Relevant bibliographies by topics / Multi-Objective Query Optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multi-Objective Query Optimization'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Multi-Objective Query Optimization"

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Trummer, Immanuel, and Christoph Koch. "Multi-objective parametric query optimization." Communications of the ACM 60, no. 10 (2017): 81–89. http://dx.doi.org/10.1145/3068612.

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Trummer, Immanuel, and Christoph Koch. "Multi-objective parametric query optimization." Proceedings of the VLDB Endowment 8, no. 3 (2014): 221–32. http://dx.doi.org/10.14778/2735508.2735512.

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Trummer, Immanuel, and Christoph Koch. "Multi-Objective Parametric Query Optimization." ACM SIGMOD Record 45, no. 1 (2016): 24–31. http://dx.doi.org/10.1145/2949741.2949748.

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Trummer, Immanuel, and Christoph Koch. "Multi-objective parametric query optimization." VLDB Journal 26, no. 1 (2016): 107–24. http://dx.doi.org/10.1007/s00778-016-0439-0.

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Wang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio, and Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning." International Journal of Database Management Systems 13, no. 1 (2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.

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In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cl
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Kumar, Deepak, Deepti Mehrotra, and Rohit Bansal. "Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer." International Journal of Information Technology and Web Engineering 14, no. 4 (2019): 50–63. http://dx.doi.org/10.4018/ijitwe.2019100103.

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Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems,
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Bansal, Rohit, Deepak Kumar, and Sushil Kumar. "Multi-objective Multi-Join Query Optimization using Modified Grey Wolf Optimization." International Journal of Advanced Intelligence Paradigms 17, no. 1/2 (2020): 1. http://dx.doi.org/10.1504/ijaip.2020.10019251.

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Sanchez-Gomez, Jesús Manuel, Miguel A. Vega-Rodríguez, and C. J. Pérez. "Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach." Applied Soft Computing 113 (June 7, 2021): 107915. https://doi.org/10.1016/j.asoc.2021.107915.

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Nowadays, the automatic text summarization is a highly relevant task in many contexts. In particular, query-focused summarization consists of generating a summary from one or multiple documents according to a query given by the user. Additionally, sentiment analysis and opinion mining analyze the polarity of the opinions contained in texts. These two issues are integrated in an approach to produce n opinionated summary according to the user’s query. Thereby, the query-focused sentiment-oriented extractive multi-document text summarization problem entails the optimization of different cri
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Sanchez-Gomez, Jesús Manuel, Miguel Ángel Vega-Rodríguez, and Sánchez Carlos Javier Pérez. "An indicator-based multi-objective variable neighborhood search approach for query-focused summarization." Swarm and Evolutionary Computation 91 (June 7, 2024): 101721. https://doi.org/10.1016/j.swevo.2024.101721.

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Currently, automatic multi-document summarization is an interesting subject in numerous fields of study. As a part of it, query-focused summarization is becoming increasingly important in recent times. These methods can automatically produce a summary based on a query given by the user, including the most relevant informationfrom the query at the same time as the redundancy among sentences is reduced. This can be achieved by developing and applying a multi-objective optimization approach. In this paper, an Indicator-based Multi-Objective Variable Neighborhood Search (IMOVNS) alg
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Kumar, Akshay, and T. V. Vijay Kumar. "A Multi-Objective Approach to Big Data View Materialization." International Journal of Knowledge and Systems Science 12, no. 2 (2021): 17–37. http://dx.doi.org/10.4018/ijkss.2021040102.

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Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this pap
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Dissertations / Theses on the topic "Multi-Objective Query Optimization"

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Garg, Vishesh. "Towards Designing PCM-Conscious Database Systems." Thesis, 2016. https://etd.iisc.ac.in/handle/2005/4889.

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Phase Change Memory (PCM) is a recently developed non-volatile memory technology that is expected to provide an attractive combination of the best features of conventional disks (persistence, capacity) and of DRAM (access speed). For instance, it is about 2 to 4 times denser than DRAM, while providing a DRAM-comparable read latency. On the other hand, it consumes much less energy than magnetic hard disks while providing substantively smaller write latency. Due to this suite of desirable features, PCM technology is expected to play a prominent role in the next generation of computing syst
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Sabih, Rafia. "Balancing Money and Time for OLAP Queries on Cloud Databases." Thesis, 2016. http://etd.iisc.ac.in/handle/2005/2931.

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Enterprise Database Management Systems (DBMSs) have to contend with resource-intensive and time-varying workloads, making them well-suited candidates for migration to cloud plat-forms { specifically, they can dynamically leverage the resource elasticity while retaining affordability through the pay-as-you-go rental interface. The current design of database engine components lays emphasis on maximizing computing efficiency, but to fully capitalize on the cloud's benefits, the outlays of these computations also need to be factored into the planning exercise. In this thesis, we investigate this c
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Sabih, Rafia. "Balancing Money and Time for OLAP Queries on Cloud Databases." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2931.

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Enterprise Database Management Systems (DBMSs) have to contend with resource-intensive and time-varying workloads, making them well-suited candidates for migration to cloud plat-forms { specifically, they can dynamically leverage the resource elasticity while retaining affordability through the pay-as-you-go rental interface. The current design of database engine components lays emphasis on maximizing computing efficiency, but to fully capitalize on the cloud's benefits, the outlays of these computations also need to be factored into the planning exercise. In this thesis, we investigate this c
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Book chapters on the topic "Multi-Objective Query Optimization"

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Yu, Qi, and Athman Bouguettaya. "Multi-objective Service Query Optimization." In Foundations for Efficient Web Service Selection. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0314-3_4.

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Cecchini, Rocío L., Carlos M. Lorenzetti, and Ana G. Maguitman. "Multi-objective Query Optimization Using Topic Ontologies." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04957-6_13.

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Vikram Singh. "Multi-objective Parametric Query Optimization for Distributed Database Systems." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_18.

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Wang, Chenxiao, Le Gruenwald, Laurent d’Orazio, and Eleazar Leal. "Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization." In Model and Data Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78428-7_12.

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Wang, Chenxiao, Le Gruenwald, and Laurent d’Orazio. "SLA-Aware Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization." In Big Data Analytics and Knowledge Discovery. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12670-3_22.

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Mittermeier, Ludwig, Richard Senington, Sunith Bandaru, and Amos Ng. "Knowledge Graphs for Supporting Group Decision Making in Manufacturing Industries." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240189.

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Group decision making is traditionally a human-centered process, where communication, synchronization and agreement are driven by the stakeholders involved. In the area of multi-objective optimization (MOO), this becomes a challenge, because MOO usually produces a large amount of trade-off solutions that need to be analyzed and discussed by the stakeholders. Moreover, for transparent group decision making, it is important that each decision maker is able to trace the entire decision process – from associated data and models to problem formulation and solution generation, as well as to the pref
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Conference papers on the topic "Multi-Objective Query Optimization"

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Yu, Hang, and Lester Litchfield. "Query Classification with Multi-objective Backoff Optimization." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2020. http://dx.doi.org/10.1145/3397271.3401320.

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Georgoulakis Misegiannis, Michail, Vasiliki (Verena) Kantere, and Laurent d'Orazio. "Multi-objective query optimization in Spark SQL." In IDEAS'22: International Database Engineered Applications Symposium. ACM, 2022. http://dx.doi.org/10.1145/3548785.3548800.

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Konstantinidis, Andreas, Demetrios Zeinalipour-Yazti, Panayiotis Andreou, and George Samaras. "Multi-objective Query Optimization in Smartphone Social Networks." In 2011 12th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2011. http://dx.doi.org/10.1109/mdm.2011.37.

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Rituraj, Rituraj, and Annamaria R. Varkonyi Koczy. "Advantages of Anytime Algorithm for Multi-Objective Query Optimization." In 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2020. http://dx.doi.org/10.1109/sami48414.2020.9108713.

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Trummer, Immanuel, and Christoph Koch. "A Fast Randomized Algorithm for Multi-Objective Query Optimization." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2882927.

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Trummer, Immanuel, and Christoph Koch. "An Incremental Anytime Algorithm for Multi-Objective Query Optimization." In SIGMOD/PODS'15: International Conference on Management of Data. ACM, 2015. http://dx.doi.org/10.1145/2723372.2746484.

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Zhang, Ting. "Mass Data Query Optimization Based on Multi-objective Co-evolutionary Algorithm." In 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/amcce-17.2017.168.

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Zhao, Hang, Qinghua Deng, Wenting Huang, and Zhenping Feng. "Thermodynamic and Economic Analysis and Multi-Objective Optimization of Supercritical CO2 Brayton Cycles." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42631.

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Supercritical CO2 Brayton cycles (SCO2BC) offer the potential of better economy and higher practicability due to their high power conversion efficiency, moderate turbine inlet temperature, compact size as compared with some traditional working fluids cycles. In this paper, the SCO2BC including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparati
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Regenwetter, Lyle, Yazan Abu Obaideh, and Faez Ahmed. "Counterfactuals for Design: A Model-Agnostic Method for Design Recommendations." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-117216.

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Abstract We introduce Multi-Objective Counter/actuals for Design (MCD), a novel method for counterfactual optimization in design problems. Counterfactuals are hypothetical situations that can lead to a different decision or choice. In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modifications to a design, leading to better functional performance. MCD improves upon existing counterfactual search methods by supporting multi-objective queries, which are crucial in design problems, and by decoupling the counterfactual search
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Gala, D., G. Becker, K. Kaul, et al. "AI-Powered, Lightning-Fast Production Modeling of Multi-Well and Multi-Bench Unconventional Development." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214818-ms.

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Abstract Production from multi-well and multi-bench unconventional development is time-consuming to model in physics-based simulators and requires multiple runs. Even with the use of high-performance computing or cloud computing, each single run can take several minutes to few hours depending on the model complexity. This challenges the development planning optimization as it is very computationally demanding and almost impractical to perform full subsurface uncertainty and multiple scenario realizations. The objective of this paper is to showcase the use of advanced deep-learning algorithms a
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