Literatura académica sobre el tema "Why-Not questions"
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Artículos de revistas sobre el tema "Why-Not questions"
Friedman, Susan Stanford. "Why Not Compare?" PMLA/Publications of the Modern Language Association of America 126, n.º 3 (mayo de 2011): 753–62. http://dx.doi.org/10.1632/pmla.2011.126.3.753.
Texto completoMaltsev, Oleg. "Why Fate is Not Popular". Newsletter on the Results of Scholarly Work in Sociology, Criminology, Philosophy and Political Science 3, n.º 1 (7 de enero de 2022): 8–23. http://dx.doi.org/10.61439/uake7898.
Texto completoOrlandi, Nico. "Why Not Reductionism?" Journal of Consciousness Studies 29, n.º 7 (14 de julio de 2022): 218–25. http://dx.doi.org/10.53765/20512201.29.7.218.
Texto completoZhong, Zhefan, Xin Lin, Liang He y Jing Yang. "Answering why-not questions on KNN queries". Frontiers of Computer Science 13, n.º 5 (17 de junio de 2019): 1062–71. http://dx.doi.org/10.1007/s11704-018-7074-4.
Texto completoWang, Meng, Jun Liu, Bifan Wei, Siyu Yao, Hongwei Zeng y Lei Shi. "Answering why-not questions on SPARQL queries". Knowledge and Information Systems 58, n.º 1 (19 de enero de 2018): 169–208. http://dx.doi.org/10.1007/s10115-018-1155-4.
Texto completoLiu, Qing, Yunjun Gao, Gang Chen, Baihua Zheng y Linlin Zhou. "Answering why-not and why questions on reverse top-k queries". VLDB Journal 25, n.º 6 (3 de septiembre de 2016): 867–92. http://dx.doi.org/10.1007/s00778-016-0443-4.
Texto completoZhian He y Eric Lo. "Answering Why-Not Questions on Top-K Queries". IEEE Transactions on Knowledge and Data Engineering 26, n.º 6 (junio de 2014): 1300–1315. http://dx.doi.org/10.1109/tkde.2012.158.
Texto completoWang, Meng, Weitong Chen, Sen Wang, Jun Liu, Xue Li y Bela Stantic. "Answering why-not questions on semantic multimedia queries". Multimedia Tools and Applications 77, n.º 3 (2 de septiembre de 2017): 3405–29. http://dx.doi.org/10.1007/s11042-017-5151-6.
Texto completoŁukaszyk, Ewa. "Why Minor, Not Major?" Colloquia Humanistica, n.º 2 (13 de junio de 2015): 13–16. http://dx.doi.org/10.11649/ch.2013.015.
Texto completoKo, Andrew J. y Brad A. Myers. "Extracting and answering why and why not questions about Java program output". ACM Transactions on Software Engineering and Methodology 20, n.º 2 (agosto de 2010): 1–36. http://dx.doi.org/10.1145/1824760.1824761.
Texto completoTesis sobre el tema "Why-Not questions"
Chen, Lei. "Answering why-not questions on spatial keyword top-k queries /Chen Lei". HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/365.
Texto completoAttolou, Hervé-Madelein. "Explications pour des recommandations manquantes basées sur les graphes". Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1337.
Texto completoIn the era of big data, Recommendation Systems play a pivotal role in helping users navigate and discover relevant content from vast amounts of data. Whilemodern Recommendation Systems have evolved to provide accurate and relevant recommendations, they often fall short in explaining their decisions to users. Thislack of transparency raises important questions about trust and user engagement, especially in cases where certain expected items are not recommended. To addressthis, recent research has focused on developing explainable Recommendation Systems, which provide users with insights into why certain items are recommended oromitted.This thesis explores the specific area of Why-Not Explanations, which focuses on explaining why certain items are missing from the recommendation list. Theneed for Why-Not Explanations is particularly crucial in complex recommendation scenarios, where the absence of certain recommendations can lead to user dissatisfaction or mistrust. For instance, a user on an e-commerce platform might wonder why a specific product was not recommended despite fulfilling certain criteria. By providing explanations for missing recommendations, we aim to improve transparency, user satisfaction, engagement, and the overall trustworthiness of the system.The main contribution of this thesis is the development of EMiGRe (Explainable Missing Graph REcommender), a novel framework that provides actionable Why-Not Explanations for graph-based Recommendation Systems. Unlike traditional explainability methods, which focus on justifying why certain items were recommended, EMiGRe focuses on the absence of specific items from recommendation lists. The framework operates by analyzing the user's interactions within a Heterogeneous Information Graph (HIN) modelization of a dataset, identifying key actions or relations that, when modified, would have led to the recommendation of the missing item. EMiGRe provides two modes for explanation:• Remove Mode identifies existing actions or interactions that are preventing the system from recommending the desired item and suggests removing these.• Add Mode suggests additional actions or items that, if interacted with, would trigger the recommendation of the missing item.To generate explanations in both Add and Remove modes, we explore the solution space using a set of heuristics tailored for specific objectives. The framework offers multiple heuristics each serving a purpose: Incremental Powerset an Exhaustive Comparison . The Incremental heuristic prioritizes faster computation by gradually increasing the set of selected items, potentially overlooking minimal explanations. In contrast, the Powerset heuristic aims to find smaller explanations by thoroughly searching the solution space. Additionally, Exhaustive Comparison comparison heuristic is included to assess the precise contribution of each neighbor to the Why-Not Item (W NI) compared to all other items, increasing the success rate.To validate the effectiveness of the EMiGRe framework, extensive experimental evaluations were conducted on both synthetic and real-world datasets. The datasets include datasets from sources like Amazon, which simulates a real-world e-commerce scenario, and the Food dataset representing a recommendation problemin a recipe-based platform. The experimental results show that EMiGRe is able to provide good-quality Why-Not Explanations. Specifically, the system demonstratesan improvement in explanation success rates compared to traditional brute-force methods, while maintaining acceptable explanation size and processing time.Moreover, this thesis introduces a novel evaluation for Why-Not Explanations, defining metrics such as success rate, explanation size, and processing time to measure the quality and efficiency of explanations
Tzompanaki, Aikaterini. "Réponses manquantes : Débogage et Réparation de requêtes". Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS223/document.
Texto completoWith the increasing amount of available data and data transformations, typically specified by queries, the need to understand them also increases. “Why are there medicine books in my sales report?” or “Why are there not any database books?” For the first question we need to find the origins or provenance of the result tuples in the source data. However, reasoning about missing query results, specified by Why-Not questions as the latter previously mentioned, has not till recently receivedthe attention it is worth of. Why-Not questions can be answered by providing explanations for the missing tuples. These explanations identify why and how data pertinent to the missing tuples were not properly combined by the query. Essentially, the causes lie either in the input data (e.g., erroneous or incomplete data) or at the query level (e.g., a query operator like join). Assuming that the source data contain all the necessary relevant information, we can identify the responsible query operators formingquery-based explanations. This information can then be used to propose query refinements modifying the responsible operators of the initial query such that the refined query result contains the expected data. This thesis proposes a framework targeted towards SQL query debugging and fixing to recover missing query results based on query-based explanations and query refinements.Our contribution to query debugging consist in two different approaches. The first one is a tree-based approach. First, we provide the formal framework around Why-Not questions, missing from the state-of-the-art. Then, we review in detail the state-of-the-art, showing how it probably leads to inaccurate explanations or fails to provide an explanation. We further propose the NedExplain algorithm that computes correct explanations for SPJA queries and unions there of, thus considering more operators (aggregation) than the state of the art. Finally, we experimentally show that NedExplain is better than the both in terms of time performance and explanation quality. However, we show that the previous approach leads to explanations that differ for equivalent query trees, thus providing incomplete information about what is wrong with the query. We address this issue by introducing a more general notion of explanations, using polynomials. The polynomial captures all the combinations in which the query conditions should be fixed in order for the missing tuples to appear in the result. This method is targeted towards conjunctive queries with inequalities. We further propose two algorithms, Ted that naively interprets the definitions for polynomial explanations and the optimized Ted++. We show that Ted does not scale well w.r.t. the size of the database. On the other hand, Ted++ is capable ii of efficiently computing the polynomial, relying on schema and data partitioning and advantageous replacement of expensive database evaluations by mathematical calculations. Finally, we experimentally evaluate the quality of the polynomial explanations and the efficiency of Ted++, including a comparative evaluation.For query fixing we propose is a new approach for refining a query by leveraging polynomial explanations. Based on the input data we propose how to change the query conditions pinpointed by the explanations by adjusting the constant values of the selection conditions. In case of joins, we introduce a novel type of query refinements using outer joins. We further devise the techniques to compute query refinements in the FixTed algorithm, and discuss how our method has the potential to be more efficient and effective than the related work.Finally, we have implemented both Ted++ and FixTed in an system prototype. The query debugging and fixing platform, short EFQ allows users to nteractively debug and fix their queries when having Why- Not questions
"Diagnosing dizziness in the emergency department: Why "What do you mean by 'dizzy'?" Should not be the first question you ask". THE JOHNS HOPKINS UNIVERSITY, 2007. http://pqdtopen.proquest.com/#viewpdf?dispub=3267879.
Texto completoLibros sobre el tema "Why-Not questions"
Willke, J. C. Why not love them both?: Questions & answers about abortion. [Cincinnati, Ohio: Hayes Pub. Co., 1997.
Buscar texto completoCobb, Vicki. Why can't I live forever?: And other not such dumb questions about life. New York: Lodestar Books, 1997.
Buscar texto completoCobb, Vicki. Why doesn't the sun burn out?: And other not such dumb questions about energy. New York: Lodestar Books, 1990.
Buscar texto completoill, Enik Ted, ed. Why can't you unscramble an egg?: And other not such dumb questions about matter. New York: Lodestar Books, 1990.
Buscar texto completoill, Enik Ted, ed. Why doesn't the earth fall up?: And other not such dumb questions about motion. New York: Lodestar Books, 1988.
Buscar texto completoPakistan Institute of Legislative Development and Transparency., ed. Why some people vote and others do not?: Penetrating answers to this and other key questions which intrigue election observers. Lahore: Pakistan Institute of Legislative Development and Transparency, 2003.
Buscar texto completoMooney, Bel. Why not? Methuen Children's, 1990.
Buscar texto completoWhy does not God intervene? and other questions. 2a ed. New York: Hodder and Stoughton, 1990.
Buscar texto completoKea, ElElise. Why Not Me?: Sometimes, We Ask the Wrong Questions. Outskirts Press, Incorporated, 2016.
Buscar texto completoHayes, Declan. God's Solution: Why Religion not Science Answers Life's Deepest Questions. iUniverse, Inc., 2007.
Buscar texto completoCapítulos de libros sobre el tema "Why-Not questions"
Faye, Jan. "Not Just Why-questions". En The Nature of Scientific Thinking, 210–40. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137389831_9.
Texto completoShagoury, Ruth y Brenda Miller Power. "Epilogue Why Not Teacher Research?" En Living the Questions, 235–39. 2a ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781032681528-9.
Texto completoZong, Chuanyu, Bin Wang, Jing Sun y Xiaochun Yang. "Minimizing Explanations of Why-Not Questions". En Database Systems for Advanced Applications, 230–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43984-5_17.
Texto completoLi, Yin y Bixin Li. "Answering Why-Not Questions on GeoSPARQL Queries". En Web and Big Data, 286–300. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25198-6_22.
Texto completoGao, Yunjun y Qing Liu. "Why-Not and Why Questions on Reverse Top-k Queries". En Preference Query Analysis and Optimization, 31–74. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6635-1_3.
Texto completoZong, Chuanyu, Xiufeng Xia, Bin Wang, Xiaochun Yang, Jiajia Li, Xiangyu Liu y Rui Zhu. "Answering Why-Not Questions on Structural Graph Clustering". En Database Systems for Advanced Applications, 255–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91452-7_17.
Texto completoStratigi, Maria, Katerina Tzompanaki y Kostas Stefanidis. "Why-Not Questions & Explanations for Collaborative Filtering". En Web Information Systems Engineering – WISE 2020, 301–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62008-0_21.
Texto completoLi, Guozhong, Nathan Ng, Peipei Yi, Zhiwei Zhang y Byron Choi. "Answering the Why-Not Questions of Graph Query Autocompletion". En Database Systems for Advanced Applications, 332–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91452-7_22.
Texto completoZong, Chuanyu, Zefang Dong, Xiaochun Yang, Bin Wang, Tao Qiu y Huaijie Zhu. "Efficiently Answering Why-Not Questions on Radius-Bounded k-Core Searches". En Database Systems for Advanced Applications, 93–109. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30675-4_7.
Texto completoZhang, Wang, Yanhong Li, Lihchyun Shu, Changyin Luo y Jianjun Li. "Shadow: Answering Why-Not Questions on Top-K Spatial Keyword Queries over Moving Objects". En Database Systems for Advanced Applications, 738–60. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73197-7_51.
Texto completoActas de conferencias sobre el tema "Why-Not questions"
Braman, Gary. "Aircraft Accidents: Investigating Human Error". En Vertical Flight Society 73rd Annual Forum & Technology Display, 1–5. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12157.
Texto completoRusovac, Dominik, Markus Hecher, Martin Gebser, Sarah Alice Gaggl y Johannes K. Fichte. "Navigating and Querying Answer Sets: How Hard Is It Really and Why?" En 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}, 642–53. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/60.
Texto completoTran, Quoc Trung y Chee-Yong Chan. "How to ConQueR why-not questions". En the 2010 international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807167.1807172.
Texto completoIslam, M. S. "On answering why and why-not questions in databases". En 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW 2013). IEEE, 2013. http://dx.doi.org/10.1109/icdew.2013.6547468.
Texto completoMyers, Brad A., David A. Weitzman, Andrew J. Ko y Duen H. Chau. "Answering why and why not questions in user interfaces". En the SIGCHI conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1124772.1124832.
Texto completoHe, Zhian y Eric Lo. "Answering Why-not Questions on Top-k Queries". En 2012 IEEE International Conference on Data Engineering (ICDE 2012). IEEE, 2012. http://dx.doi.org/10.1109/icde.2012.8.
Texto completoIslam, M. S., Rui Zhou y Chengfei Liu. "On answering why-not questions in reverse skyline queries". En 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544890.
Texto completoChen, Lu, Yunjun Gao, Kai Wang, Christian S. Jensen y Gang Chen. "Answering why-not questions on metric probabilistic range queries". En 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498288.
Texto completoBidoit, Nicole, Melanie Herschel y Aikaterini Tzompanaki. "Efficient Computation of Polynomial Explanations of Why-Not Questions". En CIKM'15: 24th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2806416.2806426.
Texto completoVermeulen, Jo, Geert Vanderhulst, Kris Luyten y Karin Coninx. "PervasiveCrystal: Asking and Answering Why and Why Not Questions about Pervasive Computing Applications". En 2010 6th International Conference on Intelligent Environments (IE). IEEE, 2010. http://dx.doi.org/10.1109/ie.2010.56.
Texto completoInformes sobre el tema "Why-Not questions"
Jordà, Òscar, Martin Kornejew, Moritz Schularick y Alan Taylor. Zombies at Large? Corporate Debt Overhang and the Macroeconomy. Institute for New Economic Thinking Working Paper Series, octubre de 2021. http://dx.doi.org/10.36687/inetwp168.
Texto completoWebb, Philip. Deployment of Parallel Kinematic Machines in Manufacturing. SAE International, abril de 2022. http://dx.doi.org/10.4271/epr2022010.
Texto completoAndrews, Matt. Getting Real about Unknowns in Complex Policy Work. Research on Improving Systems of Education (RISE), noviembre de 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/083.
Texto completoMagnoli, Alessandro. National Health Accounts in Latin America and Caribbean: Concept, Results and Policy Uses. Inter-American Development Bank, septiembre de 2001. http://dx.doi.org/10.18235/0012213.
Texto completoAntonov, Volodymyr. Natural history BBC documentaries: history and functions. Ivan Franko National University of Lviv, febrero de 2022. http://dx.doi.org/10.30970/vjo.2022.51.11402.
Texto completoPinheiro, Armando Castelar, Indermit S. Gill, Luis Servén y Mark Roland Thomas. Brazilian Economic Growth, 1900-2000: Lessons and Policy Implications. Inter-American Development Bank, mayo de 2004. http://dx.doi.org/10.18235/0008731.
Texto completoBlaxter, Tamsin, Elina Åsbjer y Walter Fraanje. Animal welfare and ethics in food and agriculture. TABLE, agosto de 2024. http://dx.doi.org/10.56661/f2d8f4c7.
Texto completoBelafi, Carmen. Where There’s a Will There’s a Way: The Role of Political Will in Creating/Producing/Shaping Education Systems for Learning. Research on Improving Systems of Education (RISE), julio de 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/043.
Texto completoBrophy, Kenny y Alison Sheridan, eds. Neolithic Scotland: ScARF Panel Report. Society of Antiquaries of Scotland, junio de 2012. http://dx.doi.org/10.9750/scarf.06.2012.196.
Texto completoGlick, Mark, Gabriel A. Lozada y Darren Bush. Why Economists Should Support Populist Antitrust Goals. Institute for New Economic Thinking Working Paper Series, diciembre de 2022. http://dx.doi.org/10.36687/inetwp195.
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