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

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

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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Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-III." International Journal of Decision Support System Technology 14, no. 1 (2022): 1–28. http://dx.doi.org/10.4018/ijdsst.311066.

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Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that mi

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Sanchez-Gomez, Jesús Manuel, Miguel Ángel Vega-Rodríguez, and Sánchez Carlos Javier Pérez. "A multi-objective memetic algorithm for query-oriented text summarization: medicine texts as a case study." Expert Systems with Applications 198 (June 7, 2022): 116769. https://doi.org/10.1016/j.eswa.2022.116769.

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Automatic text summarization is a topic of great interest in many fields of knowledge. Particularly, query-oriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user. One way to address this problem is by multi-objective optimization approaches. In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarizat

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Sanchez-Gomez, Jesus M., Miguel A. Vega-Rodríguez, and Carlos J. Pérez. "Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach." Applied Soft Computing 113 (December 2021): 107915. http://dx.doi.org/10.1016/j.asoc.2021.107915.

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Chang, Ray-I., Shu-Yu Lin, and Yuhsin Hung. "Particle swarm optimization with query-based learning for multi-objective power contract problem." Expert Systems with Applications 39, no. 3 (2012): 3116–26. http://dx.doi.org/10.1016/j.eswa.2011.08.175.

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Zhu, Lei, Chaoqun Zheng, Xu Lu, Zhiyong Cheng, Liqiang Nie, and Huaxiang Zhang. "Efficient Multi-modal Hashing with Online Query Adaption for Multimedia Retrieval." ACM Transactions on Information Systems 40, no. 2 (2022): 1–36. http://dx.doi.org/10.1145/3477180.

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Multi-modal hashing supports efficient multimedia retrieval well. However, existing methods still suffer from two problems: (1) Fixed multi-modal fusion. They collaborate the multi-modal features with fixed weights for hash learning, which cannot adaptively capture the variations of online streaming multimedia contents. (2) Binary optimization challenge. To generate binary hash codes, existing methods adopt either two-step relaxed optimization that causes significant quantization errors or direct discrete optimization that consumes considerable computation and storage cost. To address these pr

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Mo, Songsong, Yile Chen, Hao Wang, Gao Cong, and Zhifeng Bao. "Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–26. http://dx.doi.org/10.1145/3626734.

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With the expansion of modern database services, multi-user access has become a crucial feature in various practical application scenarios, including enterprise applications and e-commerce platforms. However, if multiple users submit queries within a short time frame, it can result in potential issues such as redundant computation and query concurrency. Unfortunately, most existing multi-query optimization methods, which aim to enhance query processing efficiency, have not adequately addressed these two problems, especially in the setting where multiple queries are being executed concurrently.

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Li, Yanni, Yuping Wang, Peng Jiang, and Zhensong Zhang. "Multi-objective optimization integration of query interfaces for the Deep Web based on attribute constraints." Data & Knowledge Engineering 86 (July 2013): 38–60. http://dx.doi.org/10.1016/j.datak.2013.01.003.

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Liu, Weiyi, Kun Yue, Xiaodong Fu, Zidu Yin, and Jin Li. "Multi-Objective Oriented Categorization Based on the Coalitional Game Theory." International Journal on Artificial Intelligence Tools 25, no. 03 (2016): 1650011. http://dx.doi.org/10.1142/s0218213016500111.

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Discovering different groups, or called classes, is useful for pattern recognition, data preprocessing, association analysis, query optimization, etc. To make every object satisfied as much as possible, the groups are generated by the associations or behaviors among participating objects other than the attributes owned by themselves. By mainly considering the mutual associations among the given objects and based on the game theory, in this paper we study the multi-objective oriented categorization. Based on the idea of Shapley value in the coalitional game, we first propose the concept of prio

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Molaei, Faezeh, and Shirin Ghatrehsamani. "Kinematic-Based Multi-Objective Design Optimization of a Grapevine Pruning Robotic Manipulator." AgriEngineering 4, no. 3 (2022): 606–25. http://dx.doi.org/10.3390/agriengineering4030040.

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Annual cane pruning of grape vineyards is a time-consuming and labor-intensive job, but no mechanized or automatic way has been developed to do it yet. Robotic pruning can be a perfect alternative to human labor. This article proposes a systematic seven-stage procedure to design a kinematically optimized manipulator, named ‘Prubot’, to manage vineyards’ cane pruning. The manipulator structure was chosen, resulting in a 7R (Revolute) manipulator with a spherical shoulder and wrist. To obtain the design constraints, the manipulator task space was modeled. The robot’s second and third link length

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Tejera, Eduardo, Yunierkis Pérez-Castillo, Andrea Chamorro, Alejandro Cabrera-Andrade, and Maria Eugenia Sanchez. "A Multi-Objective Approach for Drug Repurposing in Preeclampsia." Molecules 26, no. 4 (2021): 777. http://dx.doi.org/10.3390/molecules26040777.

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Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtain

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Popescu, Claudiu, Lacrimioara Grama, and Corneliu Rusu. "A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization." Symmetry 13, no. 10 (2021): 1824. http://dx.doi.org/10.3390/sym13101824.

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The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorith

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Sanchez-Gomez, Jesús Manuel, Miguel Ángel Vega-Rodríguez, and Sánchez Carlos Javier Pérez. "Automatic update summarization by a multi-objective number-one-selection genetic approach." IEEE Transactions on Cybernetics 53, no. 12 (2023): 7443–54. https://doi.org/10.1109/TCYB.2022.3223163.

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Currently, the explosive growth of the information available on the internet makes automatic text summarization systems increasingly important. A particularly relevant challenge is the update summarization task. Update summarization differs from traditional summarization in its dynamic nature. While traditional summarization is static, i.e., the document collections about a specific topic remain unchanged, update summarization addresses dynamic document collections based on a specific topic. Therefore, update summarization consists of summarizing the new document collection under the assumptio

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Yuan, Ye, Bo Tang, Tianfei Zhou, Zhiwei Zhang, and Jianbin Qin. "nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3283–89. http://dx.doi.org/10.14778/3681954.3682000.

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In this paper, we propose nsDB, a novel neuro-symbolic database system that integrates neural and symbolic system architectures natively to address the weaknesses of each, providing a strong database capable of data managing, model learning, and complex analytical query processing over multi-modal data. We employ a real-world NBA data analytical query as an example to illustrate the functionality of each component in nsDB and highlight the research challenges to build it. We then present the key design principles and our preliminary attempts to address them. In a nutshell, we envision that the

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Meng, Kun, Chunyi Cui, Haijiang Li, and Hailong Liu. "Ontology-Based Approach Supporting Multi-Objective Holistic Decision Making for Energy Pile System." Buildings 12, no. 2 (2022): 236. http://dx.doi.org/10.3390/buildings12020236.

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The traditional way of designing energy pile system is mostly single domain/objective oriented, which lacks of means to coherently consider different while relevant factors across domains. The cost for life cycle design, construction and maintenance, return of investment, CO2 emission related sustainable requirements, and so on also need to be considered, in a systematic manner, along with the main functional design objective for loading capacity and robustness. This paper presents a novel multi-objective holistic approach for energy pile system design using ontology based multi-domain knowled

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Hakmi, Sultan Hassan. "Applications of Multi-Objective OPF Solutions with Optimal Placement of Multiple and Multi-Type FACTS Units to IEEE System: Comparison of Different Approaches." International Journal of Robotics and Control Systems 4, no. 3 (2024): 1075–91. http://dx.doi.org/10.31763/ijrcs.v4i3.1472.

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Optimal power flow (OPF) problem and its implications for power system stability and efficiency is investigated in this study. OPF, a restricted optimization query with non-linearity and non-convexity, is one of the most challenging and fascinating problems in the recent power system. Based on these parameters, researchers have been working hard over the past few decades to identify the best solutions to the OPF issue that maintain system stability. This work presents multi-objective OPF solutions utilizing Newton's technique with numerous multi-type FACTS units. First, the GA is applied to id

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Piyush Dubey. "Data Lake Architecture at Uber: A Lambda-Based Approach to Real-Time and Batch Analytics with Cross-Industry Perspectives." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 325–32. https://doi.org/10.32996/jcsts.2025.7.7.35.

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The evolution of data infrastructure in modern transportation platforms demonstrates the critical role of Lambda architecture in addressing the dual challenges of real-time processing and comprehensive historical analytics. Through the implementation of sophisticated data lake architectures leveraging open-source technologies, including Apache Kafka for streaming, Apache Flink for real-time processing, Apache Hudi for data lake management, and Presto for distributed querying, organizations achieve significant reductions in data freshness latency while maintaining scalability. The architectural

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Simon Pierre Dembele. "From Performance’s Designing Feedback in the Light of Energy Efficiency Improving: How & Proofs?" Journal of Information Systems Engineering and Management 10, no. 24s (2025): 119–31. https://doi.org/10.52783/jisem.v10i24s.3878.

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At the core of any Data Storage Systems (DSS), ongoing research efforts are dedicated to refining algorithms for effective data storage and retrieval. The primary objective of any data management systems (DBMS) is improving the performance of data management such optimizing queries response times. Decades of research have borne fruit in the form of performance-oriented optimization techniques, revolutionizing DSS usability and enhancing the user experience significantly. However, the explosion of digital data and the rise of various data-driven services, combined with the ongoing decline in pr

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Lyu, Chenghao, Qi Fan, Philippe Guyard, and Yanlei Diao. "A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3565–79. http://dx.doi.org/10.14778/3681954.3682021.

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As Spark becomes a common big data analytics platform, its growing complexity makes automatic tuning of numerous parameters critical for performance. Our work on Spark parameter tuning is particularly motivated by two recent trends: Spark's Adaptive Query Execution (AQE) based on runtime statistics, and the increasingly popular Spark cloud deployments that make cost-performance reasoning crucial for the end user. This paper presents our design of a Spark optimizer that controls all tunable parameters of each query in the new AQE architecture to explore its performance benefits and, at the same

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Li, Mingzhen, Yunfeng Wang, Guangcan Yang, et al. "DGS-HSA: A Dummy Generation Scheme Adopting Hierarchical Structure of the Address." Applied Sciences 10, no. 2 (2020): 548. http://dx.doi.org/10.3390/app10020548.

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With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations

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Ye, Shuzhan, Xiaoliang Xu, Yuxiang Wang, and Tao Fu. "Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge Graphs." Mathematics 11, no. 18 (2023): 3908. http://dx.doi.org/10.3390/math11183908.

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Knowledge graphs (KGs) have gained prominence for representing real-world facts, with queries of KGs being crucial for their application. Aggregate queries, as one of the most important parts of KG queries (e.g., “ What is the average price of cars produced in Germany?”), can provide users with valuable statistical insights. An efficient solution for KG aggregate queries is approximate aggregate queries with semantic-aware sampling (AQS). This balances the query time and result accuracy by estimating an approximate aggregate result based on random samples collected from a KG, ensuring that the

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Tatarenko, Tatiana, and Jan Zimmermann. "Distributed optimization methods for N-cluster games." at - Automatisierungstechnik 70, no. 3 (2022): 237–47. http://dx.doi.org/10.1515/auto-2021-0137.

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Abstract This work provides methodological approaches to solve convex optimization problems arising in multi-agent systems which can be reformulated in terms of a so called N-cluster game. We consider different settings of information available to each agent in the system. First, we present a centralized algorithm, which requires a central coordinator having full access to information about agents’ actions and gradients of their cost functions, to demonstrate how the standard gradient descent method can be applied to achieve an optimal output in N-cluster games. After that we relax the full in

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Chen, Yuzhong, Yang Yu, and Guolong Chen. "Shortest distance estimation in large scale graphs." Engineering Computations 31, no. 8 (2014): 1635–47. http://dx.doi.org/10.1108/ec-11-2012-0286.

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Purpose – Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies. Design/methodology/approach – Diff

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Micor, Klaudia, and Roksana Poloczek. "MOQ MODELS REVIEW IN PERSPECTIVE OF 7 WASTES." Scientific Papers of Silesian University of Technology Organization and Management Series 2024, no. 208 (2024): 311–29. https://doi.org/10.29119/1641-3466.2024.208.18.

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Purpose: Paper aims to review available knowledge base about MOQ models and review their application to 7 wastes. Design/methodology/approach: Research areas were identified through author-assigned keywords linked to individual scientific publications. The multi-stage research process incorporated two bibliometric methods: a systematic literature review and a traditional literature review. Scope of the paper gathers published optimization models of MOQ. Available database was reviewed with the purpose of Collected models are reviewed under the objective of 7 wastes. Findings: The research iden

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Moskowitz, Craig H., Auayporn Nadamanee, Tamas Masszi, et al. "The Aethera Trial: Results of a Randomized, Double-Blind, Placebo-Controlled Phase 3 Study of Brentuximab Vedotin in the Treatment of Patients at Risk of Progression Following Autologous Stem Cell Transplant for Hodgkin Lymphoma." Blood 124, no. 21 (2014): 673. http://dx.doi.org/10.1182/blood.v124.21.673.673.

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Abstract Background For the past 20 years, high-dose therapy plus autologous stem cell transplant (ASCT) has been the standard of care for patients (pts) with chemosensitive relapsed/refractory Hodgkin lymphoma (HL), providing a cure for approximately 50% of pts (Sureda 2005). Despite optimization of salvage chemotherapy, supportive care, and pt selection, improvements in outcomes post-ASCT have plateaued, likely due to disease progression (PD) in pts with pre-salvage therapy risk factors. The majority of pts with multiple risk factors will progress post-ASCT and novel therapy is urgently need

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Ns, Karthik Somayaji, and Peng Li. "Pareto Optimization of Analog circuits using Reinforcement Learning." ACM Transactions on Design Automation of Electronic Systems, January 17, 2024. http://dx.doi.org/10.1145/3640463.

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Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require for the designer to optimize for multiple competing objectives which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to: (i) Extrapolate current techniques in Multi-Objective Reinforcement Learning (MORL) to continuous state and action spaces. (ii) Provide for a dynamically tunable trained model to query user

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Jiang, Wei, Shen You, Jinyu Zhan, Xupeng Wang, Hong Lei, and Deepak Adhikari. "Query-Efficient Generation of Adversarial Examples for Defensive DNNs via Multi-Objective Optimization." IEEE Transactions on Evolutionary Computation, 2022, 1. http://dx.doi.org/10.1109/tevc.2022.3231460.

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"Multi-Objective Big Data View Materialization using Improved Strength Pareto Evolutionary Algorithm." Journal of Information Technology Research 15, no. 1 (2022): 0. http://dx.doi.org/10.4018/jitr.299947.

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Big data view materialization enhances the performance of Big data queries. This is a complex problem due to large volume, heterogeneity, high rate of data generation, low integrity and low value of Big data. Big data view materialization is a bi-objective optimization problem with the objectives - minimization of query evaluation time for a set of workload queries over a window of time and minimization of update processing cost of the views. Structure of Big data views can be represented as directed graph, which can be used to identify the candidate Big data views for a given set of queries.

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Nartey, Clement, Eric Tutu Tchao, James Dzisi Gadze, et al. "Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm." EURASIP Journal on Wireless Communications and Networking 2022, no. 1 (2022). http://dx.doi.org/10.1186/s13638-021-02074-3.

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AbstractThe integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining th

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Zhao, Hang, Qinghua Deng, Wenting Huang, Dian Wang, and Zhenping Feng. "Thermodynamic and Economic Analysis and Multi-objective Optimization of Supercritical CO2 Brayton Cycles." Journal of Engineering for Gas Turbines and Power 138, no. 8 (2016). http://dx.doi.org/10.1115/1.4032666.

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Supercritical CO2 Brayton cycles (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 comparative analyses on RBC and RRBC are conducted. Nondominated Sorting Genetic Algorithm II (NSGA-II) is selected for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power (k$/kW)

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Popescu, Claudiu, Lacrimioara Grama, and Corneliu Rusu. "A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization." October 11, 2021. https://doi.org/10.3390/sym13101824.

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The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorith

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Yang, Cai, Songhao Jia, Jizheng Yang, and Haiyu Zhang. "Research on MapReduce heuristic multi table join algorithm based on binary optimization and pancake parallel strategy." Recent Patents on Engineering 17 (October 24, 2022). http://dx.doi.org/10.2174/1872212117666221024161743.

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Background: With the development of technology, the data amount has increased significantly. In data processing, multi table query is the most frequently operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper proposes a heuristic multi table join optimization method. By sharing information, the unconnected tuples are eliminated, so as to reduce the amount of data transmitting. This shortens response time

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Cheng, Jian, Zhiji Zheng, Yinan Guo, Jiayang Pu, and Shengxiang Yang. "Active broad learning with multi-objective evolution for data stream classification." Complex & Intelligent Systems, August 12, 2023. http://dx.doi.org/10.1007/s40747-023-01154-9.

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AbstractIn a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk

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Zhao, Xiaopeng, Guangxia Luo, Miao Cao, Xuanfeng Wang, Xianghui Su, and Gang Wu. "A Study of Strategies and Methods for the Application of Sports Nutrition in Fitness Training." Applied Mathematics and Nonlinear Sciences 9, no. 1 (2024). http://dx.doi.org/10.2478/amns-2024-0078.

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Abstract Dietary habits are particularly important in fitness training, and a scientific fitness diet needs to be formulated according to the fitness user’s situation, fitness goals and exercise volume. In this paper, a multimodal personalized sports nutrition recommendation model incorporating users’ visual preferences is designed to address the important impact of visual features on the task of sports nutrition recommendation. The user’s visual preference is modeled using the Query-Key-Value attention mechanism, which extracts valuable visual information from their historical data and adds i

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