Artykuły w czasopismach na temat „Query algorithm”
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Joshi, Mukul, i Praveen Ranjan Srivastava. "Query Optimization". International Journal of Intelligent Information Technologies 9, nr 1 (styczeń 2013): 40–55. http://dx.doi.org/10.4018/jiit.2013010103.
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Query optimization is an important aspect in designing database management systems, aimed to find an optimal query execution plan so that overall time of query execution is minimized. Multi join query ordering (MJQO) is an integral part of query optimizer. This paper aims to propose a solution for MJQO problem, which is an NP complete problem. This paper proposes a heuristic based algorithm as a solution of MJQO problem. The proposed algorithm is a combination of two basic search algorithms, cuckoo and tabu search. Simulation shows some exciting results in favour of the proposed algorithm and concludes that proposed algorithm can solve MJQO problem in less amount of time than the existing methods.
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Li, Feng, i Hai Ying Wang. "Study on Distributed Database Query Optimization". Applied Mechanics and Materials 536-537 (kwiecień 2014): 540–44. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.540.
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This paper presents a heterogeneous sensor networks to improve query processing mechanism. Analysis of the advantages and disadvantages of centralized query processing algorithm is proposed based on the spatial distance distributed query processing algorithm based on semantic similarity and distributed query processing algorithms for query execution processes described. Use simulation to choose better coverage and semantic similarity by half, and centralized query processing algorithm, based on the spatial distance distributed algorithms, distributed algorithms and performance-based semantic distributed hybrid algorithm were compared.
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Xu, Jialu, i Feiyue Ye. "Query Recommendation Using Hybrid Query Relevance". Future Internet 10, nr 11 (19.11.2018): 112. http://dx.doi.org/10.3390/fi10110112.
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With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms.
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Meng, Yao. "Study on Query Optimization of Distributed Database". Applied Mechanics and Materials 533 (luty 2014): 448–51. http://dx.doi.org/10.4028/www.scientific.net/amm.533.448.
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Distributed query optimization in contemporary distributed database system increasingly important role, excellent query optimizer algorithm can effectively improve the query performance of the system. This article will be distributed query optimization hybrid algorithm is applied to improve the performance of distributed query optimization algorithms. Through simulation experiments and comparing two improved algorithms and the merits of the original algorithm, results were analyzed and show that the improved algorithm is better than the original algorithm has been improved to some extent in achieving cost efficiency and other aspects.
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Yang, Ze Xue, i Zhong Xiao Hao. "Spatial Join Queries Based on QR-Tree". Advanced Materials Research 187 (luty 2011): 752–57. http://dx.doi.org/10.4028/www.scientific.net/amr.187.752.
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Spatial join query is a most important query in the spatial database.How to improve the efficiency of a spatial join is a serious question.In this paper,we propose a new algorithm to process spatial join problem.The algorithm is based on the QR-tree,which combines the good property of the R-tree and quadtree.By dividing the space into several subspaces,the algorithm implement the spatial join query on the small R-tree.We compare the algorithm with the R-tree join algorithm of Brinkhoff. Experiments demonstrate the proposed algorithms have high query efficiency.
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Li, Xin, Huayan Yu, Ligang Yuan i Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing". Sensors 22, nr 5 (23.02.2022): 1748. http://dx.doi.org/10.3390/s22051748.
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The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.
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XIE, Kun, Ji-Gang WEN, Da-Fang ZHANG i Gao-Gang XIE. "Bloom Filter Query Algorithm". Journal of Software 20, nr 1 (7.04.2009): 96–108. http://dx.doi.org/10.3724/sp.j.1001.2009.00096.
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Bai, Luyi, Jinyao Wang, Chengyu Zhang i Xiangfu Meng. "Query Relaxation and Result Ranking for Uncertain Spatiotemporal XML Data". Journal of Database Management 33, nr 1 (1.01.2022): 1–19. http://dx.doi.org/10.4018/jdm.313970.
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Due to the widespread uses of uncertain spatiotemporal data, web ordinary users have access to query these data in various ways. However, users often cannot accurately give query constraints so that the query results may be empty or very few. Traditional algorithms cannot be used to deal with uncertain spatiotemporal data because they have no relaxation query on spatiotemporal attributes. Therefore, in this paper, the authors propose new flexible query algorithms, which add relaxation query processing for spatiotemporal attributes. Considering that XML has great advantages in exchanging and representing spatiotemporal data, they propose an uncertain spatiotemporal data model based on XML. According to the different number of relaxing attributes, they give SingleRelaxation algorithm and MultipleRelaxation algorithm. In addition, a T-List structure is designed to quickly locate the nodes' positions of uncertain spatiotemporal data, and RSort algorithm is proposed to sort accurate query results and extended query results. The experimental results show the superiority of the approach.
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Kim, Hyeong-Jin, Hyunjo Lee, Yong-Ki Kim i Jae-Woo Chang. "Privacy-preserving kNN query processing algorithms via secure two-party computation over encrypted database in cloud computing". Journal of Supercomputing 78, nr 7 (17.01.2022): 9245–84. http://dx.doi.org/10.1007/s11227-021-04286-2.
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AbstractSince studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving kNN query processing algorithms have been proposed over the encrypted database. However, the existing algorithms are either insecure or inefficient. Therefore, in this paper we propose a privacy-preserving kNN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hiding data access patterns. For this, we propose efficient and secure protocols based on Yao’s garbled circuit. To achieve a high degree of efficiency in query processing, we also propose a parallel kNN query processing algorithm using encrypted random value pool. Through our performance analysis, we verify that our proposed algorithms outperform the existing ones in terms of a query processing cost.
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Xu, Hong Bo, i Nian Min Yao. "A Range Query Parallel Algorithm in High-Dimensional Space". Applied Mechanics and Materials 263-266 (grudzień 2012): 2308–13. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2308.
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The performance of the spatial range query algorithms based on Brute-Force method, R-tree, VA-file and NB-tree suffers greatly in high-dimensional space. So the reduction of the dimensionality is the key to the spatial range query in high-dimensional space. The paper uses the parallel technique to present a spatial range query parallel algorithm in high-dimensional space. The algorithm transforms d-dimensional spatial range query to the linear space on d slave node processors. The d slave node processors run parallel. The master node processor only need calculate the union of d results which d slave node processors return. The experimental results indicate that its performance is better than that of the spatial range query algorithms based on Brute-Force method, R-tree, VA-file, NB-tree.
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Nayak, Ashwin. "Deterministic algorithms for the hidden subgroup problem". Quantum Information and Computation 22, nr 9&10 (lipiec 2022): 755–69. http://dx.doi.org/10.26421/qic22.9-10-3.
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We present deterministic algorithms for the Hidden Subgroup Problem. The first algorithm, for abelian groups, achieves the same asymptotic worst-case query complexity as the optimal randomized algorithm, namely~$ \Order(\sqrt{ n}\, )$, where~$n$ is the order of the group. The analogous algorithm for non-abelian groups comes within a~$\sqrt{ \log n}$ factor of the optimal randomized query complexity. The best known randomized algorithm for the Hidden Subgroup Problem has \emph{expected\/} query complexity that is sensitive to the input, namely~$ \Order(\sqrt{ n/m}\, )$, where~$m$ is the order of the hidden subgroup. In the first version of this article~\cite[Sec.~5]{Nayak21-hsp-classical}, we asked if there is a deterministic algorithm whose query complexity has a similar dependence on the order of the hidden subgroup. Prompted by this question, Ye and Li~\cite{YL21-hsp-classical} present deterministic algorithms for \emph{abelian\/} groups which solve the problem with~$ \Order(\sqrt{ n/m }\, )$ queries, and find the hidden subgroup with~$ \Order( \sqrt{ n (\log m) / m} + \log m ) $ queries. Moreover, they exhibit instances which show that in general, the deterministic query complexity of the problem may be~$\order(\sqrt{ n/m } \,)$, and that of \emph{finding\/} the entire subgroup may also be~$\order(\sqrt{ n/m } \,)$ or even~$\upomega(\sqrt{ n/m } \,) $.}We present a different deterministic algorithm for the Hidden Subgroup Problem that also has query complexity~$ \Order(\sqrt{ n/m }\, )$ for abelian groups. The algorithm is arguably simpler. Moreover, it works for non-abelian groups, and has query complexity~$ \Order(\sqrt{ (n/m) \log (n/m) }\,) $ for a large class of instances, such as those over supersolvable groups. We build on this to design deterministic algorithms to find the hidden subgroup for all abelian and some non-abelian instances, at the cost of a~$\log m$ multiplicative factor increase in the query complexity.
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Dong, Yun Feng, i Bei Qi. "A New Approximate Matching Algorithm and its Application in Internet Music Search by Humming". Advanced Materials Research 433-440 (styczeń 2012): 3662–68. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3662.
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This paper has proposed a new approximate matching algorithm—similarity matching, and use the characteristics of algorithm to establish a system of internet music search by humming. The author compared the similarity matching algorithm and dynamic time warping (DTW) algorithm, which is most commonly used to query by humming, by the system of internet music search by humming. On the two standard of the query hit ratio and query speed, we got the result that similarity matching algorithm's comprehensive efficiency is superior, is one of QBH (query by humming) algorithm, which is applicable to the large-scale music library such as internet music search.
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Sardharwalla, Imdad S. B., Sergii Strelchuk i Richard Jozsa. "Quantum conditional query complexity". Quantum Information and Computation 17, nr 7&8 (maj 2017): 541–67. http://dx.doi.org/10.26421/qic17.7-8-1.
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We define and study a new type of quantum oracle, the quantum conditional oracle, which provides oracle access to the conditional probabilities associated with an underlying distribution. Amongst other properties, we (a) obtain highly efficient quantum algorithms for identity testing, equivalence testing and uniformity testing of probability distributions; (b) study the power of these oracles for testing properties of boolean functions, and obtain an algorithm for checking whether an n-input m-output boolean function is balanced or e-far from balanced; and (c) give an algorithm, requiring O˜(n/e) queries, for testing whether an n-dimensional quantum state is maximally mixed or not.
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Cong, SHEN, DAI Xiao-peng i LI Dong-hui. "A Research on Network Similarity Search Algorithm for Biological Networks". MATEC Web of Conferences 173 (2018): 03025. http://dx.doi.org/10.1051/matecconf/201817303025.
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The biological network database presents exponential growth, how to find the target network accurately from the network database becomes the difficult problem. This paper proposes a new network similarity search algorithm, the similar network of Top k is calculated by two methods, the similar networks returned by the two algorithms are then filtered by overlap fractions, the weighted reordering algorithm is used to reorder the two sets of data, a precise set of similar network data sets is returned finally.In this paper, the accuracy of the query is judged by the comparison of the edge correctness (EC) value and the maximum public connection subgraph (LCCS) value of the returned sorted similar network data set, and compare query time with other algorithms.From the results, this algorithm is superior to other algorithms in query accuracy and query speed.
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Yadav, Monika, i T. V. Vijay Kumar. "Distributed Query Plan Generation using Cuckoo Search Algorithm". International Journal of Energy Optimization and Engineering 6, nr 1 (styczeń 2017): 86–100. http://dx.doi.org/10.4018/ijeoe.2017010105.
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Query processing in distributed databases involves data transmission amongst sites capable of providing answers to a distributed query. For this, a distributed query processing strategy, which generates efficient query processing plans for a given distributed query, needs to be devised. Since in distributed databases, the data is fragmented and replicated at multiple sites, the number of query plans increases exponentially with increase in the number of sites capable of providing answers to a distributed query. As a result, generating efficient query processing plans, from amongst all possible query plans, becomes a complex problem. This distributed query plan generation (DQPG) problem has been addressed using the Cuckoo Search Algorithm (CSA) in this paper. Accordingly, a CSA based DQPG algorithm (DQPGCSA) that aims to generate Top-K query plans having minimum cost of processing a distributed query has been proposed. Experimental based comparison of DQPGCSA with the existing GA based DQPG algorithm shows that the former is able to generate Top-K query plans that have a comparatively lower query processing cost. This, in turn, reduces the query response time resulting in efficient decision making.
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Kim, Hyeong-Jin, Yong-Ki Kim, Hyun-Jo Lee i Jae-Woo Chang. "Privacy-Preserving Top-k Query Processing Algorithms Using Efficient Secure Protocols over Encrypted Database in Cloud Computing Environment". Electronics 11, nr 18 (11.09.2022): 2870. http://dx.doi.org/10.3390/electronics11182870.
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Recently, studies on secure database outsourcing have been highlighted for the cloud computing environment. A few secure Top-k query processing algorithms have been proposed in the encrypted database. However, the previous algorithms can support either security or efficiency. Therefore, we propose a new Top-k query processing algorithm using a homomorphic cryptosystem, which can support both security and efficiency. For security, we propose new secure and efficient protocols based on arithmetic operations. To obtain a high level of efficiency, we also propose a parallel Top-k query processing algorithm using an encrypted random value pool. Through our performance analysis, the proposed Top-k algorithms present about 1.5∼7.1 times better performance with regard to a query processing time, compared with the existing algorithms.
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Jayasudha, R., S. Subramanian i L. Sivakumar. "Genetic Algorithm and PSO Based Intelligent Software Reuse". Applied Mechanics and Materials 573 (czerwiec 2014): 612–17. http://dx.doi.org/10.4028/www.scientific.net/amm.573.612.
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Software Reuse can improve the development time, cost and quality of Software artifacts. The Storage of artifacts plays an important role of easy retrieval of the needed components according to the requirement. In this paper a great measure has been taken for the retrieval of relevant component from the Ontology based repository. Two famous evolutionary algorithms Genetic Algorithm and Particle Swarm Optimization algorithm were used for extraction of needed component. These two algorithms are separately used for component retrieval. Genetic Algorithm in Component Retrieval is best suited if the repository has more number of relevant components. PSO for Component search is best suited if the query is highly refined to get more relevant document. PSO is used for the mainly query expansion. These two methods are combined first the retrieved set of component is organized with the help of GA and PSO for best query expansion. Thus these two methods are combined for best precision and retrieval time for different sets of requirement query
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Mingyan Wang, Mingyan Wang, Qingrong Huang Mingyan Wang, Nan Wu Qingrong Huang i Ying Pan Nan Wu. "RDF Subgraph Matching by Means of Star Decomposition". 網際網路技術學刊 23, nr 7 (grudzień 2022): 1613–21. http://dx.doi.org/10.53106/160792642022122307015.
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<p>With the continuous development of the network, the scale of RDF data is becoming larger and larger. In the face of large-scale RDF data processing, the traditional database query method has been unable to meet the needs. Due to the limited characteristics of subgraph matching, most existing algorithms often have the phenomenon that many subgraphs are repeatedly traversed during the query process, resulting in a large number of intermediate result sets and low query efficiency. The core problem to be solved is how to efficiently match subgraphs. In order to improve the query efficiency of RDF subgraphs in massive RDF data graphs and solve the problem of repeated calculation of some graphs in the query process of RDF subgraphs, an RDF subgraph query algorithm based on star decomposition is proposed in this paper. The algorithm uses graph structure to decompose RDF subgraphs into stars and uses a custom node cost model to calculate the query order of the star subgraphs. By decomposing, the amount of communication among subgraphs is reduced, and the communication cost for query processing is lowered. Moreover, utilizing the query order for RDF subgraph matching can effectively reduce the generation of intermediate result sets and accelerate the efficiency of subgraph matching. On this basis, the performances of the proposed algorithm and several other widely used algorithms are compared and analyzed on two different datasets. Experiments show that the proposed algorithm has better advantages in database recreation, memory size, and execution efficiency. </p> <p> </p>
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Qian, Zhi Hong, Nan Wu, Tian Ping Li, Shi Qiang Zhao i Shuang Zhu. "A Fast Tag Anti-Collision Algorithm for RFID Systems". Advanced Materials Research 791-793 (wrzesień 2013): 1393–96. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1393.
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A fast tag anti-collision algorithm is proposed based on the binary search algorithm. First, the reader sends a query to make sure of what prefixes all the tags have, and the prefixes were pushed into a stack. Then queries containing the prefix information are sent to identify the tags. During the query process, the bits of data transmitted between the reader and the tags were greatly reduced; the back track strategy was adopted to reduce the reader query times. Simulation results show that the proposed algorithm achieves better performance than other algorithms by reducing query times and identification delay and increasing throughput, and is more suitable for the identification of great amounts of data in Internet of Things environment.
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Yan, Yushuang, Qingqi Pei, Xiang Wang i Yong Wang. "Probability-based prediction query algorithm". Ad Hoc Networks 60 (maj 2017): 52–65. http://dx.doi.org/10.1016/j.adhoc.2017.02.008.
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Ilyas, Qazi Mudassar, Muneer Ahmad, Sonia Rauf i Danish Irfan. "RDF Query Path Optimization Using Hybrid Genetic Algorithms". International Journal of Cloud Applications and Computing 12, nr 1 (styczeń 2022): 1–16. http://dx.doi.org/10.4018/ijcac.2022010101.
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Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.
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Naseri, Ardalan, Erwin Holzhauser, Degui Zhi i Shaojie Zhang. "Efficient haplotype matching between a query and a panel for genealogical search". Bioinformatics 35, nr 14 (lipiec 2019): i233—i241. http://dx.doi.org/10.1093/bioinformatics/btz347.
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Abstract Motivation With the wide availability of whole-genome genotype data, there is an increasing need for conducting genetic genealogical searches efficiently. Computationally, this task amounts to identifying shared DNA segments between a query individual and a very large panel containing millions of haplotypes. The celebrated Positional Burrows-Wheeler Transform (PBWT) data structure is a pre-computed index of the panel that enables constant time matching at each position between one haplotype and an arbitrarily large panel. However, the existing algorithm (Durbin’s Algorithm 5) can only identify set-maximal matches, the longest matches ending at any location in a panel, while in real genealogical search scenarios, multiple ‘good enough’ matches are desired. Results In this work, we developed two algorithmic extensions of Durbin’s Algorithm 5, that can find all L-long matches, matches longer than or equal to a given length L, between a query and a panel. In the first algorithm, PBWT-Query, we introduce ‘virtual insertion’ of the query into the PBWT matrix of the panel, and then scanning up and down for the PBWT match blocks with length greater than L. In our second algorithm, L-PBWT-Query, we further speed up PBWT-Query by introducing additional data structures that allow us to avoid iterating through blocks of incomplete matches. The efficiency of PBWT-Query and L-PBWT-Query is demonstrated using the simulated data and the UK Biobank data. Our results show that our proposed algorithms can detect related individuals for a given query efficiently in very large cohorts which enables a fast on-line query search. Availability and implementation genome.ucf.edu/pbwt-query Supplementary information Supplementary data are available at Bioinformatics online.
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Singh, Neha, Jay Prakash i T. V. Vijay Kumar. "Distributed Query Plan Generation Using Firefly Algorithm". International Journal of Organizational and Collective Intelligence 6, nr 1 (styczeń 2016): 29–50. http://dx.doi.org/10.4018/ijoci.2016010103.
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A large number of queries are posed daily against the distributed databases spread across the globe. Query processing strategies are used to generate efficient query plans for these queries. The number of such query plans increases exponentially with the increase in the number of involved sites and relations accessed by the query. Further, this complexity increases if the data is fragmented and replicated across multiple sites. This problem, referred to as the distributed query plan generation (DQPG) problem, is a combinatorial optimization problem. An attempt has been made in this paper to solve this DQPG problem using the Firefly Algorithm (FA), which is inspired by the flashing behaviour of fireflies in nature. The proposed FA based DQPG algorithm (DQPGFA), aims to generate distributed query plans incurring minimum Query Proximity Cost (QPC) value. The experimental results show that DQPGFA, in comparison to the GA based DQPG algorithm (DQPGGA), was able to select Top-K query plans that had a comparatively lesser average QPC value. Such generated query plans would, most likely, lead to an improvement in the query response time and thereby would result in effective and efficient decision making.
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Rojas, Oscar, Veronica Gil-Costa i Mauricio Marin. "A DFT-Based Running Time Prediction Algorithm for Web Queries". Future Internet 13, nr 8 (4.08.2021): 204. http://dx.doi.org/10.3390/fi13080204.
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Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation.
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Dong, Xiang, i Lijia Zeng. "Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing". Wireless Communications and Mobile Computing 2021 (5.08.2021): 1–11. http://dx.doi.org/10.1155/2021/6118113.
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With the changes and development of the social era, my country’s classic art is slowly being lost. In order to more effectively inherit and preserve classic art, the collection and sorting of classic art data through modern information technology has become a top priority. Database storage is a good way. However, as the amount of data grows, the requirements for computing processing power and query speed for massive amounts of data and information are also increasing day by day. Faced with this problem, this article is aimed at studying the optimization of database queries through effective algorithms to improve the efficiency of data query. Based on the traditional database query optimization algorithm, this article improves on the traditional algorithm and proposes a semi-join query optimization algorithm, which reduces the number of connection cards and the number of columns and uses the number of blocks that participate in the semi-link algorithm connection and preconnection preview and selection. And other functions reduce the size of the participating block, and the connection sent between sites reduces the cost of sending between networks. The graph data query optimization algorithm is used to optimize the graph data query in the database to reduce the extra task overhead and improve the system performance. The experimental results of this paper show that through the data query optimization algorithm of this paper, the additional task overhead is reduced by 19%, the system performance is increased by 22%, and the data query efficiency is increased by 31%.
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Chang, Tsui-Ping. "A Sliding-Window Method to Discover Recent Frequent Query Patterns from XML Query Streams". International Journal of Software Engineering and Knowledge Engineering 24, nr 06 (sierpień 2014): 955–80. http://dx.doi.org/10.1142/s021819401450034x.
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Providing efficient mining algorithm to discover recent frequent XML user query patterns is crucial, as many applications use XML to represent data in their disciplines over the Internet. These recent frequent XML user query patterns can be used to design an index mechanism or cached and thus enhance XML query performance. Several XML query pattern stream mining algorithms have been proposed to record user queries in the system and thus discover the recent frequent XML query patterns over a stream. By using these recent frequent XML query patterns, the query performance of XML data stream is improved. In this paper, user queries are modeled as a stream of XML queries and the recent frequent XML query patterns are thus mined over the stream. Data-stream mining differs from traditional data mining since its input of mining is data streams, while the latter focuses on mining static databases. To facilitate the one-pass mining process, novel schemes (i.e. XstreamCode and XstreamList) are devised in the mining algorithm (i.e. X2StreamMiner) in this paper. X2StreamMiner not only reduces the memory space, but also improves the mining performance. The simulation results also show that X2StreamMiner algorithm is both efficient and scalable. There are two major contributions in this paper. First, the novel schemes are proposed to encode and store the information of user queries in an XML query stream. Second, based on the two schemes, an efficient XML query stream mining algorithm, X2StreamMiner, is proposed to discover the recent frequent XML query patterns.
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Sukheja, Deepak, i Umesh Kumar Singh. "Novel Distributed Query Optimization Model and Hybrid Query Optimization Algorithm". International Journal of Computer Applications 75, nr 17 (23.08.2013): 22–32. http://dx.doi.org/10.5120/13203-0461.
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Jia, Lianyin, Junzhuo Tang, Mengjuan Li, Runxin Li, Jiaman Ding i Yinong Chen. "A Trie Based Set Similarity Query Algorithm". Mathematics 11, nr 1 (2.01.2023): 229. http://dx.doi.org/10.3390/math11010229.
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Set similarity query is a primitive for many applications, such as data integration, data cleaning, and gene sequence alignment. Most of the existing algorithms are inverted index based, they usually filter unqualified sets one by one and do not have sufficient support for duplicated sets, thus leading to low efficiency. To solve this problem, this paper designs T-starTrie, an efficient trie based index for set similarity query, which can naturally group sets with the same prefix into one node, and can filter all sets corresponding to the node at a time, thereby significantly improving the candidates generation efficiency. In this paper, we find that the set similarity query problem can be transformed into matching nodes of the first-layer (FMNodes) detecting problem on T-starTrie. Therefore, an efficient FLMNode detection algorithm is designed. Based on this, an efficient set similarity query algorithm, TT-SSQ, is implemented by developing a variety of filtering techniques. Experimental results show that TT-SSQ can be up to 3.10x faster than existing algorithms.
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Wei, Ming Jun, Li Chun Xia, Jian Guo Jin i Qiu Hong Fan. "Study on Clustering-Based Location Dependent Query". Advanced Materials Research 532-533 (czerwiec 2012): 897–901. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.897.
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This paper firstly analyzes the importance and necessity of location dependent query in the mobile computing. Then, it proposes a special case in the application of the location dependent query. That is as follows: Inquirers may send the same location dependent query in different but similar positions. However, the server will not deal with them together but treat them separately. Thus, it will not only cause the waste of system resources but also delay disposal of other queries. According to the principal of clustering we propose a new location Analysis Algorithms-similar merging location analysis algorithm (SMLA). By the algorithm, similar queries can be combined into the same query, so as to reduce the load on central servers, improve system efficiency and query processing performance.
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Liu, Liang, Zhenhai Hu i Lisong Wang. "Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks". International Journal of Distributed Sensor Networks 15, nr 7 (lipiec 2019): 155014771986100. http://dx.doi.org/10.1177/1550147719861005.
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The existing privacy-preserving aggregation query processing methods in sensor networks rely on pre-established network topology and require all nodes in the network to participate in query processing. Maintaining the topology results in a large amount of energy overhead, and in many cases, the user is interested only in the aggregated query results of some areas in the network, and thus, the participation of the entire network node is not necessary. Aiming to solve this problem, this article proposes a spatial range aggregation query algorithm for a dynamic sensor network with privacy protection (energy-efficient privacy-preserving data aggregation). The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. The analysis and experimental results show that the proposed algorithm outperforms the existing algorithms in terms of energy and privacy protection.
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Li, Jincao, i Ming Xu. "A parametric approximation algorithm for spatial group keyword queries". Intelligent Data Analysis 25, nr 2 (4.03.2021): 305–19. http://dx.doi.org/10.3233/ida-195071.
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With the application of big data, various queries arise for information retrieval. Spatial group keyword queries aim to find a set of spatial objects that cover the query keywords and minimize a goal function such as the total distance between the objects and the query point. This problem is widely found in database applications and is known to be NP-hard. Efficient algorithms for solving this problem can only provide approximate solutions, and most of these algorithms achieve a fixed approximation ratio (the upper bound of the ratio of an approximate goal value to the optimal goal value). Thus, to obtain a self-adjusting algorithm, we propose an approximation algorithm for achieving a parametric approximation ratio. The algorithm makes a trade-off between the approximation ratio and time consumption enabling the users to assign arbitrary query accuracy. Additionally, it runs in an on-the-fly manner, making it scalable to large-scale applications. The efficiency and scalability of the algorithm were further validated using benchmark datasets.
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Di, Chun Yun, Chen Lei Cao, Ru Zhang i Jian Yi Liu. "Homomorphic Encryption Based Data Storage and Query Algorithm". Advanced Materials Research 846-847 (listopad 2013): 1582–89. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1582.
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To solve the problems of ciphertext storage and query on cloud, this paper proposes a ciphertext query algorithm according to the multiplication homomorphic encryption characteristic of RSA. Moreover, this paper sorts and displays the query result of the ciphertext efficiently by the ciphertext similarity, which is the combination of inclined angle's cosine of vector, vector's mapping length and words' weight. Finally, the ciphertext query time is investigated in a lab environment. The ciphertext query time is considered as acceptable to users.
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Chen, Zijun, Tingting Zhao i Wenyuan Liu. "Time-aware collective spatial keyword query". Computer Science and Information Systems 18, nr 3 (2021): 1077–100. http://dx.doi.org/10.2298/csis200131034c.
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The collective spatial keyword query is a hot research topic in the database community in recent years, which considers both the positional relevance to the query location and textual relevance to the query keywords. However, in real life, the temporal information of object is not always valid. Based on this, we define a new query, namely time-aware collective spatial keyword query (TCoSKQ), which considers the positional relevance, textual relevance, and temporal relevance between objects and query at the same time. Two evaluation functions are defined to meet different needs of users, for each of which we propose an algorithm. Effective pruning strategies are proposed to improve query efficiency based on the two algorithms. Finally, the experimental results show that the proposed algorithms are efficient and scalable.
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Dong, Yuan, Shang, Ye i Zhang. "Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks". ISPRS International Journal of Geo-Information 8, nr 9 (29.08.2019): 379. http://dx.doi.org/10.3390/ijgi8090379.
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Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. The existing methods of CKNN queries often recommend K objects to users based on distance, but they do not consider the moving directions of objects in a road network. Although a few CKNN query methods consider the movement directions of moving objects in Euclidean space, no efficient direction determination algorithm has been applied to CKNN queries over data streams in spatial road networks until now. In order to find the top K-nearest objects move towards the query object within a period of time, this paper presents a novel algorithm of direction-aware continuous moving K-nearest neighbor (DACKNN) queries in road networks. In this method, the objects’ azimuth information is adopted to determine the moving direction, ensuring the moving objects in the result set towards the query object. In addition, we evaluate the DACKNN query algorithm via comprehensive tests on the Los Angeles network TIGER/LINE data and compare DACKNN with other existing algorithms. The comparative test results demonstrate that our algorithm can perform the direction-aware CKNN query accurately and efficiently.
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Panicker, Shina, i T. V. Vijay Kumar. "Distributed Query Plan Generation Using Multiobjective Genetic Algorithm". Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/628471.
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A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.
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Zervoudakis, Petros, Haridimos Kondylakis, Nicolas Spyratos i Dimitris Plexousakis. "Query Rewriting for Incremental Continuous Query Evaluation in HIFUN". Algorithms 14, nr 5 (8.05.2021): 149. http://dx.doi.org/10.3390/a14050149.
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HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.
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LIU, LING, CALTON PU i KIRILL RICHINE. "DISTRIBUTED QUERY SCHEDULING SERVICE: AN ARCHITECTURE AND ITS IMPLEMENTATION". International Journal of Cooperative Information Systems 07, nr 02n03 (czerwiec 1998): 123–66. http://dx.doi.org/10.1142/s0218843098000088.
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We present the systematic design and development of a distributed query scheduling service (DQS) in the context of DIOM, a distributed and interoperable query mediation system.26 DQS consists of an extensible architecture for distributed query processing, a three-phase optimization algorithm for generating efficient query execution schedules, and a prototype implementation. Functionally, two important execution models of distributed queries, namely moving query to data or moving data to query, are supported and combined into a unified framework, allowing the data sources with limited search and filtering capabilities to be incorporated through wrappers into the distributed query scheduling process. Algorithmically, conventional optimization factors (such as join order) are considered separately from and refined by distributed system factors (such as data distribution, execution location, heterogeneous host capabilities), allowing for stepwise refinement through three optimization phases: Compilation, parallelization, site selection and execution. A subset of DQS algorithms has been implemented in Java to demonstrate the practicality of the architecture and the usefulness of the distributed query scheduling algorithm in optimizing execution schedules for inter-site queries.
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Yang, Yang, i Hai Ge Li. "XML Query Based on Indexed Sequential Table". Advanced Materials Research 532-533 (czerwiec 2012): 1177–81. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1177.
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The current study based on XML index and query mostly focuses on encoding and the structural relation. Region codings are widely used to improve XML query. In this paper postorder-traversal region coding is proposed. The postorder of a node’s all descendants consists of the region. Judging and ensuring structural relation of any two nodes just depend on this region, if the postorder of a node is in a region, ancestor/descendant structural relation can be ensured. Consequently, postorder-traversal region coding can effectively judge structural relation and avoid traversing the XML document tree. Based on region coding, many constructive structural query algorithms have been put forward. As we all know that Stack-Tree-Desc algorithm is one of these fine algorithms, AList and DList only need separately scan one time to judge structural relation, however some unnecessary nodes still be scanned. In order to solve this problem, Indexed Sequential Table algorithm is introduced. The optimized algorithm introduces Indexed Sequential Table to avoid scanning unwanted nodes when the two lists join to locate next node which participates in structural join. In this case, some nodes of AList and DList which don’t participate in structural joins can be jumped, the query efficiency is enhanced. As a result, ordered scanning is prevented, the consuming time of XML query shortens accordingly. Experiment results demonstrate the effectiveness of the improved coding and algorithm.
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Wang, Ying Jie. "Research in Continuous Nearest Neighbor Queries". Advanced Materials Research 457-458 (styczeń 2012): 461–66. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.461.
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This paper analyzes several methods of the present continuous nearest neighbor queries and proposes a query algorithm based on R – tree through the geometric feature of this problem, the algorithms can not only avoid the loss of dividing points and high cost of the query, but also can finish the continuous nearest neighbor query for moving objects effectively.
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Li, Su Duo, Jing Lian Huang, Ying Xing Li, Jing Wei Deng i Kai Ying Deng. "A Model of LBS Privacy Protection Based on Collaboration". Applied Mechanics and Materials 411-414 (wrzesień 2013): 172–76. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.172.
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How to protect users privacy effectively while providing location based service (LBS) is a very important study of the mobile Internet. Where location k-anonymity algorithm is the most commonly used algorithm at present. However the algorithm also has some disadvantages. Therefore, this paper presents a model of LBS privacy protection based on collaboration. It divides a location-based query into two parts and sends to two entities separately. One is the location cloaking server (LCS) which calculates k-anonymous locations, and the other is location service provider (LSP) which implements the query. Except the user none of the servers could have the complete query information. By this way it protects the users privacy very well. The algorithm partly solved the problems existing in the traditional k-anonymity algorithm, and the algorithms performance was significantly increased.
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Huang, Lan, Yuanwei Zhao, Pedro Mestre, Laipeng Han, Kangping Wang, Wenjuan Gao i Rui Zhang. "Research on Reverse Skyline Query Algorithm Based on Decision Set". Journal of Database Management 33, nr 1 (1.01.2022): 1–28. http://dx.doi.org/10.4018/jdm.313971.
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Reverse skyline query is an extension of the classical skyline query, widely used in the decision support in e-business. The vast burst of big data in e-business challenges the classical algorithms for such queries. This paper provides a novel definition of decision set and a decision set based reverse skyline query method called DRS on the double-layer R tree indexing in a map-reduce manner. Theoretical proofs are provided for the correctness and complexity of the DRS algorithm. Experiments made using several large data sets are presented and analyzed to illustrate the applicability and the outperformance of DRS over the state-of-the-art reverse skyline query methods.
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Wang, Yingqi, Nianbin Wang i Lianke Zhou. "Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases". Scientific Programming 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7613026.
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Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users’ query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method.
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Li, Zhiyuan, Junlei Bi i Carlos Borrego. "Exploiting Temporal and Spatial Regularities for Content Dissemination in Opportunistic Social Network". Wireless Communications and Mobile Computing 2019 (6.03.2019): 1–16. http://dx.doi.org/10.1155/2019/3173152.
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Recently, content dissemination has become more and more important for opportunistic social networks. The challenges of opportunistic content dissemination result from random movement of nodes and uncertain positions of a destination, which seriously affect the efficiency of content dissemination. In this paper, we firstly construct time-varying interest communities based on the temporal and spatial regularities of users. Next, we design a content dissemination algorithm on the basis of time-varying interest communities. Our proposed content dissemination algorithm can run in O(nlogn) time. Finally, the comparisons between the proposed content dissemination algorithm and state-of-the-art content dissemination algorithms show that our proposed content dissemination algorithm can (a) keep high query success rate, (b) reduce the average query latency, (c) reduce the hop count of a query, and (d) maintain low system overhead.
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Liu, Zezheng, Yifu Zeng, Siyuan He i Yantao Zhou. "An Adaptive Parallel PI-Skyline Query for Probabilistic and Incomplete Database". International Journal of Computational Methods 17, nr 07 (31.05.2019): 1950036. http://dx.doi.org/10.1142/s0219876219500361.
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In the context of large quantities of information, the skyline query is a particularly useful tool for data mining and decision-making. However, the massive amounts of information on the Internet are frequently incomplete and uncertain due to data randomness, transmission errors, and many other reasons. Therefore, an efficient skyline query algorithm over an incomplete uncertain database is imperative. To address this issue, this paper proposes an efficient algorithm to apply skyline query on probabilistic incomplete data. The algorithm is based on U-Skyline model to avoid disadvantages of traditional P-Skyline model. The proposed methods introduce some novel concepts including transferred tuples, leading tuples and the new dominance relationship between probabilistic incomplete data. Besides, it is a parallel processing algorithm. Extensive experiments demonstrate the effectiveness and efficiency of the proposed algorithms.
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Moutafis, Panagiotis, George Mavrommatis, Michael Vassilakopoulos i Antonio Corral. "Efficient Group K Nearest-Neighbor Spatial Query Processing in Apache Spark". ISPRS International Journal of Geo-Information 10, nr 11 (11.11.2021): 763. http://dx.doi.org/10.3390/ijgi10110763.
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Aiming at the problem of spatial query processing in distributed computing systems, the design and implementation of new distributed spatial query algorithms is a current challenge. Apache Spark is a memory-based framework suitable for real-time and batch processing. Spark-based systems allow users to work on distributed in-memory data, without worrying about the data distribution mechanism and fault-tolerance. Given two datasets of points (called Query and Training), the group K nearest-neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum of distances to every point of the Query. This spatial query has been actively studied in centralized environments and several performance improving techniques and pruning heuristics have been also proposed, while, a distributed algorithm in Apache Hadoop was recently proposed by our team. Since, in general, Apache Hadoop exhibits lower performance than Spark, in this paper, we present the first distributed GKNN query algorithm in Apache Spark and compare it against the one in Apache Hadoop. This algorithm incorporates programming features and facilities that are specific to Apache Spark. Moreover, techniques that improve performance and are applicable in Apache Spark are also incorporated. The results of an extensive set of experiments with real-world spatial datasets are presented, demonstrating that our Apache Spark GKNN solution, with its improvements, is efficient and a clear winner in comparison to processing this query in Apache Hadoop.
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Luo, Yuan, i Peter Szolovits. "Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records". Biomedical Informatics Insights 8 (styczeń 2016): BII.S38916. http://dx.doi.org/10.4137/bii.s38916.
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In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen's interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen's relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions.
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Liu, Fu Min, i Jing Yong Wang. "Research of Query Optimization Based on Improved Quantum Particle Swarm Optimization Algorithm in Distributed Database". Advanced Materials Research 532-533 (czerwiec 2012): 1365–69. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1365.
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Database query optimization is a very complicated issue, also is the key influencing factor in database systems performance. Database query operation efficiency is one of the key factors that affect system response time. Therefore, how to improve the efficiency of database query system becomes particularly important. This paper, on the basis of the advantages of Quantum particle swarm optimization algorithm, proposes distributed database query optimization methods based on Quantum particle swarm optimization algorithm, and improves algorithm. Simulation comparison experiments show that Quantum particle swarm optimization algorithm can improve the efficiency of the distributed database query, and is an effective way to solve the optimization of distributed database query.
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Zhu, Bin, Fang Yuan i Yu Wang. "Keyword Aggregate Query Based on Query Template". Advanced Materials Research 756-759 (wrzesień 2013): 932–39. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.932.
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For keywords query, we propose a keyword aggregate query method based on query template. During the keywords processing, symbol table is used to locate the position of the keywords in the database to get a series of query items. In the query template generating stage, we design a heuristic query template generation algorithm. We use the improved scoring rules to rate the query templates, and select the optimal query template. The experimental results have shown that the aggregate query method is effective.
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Zhang, Lei, Songtao Yang, Jing Li i Lili Yu. "A Particle Swarm Optimization Clustering-Based Attribute Generalization Privacy Protection Scheme". Journal of Circuits, Systems and Computers 27, nr 11 (6.06.2018): 1850179. http://dx.doi.org/10.1142/s0218126618501797.
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Continuous query in location-based services may reveal the attribute information of the user obliviously, and an adversary may utilize the attribute as background knowledge to correlate the real locations and to generate location trajectory. Thus, the adversary can obtain the personal privacy of the user. In order to cope with this problem, several algorithms had been proposed. However, these algorithms were mainly designed for snapshot query and failed to provide privacy protection service for continuous query. As a matter of fact, continuous anonymous regions can also be used as the trajectory of regions and one can obtain the real location trajectory through calibration. In addition, other algorithms designed for continuous query may also utilize a longer running time to achieve the attribute anonymity and affect the balance of quality of service and personal privacy. Therefore, in order to cope with the above two problems, this paper provides a PSO anonymization, short for particle swarm optimization anonymization algorithm. This algorithm utilizes the particle swarm optimization clustering algorithm to accelerate the process of finding similar attributes in attribute generalization. Furthermore, this algorithm also utilizes the randomly chosen anonymous cells to further generalize the anonymous region, so that it can provide better privacy protection and better service quality. At last, this paper utilizes security analysis and experimental verification to further verify the effectiveness and efficiency of both the level of privacy protection and algorithm execution.
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Guo, Ya Hong, Xu Liu i Qian Qian Ren. "A Grouping Cache Based Joins Query Algorithm in MANET". Advanced Materials Research 532-533 (czerwiec 2012): 914–18. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.914.
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Aiming at the problem of joins query in MANET, we proposed a grouping cache mechanism, which builds grouping cache considering the features of data. The proposed mechanism can implement the cooperating cache between groups and update cache information dynamically, which leads to the improvement of query hit ratio and decrease of response time. Based on the grouping cache, a novel joins query algorithm is presented. The algorithm optimizes the query plan using dynamic programming scheme. Then it constructs an optimal execution plan for each sub join query with available cache data taken into account. Simulation results indicated that the group-based cache mechanism can improve the hit ratio, reduce query response time and conserve energy of the network efficiently.