Academic literature on the topic 'Group-query'
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Journal articles on the topic "Group-query"
Deng, Ke, Shazia Sadiq, Xiaofang Zhou, Hu Xu, Gabriel Pui Cheong Fung, and Yansheng Lu. "On Group Nearest Group Query Processing." IEEE Transactions on Knowledge and Data Engineering 24, no. 2 (February 2012): 295–308. http://dx.doi.org/10.1109/tkde.2010.230.
Full textHaryanto, Anasthasia Agnes, David Taniar, and Kiki Maulana Adhinugraha. "Group Reverse kNN Query optimisation." Journal of Computational Science 11 (November 2015): 205–21. http://dx.doi.org/10.1016/j.jocs.2015.09.006.
Full textShan, Jing, Derong Shen, Tiezheng Nie, Yue Kou, and Ge Yu. "Searching overlapping communities for group query." World Wide Web 19, no. 6 (December 2, 2015): 1179–202. http://dx.doi.org/10.1007/s11280-015-0378-5.
Full textLi, Yafei, Rui Chen, Lei Chen, and Jianliang Xu. "Towards Social-Aware Ridesharing Group Query Services." IEEE Transactions on Services Computing 10, no. 4 (July 1, 2017): 646–59. http://dx.doi.org/10.1109/tsc.2015.2508440.
Full textLukasiewicz, Thomas, Maria Vanina Martinez, Gerardo I. Simari, and Oana Tifrea-Marciuska. "Ontology-Based Query Answering with Group Preferences." ACM Transactions on Internet Technology 14, no. 4 (December 17, 2014): 1–24. http://dx.doi.org/10.1145/2677207.
Full textSharma, Anjali, and Ajay Kr. Singh. "CBIR through CDH using Query by Group." International Journal of Computer Trends and Technology 28, no. 1 (October 25, 2015): 21–27. http://dx.doi.org/10.14445/22312803/ijctt-v28p106.
Full textZhou, Yinglian, and Jifeng Chen. "Time Series Geographic Social Network Dynamic Preference Group Query." International Journal of Information Systems in the Service Sector 13, no. 4 (October 2021): 18–39. http://dx.doi.org/10.4018/ijisss.2021100102.
Full textLiu, Jia, Wei Chen, Ziyang Chen, Lin Liu, Yuhong Wu, Kaiyu Liu, Amar Jain, and Yasser H. Elawady. "Optimized Query Algorithms for Top- K Group Skyline." Wireless Communications and Mobile Computing 2022 (January 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/3404906.
Full textYang, De-Nian, Yi-Ling Chen, Wang-Chien Lee, and Ming-Syan Chen. "On social-temporal group query with acquaintance constraint." Proceedings of the VLDB Endowment 4, no. 6 (March 2011): 397–408. http://dx.doi.org/10.14778/1978665.1978671.
Full textLiu, Yongshan, Xiang Gong, Dehan Kong, Tianbao Hao, and Xiaoqi Yan. "Research on Group Reverse Farthest Neighbour Query Algorithm." Journal of Physics: Conference Series 1624 (October 2020): 042011. http://dx.doi.org/10.1088/1742-6596/1624/4/042011.
Full textDissertations / Theses on the topic "Group-query"
Mahendiran, Aravindan. "Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25430.
Full textMaster of Science
Shen, Qiong Mao. "Group nearest neighbor queries /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20SHEN.
Full textDumas, Menjivar Marlon. "TEMPOS : une plate-forme pour le développement d'applications temporelles au dessus de SGBD à objets." Phd thesis, Université Joseph Fourier (Grenoble), 2000. http://tel.archives-ouvertes.fr/tel-00006741.
Full textStehura, Igor. "Návrh testů komunikace se skupinovým adresováním v IP." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235947.
Full textChang, Yu-Tu, and 張玉圖. "An Efficient Skyline Query Algorithm for Path Group." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/j4qngb.
Full text逢甲大學
資訊工程學系
104
In recent years the skyline path query has been used very effectively in many business areas like logistics and transportation. A path group consists of a set of paths which cover all user interesting nodes. It’s useful for planning logistic routes or individual case visiting paths for social workers . Given a starting node s, a set of k interesting nodes and the cardinality of path group c, the skyline path group query retrieves a set of non-dominated path groups from a road network, where each edge has multi-dimension costs. Each path group includes all interesting nodes and meets the constraint of path group cardinality. Computing the skyline path group query is quite complex due to the extensive dominance test with path group tuples. This paper proposes an efficient approach for computing skyline path groups. The core of our method is pruning those path group sets that can’t become outstanding tuples while generating path groups. As shown in our experimental evaluation, we narrow down the search space and improve process time.
Chen, Yi-Ling, and 陳怡伶. "Efficient Link Prediction and Group Query in Social Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/01584524839096799042.
Full text國立臺灣大學
電機工程學研究所
105
As the development and popularization of social networking websites, many recommendation systems tend to leverage the information in social networks to provide helpful suggestions for users, and a great deal of research studies on social network analysis are thereby motivated. Recently, the sizes of social networks have been increasing rapidly, and this growth results in a significant increase in the computational cost of the sophisticated recommendations. The huge size and complexity of social networks create a considerable burden for recommendation systems while processing the information from social networks to provide suggestions. Therefore, in this dissertation, we study three important recommendation problems in social networks and aim to improve their efficiency. First, we focus on the relationship between two users and study the link prediction problem in large-scale networks. During the link prediction, numerous feature values need to be calculated and then combined to make recommendations, and the computational cost grows quickly as the network size becomes larger. Some previous studies involving network processing attempt to lower the computational cost by reducing the network size via sparsification. However, sparsification might remove important information and hurt the prediction accuracy. To address this issue, we propose a framework called Diverse Ensemble of Drastic Sparsification (DEDS), which constructs ensemble classifiers with good accuracy while keeping the prediction time short. DEDS includes various sparsification methods that are designed to preserve different measures of a network. Therefore, DEDS can generate sparsified networks with significant structural differences and increase the diversity of the ensemble classifier, which is key to improving prediction performance. Second, we extend the scope from the relationship between two users to the relationship among a group of users, and study the social group query problem with its applications in activity planning. Considering social links among all users to recommend a mutually acquainted group of attendees for an activity is an NP-hard problem. In addition to finding a group of attendees familiar with each other, selecting an activity period available to all is also essential for activity planning. Therefore, we need to further consider the available time of users, which makes the problem even harder due to the complexity of social connectivity and the diversity of user schedules. In this dissertation, we propose the Social-Temporal Group Query (STGQ) to find suitable time and attendees with minimum total social distance. We design two algorithms, SGSelect and STGSelect, which include various effective pruning strategies to substantially reduce running time. Experimental results indicate that SGSelect and STGSelect are significantly more efficient than baseline approaches. We also conduct a user study to compare the proposed approach with manual activity coordination. The results show that our approach obtains higher quality solutions with less coordination effort, thereby increasing users'' willingness to organize activities. Finally, we study the consecutive group query problem to support a sequence of recommendations. When planning an activity, it is difficult for a user to specify all the conditions right at once to find the perfect group of attendees and time. Fortunately, with the aforementioned social-temporal group query, it is easy for the user to tune the parameters to find alternative recommendations. As users may iteratively adjust query parameters to fine tune the results, we further propose Consecutive Social Group Query (CSGQ) to support such needs. Envisaging that exploiting the intermediate solutions of previous queries may improve processing of the succeeding queries, we design a new tree structure, namely, Accumulative Search Tree, which caches the intermediate solutions of historical queries in a compact form for reuse. To facilitate efficient lookup, we further propose a new index structure, called Social Boundary, which effectively indexes the intermediate solutions required for processing each CSGQ with specified parameters. According to the experimental results, with the caching mechanisms, processing time of consecutive queries can be further reduced considerably.
Huang, Chih-Chung, and 黃治中. "Group-by and Order-by Query Processing for XML data." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/06026324499597081893.
Full text國立臺灣海洋大學
資訊工程學系
97
In this thesis, we discuss the performance of processing Group-By queries over XML data. We consider the query with mutiple by expressions or nested structures, and can have Having and Rank constraints. We design two indexes. The first one is based on the value of elements with same tags, called Value-Index. The second one is called Master Entity Index. It is used to retrieve the data for each returned clause. We design a special structure called ABList, to facilitate the grouping process. We have performed a series of experiments to evaluate the performance of the proposed approach. We also contrast with a well-know system. The experimental results show that our system is more efficient when the query has less grouping clauses.
wei, kat ming, and 闕銘威. "An Opinion Mining Approach Based on Localized Social Group and Query Expansion." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/07906177749304063162.
Full text逢甲大學
資訊工程學系
104
A great deal of research has attempted to find information from the increasing use of social networks. Research indicates that using social network to analyze event occurrence may be faster and have a wider range than government published information. Based on observations, we have designed a system that can easily switch between events and find out the most suitable data collection social group, keywordlist and classification feature combination. Different types of events keyword-lists will grow following event occurrence. Based on the experiment on event detention flooding, we conclude that our approach can detect the flooding precisely. For event detection, we hope to obtain more detailed information, including occurrence time, locations, degrees of disasters, among other factors, but most of the time we could not place them in a single post. With the system characteristics and results, we can compose the missing information, and allow the analysis results to have more flexible usage.
Chen, Yanzhi. "Efficient and robust image ranking for object retrieval." Thesis, 2013. http://hdl.handle.net/2440/90334.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013
Books on the topic "Group-query"
Library of Congress. Copyright Office, ed. In answer to your query: Group registration of published photographs. [Washington, D.C: U.S. Copyright Office, 2004.
Find full textBoudreau, Joseph F., and Eric S. Swanson. Templates, the standard C++ library, and modern C++. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0017.
Full textBook chapters on the topic "Group-query"
Ahmad, Sabbir, Rafi Kamal, Mohammed Eunus Ali, Jianzhong Qi, Peter Scheuermann, and Egemen Tanin. "The Flexible Group Spatial Keyword Query." In Lecture Notes in Computer Science, 3–16. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68155-9_1.
Full textCholvy, Laurence. "Flexible Query-Answering in a Group of Databases." In Flexible Query Answering Systems, 141–61. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6075-3_8.
Full textWang, Yi, Fan Xia, and Aoying Zhou. "Group-Scope Query and Its Access Method." In Web Technologies and Applications, 552–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29253-8_49.
Full textLukasiewicz, Thomas, Maria Vanina Martinez, Gerardo I. Simari, and Oana Tifrea-Marciuska. "Group Preferences for Query Answering in Datalog+/- Ontologies." In Lecture Notes in Computer Science, 360–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40381-1_28.
Full textXu, Jin, Lan Yao, and Fuxiang Gao. "Group Signature Based Trace Hiding in Web Query." In Big Data Computing and Communications, 503–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22047-5_41.
Full textYao, Kai, Jianjun Li, Guohui Li, and Changyin Luo. "Efficient Group Top-k Spatial Keyword Query Processing." In Web Technologies and Applications, 153–65. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45814-4_13.
Full textDamaschke, Peter, and Azam Sheikh Muhammad. "Randomized Group Testing Both Query-Optimal and Minimal Adaptive." In SOFSEM 2012: Theory and Practice of Computer Science, 214–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27660-6_18.
Full textEkomie, Hermann B., Kai Yao, Jianjun Li, Guohui Li, and Yanhong Li. "Group Top-k Spatial Keyword Query Processing in Road Networks." In Lecture Notes in Computer Science, 395–408. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64468-4_30.
Full textSinghal, Mayank, and Suman Banerjee. "Envy-Free Trip Planning in Group Trip Planning Query Problem." In Lecture Notes in Networks and Systems, 212–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14314-4_21.
Full textBuchmann, Alejandro P., and Ming-Chuan Wu. "Supporting Group-By and Pipelining in Bitmap-Enabled Query Processors." In SOFSEM’99: Theory and Practice of Informatics, 249–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-47849-3_14.
Full textConference papers on the topic "Group-query"
Deng, Ke, Hu Xu, Shazia Sadiq, Yansheng Lu, Gabriel Pui Cheong Fung, and Heng Tao Shen. "Processing Group Nearest Group Query." In 2009 IEEE 25th International Conference on Data Engineering (ICDE). IEEE, 2009. http://dx.doi.org/10.1109/icde.2009.186.
Full textXu, Hu, Yansheng Lu, and Zhicheng Li. "Continuous Group Nearest Group Query on Moving Objects." In 2010 Second International Workshop on Education Technology and Computer Science. IEEE, 2010. http://dx.doi.org/10.1109/etcs.2010.66.
Full textLi, Xiang, and Ling Feng. "Context-aware group top-k query." In 2012 Seventh International Conference on Digital Information Management (ICDIM). IEEE, 2012. http://dx.doi.org/10.1109/icdim.2012.6360135.
Full textChen, Xiaoying, Chong Zhang, Yanli Hu, Bin Ge, and Weidong Xiao. "Temporal Social Network: Group Query Processing." In 2016 27th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2016. http://dx.doi.org/10.1109/dexa.2016.047.
Full textLiu, Dan, and Wenjun Xie. "Spatial Range Query on Group P2P Networks." In 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC). IEEE, 2009. http://dx.doi.org/10.1109/nswctc.2009.380.
Full textMa, Hanchao, Sheng Guan, Christopher Toomey, and Yinghui Wu. "Diversified Subgraph Query Generation with Group Fairness." In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3488560.3498525.
Full textZhang, Haoran, Dongpu Sun, Fahu Ji, Mingqiu Xu, Shang Gao, and Yang Xu. "Group Visible Nearest Surrounder Query in Obstacle Space." In 2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI). IEEE, 2019. http://dx.doi.org/10.1109/csei47661.2019.8939019.
Full textChandrasekaran, Ramji, Harsh Nilesh Pathak, and Tae Yano. "Deep Neural Query Understanding System at Expedia Group." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378495.
Full textLuk, Ming-Hay, Man Lung Yiu, and Eric Lo. "Group-by skyline query processing in relational engines." In Proceeding of the 18th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646138.
Full textLiu, Kai, Tianyi Wu, Cong Liu, and Guodong Guo. "Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/166.
Full textReports on the topic "Group-query"
Katzir, Nurit, James Giovannoni, Marla Binzel, Efraim Lewinsohn, Joseph Burger, and Arthur Schaffer. Genomic Approach to the Improvement of Fruit Quality in Melon (Cucumis melo) and Related Cucurbit Crops II: Functional Genomics. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7592123.bard.
Full textKhan, Mahreen. Public Financial Management and Transitioning out of Aid. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.145.
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