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Статті в журналах з теми "Querying (Computer science)"

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Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 20 лютого 2023

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1

Lai, Catherine, and Steven Bird. "Querying Linguistic Trees." Journal of Logic, Language and Information 19, no. 1 (May 28, 2009): 53–73. http://dx.doi.org/10.1007/s10849-009-9086-9.

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2

Vardi, Moshe Y. "Querying logical databases." Journal of Computer and System Sciences 33, no. 2 (October 1986): 142–60. http://dx.doi.org/10.1016/0022-0000(86)90016-4.

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3

Ganesan, K., and G. Siva. "Multilingual Querying and Information Processing." Information Technology Journal 6, no. 5 (June 15, 2007): 751–55. http://dx.doi.org/10.3923/itj.2007.751.755.

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4

Buneman, O. P., S. B. Davidson, and A. Watters. "Querying independent databases." Information Sciences 52, no. 1 (October 1990): 1–34. http://dx.doi.org/10.1016/0020-0255(90)90033-7.

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5

Barton, Carl, Mathieu Giraud, Costas S. Iliopoulos, Thierry Lecroq, Laurent Mouchard, and Solon P. Pissis. "Querying highly similar sequences." International Journal of Computational Biology and Drug Design 6, no. 1/2 (2013): 119. http://dx.doi.org/10.1504/ijcbdd.2013.052206.

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6

Chabrier-Rivier, Nathalie, Marc Chiaverini, Vincent Danos, François Fages, and Vincent Schächter. "Modeling and querying biomolecular interaction networks." Theoretical Computer Science 325, no. 1 (September 2004): 25–44. http://dx.doi.org/10.1016/j.tcs.2004.03.063.

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7

Siddiqui, Tarique, Paul Luh, Zesheng Wang, Karrie Karahalios, and Aditya G. Parameswaran. "Expressive querying for accelerating visual analytics." Communications of the ACM 65, no. 7 (July 2022): 85–94. http://dx.doi.org/10.1145/3535337.

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Анотація:

Data visualization is the primary means by which data analysts explore patterns, trends, and insights in their data. Unfortunately, existing visual analytics tools offer limited expressiveness and scalability when it comes to searching for visualizations over large datasets, making visual data exploration labor-intensive and time-consuming. In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching. To address them, we first present our work on Zenvisage that helps enumerate large collections of visualizations and supports simple visualization matching with the help of an interactive interface and an expressive visualization query language. For more finegrained and flexible visualization matching, including search for underspecified and approximate patterns, we extend Zenvisage to develop ShapeSearch. ShapeSearch supports a novel shape querying algebra that helps express a large class of pattern queries that are hard to specify with existing systems. ShapeSearch exposes multiple specification mechanisms: sketch, natural-language, and visual regular expressions that help users easily issue shape queries, while applying query-aware and perceptually-aware optimizations to efficiently execute them within interactive response times. To conclude, we discuss a number of open research problems to further improve the usability and performance of both Zenvisage and ShapeSearch.

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8

Cao, Zhongsheng, Zongda Wu, Yuanzhen Wang, and Guiling Li. "Processing Techniques for Querying Multimedia Contents." Information Technology Journal 8, no. 8 (November 1, 2009): 1115–28. http://dx.doi.org/10.3923/itj.2009.1115.1128.

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9

Flesca, S., F. Furfaro, and S. Greco. "Graph Grammars for Querying Graph-like Data." Electronic Notes in Theoretical Computer Science 50, no. 3 (August 2001): 247–56. http://dx.doi.org/10.1016/s1571-0661(04)00176-8.

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10

Belazzougui, Djamal, Roman Kolpakov, and Mathieu Raffinot. "Indexing and querying color sets of images." Theoretical Computer Science 647 (September 2016): 74–84. http://dx.doi.org/10.1016/j.tcs.2016.07.041.

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11

COSTAGLIOLA, GENNARO, and ERLAND JUNGERT. "Towards Querying Multiple Data Sources." Journal of Visual Languages & Computing 12, no. 1 (February 2001): 1–2. http://dx.doi.org/10.1006/jvlc.2000.0196.

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12

Grzegorzewski, Przemysław, and Edyta Mrówka. "Flexible querying via if-sets." International Journal of Intelligent Systems 22, no. 6 (2007): 587–97. http://dx.doi.org/10.1002/int.20215.

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13

Knappe, Rasmus, Henrik Bulskov, and Troels Andreasen. "Perspectives on ontology-based querying." International Journal of Intelligent Systems 22, no. 7 (2007): 739–61. http://dx.doi.org/10.1002/int.20226.

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14

Liu, H., P. Van Oosterom, M. Meijers, and E. Verbree. "AN OPTIMIZED SFC APPROACH FOR ND WINDOW QUERYING ON POINT CLOUDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-4/W1-2020 (September 3, 2020): 119–28. http://dx.doi.org/10.5194/isprs-annals-vi-4-w1-2020-119-2020.

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Анотація:

Abstract. Dramatically increasing collection of point clouds raises an essential demand for highly efficient data management. It can also facilitate modern applications such as robotics and virtual reality. Extensive studies have been performed on point data management and querying, but most of them concentrate on low dimensional spaces. High dimensional data management solutions from computer science have not considered the special features of spatial data; so, they may not be optimal. A Space Filling Curve (SFC) based approach, PlainSFC which is capable of nD point querying has been proposed and tested in low dimensional spaces. However, its efficiency in nD space is still unknown. Besides that, PlainSFC performs poorly on skewed data querying. This paper develops HistSFC which utilizes point distribution information to improve the querying efficiency on skewed data. Then, the paper presents statistical analysis of how PlainSFC and HistSFC perform when dimensionality increases. By experimenting on simulated nD data and real data, we confirmed the patterns deduced: for inhomogeneous data querying, the false positive rate (FPR) of PlainSFC increases drastically as dimensionality goes up. HistSFC alleviates such deterioration to a large extent. Despite performance degeneration in ultra high dimensional spaces, HistSFC can be applied with high efficiency for most spatial applications. The generic theoretical framework developed also allows us to study related topics such as visualization and data transmission in the future.

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15

Gehani, Ashish, Raza Ahmad, Hassaan Irshad, Jianqiao Zhu, and Jignesh Patel. "Digging into big provenance (with SPADE)." Communications of the ACM 64, no. 12 (December 2021): 48–56. http://dx.doi.org/10.1145/3475358.

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16

Nasir, J. A., M. K. Shahzad ., and M. A. Pasha . "Transparent Querying Multiple-versions of Data Warehouse." Information Technology Journal 5, no. 2 (February 15, 2006): 250–59. http://dx.doi.org/10.3923/itj.2006.250.259.

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17

Xin, Guanlin, and Jinglei Liu. "Exact Dominance Querying Algorithm on CP-nets." International Journal of Database Theory and Application 9, no. 5 (May 31, 2016): 21–36. http://dx.doi.org/10.14257/ijdta.2016.9.5.03.

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18

Chen, Bo, Huaijie Zhu, Wei Liu, Jian Yin, Wang-Chien Lee, and Jianliang Xu. "Querying Optimal Routes for Group Meetup." Data Science and Engineering 6, no. 2 (March 15, 2021): 180–91. http://dx.doi.org/10.1007/s41019-021-00153-5.

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Анотація:

AbstractMotivated by location-based social networks which allow people to access location-based services as a group, we study a novel variant of optimal sequenced route (OSR) queries, optimal sequenced route for group meetup (OSR-G) queries. OSR-G query aims to find the optimal meeting POI (point of interest) such that the maximum users’ route distance to the meeting POI is minimized after each user visits a number of POIs of specific categories (e.g., gas stations, restaurants, and shopping malls) in a particular order. To process OSR-G queries, we first propose an OSR-Based (OSRB) algorithm as our baseline, which examines every POI in the meeting category and utilizes existing OSR (called E-OSR) algorithm to compute the optimal route for each user to the meeting POI. To address the shortcomings (i.e., requiring to examine every POI in the meeting category) of OSRB, we propose an upper bound based filtering algorithm, called circle filtering (CF) algorithm, which exploits the circle property to filter the unpromising meeting POIs. In addition, we propose a lower bound based pruning (LBP) algorithm, namely LBP-SP which exploits a shortest path lower bound to prune the unqualified meeting POIs to reduce the search space. Furthermore, we develop an approximate algorithm, namely APS, to accelerate OSR-G queries with a good approximation ratio. Finally the experimental results show that both CF and LBP-SP outperform the OSRB algorithm and have high pruning rates. Moreover, the proposed approximate algorithm runs faster than the exact OSR-G algorithms and has a good approximation ratio.

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19

Gehani, Ashish, Raza Ahmad, Hassan Irshad, Jianqiao Zhu, and Jignesh Patel. "Digging into Big Provenance (with SPADE)." Queue 19, no. 3 (June 30, 2021): 77–106. http://dx.doi.org/10.1145/3475965.3476885.

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Анотація:

Several interfaces exist for querying provenance. Many are not flexible in allowing users to select a database type of their choice. Some provide query functionality in a data model that is different from the graph-oriented one that is natural for provenance. Others have intuitive constructs for finding results but have limited support for efficiently chaining responses, as needed for faceted search. This article presents a user interface for querying provenance that addresses these concerns and is agnostic to the underlying database being used.

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20

Kejriwal, Mayank, Juan Sequeda, and Vanessa Lopez. "Knowledge graphs: Construction, management and querying." Semantic Web 10, no. 6 (October 28, 2019): 961–62. http://dx.doi.org/10.3233/sw-190370.

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21

Rudi, Ali Gholami. "Identifying and Querying Regularly Visited Places." Scientific Annals of Computer Science 19, no. 2 (December 30, 2019): 185–201. http://dx.doi.org/10.7561/sacs.2019.2.185.

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22

Yadamjav, Munkh-Erdene, Zhifeng Bao, Baihua Zheng, Farhana M. Choudhury, and Hanan Samet. "Querying Recurrent Convoys over Trajectory Data." ACM Transactions on Intelligent Systems and Technology 11, no. 5 (September 5, 2020): 1–24. http://dx.doi.org/10.1145/3400730.

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23

Pereira, Arnaldo, João Rafael Almeida, Rui Pedro Lopes, and José Luís Oliveira. "Querying semantic catalogues of biomedical databases." Journal of Biomedical Informatics 137 (January 2023): 104272. http://dx.doi.org/10.1016/j.jbi.2022.104272.

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24

Rajaraman, Anand, and Jeffrey D. Ullman. "Querying websites using compact skeletons." Journal of Computer and System Sciences 66, no. 4 (June 2003): 809–51. http://dx.doi.org/10.1016/s0022-0000(03)00029-1.

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25

Bai, Luyi, Li Yan, and Z. M. Ma. "Querying fuzzy spatiotemporal data using XQuery." Integrated Computer-Aided Engineering 21, no. 2 (March 1, 2014): 147–62. http://dx.doi.org/10.3233/ica-130454.

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26

Ricci, F., and H. Werthner. "Case Base Querying for Travel Planning Recommendation." Information Technology & Tourism 4, no. 3 (March 1, 2001): 215–26. http://dx.doi.org/10.3727/109830501108751001.

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27

CAKMAK, ALI, GULTEKIN OZSOYOGLU, and RICHARD W. HANSON. "QUERYING METABOLISM UNDER DIFFERENT PHYSIOLOGICAL CONSTRAINTS." Journal of Bioinformatics and Computational Biology 08, no. 02 (April 2010): 247–93. http://dx.doi.org/10.1142/s0219720010004604.

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Анотація:

Metabolism is a representation of the biochemical principles that govern the production, consumption, degradation, and biosynthesis of metabolites in living cells. Organisms respond to changes in their physiological conditions or environmental perturbations (i.e. constraints) via cooperative implementation of such principles. Querying inner working principles of metabolism under different constraints provides invaluable insights for both researchers and educators. In this paper, we propose a metabolism query language (MQL) and discuss its query processing. MQL enables researchers to explore the behavior of the metabolism with a wide-range of predicates including dietary and physiological condition specifications. The query results of MQL are enriched with both textual and visual representations, and its query processing is completely tailored based on the underlying metabolic principles.

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28

Wu, Zongda, Zhongsheng Cao, and Yuanzhen Wang. "UMQA: An Internal Algebra for Querying Multimedia Contents." Information Technology Journal 8, no. 4 (May 1, 2009): 411–26. http://dx.doi.org/10.3923/itj.2009.411.426.

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29

Hasany, N., A. B. Jantan, M. H. B. Selamat, and M. I. Saripan. "Querying Ontology using Keywords and Quantitative Restriction Phrases." Information Technology Journal 9, no. 1 (December 15, 2009): 67–78. http://dx.doi.org/10.3923/itj.2010.67.78.

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30

Demirbas, Murat, Anish Arora, and Vinodkrishnan Kulathumani. "Glance: A lightweight querying service for wireless sensor networks." Theoretical Computer Science 410, no. 6-7 (February 2009): 500–513. http://dx.doi.org/10.1016/j.tcs.2008.10.013.

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31

Fionda, Valeria, Giuseppe Pirrò, and Mariano P. Consens. "Querying knowledge graphs with extended property paths." Semantic Web 10, no. 6 (October 28, 2019): 1127–68. http://dx.doi.org/10.3233/sw-190365.

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32

Tré, Guy De, Janusz Kacprzyk, Adnan Yazici, and Sławomir Zadrożny. "Advances in fuzzy querying: Theory and applications." International Journal of Intelligent Systems 26, no. 10 (July 29, 2011): 887–89. http://dx.doi.org/10.1002/int.20504.

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33

Matthé, Tom, Guy De Tré, Sławomir Zadrożny, Janusz Kacprzyk, and Antoon Bronselaer. "Bipolar database querying using bipolar satisfaction degrees." International Journal of Intelligent Systems 26, no. 10 (August 1, 2011): 890–910. http://dx.doi.org/10.1002/int.20505.

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34

Deng, Lei, Zhixiang Chen, Baohua Chen, Yueqi Duan, and Jie Zhou. "Incremental image set querying based localization." Neurocomputing 208 (October 2016): 315–24. http://dx.doi.org/10.1016/j.neucom.2015.11.117.

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35

Combi, Carlo, Giuseppe Pozzi, and Rosalba Rossato. "Querying temporal clinical databases on granular trends." Journal of Biomedical Informatics 45, no. 2 (April 2012): 273–91. http://dx.doi.org/10.1016/j.jbi.2011.11.005.

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36

Cheng, Siu-Wing, Hyeon-Suk Na, Antoine Vigneron, and Yajun Wang. "Querying Approximate Shortest Paths in Anisotropic Regions." SIAM Journal on Computing 39, no. 5 (January 2010): 1888–918. http://dx.doi.org/10.1137/080742166.

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37

Goldin, Dina Q., Todd D. Millstein, and Ayferi Kutlu. "Bounded similarity querying for time-series data." Information and Computation 194, no. 2 (November 2004): 203–41. http://dx.doi.org/10.1016/j.ic.2004.07.001.

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38

Doka, Katerina, Dimitrios Tsoumakos, and Nectarios Koziris. "Online querying of d-dimensional hierarchies." Journal of Parallel and Distributed Computing 71, no. 3 (March 2011): 424–37. http://dx.doi.org/10.1016/j.jpdc.2010.10.005.

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39

Segoufin, Luc, and Victor Vianu. "Querying Spatial Databases via Topological Invariants." Journal of Computer and System Sciences 61, no. 2 (October 2000): 270–301. http://dx.doi.org/10.1006/jcss.2000.1712.

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40

Kanza, Yaron, Werner Nutt, and Yehoshua Sagiv. "Querying Incomplete Information in Semistructured Data." Journal of Computer and System Sciences 64, no. 3 (May 2002): 655–93. http://dx.doi.org/10.1006/jcss.2001.1811.

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41

Koprulu, Mesru, Nihan Kesim Cicekli, and Adnan Yazici. "Spatio-temporal querying in video databases." Information Sciences 160, no. 1-4 (March 2004): 131–52. http://dx.doi.org/10.1016/j.ins.2003.08.011.

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42

Erozel, Guzen, Nihan Kesim Cicekli, and Ilyas Cicekli. "Natural language querying for video databases." Information Sciences 178, no. 12 (June 2008): 2534–52. http://dx.doi.org/10.1016/j.ins.2008.02.001.

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43

Sözer, Aziz, Adnan Yazıcı, Halit Oğuztüzün, and Osman Taş. "Modeling and querying fuzzy spatiotemporal databases." Information Sciences 178, no. 19 (October 2008): 3665–82. http://dx.doi.org/10.1016/j.ins.2008.05.034.

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44

Chebotko, Artem, Seunghan Chang, Shiyong Lu, and Farshad Fotouhi. "Secure XML querying based on authorization graphs." Information Systems Frontiers 14, no. 3 (November 5, 2010): 617–32. http://dx.doi.org/10.1007/s10796-010-9289-2.

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45

Du, Shihong, and Luo Guo. "Modeling and querying approximate direction relations." ISPRS Journal of Photogrammetry and Remote Sensing 65, no. 4 (July 2010): 328–40. http://dx.doi.org/10.1016/j.isprsjprs.2010.03.001.

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46

Nguyen, Vu-Linh, Sébastien Destercke, and Marie-Hélène Masson. "Partial data querying through racing algorithms." International Journal of Approximate Reasoning 96 (May 2018): 36–55. http://dx.doi.org/10.1016/j.ijar.2018.03.005.

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47

Prakasam, Elakkiya, and Arun Manoharan. "A Cache Efficient One Hashing Blocked Bloom Filter (OHBB) for Random Strings and the K-mer Strings in DNA Sequence." Symmetry 14, no. 9 (September 13, 2022): 1911. http://dx.doi.org/10.3390/sym14091911.

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Анотація:

Bloom filters are widely used in genome assembly, IoT applications and several network applications such as symmetric encryption algorithms, and blockchain applications owing to their advantages of fast querying, despite some false positives in querying the input elements. There are many research works carried out to improve both the insertion and querying speed or reduce the false-positive or reduce the storage requirements separately. However, the optimization of all the aforementioned parameters is quite challenging with the existing reported systems. This work proposes to simultaneously improve the insertion and querying speeds by introducing a Cache-efficient One-Hashing Blocked Bloom filter. The proposed method aims to reduce the number of memory accesses required for querying elements into one by splitting the memory into blocks where the block size is equal to the cache line size of the memory. In the proposed filter, each block has further been split into partitions where the size of each partition is the prime number. For insertion and query, one hash value is required, which yields different values when modulo divided with prime numbers. The speed is accelerated using simple hash functions where the hash function is called only once. The proposed method has been implemented and validated using random strings and symmetric K-mer datasets used in the gene assembly. The simulation results show that the proposed filter outperforms the Standard Bloom Filter in terms of the insertion and querying speed.

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48

Cao, Tuan-Dung, and Quang-Minh Nguyen. "Semantic Querying of News Articles With Natural Language Questions." Journal of Information Technology Research 14, no. 3 (July 2021): 38–57. http://dx.doi.org/10.4018/jitr.2021070103.

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Анотація:

The heterogeneity and the increasing amount of the news published on the web create challenges in accessing them. In the authors' previous studies, they introduced a semantic web-based sports news aggregation system called BKSport, which manages to generate metadata for every news item. Providing an intuitive and expressive way to retrieve information and exploiting the advantages of semantic search technique is within their consideration. In this paper, they propose a method to transform natural language questions into SPARQL queries, which could be applied to existing semantic data. This method is mainly based on the following tasks: the construction of a semantic model representing a question, detection of ontology vocabularies and knowledge base elements in question, and their mapping to generate a query. Experiments are performed on a set of questions belonging to various categories, and the results show that the proposed method provides high precision.

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49

Wang, Hongzhi, and Jianzhong Li. "GXQuery: Extending XQuery for Querying Graph-structured XML Data." Journal of Computing and Information Technology 19, no. 2 (2011): 83. http://dx.doi.org/10.2498/cit.1001006.

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50

Almendros-Jiménez, Jesús M., Antonio Becerra-Terón, and Manuel Torres. "Integrating and Querying OpenStreetMap and Linked Geo Open Data." Computer Journal 62, no. 3 (September 23, 2017): 321–45. http://dx.doi.org/10.1093/comjnl/bxx079.

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