Colorant-Based Direct Binary Search Halftoning." IEEE Transactions on Image Processing 19, no. 7 (2010): 1824–36. http://dx.doi.org/10.1109/tip.2010.2045690.

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20

Tian, Xinmei, Linjun Yang, Yijuan Lu, Qi Tian, and Dacheng Tao. "Image Search Reranking With Hierarchical Topic Awareness." IEEE Transactions on Cybernetics 45, no. 10 (2015): 2177–89. http://dx.doi.org/10.1109/tcyb.2014.2366740.

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21

Fang, Xiaowen. "A Hierarchical Search History for Web Searching." International Journal of Human-Computer Interaction 12, no. 1 (2000): 73–88. http://dx.doi.org/10.1207/s15327590ijhc1201_3.

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22

Mihm, Jürgen, Christoph H. Loch, Dennis Wilkinson, and Bernardo A. Huberman. "Hierarchical Structure and Search in Complex Organizations." Management Science 56, no. 5 (2010): 831–48. http://dx.doi.org/10.1287/mnsc.1100.1148.

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23

Montgomery, Thomas A., and Edmund H. Durfee. "Search reduction in hierarchical distributed problem solving." Group Decision and Negotiation 2, no. 3 (1993): 301–17. http://dx.doi.org/10.1007/bf01384251.

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24

Gou, Qianwen, Yunwei Dong, Yujiao Wu, and Qiao Ke. "RRGcode: Deep hierarchical search-based code generation." Journal of Systems and Software 211 (May 2024): 111982. http://dx.doi.org/10.1016/j.jss.2024.111982.

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25

Larsen, Bradford, Ethan Burns, Wheeler Ruml, and Robert Holte. "Searching Without a Heuristic: Efficient Use of Abstraction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 114–20. http://dx.doi.org/10.1609/aaai.v24i1.7563.

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In problem domains where an informative heuristic evaluation function is not known or not easily computed, abstraction can be used to derive admissible heuristic values. Optimal path lengths in the abstracted problem are consistent heuristic estimates for the original problem. Pattern databases are the traditional method of creating such heuristics, but they exhaustively compute costs for all abstract states and are thus usually appropriate only when all instances share the same single goal state. Hierarchical heuristic search algorithms address these shortcomings by searching for paths in the

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26

Elkawkagy*, Mohamed, and Elbeh Heba. "Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm." Regular issue 10, no. 7 (2021): 24–32. http://dx.doi.org/10.35940/ijitee.g8902.0510721.

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While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to div

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27

Mohamed, Elkawkagy, and Elbeh Heba. "Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 7 (2021): 24–32. https://doi.org/10.35940/ijitee.G8902.0510721.

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While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to div

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28

Unger, Christoph. "The scope of discourse connectives: implications for discourse organization." Journal of Linguistics 32, no. 2 (1996): 403–38. http://dx.doi.org/10.1017/s0022226700015942.

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The main aim of this paper is to discuss the claim that discourse connectives are best treated as indicators of coherence relations between hierarchically organized discourse units. It will be argued that coherence relations cannot be seen as cognitively real entities. Furthermore, there is no evidence for hierarchical organization in discourse. The intuitions underlying the notion of hierarchical discourse structure are instead explained in terms of consequences of processing a text in the search for optimal relevance. This account draws attention to a hitherto not widely discussed set of dat

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29

Prakash, Miss Pawar Pratima. "A Review on Enabling Synonym Based Fined-grained Multi-keyword Search Using Hierarchical Clustering." International journal of Emerging Trends in Science and Technology 03, no. 12 (2016): 4866–70. http://dx.doi.org/10.18535/ijetst/v3i12.11.

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30

Nie, Mengdie, Zhi-Jie Wang, Chunjing Gan, Zhe Quan, Bin Yao, and Jian Yin. "An Improved Hierarchical Datastructure for Nearest Neighbor Search." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10001–2. http://dx.doi.org/10.1609/aaai.v33i01.330110001.

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Nearest neighbor search is a fundamental computational tool and has wide applications. In past decades, many datastructures have been developed to speed up this operation. In this paper, we propose a novel hierarchical datastructure for nearest neighbor search in moderately high dimension. Our proposed method maintains good run time guarantees, and it outperforms several state-of-the-art methods in practice.

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31

Hutchinson, Hilary Browne. "Children's interface design for hierarchical search and browse." ACM SIGCAPH Computers and the Physically Handicapped, no. 75 (January 2003): 11–12. http://dx.doi.org/10.1145/976261.976265.

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32

Parikh, N. P., C. Y. Lo, A. Singhal, and K. W. Wu. "HS: a hierarchical search package for CAD data." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 12, no. 1 (1993): 1–5. http://dx.doi.org/10.1109/43.184838.

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33

Serrano, D. P., J. M. Escola, and P. Pizarro. "Synthesis strategies in the search for hierarchical zeolites." Chem. Soc. Rev. 42, no. 9 (2013): 4004–35. http://dx.doi.org/10.1039/c2cs35330j.

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34

SASSA, Fumihiro, and Manabu HASEGAWA. "20606 A Hierarchical Design of Iterated Local Search." Proceedings of Conference of Kanto Branch 2006.12 (2006): 439–40. http://dx.doi.org/10.1299/jsmekanto.2006.12.439.

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35

SHIMIZU, Yoshiaki, and Takeshi WADA. "Hybrid Tabu Search Approach for Hierarchical Logistics Optimization." Transactions of the Institute of Systems, Control and Information Engineers 17, no. 6 (2004): 241–48. http://dx.doi.org/10.5687/iscie.17.241.

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36

WADA, Takeshi, Shigeharu MATSUDA, and Yoshiaki SHIMIZU. "Hybrid Tabu Search Approach for Hierarchical Logistics Optimization." Proceedings of Manufacturing Systems Division Conference 2003 (2003): 25–26. http://dx.doi.org/10.1299/jsmemsd.2003.25.

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37

Frasca, S., and C. Palomba. "Spectral filtering for hierarchical search of periodic sources." Classical and Quantum Gravity 21, no. 20 (2004): S1645—S1654. http://dx.doi.org/10.1088/0264-9381/21/20/007.

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38

Zaid, Norasykin Mohd, Sim Kim Lau, and Mohd Nihra Haruzuan Mohamad Said. "Ontology-based Search System Using Hierarchical Structure Design." Procedia - Social and Behavioral Sciences 97 (November 2013): 566–70. http://dx.doi.org/10.1016/j.sbspro.2013.10.274.

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39

Xu, Jiajie, Yunjun Gao, Chengfei Liu, Lei Zhao, and Zhiming Ding. "Efficient route search on hierarchical dynamic road networks." Distributed and Parallel Databases 33, no. 2 (2014): 227–52. http://dx.doi.org/10.1007/s10619-014-7146-x.

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40

Svanberg, K., and M. Werme. "A hierarchical neighbourhood search method for topology optimization." Structural and Multidisciplinary Optimization 29, no. 5 (2005): 325–40. http://dx.doi.org/10.1007/s00158-004-0493-x.

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41

Jau-Yuen Chen, C. A. Bouman, and J. C. Dalton. "Hierarchical browsing and search of large image databases." IEEE Transactions on Image Processing 9, no. 3 (2000): 442–55. http://dx.doi.org/10.1109/83.826781.

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42

Tsay, Jyh-Jong, and Chi-Hsiang Lin. "Hierarchical directory mapping for category-constrained meta-search." Journal of Intelligent Information Systems 42, no. 1 (2013): 75–94. http://dx.doi.org/10.1007/s10844-013-0256-5.

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43

Girill, T. R., and Clement H. Luk. "Hierarchical search support for hypertext on-line documentation." International Journal of Man-Machine Studies 36, no. 4 (1992): 571–85. http://dx.doi.org/10.1016/0020-7373(92)90097-5.

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44

Tran, D. A., and T. Nguyen. "Hierarchical multidimensional search in peer-to-peer networks." Computer Communications 31, no. 2 (2008): 346–57. http://dx.doi.org/10.1016/j.comcom.2007.08.007.

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45

Ouellette, Paul, Aidan Sciortino, Fatemeh Nargesian, et al. "RONIN." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2863–66. http://dx.doi.org/10.14778/3476311.3476364.

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Dataset discovery can be performed using search (with a query or keywords) to find relevant data. However, the result of this discovery can be overwhelming to explore. Existing navigation techniques mostly focus on linkage graphs that enable navigation from one data set to another based on similarity or joinability of attributes. However, users often do not know which data set to start the navigation from. RONIN proposes an alternative way to navigate by building a hierarchical structure on a collection of data sets: the user navigates between groups of data sets in a hierarchical manner to na

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46

Churchill, David, and Michael Buro. "Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 1 (2021): 16–22. http://dx.doi.org/10.1609/aiide.v11i1.12787.

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Online strategy video games offer several unique challenges to the field of AI research. Due to their large state and action spaces, existing search algorithms have difficulties in making strategically strong decisions. Additionally, the nature of competitive on-line video games adds the requirement that game designers be able to tweak game properties regularly when strategic imbalances are found. This means that an AI system for a game like this needs to be robust to such changes and less reliant on expert knowledge. This paper makes two main contributions to advancing the state of the art fo

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47

Shin, Jungpil, and Hsien-Chou Liao. "An Interactive Map Search System Using Wavelet and Shape Contexts." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1256–61. http://dx.doi.org/10.20965/jaciii.2011.p1256.

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In this paper a new interactive map search system is presented using shape context and bipartite graph matching. Shape context is used for measuring shape similarity and the recovering of point correspondences. After the above information is generated from the shape context bipartite graph matching is used to obtain the optimal correspondence between two shapes. Hierarchical description is also used to increase the recognition rate. Shape context is a method to treat shapes as a set of points and generate the histogram of the distribution of points. Wavelet analysis is used in hierarchical des

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48

Arief, Rifiana, Suryarini Widodo, Ary Bima Kurniawan, Hustinawaty Hustinawaty, and Faisal Arkan. "Advanced content-based retrieval for digital correspondence documents with ontology classification." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1665–77. http://dx.doi.org/10.11591/eei.v11i3.3376.

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The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using

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49

Rifiana, Arief, Widodo Suryarini, Bima Kurniawan Ary, Hustinawaty, and Arkan Faisal. "Advanced content-based retrieval for digital correspondence documents with ontology classification." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1665~1677. https://doi.org/10.11591/eei.v11i3.3376.

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The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using

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50

Fisher, D. "Iterative Optimization and Simplification of Hierarchical Clusterings." Journal of Artificial Intelligence Research 4 (April 1, 1996): 147–78. http://dx.doi.org/10.1613/jair.276.

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Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved

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