Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pP...Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
This paper addresses the challenge of efficiently querying multimodal related data in data lakes,a large-scale storage and management system that supports heterogeneous data formats,including structured,semi-structure...This paper addresses the challenge of efficiently querying multimodal related data in data lakes,a large-scale storage and management system that supports heterogeneous data formats,including structured,semi-structured,and unstructured data.Multimodal data queries are crucial because they enable seamless retrieval of related data across modalities,such as tables,images,and text,which has applications in fields like e-commerce,healthcare,and education.However,existing methods primarily focus on single-modality queries,such as joinable or unionable table discovery,and struggle to handle the heterogeneity and lack of metadata in data lakes while balancing accuracy and efficiency.To tackle these challenges,we propose a Multimodal data Query mechanism for Data Lakes(MQDL),which employs a modality-adaptive indexing mechanism raleted and contrastive learning based embeddings to unify representations across modalities.Additionally,we introduce product quantization to optimize candidate verification during queries,reducing computational overhead while maintaining precision.We evaluate MQDL using a table-image dataset across multiple business scenarios,measuring metrics such as precision,recall,and F1-score.Results show that MQDL achieves an accuracy rate of approximately 90%,while demonstrating strong scalability and reduced query response time compared to traditional methods.These findings highlight MQDL's potential to enhance multimodal data retrieval in complex data lake environments.展开更多
Cultural landscape zoning research of traditional villages is the basic premise for carrying out overall protection and regional development.Through the clustering algorithm,cultural area zoning research of traditiona...Cultural landscape zoning research of traditional villages is the basic premise for carrying out overall protection and regional development.Through the clustering algorithm,cultural area zoning research of traditional villages can provide objective basis for its overall protection and development.Based on the field research,drawing on the theory of cultural landscape,southwest Hubei is taken as the research object,and the index system of cultural landscape type division of traditional villages is constructed from three levels of culture,geography and village carrier.Adopting the multi-attribute weighted k-modes clustering algorithm,92 traditional villages in southwest Hubei are divided into three major types,which are the western Tujia cultural characteristic area,the southern Tujia-Miao cultural penetration area,and the northern multi-ethnic cultural mixed area,and the characteristics of each area are summarized.The regional characteristics of traditional villages in southwest Hubei at the cultural landscape level are analysed from a macro point of view,which provides a reference for more objective cognition of the distribution law of traditional villages in southwest Hubei,and carrying out the contiguous protection of traditional villages.展开更多
The diferential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specifed parametric distribution model with one or two degrees of freedom. ...The diferential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specifed parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defning the noise distribution are fnite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and fnite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfes privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meet-ingprivacy constraints, our framework seeks to optimize the noise distribution under an arbitrary (ǫ, δ) constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specifc cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy pri-vacyrequirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, ofering signifcant enhancements in response accuracy and utility.展开更多
The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution,primarily by minimizing the number of scanned block files.Existing p...The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution,primarily by minimizing the number of scanned block files.Existing partitioning techniques predominantly focus on query accesses on numeric columns for constructing partitions,often overlooking non-numeric columns and thus limiting optimization potential.Additionally,these techniques,despite creating fine-grained partitions from representative queries to enhance system performance,experience from notable performance declines due to unpredictable fluctuations in future queries.To tackle these issues,we introduce LRP,a learned robust partitioning system for dynamic query processing.LRP first proposes a method for data and query encoding that captures comprehensive column access patterns from historical queries.It then employs Multi-Layer Perceptron and Long Short-Term Memory networks to predict shifts in the distribution of historical queries.To create high-quality,robust partitions based on these predictions,LRP adopts a greedy beam search algorithm for optimal partition division and implements a data redundancy mechanism to share frequently accessed data across partitions.Experimental evaluations reveal that LRP yields partitions with more stable performance under incoming queries and significantly surpasses state-of-the-art partitioning methods.展开更多
Graphs have been widely used in fields ranging from chemical informatics to social network analysis.Graph-related problems become increasingly significant,with subgraph matching standing out as one of the most challen...Graphs have been widely used in fields ranging from chemical informatics to social network analysis.Graph-related problems become increasingly significant,with subgraph matching standing out as one of the most challenging tasks.The goal of subgraph matching is to find all subgraphs in the data graph that are isomorphic to the query graph.Traditional methods mostly rely on search strategies with high computational complexity and are hard to apply to large-scale real datasets.With the advent of graph neural networks(GNNs),researchers have turned to GNNs to address subgraph matching problems.However,the multi-attributed features on nodes and edges are overlooked during the learning of graphs,which causes inaccurate results in real-world scenarios.To tackle this problem,we propose a novel model called subgraph matching on multi-attributed graph network(SGMAN).SGMAN first utilizes improved line graphs to capture node and edge features.Then,SGMAN integrates GNN and contrastive learning(CL)to derive graph representation embeddings and calculate the matching matrix to represent the matching results.We conduct experiments on public datasets,and the results affirm the superior performance of our model.展开更多
Spatial relationships are core components in the design and definition of spatial queries.A spatial relationship determines how two or more spatial objects are related or connected in space.Hence,given a spatial datas...Spatial relationships are core components in the design and definition of spatial queries.A spatial relationship determines how two or more spatial objects are related or connected in space.Hence,given a spatial dataset,users can retrieve spatial objects in a given relationship with a search object.Different interpretations of spatial relationships are conceivable,leading to different types of relationships.The main types are(i)topological relationships(e.g.overlap,meet,inside),(ii)metric relationships(e.g.nearest neighbors),and(iii)direction relationships(e.g.cardinal directions).Although spatial information retrieval has been extensively studied in the literature,it is unclear which types of spatial queries can be defined using spatial relationships.In this article,we introduce a taxonomy for naming,describing,and classifying types of spatial queries frequently found in the literature.This taxonomy is based on the types of spatial relationships that are employed by spatial queries.By using this taxonomy,we discuss the intuitive descriptions,formal definitions,and possible implementation techniques of several types of spatial queries.The discussions lead to the identification of correspondences between types of spatial queries.Further,we identify challenges and open research topics in the spatial information retrieval area.展开更多
An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attr...An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.展开更多
In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternati...In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.展开更多
Through the mapping from UMQL （ unified multimedia query language） conditional expressions to UMQA （unified multimedia query algebra） query operations, a translation algorithm from a UMQL query to a UMQA query pla...Through the mapping from UMQL （ unified multimedia query language） conditional expressions to UMQA （unified multimedia query algebra） query operations, a translation algorithm from a UMQL query to a UMQA query plan is put forward, which can generate an equivalent UMQA internal query plan for any UMQL query. Then, to improve the execution costs of UMQA query plans effectively, equivalent UMQA translation formulae and general optimization strategies are studied, and an optimization algorithm for UMQA internal query plans is presented. This algorithm uses equivalent UMQA translation formulae to optimize query plans, and makes the optimized query plans accord with the optimization strategies as much as possible. Finally, the logic implementation methods of UMQA plans, i.e., logic implementation methods of UMQA operators, are discussed to obtain useful target data from a muifirnedia database. All of these algorithms are implemented in a UMQL prototype system. Application results show that these query processing techniques are feasible and applicable.展开更多
[Objective]The aim was to establish a multi-attribute decision making method and introduce its application in rice breeding.[Method]Based on the defined closeness degree among attributes,the difference degrees among a...[Objective]The aim was to establish a multi-attribute decision making method and introduce its application in rice breeding.[Method]Based on the defined closeness degree among attributes,the difference degrees among attributes were discussed.Furthermore,the weights of attributes were determined based on the difference degrees among the attributes.[Result]A multi-attribute decision making method based on difference degrees among attributes was established,the feasibility of applying it in rice breeding was also analyzed.[Conclusion]This study enriched the methods to determine attribute weights in multi-attribute decision making and provided the necessary theoretical support for selecting rice varieties scientifically and rationally.展开更多
To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation...To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation weights.The comprehensive attribute weights are gotten through the product of the above three kinds of weights.And each decision maker＇s weighted decision matrices are also received by using the integrated attribute weights.The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution（TOPSIS） through dealing with the weighted decision matrices.At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker＇s closeness degree to every project.Then the vertical TOPSIS method is used to calculate the closeness degree of each project.So these projects can be ranked according to their values of the closeness degree.The process of the method is also given step by step.Finally,a numerical example demonstrates the feasibility and effectiveness of the approach.展开更多
Due to the complexity of decision-making problems and the subjectivity of decision-makers in practical application,it is necessary to adopt different forms of information expression according to the actual situation o...Due to the complexity of decision-making problems and the subjectivity of decision-makers in practical application,it is necessary to adopt different forms of information expression according to the actual situation of specific decision-making problems and choose the best method to solve them.Multi-valued neutrosophic set,as an extension of neutrosophic set,can more effectively and accurately describe incomplete,uncertain or inconsistent information.TODIM and TOPSIS methods are two commonly used multi-attribute decision-making methods,each of which has its advantages and disadvantages.This paper proposes a new method based on TODIM and TOPSIS to solve multi-attribute decision-making problems under multi-valued neutrosophic environment.After introducing the related theory of multi-valued neutrosophic set and the traditional TODIM and TOPSIS methods,the new method based on a combination of TODIM and TOPSIS methods is described.And then,two illustrative examples proved the feasibility and validity of the proposed method.Finally,the result has been compared with some existing methods under the same examples and the proposed method’s superiority has been proved.This paper studies this kind of decision-making problem from algorithm idea,algorithm steps and decision-making influencing factors.展开更多
To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy gr...To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.展开更多
In the case of unknown weights, theories of multi-attributed decision making based on interval numbers and grey related analysis were used to optimize mining methods. As the representative of independence for the indi...In the case of unknown weights, theories of multi-attributed decision making based on interval numbers and grey related analysis were used to optimize mining methods. As the representative of independence for the indicator, the smaller the correlation of indicators is, the greater the weight is. Hence, the weights of interval numbers of indicators were determined by using correlation coefficient. Relative closeness based on positive and negative ideal methods was calculated by introducing distance between interval numbers, which made decision making more rational and comprehensive. A new method of ranking interval numbers based on normal distribution was proposed for the optimization of mining methods, whose basic properties were discussed. Finally, the feasibility and effectiveness of this method were verified by theories and practice.展开更多
A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the...A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.展开更多
Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with resp...Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.展开更多
The function of the air target threat evaluation （TE） is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to...The function of the air target threat evaluation （TE） is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes. Meanwhile, the intuitionistic fuzzy set （IFS） is employed to deal with information uncertainty in the TE process. Furthermore, on the basis of the entropy weight and inclusion-comparison probability, a hybrid TE method is developed. In order to accommodate the demands of naturalistic decision making, the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target. An illustrative example is provided to indicate the feasibility and advantage of the proposed method.展开更多
Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information ac...Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information acquired from the reservoir.The use of multi-attribute matching technology to predict sedimentary system has always been a very important but challenging task.To resolve the challenges,we utilized a quantitative analysis method of seismic attributes based on geological models involving high resolution 3D seismic data for sedimentary facies.We developed a workflow that includes core data,seismic attribute analysis,and well logging to highlight the benefit of understanding the facies distribution in the 3 rd Member of the Lower Jurassic Badaowan Formation,Hongshanzui area,Junggar Basin,China.1)Data preprocessing.2)Cluster analysis.3)RMS attribute based on a normal distribution constrains facies boundary.4)Mapping the sedimentary facies by using MRA(multiple regression analysis)prediction model combined with the lithofacies assemblages and logging facies assemblages.The confident level presented in this research is 0.745,which suggests that the methods and data-mining techniques are practical and efficient,and also be used to map facies in other similar geological settings.展开更多
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University(QU-APC-2024-9/1).
文摘Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
文摘This paper addresses the challenge of efficiently querying multimodal related data in data lakes,a large-scale storage and management system that supports heterogeneous data formats,including structured,semi-structured,and unstructured data.Multimodal data queries are crucial because they enable seamless retrieval of related data across modalities,such as tables,images,and text,which has applications in fields like e-commerce,healthcare,and education.However,existing methods primarily focus on single-modality queries,such as joinable or unionable table discovery,and struggle to handle the heterogeneity and lack of metadata in data lakes while balancing accuracy and efficiency.To tackle these challenges,we propose a Multimodal data Query mechanism for Data Lakes(MQDL),which employs a modality-adaptive indexing mechanism raleted and contrastive learning based embeddings to unify representations across modalities.Additionally,we introduce product quantization to optimize candidate verification during queries,reducing computational overhead while maintaining precision.We evaluate MQDL using a table-image dataset across multiple business scenarios,measuring metrics such as precision,recall,and F1-score.Results show that MQDL achieves an accuracy rate of approximately 90%,while demonstrating strong scalability and reduced query response time compared to traditional methods.These findings highlight MQDL's potential to enhance multimodal data retrieval in complex data lake environments.
基金Philosophy and Social Sciences Research Project of Hubei Provincial Department of Education(22D057).
文摘Cultural landscape zoning research of traditional villages is the basic premise for carrying out overall protection and regional development.Through the clustering algorithm,cultural area zoning research of traditional villages can provide objective basis for its overall protection and development.Based on the field research,drawing on the theory of cultural landscape,southwest Hubei is taken as the research object,and the index system of cultural landscape type division of traditional villages is constructed from three levels of culture,geography and village carrier.Adopting the multi-attribute weighted k-modes clustering algorithm,92 traditional villages in southwest Hubei are divided into three major types,which are the western Tujia cultural characteristic area,the southern Tujia-Miao cultural penetration area,and the northern multi-ethnic cultural mixed area,and the characteristics of each area are summarized.The regional characteristics of traditional villages in southwest Hubei at the cultural landscape level are analysed from a macro point of view,which provides a reference for more objective cognition of the distribution law of traditional villages in southwest Hubei,and carrying out the contiguous protection of traditional villages.
基金supported by the Director,Cybersecurity,Energy Security,and Emergency Response,Cybersecurity for Energy Delivery Systems pro-gram,of the U.S.Department of Energy,under contract DE-AC02-05CH11231。
文摘The diferential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specifed parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defning the noise distribution are fnite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and fnite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfes privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meet-ingprivacy constraints, our framework seeks to optimize the noise distribution under an arbitrary (ǫ, δ) constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specifc cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy pri-vacyrequirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, ofering signifcant enhancements in response accuracy and utility.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB4503600)the National Natural Science Foundation of China(Grant Nos.U23A20299,62072460,62172424,62276270,and 62322214).
文摘The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution,primarily by minimizing the number of scanned block files.Existing partitioning techniques predominantly focus on query accesses on numeric columns for constructing partitions,often overlooking non-numeric columns and thus limiting optimization potential.Additionally,these techniques,despite creating fine-grained partitions from representative queries to enhance system performance,experience from notable performance declines due to unpredictable fluctuations in future queries.To tackle these issues,we introduce LRP,a learned robust partitioning system for dynamic query processing.LRP first proposes a method for data and query encoding that captures comprehensive column access patterns from historical queries.It then employs Multi-Layer Perceptron and Long Short-Term Memory networks to predict shifts in the distribution of historical queries.To create high-quality,robust partitions based on these predictions,LRP adopts a greedy beam search algorithm for optimal partition division and implements a data redundancy mechanism to share frequently accessed data across partitions.Experimental evaluations reveal that LRP yields partitions with more stable performance under incoming queries and significantly surpasses state-of-the-art partitioning methods.
文摘Graphs have been widely used in fields ranging from chemical informatics to social network analysis.Graph-related problems become increasingly significant,with subgraph matching standing out as one of the most challenging tasks.The goal of subgraph matching is to find all subgraphs in the data graph that are isomorphic to the query graph.Traditional methods mostly rely on search strategies with high computational complexity and are hard to apply to large-scale real datasets.With the advent of graph neural networks(GNNs),researchers have turned to GNNs to address subgraph matching problems.However,the multi-attributed features on nodes and edges are overlooked during the learning of graphs,which causes inaccurate results in real-world scenarios.To tackle this problem,we propose a novel model called subgraph matching on multi-attributed graph network(SGMAN).SGMAN first utilizes improved line graphs to capture node and edge features.Then,SGMAN integrates GNN and contrastive learning(CL)to derive graph representation embeddings and calculate the matching matrix to represent the matching results.We conduct experiments on public datasets,and the results affirm the superior performance of our model.
基金financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil(CAPES)-Finance Code 001.Anderson C.Carniel was supported by Google as a recipient of the 2022 Google Research Scholar program.
文摘Spatial relationships are core components in the design and definition of spatial queries.A spatial relationship determines how two or more spatial objects are related or connected in space.Hence,given a spatial dataset,users can retrieve spatial objects in a given relationship with a search object.Different interpretations of spatial relationships are conceivable,leading to different types of relationships.The main types are(i)topological relationships(e.g.overlap,meet,inside),(ii)metric relationships(e.g.nearest neighbors),and(iii)direction relationships(e.g.cardinal directions).Although spatial information retrieval has been extensively studied in the literature,it is unclear which types of spatial queries can be defined using spatial relationships.In this article,we introduce a taxonomy for naming,describing,and classifying types of spatial queries frequently found in the literature.This taxonomy is based on the types of spatial relationships that are employed by spatial queries.By using this taxonomy,we discuss the intuitive descriptions,formal definitions,and possible implementation techniques of several types of spatial queries.The discussions lead to the identification of correspondences between types of spatial queries.Further,we identify challenges and open research topics in the spatial information retrieval area.
文摘An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.
文摘In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.
基金The National High Technology Research and Development Program of China(863 Program) (No.2006AA01Z430)
文摘Through the mapping from UMQL （ unified multimedia query language） conditional expressions to UMQA （unified multimedia query algebra） query operations, a translation algorithm from a UMQL query to a UMQA query plan is put forward, which can generate an equivalent UMQA internal query plan for any UMQL query. Then, to improve the execution costs of UMQA query plans effectively, equivalent UMQA translation formulae and general optimization strategies are studied, and an optimization algorithm for UMQA internal query plans is presented. This algorithm uses equivalent UMQA translation formulae to optimize query plans, and makes the optimized query plans accord with the optimization strategies as much as possible. Finally, the logic implementation methods of UMQA plans, i.e., logic implementation methods of UMQA operators, are discussed to obtain useful target data from a muifirnedia database. All of these algorithms are implemented in a UMQL prototype system. Application results show that these query processing techniques are feasible and applicable.
基金Supported by the Science Research and Development Project of Nanning City(201002030B)~~
文摘[Objective]The aim was to establish a multi-attribute decision making method and introduce its application in rice breeding.[Method]Based on the defined closeness degree among attributes,the difference degrees among attributes were discussed.Furthermore,the weights of attributes were determined based on the difference degrees among the attributes.[Result]A multi-attribute decision making method based on difference degrees among attributes was established,the feasibility of applying it in rice breeding was also analyzed.[Conclusion]This study enriched the methods to determine attribute weights in multi-attribute decision making and provided the necessary theoretical support for selecting rice varieties scientifically and rationally.
基金supported by the Research Innovation Project of Shanghai Education Committee (08YS19)the Excellent Young Teacher Project of Shanghai University
文摘To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation weights.The comprehensive attribute weights are gotten through the product of the above three kinds of weights.And each decision maker＇s weighted decision matrices are also received by using the integrated attribute weights.The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution（TOPSIS） through dealing with the weighted decision matrices.At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker＇s closeness degree to every project.Then the vertical TOPSIS method is used to calculate the closeness degree of each project.So these projects can be ranked according to their values of the closeness degree.The process of the method is also given step by step.Finally,a numerical example demonstrates the feasibility and effectiveness of the approach.
基金This research was funded by the Humanities and Social Sciences Foundation of Ministry of Education of the Peoples Republic of China(17YJA630115)The recipient of the founding is DX.
文摘Due to the complexity of decision-making problems and the subjectivity of decision-makers in practical application,it is necessary to adopt different forms of information expression according to the actual situation of specific decision-making problems and choose the best method to solve them.Multi-valued neutrosophic set,as an extension of neutrosophic set,can more effectively and accurately describe incomplete,uncertain or inconsistent information.TODIM and TOPSIS methods are two commonly used multi-attribute decision-making methods,each of which has its advantages and disadvantages.This paper proposes a new method based on TODIM and TOPSIS to solve multi-attribute decision-making problems under multi-valued neutrosophic environment.After introducing the related theory of multi-valued neutrosophic set and the traditional TODIM and TOPSIS methods,the new method based on a combination of TODIM and TOPSIS methods is described.And then,two illustrative examples proved the feasibility and validity of the proposed method.Finally,the result has been compared with some existing methods under the same examples and the proposed method’s superiority has been proved.This paper studies this kind of decision-making problem from algorithm idea,algorithm steps and decision-making influencing factors.
基金This project was supported by the National Natural Science Foundation of China (70671050 70471019)the Key Project of Hubei Provincial Department of Education (D200627005).
文摘To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.
基金Project(50774095) supported by the National Natural Science Foundation of ChinaProject(200449) supported by the National Outstanding Doctoral Dissertations Special Funds of China
文摘In the case of unknown weights, theories of multi-attributed decision making based on interval numbers and grey related analysis were used to optimize mining methods. As the representative of independence for the indicator, the smaller the correlation of indicators is, the greater the weight is. Hence, the weights of interval numbers of indicators were determined by using correlation coefficient. Relative closeness based on positive and negative ideal methods was calculated by introducing distance between interval numbers, which made decision making more rational and comprehensive. A new method of ranking interval numbers based on normal distribution was proposed for the optimization of mining methods, whose basic properties were discussed. Finally, the feasibility and effectiveness of this method were verified by theories and practice.
基金supported by the National Natural Science Foundation of China(51405499)
文摘A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.
文摘Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.
基金supported by the National Natural Science Foundation of China (70871117 70571086)the Development Foundation of Dalian Naval Academy
文摘The function of the air target threat evaluation （TE） is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes. Meanwhile, the intuitionistic fuzzy set （IFS） is employed to deal with information uncertainty in the TE process. Furthermore, on the basis of the entropy weight and inclusion-comparison probability, a hybrid TE method is developed. In order to accommodate the demands of naturalistic decision making, the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target. An illustrative example is provided to indicate the feasibility and advantage of the proposed method.
基金supported by the National Natural Science Foundation of China(41902109)Tianshan Youth Program(2020Q064)+1 种基金National Major Projects(2017ZX05001004)Tianshan Innovation Team Program(2020D14023)。
文摘Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information acquired from the reservoir.The use of multi-attribute matching technology to predict sedimentary system has always been a very important but challenging task.To resolve the challenges,we utilized a quantitative analysis method of seismic attributes based on geological models involving high resolution 3D seismic data for sedimentary facies.We developed a workflow that includes core data,seismic attribute analysis,and well logging to highlight the benefit of understanding the facies distribution in the 3 rd Member of the Lower Jurassic Badaowan Formation,Hongshanzui area,Junggar Basin,China.1)Data preprocessing.2)Cluster analysis.3)RMS attribute based on a normal distribution constrains facies boundary.4)Mapping the sedimentary facies by using MRA(multiple regression analysis)prediction model combined with the lithofacies assemblages and logging facies assemblages.The confident level presented in this research is 0.745,which suggests that the methods and data-mining techniques are practical and efficient,and also be used to map facies in other similar geological settings.
