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Artículos de revistas sobre el tema "Search Log"

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Autor: Grafiati

Publicado: 11 de marzo de 2023

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1

V. S, Stency y Sureshkumar R. "Semantic Search Log for Social Personalized Search". International Journal of Computer Trends and Technology 11, n.º 3 (25 de mayo de 2014): 122–27. http://dx.doi.org/10.14445/22312803/ijctt-v11p126.

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2

Garey, L. E. y R. D. Gupta. "A note on continuous search algorithms". Journal of Applied Probability 24, n.º 1 (marzo de 1987): 277–80. http://dx.doi.org/10.2307/3214081.

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Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.

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3

Garey, L. E. y R. D. Gupta. "A note on continuous search algorithms". Journal of Applied Probability 24, n.º 01 (marzo de 1987): 277–80. http://dx.doi.org/10.1017/s0021900200030837.

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Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.

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4

Buzikashvili, Nikolai. "Towards Query Log-Based Sociology of Search". International Journal of Chaotic Computing 4, n.º 1 (30 de junio de 2016): 53–61. http://dx.doi.org/10.20533/ijcc.2046.3359.2016.0007.

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5

Jiang, Di y Lingxiao Yang. "Query intent inference via search engine log". Knowledge and Information Systems 49, n.º 2 (18 de enero de 2016): 661–85. http://dx.doi.org/10.1007/s10115-015-0915-7.

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6

Maheswari, K. y Dr S. Kiruba karan. "Enhancing Social Personalized Search Based on Semantic Search Log using Ontology". International Journal of Innovative Research in Science, Engineering and Technology 03, n.º 09 (15 de septiembre de 2014): 16238–42. http://dx.doi.org/10.15680/ijirset.2014.0309061.

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7

Arunachalam, Srinivasan y Ronald de Wolf. "Optimizing the number of gates in quantum search". Quantum Information and Computation 17, n.º 3&4 (marzo de 2017): 251–61. http://dx.doi.org/10.26421/qic17.3-4-4.

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In its usual form, Grover’s quantum search algorithm uses O( √ N) queries and O( √ N log N) other elementary gates to find a solution in an N-bit database. Grover in 2002 showed how to reduce the number of other gates to O( √ N log log N) for the special case where the database has a unique solution, without significantly increasing the number of queries. We show how to reduce this further to O( √ N log(r) N) gates for every constant r, and sufficiently large N. This means that, on average, the circuits between two queries barely touch more than a constant number of the log N qubits on which the algorithm acts. For a very large N that is a power of 2, we can choose r such that the algorithm uses essentially the minimal number π 4 √ N of queries, and only O( √ N log(log? N)) other gates.

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8

Belinsha, S. y A. P. V. Raghavendra. "Enhanced Utility in Search Log Publishing of Infrequent Items". International Journal of Information Technology and Web Engineering 9, n.º 2 (abril de 2014): 65–77. http://dx.doi.org/10.4018/ijitwe.2014040104.

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Search engines are being widely used by the web users. The search engine companies are concerned to produce best search results. Search logs are the records which records the interactions between the user and the search engine. Various search patterns, user's behaviors can be analyzed from these logs, which will help to enhance the search results. Publishing these search logs to third party for analysis is a privacy issue. Zealous algorithm of filtering the frequent search items in the search log looses its utility in the course of providing privacy. The proposed confess algorithm extends the work by qualifying the infrequent search items in the log which tends to increase the utility of the search log by preserving the privacy. Confess algorithm involves qualifying the infrequent keywords, URL clicks in the search log and publishing it along with the frequent items.

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9

Creasey, Peter E. "Two hide-search games with rapid strategies for multiple parallel searches". Open Computer Science 12, n.º 1 (1 de enero de 2022): 171–80. http://dx.doi.org/10.1515/comp-2022-0243.

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Abstract Making a rapid unpredictable decision from N N choices of unequal value is a common control task. When the cost of predictability can be modelled as a penalty hidden under a single option by an intelligent adversary, then an optimal strategy can be found efficiently in O ( N log N ) O\left(N\log N) steps using an approach described by Sakaguchi for a zero-sum hide-search game. In this work, we extend this to two games with multiple parallel predictions, either coordinated or drawn independently from the optimal distribution, both of which can be solved with the same scaling. An open-source code is provided online at https://github.com/pec27/rams.

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10

Jiang, Ming, PeiSi Cen, JingFan Tang y Xing Qi Wang. "Personalized Video Search Based on User Log Mining". Information Technology Journal 12, n.º 4 (1 de abril de 2013): 770–76. http://dx.doi.org/10.3923/itj.2013.770.776.

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11

Ni, Weijian, Tong Liu, Qingtian Zeng y Nengfu Xie. "Mining Domain Terminologies Using Search Engine's Query Log". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 6 (30 de noviembre de 2021): 1–32. http://dx.doi.org/10.1145/3462327.

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Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log—a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.

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12

Ntzoufras, Ioannis, Jonathan J. Forster y Petros Dellaportas. "Stochastic search variable selection for log-linear models". Journal of Statistical Computation and Simulation 68, n.º 1 (diciembre de 2000): 23–37. http://dx.doi.org/10.1080/00949650008812054.

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13

Yoon, Taebok y Jee-Hyong Lee. "Adaptive Web Search based on User Web Log". Journal of the Korea Academia-Industrial cooperation Society 15, n.º 11 (30 de noviembre de 2014): 6856–62. http://dx.doi.org/10.5762/kais.2014.15.11.6856.

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14

Burt, Wayne V. "The Search for the Manila Galleon Log Books". Bulletin of the American Meteorological Society 71, n.º 11 (noviembre de 1990): 1630–36. http://dx.doi.org/10.1175/1520-0477(1990)071<1630:tsftmg>2.0.co;2.

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15

MYERS, E. y X. HUANG. "An ( log ) restriction map comparison and search algorithm". Bulletin of Mathematical Biology 54, n.º 4 (julio de 1992): 599–618. http://dx.doi.org/10.1016/s0092-8240(05)80078-3.

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16

Mat-Hassan, Mazlita y Mark Levene. "Associating search and navigation behavior through log analysis". Journal of the American Society for Information Science and Technology 56, n.º 9 (2005): 913–34. http://dx.doi.org/10.1002/asi.20185.

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17

Schultheiß, Sebastian, Alexandra Linhart, Christiane Behnert, Imke Rulik y Dirk Lewandowski. "Known-item searches and search tactics in library search systems: Results from four transaction log analysis studies". Journal of Academic Librarianship 46, n.º 5 (septiembre de 2020): 102202. http://dx.doi.org/10.1016/j.acalib.2020.102202.

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18

Chawla, Suruchi. "Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering". International Journal of Information Retrieval Research 12, n.º 1 (enero de 2022): 1–14. http://dx.doi.org/10.4018/ijirr.300367.

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The volume of web search data stored in search engine log is increasing and has become big search log data. The web search log has been the source of data for mining based on web document clustering techniques to improve the efficiency and effectiveness of information retrieval. In this paper Deep Learning Model Convolution Neural Network(CNN) is used in big web search log data mining to learn the semantic representation of a document. These semantic documents vectors are clustered using K-means to group relevant documents for effective web document clustering. Experiment was done on the data set of web search query and associated clicked URLs to measure the quality of clusters based on document semantic representation using Deep learning model CNN. The clusters analysis was performed based on WCSS(the sum of squared distances of documents samples to their closest cluster center) and decrease in the WCSS in comparison to TF.IDF keyword based clusters confirm the effectiveness of CNN in web search log mining for effective web document clustering.

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19

Hollink, Vera, Theodora Tsikrika y Arjen P. de Vries. "Semantic search log analysis: A method and a study on professional image search". Journal of the American Society for Information Science and Technology 62, n.º 4 (9 de febrero de 2011): 691–713. http://dx.doi.org/10.1002/asi.21484.

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20

Sholihah, Walidatush, Sangga Pripambudi y Anggi Mardiyono. "Log Event Management Server Menggunakan Elastic Search Logstash Kibana (ELK Stack)". JTIM : Jurnal Teknologi Informasi dan Multimedia 2, n.º 1 (20 de mayo de 2020): 12–20. http://dx.doi.org/10.35746/jtim.v2i1.79.

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This study aims to build an Event Management Server Log using ELK Stack (Elastic searchLogstash Kibana) which can make it easier to read and analyze log services on the server. TheEvent Management Server log in this study uses CentOS 7 as the Central Server and CentOS7 as a client-server with ssh services installed. This research consists of five stages. The stagesare analysis, network design, server configuration, client configuration, and testing. Theexperimental results show that all ssh log services that occur on the client-server sent inrealtime to the central server. Even though the contents of the log file on the client-server hasdeleted. In This study, in addition to sending logs, it can also display a percentage of successreferences.

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21

TERABE, Shintaro, Kosuke JYURI y Hisao UCHIYAMA. "Log Analysis of Web Site for Railway Route Search". INFRASTRUCTURE PLANNING REVIEW 26 (2009): 569–74. http://dx.doi.org/10.2208/journalip.26.569.

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22

Hanna, Wael K., Aziza Saad Asem y M. B. Senousy. "Dynamic Query Intent Prediction from a Search Log Stream". International Journal of Information Retrieval Research 6, n.º 2 (abril de 2016): 66–85. http://dx.doi.org/10.4018/ijirr.2016040104.

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The users that used search engines are obligated to express their goals in few words (queries). Sometimes search queries are ambiguous. Moreover, the users' intents are dynamically evolving. This paper analyzes the user's query logs to classify the related queries, the related intent topic categories and the related intent types and use this classification to dynamically predict the users' future queries, its intent topic and its intent type. AOL Search Query Log is taken as an experimental data set. Then use evaluation metrics to evaluate the prediction results.

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23

Islamaj Dogan, R., G. C. Murray, A. Neveol y Z. Lu. "Understanding PubMed(R) user search behavior through log analysis". Database 2009 (27 de noviembre de 2009): bap018. http://dx.doi.org/10.1093/database/bap018.

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24

PANDEY, RAS B. y DIETRICH STAUFFER. "SEARCH FOR LOG-PERIODIC OSCILLATIONS IN STOCK MARKET SIMULATIONS". International Journal of Theoretical and Applied Finance 03, n.º 03 (julio de 2000): 479–82. http://dx.doi.org/10.1142/s0219024900000437.

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Using Chowdhury's simplification of the Cont–Bouchaud percolation algorithm, and including inertia (trading momentum) as well as a nonlinear restoring force (pricing pressure), we find spontaneous oscillations, which for suitable parameters have at first an exponentially increasing period. They seem to mimic the approximate log-periodic oscillations found by Johansen and Sornette in the Nikkei index after the burst of the Japanese bubble a decade ago.

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25

Malyutov, Mikhail B. "Log-efficient search for significant inputs of linear model". Journal of Statistical Planning and Inference 100, n.º 2 (febrero de 2002): 269–80. http://dx.doi.org/10.1016/s0378-3758(01)00139-2.

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26

Bajracharya, Sushil Krishna y Cristina Videira Lopes. "Analyzing and mining a code search engine usage log". Empirical Software Engineering 17, n.º 4-5 (18 de septiembre de 2010): 424–66. http://dx.doi.org/10.1007/s10664-010-9144-6.

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27

LeemaPriyadharshini, L., S. Florence, K. Prema y C. Shyamala Kumari. "Predicting implicit search behaviors usinglog analysis". International Journal of Engineering & Technology 7, n.º 1.7 (5 de febrero de 2018): 91. http://dx.doi.org/10.14419/ijet.v7i1.7.9582.

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Search engines provide ranked information based on the query given by the user. Understanding user search behavior is an important task for satisfaction of the users with the needed information. Understanding user search behaviors and recommending more information or more sites to the user is an emerging task. The work is based on the queries given by the user, the amount of time the user spending on the particular page, the number of clicks done by the user particular URL. These details will be available in the dataset of web search log. The web search log is nothing but the log which contains the user searching activities and other details like machine ID, browser ID, timestamp, query given by the user, URL accessed etc., four things considered as the important: 1) Extraction of tasks from the sequence of queries given by the user 2) suggesting some similar query to the user 3) ranking URLs based on the implicit user behaviors 4) increasing web page utilities based on the implicit behaviors. For increasing the web page utility and ranking the URLs predicting implicit user behavior is a needed task. For each of these four things designing and implementation of some algorithms and techniques are needed to increase the efficiency and effectiveness.

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28

Huntington, Paul, David Nicholas y Hamid R. Jamali. "Employing log metrics to evaluate search behaviour and success: case study BBC search engine". Journal of Information Science 33, n.º 5 (10 de abril de 2007): 584–97. http://dx.doi.org/10.1177/0165551506077407.

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29

Blecic, Deborah D., Nirmala S. Bangalore, Josephine L. Dorsch, Cynthia L. Henderson, Melissa H. Koenig y Ann C. Weller. "Using Transaction Log Analysis to Improve OPAC Retrieval Results". College & Research Libraries 59, n.º 1 (1 de enero de 1998): 39–50. http://dx.doi.org/10.5860/crl.59.1.39.

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This OPAC transaction log analysis study compared data derived from two sets of logs within a six-month period. Analysis of the first set of data revealed that users experienced difficulty with basic searching techniques. The OPAC introductory screens were simplified and clarified to help users improve search success rates. The second set of data, analyzed after screen changes had been made, showed statistically significant differences in search results. Regular monitoring of OPACs through transaction log analysis can lead to improved retrieval when changes are made in response to an analysis of user search patterns.

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30

Ambainis, Andris, Renato Portugal y Nikolay Nahimov. "Spatial search on grids with minimum memory". Quantum Information and Computation 15, n.º 13&14 (octubre de 2015): 1233–47. http://dx.doi.org/10.26421/qic15.13-14-9.

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We study quantum algorithms for spatial search on finite dimensional grids. Patel \textit{et al.}~and Falk have proposed algorithms based on a quantum walk without a coin, with different operators applied at even and odd steps. Until now, such algorithms have been studied only using numerical simulations. In this paper, we present the first rigorous analysis for an algorithm of this type, showing that the optimal number of steps is $O(\sqrt{N\log N})$ and the success probability is $O(1/\log N)$, where $N$ is the number of vertices. This matches the performance achieved by algorithms that use other forms of quantum walks.

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31

Dereniowski, Dariusz y Dorota Osula. "On-line Search in Two-Dimensional Environment". Theory of Computing Systems 63, n.º 8 (11 de septiembre de 2019): 1819–48. http://dx.doi.org/10.1007/s00224-019-09948-6.

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Abstract We consider the following on-line pursuit-evasion problem. A team of mobile agents called searchers starts at an arbitrary node of an unknown network. Their goal is to execute a search strategy that guarantees capturing a fast and invisible intruder regardless of its movements using as few searchers as possible. We require that the strategy is connected and monotone, that is, at each point of the execution the part of the graph that is guaranteed to be free of the fugitive is connected and whenever some node gains a property that it cannot be occupied by the fugitive, the strategy must operate in such a way to keep this property till its end. As a way of modeling two-dimensional shapes, we restrict our attention to networks that are embedded into partial grids: nodes are placed on the plane at integer coordinates and only nodes at distance one can be adjacent. Agents do not have any knowledge about the graph a priori, but they recognize the direction of the incident edge (up, down, left or right). We give an on-line algorithm for the searchers that allows them to compute a connected and monotone strategy that guarantees searching any unknown partial grid with the use of $O(\sqrt {n})$ O ( n ) searchers, where n is the number of nodes in the grid. As for a lower bound, there exist partial grids that require ${\varOmega }(\sqrt {n})$ Ω ( n ) searchers. Moreover, we prove that for each on-line searching algorithm there is a partial grid that forces the algorithm to use ${\varOmega }(\sqrt {n})$ Ω ( n ) searchers but $O(\log n)$ O ( log n ) searchers are sufficient in the off-line scenario. This gives a lower bound on ${\varOmega }(\sqrt {n}/\log n)$ Ω ( n / log n ) in terms of achievable competitive ratio of any on-line algorithm.

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32

Klose, Maria, Diana Steger, Julian Fick y Cordula Artelt. "Decrypting Log Data". Zeitschrift für Psychologie 230, n.º 1 (enero de 2022): 3–15. http://dx.doi.org/10.1027/2151-2604/a000484.

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Abstract. Analyzing log data from digital learning environments provides information about online learning. However, it remains unclear how this information can be transferred to psychologically meaningful variables or how it is linked to learning outcomes. The present study summarizes findings on correlations between general online activity and learning outcomes in university settings. The course format, instructions to engage in online discussions, requirements, operationalization of general online activity, and publication year are considered moderators. A multi-source search provided 41 studies ( N = 28,986) reporting 69 independent samples and 104 effect sizes. The three-level random-effects meta-analysis identified a pooled effect of r = .25 p = .003, 95% CI [.09, .41], indicating that students who are more active online have better grades. Despite high heterogeneity, Q(103) = 3,960.04, p < .001, moderator analyses showed no statistically significant effect. We discuss further potential influencing factors in online courses and highlight the potential of learning analytics.

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33

Hale, Meredith L. "In Search of Art: A Log Analysis of the Ackland Art Museum’s Collection Search System". Art Documentation: Journal of the Art Libraries Society of North America 35, n.º 2 (septiembre de 2016): 182–205. http://dx.doi.org/10.1086/688722.

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34

HAMADI, YOUSSEF y MARC SHAPIRO. "PUSHING LOG-BASED RECONCILIATION". International Journal on Artificial Intelligence Tools 14, n.º 03 (junio de 2005): 445–58. http://dx.doi.org/10.1142/s0218213005002193.

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Optimistic reconciliation allows, multiple update of shared data without synchronization. The assumption is that the vast majority of the actions will not conflict. In these systems, write availability is raised in the presence of network failures, high latencies or parallel development. However, in order to remain consistent, optimistic systems repair divergences. To produce a new consistent state, they use the logs of each user in a process called log-based reconciliation. The purpose of an efficient reconciliation engine is then to compute a new consistent state which preserves the maximum of previous actions. This work leverages the efficiency of constraint-based reconciliation. It provides a new efficient two step procedure built by connecting theoretical results on backtrack-free search with this hard optimisation problem.

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35

Raab, Christian. "In Search of the Masculine Genius: The Contribution of Walter J. Ong". Logos: A Journal of Catholic Thought and Culture 21, n.º 1 (2018): 83–117. http://dx.doi.org/10.1353/log.2018.0004.

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FALCONER, SEAN M. y BRADFORD G. NICKERSON. "ON MULTI-LEVEL k-RANGES FOR RANGE SEARCH". International Journal of Computational Geometry & Applications 15, n.º 06 (diciembre de 2005): 565–73. http://dx.doi.org/10.1142/s0218195905001853.

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We investigate an implementation of the multi-level or ℓ-level k-range data structure. The ℓ-level k-range is compared to naive and R*tree search over N randomly generated k-dimensional points. Results indicate that multi-level k-ranges are not competitive due to their (previously unreported) complexity. We show that storage is S(N,k,ℓ) = O(N1+2(k-1)/ℓ) and S(N,k) = Θ(N1+2(k-1)/ log 2N). Our results also indicate that the ℓ-level k-range requires Q(N,k,ℓ) = O((2ℓ)k( log N + A)) time for range search, for A = number of points reported in range.

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37

Hong, Yuan, Jaideep Vaidya, Haibing Lu, Panagiotis Karras y Sanjay Goel. "Collaborative Search Log Sanitization: Toward Differential Privacy and Boosted Utility". IEEE Transactions on Dependable and Secure Computing 12, n.º 5 (1 de septiembre de 2015): 504–18. http://dx.doi.org/10.1109/tdsc.2014.2369034.

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38

White, Ryen W., Sheng Wang, Apurv Pant, Rave Harpaz, Pushpraj Shukla, Walter Sun, William DuMouchel y Eric Horvitz. "Early identification of adverse drug reactions from search log data". Journal of Biomedical Informatics 59 (febrero de 2016): 42–48. http://dx.doi.org/10.1016/j.jbi.2015.11.005.

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39

Zhang, Ying, Bernard J. Jansen y Amanda Spink. "Time series analysis of a Web search engine transaction log". Information Processing & Management 45, n.º 2 (marzo de 2009): 230–45. http://dx.doi.org/10.1016/j.ipm.2008.07.003.

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40

Jiang, Di, Yongxin Tong y Yuanfeng Song. "Cross-Lingual Topic Discovery From Multilingual Search Engine Query Log". ACM Transactions on Information Systems 35, n.º 2 (21 de diciembre de 2016): 1–28. http://dx.doi.org/10.1145/2956235.

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41

Silverstein, Craig, Hannes Marais, Monika Henzinger y Michael Moricz. "Analysis of a very large web search engine query log". ACM SIGIR Forum 33, n.º 1 (septiembre de 1999): 6–12. http://dx.doi.org/10.1145/331403.331405.

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42

Hagerup, Torben. "Planar Depth-First Search in $O(\log n)$ Parallel Time". SIAM Journal on Computing 19, n.º 4 (agosto de 1990): 678–704. http://dx.doi.org/10.1137/0219047.

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43

Zycinski, Jozef. "Between Mathematics and Transcendence: The Search for the Spiritual Dimension of Scientific Discovery". Logos: A Journal of Catholic Thought and Culture 6, n.º 2 (2003): 38–45. http://dx.doi.org/10.1353/log.2003.0028.

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44

FLEISCHER, RUDOLF. "A SIMPLE BALANCED SEARCH TREE WITH O(1) WORST-CASE UPDATE TIME". International Journal of Foundations of Computer Science 07, n.º 02 (junio de 1996): 137–49. http://dx.doi.org/10.1142/s0129054196000117.

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In this paper we show how a slight modification of (a, 2b)-trees allows us to perform member and neighbor queries in O( log n) time and updates in O(1) worst-case time (once the position of the inserted or deleted key is known). Our data structure is quite natural and much simpler than previous worst-case optimal solutions. It is based on two techniques : 1) bucketing, i.e., storing an ordered list of 2 log n keys in each leaf of an (a, 2b) tree, and 2) preventive splitting, i.e., splitting nodes before they can grow bigger than allowed. If only insertions are allowed, it can also be used as a finger search tree with O( log * n) worst-case update time.

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45

Ullah, Mohib, Rafi Ullah Khan, Irfan Ullah Khan, Nida Aslam, Sumayh S. Aljameel, Muhammad Inam Ul Haq y Muhammad Arshad Islam. "Profile Aware ObScure Logging (PaOSLo): A Web Search Privacy-Preserving Protocol to Mitigate Digital Traces". Security and Communication Networks 2022 (3 de febrero de 2022): 1–13. http://dx.doi.org/10.1155/2022/2109024.

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Web search querying is an inevitable activity of any Internet user. The web search engine (WSE) is the easiest way to search and retrieve data from the Internet. The WSE stores the user’s search queries to retrieve the personalized search result in a form of query log. A user often leaves digital traces and sensitive information in the query log. WSE is known to sell the query log to a third party to generate revenue. However, the release of the query log can compromise the security and privacy of a user. In this work, we propose a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching. PaOSLo systematically groups users based on profile similarity. The primary objective of this work is to evaluate the impact of the systematic group compared to random grouping. We first computed the similarity between the users’ profiles and then clustered them using the K-mean algorithm to group the users systematically. Unlikability and indistinguishability are the two dimensions in which we have measured the privacy of a user. To compute the impact of systematic grouping on a user’s privacy, we have experimented with and compared the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e). Results show that, at the top degree of the ODP hierarchy, PaOSLo preserved 10% and 3% better profile privacy than the modern distributed protocols mentioned above. In addition, the PaOSLo has less profile exposure for any group size and at each degree of the ODP hierarchy.

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46

Zahera, Hamada M., Gamal F. El-Hady y W. F. Abd El-Wahed. "Query Recommendation for Improving Search Engine Results". International Journal of Information Retrieval Research 1, n.º 1 (enero de 2011): 45–52. http://dx.doi.org/10.4018/ijirr.2011010104.

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As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.

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47

Tiwari, Ankita. "A Survey On Page Ranking And Search Engine For Query Log". International Journal for Research in Applied Science and Engineering Technology V, n.º IX (30 de septiembre de 2017): 20–26. http://dx.doi.org/10.22214/ijraset.2017.9005.

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48

Chaudhari, Soniya P., Prof Hitesh Gupta y S. J. Patil. "Review of Improvement of Web Search Based on Web Log File". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, n.º 2 (30 de octubre de 2012): 298–300. http://dx.doi.org/10.24297/ijct.v3i2b.2880.

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In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

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49

Hider, Philip M. "Constructing an index of search goal redefinition through transaction log analysis". Journal of Documentation 63, n.º 2 (13 de marzo de 2007): 175–87. http://dx.doi.org/10.1108/00220410710737169.

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50

Lee, Sung-Sook. "Trends of Web-based OPAC Search Behavior via Transaction Log Analysis". Journal of the Korean BIBLIA Society for library and Information Science 23, n.º 2 (30 de junio de 2012): 209–33. http://dx.doi.org/10.14699/kbiblia.2012.23.2.209.

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