Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data最新文献_第10页

Optimal Spatial Dominance: An Effective Search of Nearest Neighbor Candidates 最优空间优势:最近邻候选的有效搜索

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2749442

Xiaoyang Wang, Ying Zhang, W. Zhang, Xuemin Lin, M. A. Cheema

{"title":"Optimal Spatial Dominance: An Effective Search of Nearest Neighbor Candidates","authors":"Xiaoyang Wang, Ying Zhang, W. Zhang, Xuemin Lin, M. A. Cheema","doi":"10.1145/2723372.2749442","DOIUrl":"https://doi.org/10.1145/2723372.2749442","url":null,"abstract":"In many domains such as computational geometry and database management, an object may be described by multiple instances (points). Then the distance (or similarity) between two objects is captured by the pair-wise distances among their instances. In the past, numerous nearest neighbor (NN) functions have been proposed to define the distance between objects with multiple instances and to identify the NN object. Nevertheless, considering that a user may not have a specific NN function in mind, it is desirable to provide her with a set of NN candidates. Ideally, the set of NN candidates must include every object that is NN for at least one of the NN functions and must exclude every non-promising object. However, no one has studied the problem of NN candidates computation from this perspective. Although some of the existing works aim at returning a set of candidate objects, they do not focus on the NN functions while computing the candidate objects. As a result, they either fail to include an NN object w.r.t. some NN functions or include a large number of unnecessary objects that have no potential to be the NN regardless of the NN functions. Motivated by this, we classify the existing NN functions for objects with multiple instances into three families by characterizing their key features. Then, we advocate three spatial dominance operators to compute NN candidates where each operator is optimal w.r.t. different coverage of NN functions. Efficient algorithms are proposed for the dominance check and corresponding NN candidates computation. Extensive empirical study on real and synthetic datasets shows that our proposed operators can significantly reduce the number of NN candidates. The comprehensive performance evaluation demonstrates the efficiency of our computation techniques.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126323886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Modular Order-Preserving Encryption, Revisited 模保序加密，重访

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2749455

Charalampos Mavroforakis, Nathan Chenette, Adam O'Neill, G. Kollios, R. Canetti

{"title":"Modular Order-Preserving Encryption, Revisited","authors":"Charalampos Mavroforakis, Nathan Chenette, Adam O'Neill, G. Kollios, R. Canetti","doi":"10.1145/2723372.2749455","DOIUrl":"https://doi.org/10.1145/2723372.2749455","url":null,"abstract":"Order-preserving encryption (OPE) schemes, whose ciphertexts preserve the natural ordering of the plaintexts, allow efficient range query processing over outsourced encrypted databases without giving the server access to the decryption key. Such schemes have recently received increased interest in both the database and the cryptographic communities. In particular, modular order-preserving encryption (MOPE), due to Boldyreva et al., is a promising extension that increases the security of the basic OPE by introducing a secret modular offset to each data value prior to encrypting it. However, executing range queries via MOPE in a naive way allows the adversary to learn this offset, negating any potential security gains of this approach. In this paper, we systematically address this vulnerability and show that MOPE can be used to build a practical system for executing range queries on encrypted data while providing a significant security improvement over the basic OPE. We introduce two new query execution algorithms for MOPE: our first algorithm is efficient if the user's query distribution is well-spread, while the second scheme is efficient even for skewed query distributions. Interestingly, our second algorithm achieves this efficiency by leaking the least-important bits of the data, whereas OPE is known to leak the most-important bits of the data. We also show that our algorithms can be extended to the case where the query distribution is adaptively learned online. We present new, appropriate security models for MOPE and use them to rigorously analyze the security of our proposed schemes. Finally, we design a system prototype that integrates our schemes on top of an existing database system and apply query optimization methods to execute SQL queries with range predicates efficiently. We provide a performance evaluation of our prototype under a number of different database and query distributions, using both synthetic and real datasets","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126191297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 72

Minimizing Efforts in Validating Crowd Answers 最小化验证人群答案的努力

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2723731

Nguyen Quoc Viet Hung, Chi Thang Duong, M. Weidlich, K. Aberer

{"title":"Minimizing Efforts in Validating Crowd Answers","authors":"Nguyen Quoc Viet Hung, Chi Thang Duong, M. Weidlich, K. Aberer","doi":"10.1145/2723372.2723731","DOIUrl":"https://doi.org/10.1145/2723372.2723731","url":null,"abstract":"In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain faulty workers. Although various techniques for quality control have been proposed, a post-processing phase in which crowd answers are validated is still required. Validation is typically conducted by experts, whose availability is limited and who incur high costs. Therefore, we develop a probabilistic model that helps to identify the most beneficial validation questions in terms of both, improvement of result correctness and detection of faulty workers. Our approach allows us to guide the expert's work by collecting input on the most problematic cases, thereby achieving a set of high quality answers even if the expert does not validate the complete answer set. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 50% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 20% of the questions.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126232607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 90

How to Build Templates for RDF Question/Answering: An Uncertain Graph Similarity Join Approach 如何为RDF问答构建模板:一种不确定图相似度连接方法

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2747648

Weiguo Zheng, Lei Zou, Xiang Lian, J. Yu, Shaoxu Song, Dongyan Zhao

{"title":"How to Build Templates for RDF Question/Answering: An Uncertain Graph Similarity Join Approach","authors":"Weiguo Zheng, Lei Zou, Xiang Lian, J. Yu, Shaoxu Song, Dongyan Zhao","doi":"10.1145/2723372.2747648","DOIUrl":"https://doi.org/10.1145/2723372.2747648","url":null,"abstract":"A challenging task in the natural language question answering (Q/A for short) over RDF knowledge graph is how to bridge the gap between unstructured natural language questions (NLQ) and graph-structured RDF data (GOne of the effective tools is the \"template\", which is often used in many existing RDF Q/A systems. However, few of them study how to generate templates automatically. To the best of our knowledge, we are the first to propose a join approach for template generation. Given a workload D of SPARQL queries and a set N of natural language questions, the goal is to find some pairs q, n, for q∈ D ∧ n ∈, N, where SPARQL query q is the best match for natural language question n. These pairs provide promising hints for automatic template generation. Due to the ambiguity of the natural languages, we model the problem above as an uncertain graph join task. We propose several structural and probability pruning techniques to speed up joining. Extensive experiments over real RDF Q/A benchmark datasets confirm both the effectiveness and efficiency of our approach.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127115814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 61

Divide & Conquer: I/O Efficient Depth-First Search 分而治之:I/O高效深度优先搜索

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2723740

Zhiwei Zhang, J. Yu, Lu Qin, Zechao Shang

{"title":"Divide & Conquer: I/O Efficient Depth-First Search","authors":"Zhiwei Zhang, J. Yu, Lu Qin, Zechao Shang","doi":"10.1145/2723372.2723740","DOIUrl":"https://doi.org/10.1145/2723372.2723740","url":null,"abstract":"Depth-First Search (DFS), which traverses a graph in the depth- first order, is one of the fundamental graph operations, and the result of DFS over all nodes in G is a spanning tree known as a DFS-Tree. There are many graph algorithms that need DFS such as connected component computation, topological sort, community detection, eulerian path computation, graph bipartiteness testing, planar graph testing, etc, because the in-memory DFS algorithm shows it can be done in linear time w.r.t. the size of G. However, given the fact that real-world graphs grow rapidly in the big data era, the in-memory DFS algorithm cannot be used to handle a large graph that cannot be entirely held in main memory. In this paper, we focus on I/O efficiency and study semi-external algorithms to DFS a graph G which is on disk. Here, like the existing semi-external algorithms, we assume that a spanning tree of G can be held in main memory and the remaining edges of G are kept on disk, and compute the DFS-Tree in main memory with which DFS can be identified. We propose novel divide & conquer algorithms to DFS over a graph G on disk. In brief, we divide a graph into several subgraphs, compute the DFS-Tree for each subgraph independently, and then merge them together to compute the DFS-Tree for the whole graph. With the global DFS-Tree computed we identify DFS. We discuss the valid division, that can lead to the correct DFS, and the challenges to do so. We propose two division algorithms, named Divide-Star and Divide-TD, and a merge algorithm. We conduct extensive experimental studies using four real massive datasets and several synthetic datasets to confirm the I/O efficiency of our approach.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126993722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs BEAR:大型图上随机行走的块消除方法

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2723716

Kijung Shin, Jinhong Jung, Lee Sael, U. Kang

{"title":"BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs","authors":"Kijung Shin, Jinhong Jung, Lee Sael, U. Kang","doi":"10.1145/2723372.2723716","DOIUrl":"https://doi.org/10.1145/2723372.2723716","url":null,"abstract":"Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes long, various preprocessing methods, most of which are related to inverting adjacency matrices, have been proposed to speed up the calculation. However, these methods do not scale to large graphs because they usually produce large and dense matrices which do not fit into memory. In this paper, we propose BEAR, a fast, scalable, and accurate method for computing RWR on large graphs. BEAR comprises the preprocessing step and the query step. In the preprocessing step, BEAR reorders the adjacency matrix of a given graph so that it contains a large and easy-to-invert submatrix, and precomputes several matrices including the Schur complement of the submatrix. In the query step, BEAR computes the RWR scores for a given query node quickly using a block elimination approach with the matrices computed in the preprocessing step. Through extensive experiments, we show that BEAR significantly outperforms other state-of-the-art methods in terms of preprocessing and query speed, space efficiency, and accuracy.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127243041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 79

Fast Serializable Multi-Version Concurrency Control for Main-Memory Database Systems 面向主存数据库系统的快速可串行多版本并发控制

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2749436

Thomas Neumann, Tobias Mühlbauer, A. Kemper

{"title":"Fast Serializable Multi-Version Concurrency Control for Main-Memory Database Systems","authors":"Thomas Neumann, Tobias Mühlbauer, A. Kemper","doi":"10.1145/2723372.2749436","DOIUrl":"https://doi.org/10.1145/2723372.2749436","url":null,"abstract":"Multi-Version Concurrency Control (MVCC) is a widely employed concurrency control mechanism, as it allows for execution modes where readers never block writers. However, most systems implement only snapshot isolation (SI) instead of full serializability. Adding serializability guarantees to existing SI implementations tends to be prohibitively expensive. We present a novel MVCC implementation for main-memory database systems that has very little overhead compared to serial execution with single-version concurrency control, even when maintaining serializability guarantees. Updating data in-place and storing versions as before-image deltas in undo buffers not only allows us to retain the high scan performance of single-version systems but also forms the basis of our cheap and fine-grained serializability validation mechanism. The novel idea is based on an adaptation of precision locking and verifies that the (extensional) writes of recently committed transactions do not intersect with the (intensional) read predicate space of a committing transaction. We experimentally show that our MVCC model allows very fast processing of transactions with point accesses as well as read-heavy transactions and that there is little need to prefer SI over full serializability any longer.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 194

Why Big Data Industrial Systems Need Rules and What We Can Do About It 为什么大数据工业系统需要规则，我们能做些什么

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2742784

C. PaulSuganthanG., Chong Sun, K. KrishnaGayatri, Haojun Zhang, Frank Yang, Narasimhan Rampalli, Shishir Prasad, Esteban Arcaute, Ganesh Krishnan, Rohit Deep, V. Raghavendra, A. Doan

{"title":"Why Big Data Industrial Systems Need Rules and What We Can Do About It","authors":"C. PaulSuganthanG., Chong Sun, K. KrishnaGayatri, Haojun Zhang, Frank Yang, Narasimhan Rampalli, Shishir Prasad, Esteban Arcaute, Ganesh Krishnan, Rohit Deep, V. Raghavendra, A. Doan","doi":"10.1145/2723372.2742784","DOIUrl":"https://doi.org/10.1145/2723372.2742784","url":null,"abstract":"Big Data industrial systems that address problems such as classification, information extraction, and entity matching very commonly use hand-crafted rules. Today, however, little is understood about the usage of such rules. In this paper we explore this issue. We discuss how these systems differ from those considered in academia. We describe default solutions, their limitations, and reasons for using rules. We show examples of extensive rule usage in industry. Contrary to popular perceptions, we show that there is a rich set of research challenges in rule generation, evaluation, execution, optimization, and maintenance. We discuss ongoing work at WalmartLabs and UW-Madison that illustrate these challenges. Our main conclusions are (1) using rules (together with techniques such as learning and crowdsourcing) is fundamental to building semantics-intensive Big Data systems, and (2) it is increasingly critical to address rule management, given the tens of thousands of rules industrial systems often manage today in an ad-hoc fashion.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131284685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Large-scale Predictive Analytics in Vertica: Fast Data Transfer, Distributed Model Creation, and In-database Prediction Vertica中的大规模预测分析:快速数据传输，分布式模型创建和数据库内预测

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2742789

S. Prasad, A. Fard, Vishrut Gupta, Jorge Martinez, J. LeFevre, Vincent Xu, M. Hsu, Indrajit Roy

{"title":"Large-scale Predictive Analytics in Vertica: Fast Data Transfer, Distributed Model Creation, and In-database Prediction","authors":"S. Prasad, A. Fard, Vishrut Gupta, Jorge Martinez, J. LeFevre, Vincent Xu, M. Hsu, Indrajit Roy","doi":"10.1145/2723372.2742789","DOIUrl":"https://doi.org/10.1145/2723372.2742789","url":null,"abstract":"A typical predictive analytics workflow will pre-process data in a database, transfer the resulting data to an external statistical tool such as R, create machine learning models in R, and then apply the model on newly arriving data. Today, this workflow is slow and cumbersome. Extracting data from databases, using ODBC connectors, can take hours on multi-gigabyte datasets. Building models on single-threaded R does not scale. Finally, it is nearly impossible to use R or other common tools, to apply models on terabytes of newly arriving data. We solve all the above challenges by integrating HP Vertica with Distributed R, a distributed framework for R. This paper presents the design of a high performance data transfer mechanism, new data-structures in Distributed R to maintain data locality with database table segments, and extensions to Vertica for saving and deploying R models. Our experiments show that data transfers from Vertica are 6x faster than using ODBC connections. Even complex predictive analysis on 100s of gigabytes of database tables can complete in minutes, and is as fast as in-memory systems like Spark running directly on a distributed file system.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131579062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Query-Oriented Data Cleaning with Oracles 使用oracle进行面向查询的数据清理

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Pub Date : 2015-05-27 DOI: 10.1145/2723372.2737786

M. Bergman, T. Milo, Slava Novgorodov, W. Tan

{"title":"Query-Oriented Data Cleaning with Oracles","authors":"M. Bergman, T. Milo, Slava Novgorodov, W. Tan","doi":"10.1145/2723372.2737786","DOIUrl":"https://doi.org/10.1145/2723372.2737786","url":null,"abstract":"As key decisions are often made based on information contained in a database, it is important for the database to be as complete and correct as possible. For this reason, many data cleaning tools have been developed to automatically resolve inconsistencies in databases. However, data cleaning tools provide only best-effort results and usually cannot eradicate all errors that may exist in a database. Even more importantly, existing data cleaning tools do not typically address the problem of determining what information is missing from a database. To overcome the limitations of existing data cleaning techniques, we present QOCO, a novel query-oriented system for cleaning data with oracles. Under this framework, incorrect (resp. missing) tuples are removed from (added to) the result of a query through edits that are applied to the underlying database, where the edits are derived by interacting with domain experts which we model as oracle crowds. We show that the problem of determining minimal interactions with oracle crowds to derive database edits for removing (adding) incorrect (missing) tuples to the result of a query is NP-hard in general and present heuristic algorithms that interact with oracle crowds. Finally, we implement our algorithms in our prototype system QOCO and show that it is effective and efficient through a comprehensive suite of experiments.","PeriodicalId":168391,"journal":{"name":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131638435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54