Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),ha...Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.展开更多
聚焦于中小型企业,深入探讨借助Excel Power Query工具批量生成记账凭证的方法。通过分析中小型企业记账凭证处理的现状,对比手工录入的会计电算化记账方式(以下简称手工录账)与借助Excel Power Query批量生成记账凭证的模式,阐述Excel ...聚焦于中小型企业,深入探讨借助Excel Power Query工具批量生成记账凭证的方法。通过分析中小型企业记账凭证处理的现状,对比手工录入的会计电算化记账方式(以下简称手工录账)与借助Excel Power Query批量生成记账凭证的模式,阐述Excel Power Query在数据处理各环节的应用优势,详细介绍应用该工具批量生成记账凭证的具体步骤,并结合实际案例展示其应用效果。展开更多
In order to protect the privacy of the query user and database,some QKD-based quantum private query(QPQ)protocols were proposed.One example is the protocol proposed by Zhou et al,in which the user makes initial quantu...In order to protect the privacy of the query user and database,some QKD-based quantum private query(QPQ)protocols were proposed.One example is the protocol proposed by Zhou et al,in which the user makes initial quantum states and derives the key bit by comparing the initial quantum state and the outcome state returned from the database by ctrl or shift mode,instead of announcing two non-orthogonal qubits as others which may leak part secret information.To some extent,the security of the database and the privacy of the user are strengthened.Unfortunately,we find that in this protocol,the dishonest user could be obtained,utilizing unambiguous state discrimination,much more database information than that is analyzed in Zhou et al's original research.To strengthen the database security,we improved the mentioned protocol by modifying the information returned by the database in various ways.The analysis indicates that the security of the improved protocols is greatly enhanced.展开更多
With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issue...With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.展开更多
To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are cla...To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.展开更多
A data lake(DL),abbreviated as DL,denotes a vast reservoir or repository of data.It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various form...A data lake(DL),abbreviated as DL,denotes a vast reservoir or repository of data.It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured,structured,and unstructured information.These systems use a flat architecture and run different types of data analytics.NoSQL databases are nontabular and store data in a different manner than the relational table.NoSQL databases come in various forms,including key-value pairs,documents,wide columns,and graphs,each based on its data model.They offer simpler scalability and generally outperform traditional relational databases.While NoSQL databases can store diverse data types,they lack full support for atomicity,consistency,isolation,and durability features found in relational databases.Consequently,employing machine learning approaches becomes necessary to categorize complex structured query language(SQL)queries.Results indicate that the most frequently used automatic classification technique in processing SQL queries on NoSQL databases is machine learning-based classification.Overall,this study provides an overview of the automatic classification techniques used in processing SQL queries on NoSQL databases.Understanding these techniques can aid in the development of effective and efficient NoSQL database applications.展开更多
This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagno...This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.展开更多
The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimizati...The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.展开更多
With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enha...With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.展开更多
To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,al...To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.展开更多
Query expansion with thesaurus is one of the useful techniques in modern information retrieval (IR). In this paper, a method of query expansion for Chinese IR by using a decaying co-occurrence model is proposed and re...Query expansion with thesaurus is one of the useful techniques in modern information retrieval (IR). In this paper, a method of query expansion for Chinese IR by using a decaying co-occurrence model is proposed and realized. The model is an extension of the traditional co-occurrence model by adding a decaying factor that decreases the mutual information when the distance between the terms increases. Experimental results on TREC-9 collections show this query expansion method results in significant improvements over the IR without query expansion.展开更多
Aiming at the problem that only some types of SPARQL （ simple protocal and resource description framework query language） queries can be answered by using the current resource description framework link traversal ba...Aiming at the problem that only some types of SPARQL （ simple protocal and resource description framework query language） queries can be answered by using the current resource description framework link traversal based query execution （RDF-LTE） approach, this paper discusses how the execution order of the triple pattern affects the query results and cost based on concrete SPARQL queries, and analyzes two properties of the web of linked data, missing backward links and missing contingency solution. Then three heuristic principles for logic query plan optimization, namely, the filtered basic graph pattern （FBGP） principle, the triple pattern chain principle and the seed URIs principle, are proposed. The three principles contribute to decrease the intermediate solutions and increase the types of queries that can be answered. The effectiveness and feasibility of the proposed approach is evaluated. The experimental results show that more query results can be returned with less cost, thus enabling users to develop the full potential of the web of linked data.展开更多
Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data po...Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.展开更多
In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image ...In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.展开更多
基金supported by the Special Project of Henan Provincial Key Research,Development and Promotion(Key Science and Technology Program)under Grant 252102210154in part by the National Natural Science Foundation of China under Grant 62403437.
文摘Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.
文摘聚焦于中小型企业,深入探讨借助Excel Power Query工具批量生成记账凭证的方法。通过分析中小型企业记账凭证处理的现状,对比手工录入的会计电算化记账方式(以下简称手工录账)与借助Excel Power Query批量生成记账凭证的模式,阐述Excel Power Query在数据处理各环节的应用优势,详细介绍应用该工具批量生成记账凭证的具体步骤,并结合实际案例展示其应用效果。
基金supported by the National Key R&D Program of China(Grant No.2022YFC3801700)the National Natural Science Foundation of China(Grant No.62472052)Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security(Grant No.CZ002702-3)。
文摘In order to protect the privacy of the query user and database,some QKD-based quantum private query(QPQ)protocols were proposed.One example is the protocol proposed by Zhou et al,in which the user makes initial quantum states and derives the key bit by comparing the initial quantum state and the outcome state returned from the database by ctrl or shift mode,instead of announcing two non-orthogonal qubits as others which may leak part secret information.To some extent,the security of the database and the privacy of the user are strengthened.Unfortunately,we find that in this protocol,the dishonest user could be obtained,utilizing unambiguous state discrimination,much more database information than that is analyzed in Zhou et al's original research.To strengthen the database security,we improved the mentioned protocol by modifying the information returned by the database in various ways.The analysis indicates that the security of the improved protocols is greatly enhanced.
文摘With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.
基金The State Grid Technology Project(No.5108202340042A-1-1-ZN).
文摘To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.
基金supported by the Student Scheme provided by Universiti Kebangsaan Malaysia with the Code TAP-20558.
文摘A data lake(DL),abbreviated as DL,denotes a vast reservoir or repository of data.It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured,structured,and unstructured information.These systems use a flat architecture and run different types of data analytics.NoSQL databases are nontabular and store data in a different manner than the relational table.NoSQL databases come in various forms,including key-value pairs,documents,wide columns,and graphs,each based on its data model.They offer simpler scalability and generally outperform traditional relational databases.While NoSQL databases can store diverse data types,they lack full support for atomicity,consistency,isolation,and durability features found in relational databases.Consequently,employing machine learning approaches becomes necessary to categorize complex structured query language(SQL)queries.Results indicate that the most frequently used automatic classification technique in processing SQL queries on NoSQL databases is machine learning-based classification.Overall,this study provides an overview of the automatic classification techniques used in processing SQL queries on NoSQL databases.Understanding these techniques can aid in the development of effective and efficient NoSQL database applications.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Korea(No.20204010600090).
文摘This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.
基金partially supported by NSFC under Grant Nos.61832001 and 62272008ZTE Industry-University-Institute Fund Project。
文摘The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.
文摘With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.
基金Weaponry Equipment Pre-Research Foundation of PLA Equipment Ministry (No. 9140A06050409JB8102)Pre-Research Foundation of PLA University of Science and Technology (No. 2009JSJ11)
文摘To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.
文摘Query expansion with thesaurus is one of the useful techniques in modern information retrieval (IR). In this paper, a method of query expansion for Chinese IR by using a decaying co-occurrence model is proposed and realized. The model is an extension of the traditional co-occurrence model by adding a decaying factor that decreases the mutual information when the distance between the terms increases. Experimental results on TREC-9 collections show this query expansion method results in significant improvements over the IR without query expansion.
基金The National Natural Science Foundation of China(No.61070170)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.11KJB520017)Suzhou Application Foundation Research Project(No.SYG201238)
文摘Aiming at the problem that only some types of SPARQL （ simple protocal and resource description framework query language） queries can be answered by using the current resource description framework link traversal based query execution （RDF-LTE） approach, this paper discusses how the execution order of the triple pattern affects the query results and cost based on concrete SPARQL queries, and analyzes two properties of the web of linked data, missing backward links and missing contingency solution. Then three heuristic principles for logic query plan optimization, namely, the filtered basic graph pattern （FBGP） principle, the triple pattern chain principle and the seed URIs principle, are proposed. The three principles contribute to decrease the intermediate solutions and increase the types of queries that can be answered. The effectiveness and feasibility of the proposed approach is evaluated. The experimental results show that more query results can be returned with less cost, thus enabling users to develop the full potential of the web of linked data.
基金Supported by the National Natural Science Foundation of China (60673136)the Natural Science Foundation of Heilongjiang Province of China (F200601)~~
文摘Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.
基金The National High Technology Research and Develop-ment Program of China (863 Program) (No.2002AA413420).
文摘In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.
