Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

Bhaskar, N.; Kumar, Praveen

doi:10.1007/s00500-020-04722-0

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…Take a region as an example: the two objective functions of optimal economic benefit and optimal social benefit adopt the weighting method (in which the economic benefit weight is 0.4 and the social benefit weight is 0.6). After converting the multiobjective problem into a single objective, calculate the fitness value of each individual in the whale population, and find and save the best whale individual Q d in the current population (the current optimal solution) [22].…”

Section: Optimal Allocation Of Resources For the Integrated Medical C...mentioning

confidence: 99%

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

Jun-xia

2022

Scientific Programming

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Aiming at the problems of poor fairness of resource allocation and long time-consuming allocation of traditional methods, a method of resource optimization allocation under dynamic monitoring of medical care combined with elderly care mode is proposed. The integrated medical and elderly care model is analyzed, the role of dynamic monitoring in the optimal allocation of resources in the integrated medical and elderly care model is discussed, and a general model for optimal allocation of medical and elderly resources based on the analysis results is established. Based on the criterion of “minimum variance of estimation error,” a combined prediction model is established to obtain the combined prediction results. The investment objects of the optimal allocation of resources in the elderly care model are clustered in advance by using the clustering method, and the investment objects of the optimal allocation of resources in the elderly care model are comprehensively evaluated by using the principal component analysis method. According to the actual results of the comprehensive evaluation, the improved whale algorithm is used to allocate pension resources reasonably, and the fairness and applicability of the allocation results are analyzed experimentally. The results show that the method proposed in this paper has strong rationality and can effectively realize the rational distribution of pensions.

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Section: Optimal Allocation Of Resources For the Integrated Medical C...mentioning

confidence: 99%

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

Jun-xia

2022

Scientific Programming

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“…The query optimization overview and objective functions are explained in this section. In the existing work, 33 authors have optimized the objective functions of monetary cost and latency during query optimization. But, one more additional term such as data privacy is taken as the objective function and the homomorphic encryption-based protocol is used for data privacy (Encryption).…”

Section: Objective Functions Of Querymentioning

confidence: 99%

An efficient privacy‐preserving model based on OMFTSA for query optimization in crowdsourcing

Renukadevi¹,

Anita

Geetha³

2021

Concurrency and Computation

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Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence-intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF-TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd-controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF-TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, andcrowdsourcing, moth flame-based tunicate swarm algorithm, query, select and join queries INTRODUCTIONRecently, large-scale user representations of extensive collaboration have been made available through the Internet via various types of technologies and mechanisms. The collection of text documents and data source connections is an important part of the World Wide Web (WWW). The emerging crowdsourcing platform has been authorized to collect and distribute human computing for a wide range of tasks. 1 To generate an accurate and efficient solution, crowdsourcing targets a massive audience. The consensus is shared between the crowdsourcing system and implementations, such as employees who participate and delegate tasks that differ. When it comes to tasks such as answering questions, some workers are smarter and faster than others. 2,3

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“…The crowdsourced platforms became an effective solution for various types of queries by exploiting knowledge, ideas, experiences, and skills of crowd workers to process information and obtain accurate answers for queries. The hybrid human/machine systems defined as the process that integrates the crowd workers with computer systems to get high-quality results (Parameswaran et al, 2011;Li et al, 2016;Swidan et al, 2018;Bhaskar et al, 2020;Lian et al, 2020;Swidan et al, 2020). The crowd-sourced system consists of three main components that work together to provide the users with the best answers for the given queries.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the DBMS expects that the information entered by the user has been appropriately cleaned and validated before inserting it into the database. Typically, query processing in the crowd databases has influenced by three critical factors, namely: monetary cost, time latency, and the accuracy of the results (Parameswaran et al, 2011;EL Maarry et al, 2015;Li et al, 2016;Chai et al, 2019;Bhaskar et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Overview of Query Processing on Crowdsourced Databases

Swidan¹,

Alwan²

2020

SJKFU

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Crowd-sourcing is a powerful solution to correctly answer expensive and unanswered queries in the database. This includes queries on a database with uncertain and incomplete data. The crowd-sourcing attempts to exploit human abilities to process these difficult tasks and workers helped to provide accurate results utilizing the available data in the crowd. The crowd-sourcing database systems (CSDB) combined the ability of the crowd with the relational database by using some variant of the relational database with minor changes. This paper surveys and examines the leading studies conducted on the area of query processing for both traditional and preference queries in crowd-sourcing databases. The focus is given on highlights the strengths and the weakness of each approach. A detailed discussion for the current and future trends research relevant to query processing in the area of crow-sourced databases is also demonstrated.

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Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

Cited by 15 publications

References 32 publications

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

An efficient privacy‐preserving model based on OMFTSA for query optimization in crowdsourcing

An Overview of Query Processing on Crowdsourced Databases

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