Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544899
Wentao Wu, Yun Chi, Shenghuo Zhu, J. Tatemura, Hakan Hacıgümüş, J. Naughton
Predicting query execution time is useful in many database management issues including admission control, query scheduling, progress monitoring, and system sizing. Recently the research community has been exploring the use of statistical machine learning approaches to build predictive models for this task. An implicit assumption behind this work is that the cost models used by query optimizers are insufficient for query execution time prediction. In this paper we challenge this assumption and show while the simple approach of scaling the optimizer's estimated cost indeed fails, a properly calibrated optimizer cost model is surprisingly effective. However, even a well-tuned optimizer cost model will fail in the presence of errors in cardinality estimates. Accordingly we investigate the novel idea of spending extra resources to refine estimates for the query plan after it has been chosen by the optimizer but before execution. In our experiments we find that a well calibrated query optimizer model along with cardinality estimation refinement provides a low overhead way to provide estimates that are always competitive and often much better than the best reported numbers from the machine learning approaches.
{"title":"Predicting query execution time: Are optimizer cost models really unusable?","authors":"Wentao Wu, Yun Chi, Shenghuo Zhu, J. Tatemura, Hakan Hacıgümüş, J. Naughton","doi":"10.1109/ICDE.2013.6544899","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544899","url":null,"abstract":"Predicting query execution time is useful in many database management issues including admission control, query scheduling, progress monitoring, and system sizing. Recently the research community has been exploring the use of statistical machine learning approaches to build predictive models for this task. An implicit assumption behind this work is that the cost models used by query optimizers are insufficient for query execution time prediction. In this paper we challenge this assumption and show while the simple approach of scaling the optimizer's estimated cost indeed fails, a properly calibrated optimizer cost model is surprisingly effective. However, even a well-tuned optimizer cost model will fail in the presence of errors in cardinality estimates. Accordingly we investigate the novel idea of spending extra resources to refine estimates for the query plan after it has been chosen by the optimizer but before execution. In our experiments we find that a well calibrated query optimizer model along with cardinality estimation refinement provides a low overhead way to provide estimates that are always competitive and often much better than the best reported numbers from the machine learning approaches.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125836514","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544885
Ming Gao, Cheqing Jin, Wei Wang, Xuemin Lin, Aoying Zhou
Evaluating similarity between sets is a fundamental task in computer science. However, there are many applications in which elements in a set may be uncertain due to various reasons. Existing work on modeling such probabilistic sets and computing their similarities suffers from huge model sizes or significant similarity evaluation cost, and hence is only applicable to small probabilistic sets. In this paper, we propose a simple yet expressive model that supports many applications where one probabilistic set may have thousands of elements. We define two types of similarities between two probabilistic sets using the possible world semantics; they complement each other in capturing the similarity distributions in the cross product of possible worlds. We design efficient dynamic programming-based algorithms to calculate both types of similarities. Novel individual and batch pruning techniques based on upper bounding the similarity values are also proposed. To accommodate extremely large probabilistic sets, we also design sampling-based approximate query processing methods with strong probabilistic guarantees. We have conducted extensive experiments using both synthetic and real datasets, and demonstrated the effectiveness and efficiency of our proposed methods.
{"title":"Similarity query processing for probabilistic sets","authors":"Ming Gao, Cheqing Jin, Wei Wang, Xuemin Lin, Aoying Zhou","doi":"10.1109/ICDE.2013.6544885","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544885","url":null,"abstract":"Evaluating similarity between sets is a fundamental task in computer science. However, there are many applications in which elements in a set may be uncertain due to various reasons. Existing work on modeling such probabilistic sets and computing their similarities suffers from huge model sizes or significant similarity evaluation cost, and hence is only applicable to small probabilistic sets. In this paper, we propose a simple yet expressive model that supports many applications where one probabilistic set may have thousands of elements. We define two types of similarities between two probabilistic sets using the possible world semantics; they complement each other in capturing the similarity distributions in the cross product of possible worlds. We design efficient dynamic programming-based algorithms to calculate both types of similarities. Novel individual and batch pruning techniques based on upper bounding the similarity values are also proposed. To accommodate extremely large probabilistic sets, we also design sampling-based approximate query processing methods with strong probabilistic guarantees. We have conducted extensive experiments using both synthetic and real datasets, and demonstrated the effectiveness and efficiency of our proposed methods.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121979885","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544849
Lingkun Wu, Wenqing Lin, Xiaokui Xiao, Yabo Xu
Indexing microblogs for real-time search is challenging given the efficiency issue caused by the tremendous speed at which new microblogs are created by users. Existing approaches address this efficiency issue at the cost of query accuracy, as they either (i) exclude a significant portion of microblogs from the index to reduce update cost or (ii) rank microblogs mostly by their timestamps (without sufficient consideration of their relevance to the queries) to enable append-only index insertion. As a consequence, the search results returned by the existing approaches do not satisfy the users who demand timely and high-quality search results. To remedy this deficiency, we propose the Log-Structured Inverted Indices (LSII), a structure for exact real-time search on microblogs. The core of LSII is a sequence of inverted indices with exponentially increasing sizes, such that new microblogs are (i) first inserted into the smallest index and (ii) later moved into the larger indices in a batch manner. The batch insertion mechanism leads to a small amortize update cost for each new microblog, without significantly degrading query performance. We present a comprehensive study on LSII, exploring various design options to strike a good balance between query and update performance. In addition, we propose extensions of LSII to support personalized search and to exploit multi-threading for performance improvement. Extensive experiments demonstrate the efficiency of LSII with experiments on real data.
{"title":"LSII: An indexing structure for exact real-time search on microblogs","authors":"Lingkun Wu, Wenqing Lin, Xiaokui Xiao, Yabo Xu","doi":"10.1109/ICDE.2013.6544849","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544849","url":null,"abstract":"Indexing microblogs for real-time search is challenging given the efficiency issue caused by the tremendous speed at which new microblogs are created by users. Existing approaches address this efficiency issue at the cost of query accuracy, as they either (i) exclude a significant portion of microblogs from the index to reduce update cost or (ii) rank microblogs mostly by their timestamps (without sufficient consideration of their relevance to the queries) to enable append-only index insertion. As a consequence, the search results returned by the existing approaches do not satisfy the users who demand timely and high-quality search results. To remedy this deficiency, we propose the Log-Structured Inverted Indices (LSII), a structure for exact real-time search on microblogs. The core of LSII is a sequence of inverted indices with exponentially increasing sizes, such that new microblogs are (i) first inserted into the smallest index and (ii) later moved into the larger indices in a batch manner. The batch insertion mechanism leads to a small amortize update cost for each new microblog, without significantly degrading query performance. We present a comprehensive study on LSII, exploring various design options to strike a good balance between query and update performance. In addition, we propose extensions of LSII to support personalized search and to exploit multi-threading for performance improvement. Extensive experiments demonstrate the efficiency of LSII with experiments on real data.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122065622","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544904
Daniela Fabbri, Ravishankar Ramamurthy, R. Kaushik
Auditing is a key part of the security infrastructure in a database system. While commercial database systems provide mechanisms such as triggers that can be used to track and log any changes made to “sensitive” data using UPDATE queries, they are not useful for tracking accesses to sensitive data using complex SQL queries, which is important for many applications given recent laws such as HIPAA. In this paper, we propose the notion of SELECT triggers that extends triggers to work for SELECT queries in order to facilitate data auditing. We discuss the challenges in integrating SELECT triggers in a database system including specification, semantics as well as efficient implementation techniques. We have prototyped our framework in a commercial database system and present an experimental evaluation of our framework using the TPC-H benchmark.
{"title":"SELECT triggers for data auditing","authors":"Daniela Fabbri, Ravishankar Ramamurthy, R. Kaushik","doi":"10.1109/ICDE.2013.6544904","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544904","url":null,"abstract":"Auditing is a key part of the security infrastructure in a database system. While commercial database systems provide mechanisms such as triggers that can be used to track and log any changes made to “sensitive” data using UPDATE queries, they are not useful for tracking accesses to sensitive data using complex SQL queries, which is important for many applications given recent laws such as HIPAA. In this paper, we propose the notion of SELECT triggers that extends triggers to work for SELECT queries in order to facilitate data auditing. We discuss the challenges in integrating SELECT triggers in a database system including specification, semantics as well as efficient implementation techniques. We have prototyped our framework in a commercial database system and present an experimental evaluation of our framework using the TPC-H benchmark.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114981380","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544856
Xiaofei Zhang, Lei Chen, Yongxin Tong, Min Wang
To benefit from the Cloud platform's unlimited resources, managing and evaluating huge volume of RDF data in a scalable manner has attracted intensive research efforts recently. Progresses have been made on evaluating SPARQL queries with either high-level declarative programming languages, like Pig [1], or a sequence of sophisticated designed MapReduce jobs, both of which tend to answer the query with multiple join operations. However, due to the simplicity of Cloud storage and the coarse organization of RDF data in existing solutions, multiple join operations easily bring significant I/O and network traffic which can severely degrade the system performance. In this work, we first propose EAGRE, an Entity-Aware Graph compREssion technique to form a new representation of RDF data on Cloud platforms, based on which we propose an I/O efficient strategy to evaluate SPARQL queries as quickly as possible, especially queries with specified solution sequence modifiers, e.g., PROJECTION, ORDER BY, etc. We implement a prototype system and conduct extensive experiments over both real and synthetic datasets on an in-house cluster. The experimental results show that our solution can achieve over an order of magnitude of time saving for the SPARQL query evaluation compared to the state-of-art MapReduce-based solutions.
为了从云平台的无限资源中获益，以可伸缩的方式管理和评估大量RDF数据最近吸引了大量的研究工作。在使用高级声明性编程语言(如Pig[1])或一系列设计复杂的MapReduce作业来评估SPARQL查询方面已经取得了进展，这两种语言都倾向于使用多个连接操作来回答查询。然而，由于云存储的简单性和现有解决方案中RDF数据的粗糙组织，多次连接操作很容易带来大量的I/O和网络流量，从而严重降低系统性能。在这项工作中，我们首先提出了EAGRE，一种实体感知图压缩技术，用于在云平台上形成RDF数据的新表示，在此基础上，我们提出了一种高效的I/O策略，以尽可能快地评估SPARQL查询，特别是具有指定解决方案序列修饰符的查询，例如，PROJECTION, ORDER BY等。我们实现了一个原型系统，并在内部集群上对真实和合成数据集进行了广泛的实验。实验结果表明，与最先进的基于mapreduce的解决方案相比，我们的解决方案可以为SPARQL查询评估节省超过一个数量级的时间。
{"title":"EAGRE: Towards scalable I/O efficient SPARQL query evaluation on the cloud","authors":"Xiaofei Zhang, Lei Chen, Yongxin Tong, Min Wang","doi":"10.1109/ICDE.2013.6544856","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544856","url":null,"abstract":"To benefit from the Cloud platform's unlimited resources, managing and evaluating huge volume of RDF data in a scalable manner has attracted intensive research efforts recently. Progresses have been made on evaluating SPARQL queries with either high-level declarative programming languages, like Pig [1], or a sequence of sophisticated designed MapReduce jobs, both of which tend to answer the query with multiple join operations. However, due to the simplicity of Cloud storage and the coarse organization of RDF data in existing solutions, multiple join operations easily bring significant I/O and network traffic which can severely degrade the system performance. In this work, we first propose EAGRE, an Entity-Aware Graph compREssion technique to form a new representation of RDF data on Cloud platforms, based on which we propose an I/O efficient strategy to evaluate SPARQL queries as quickly as possible, especially queries with specified solution sequence modifiers, e.g., PROJECTION, ORDER BY, etc. We implement a prototype system and conduct extensive experiments over both real and synthetic datasets on an in-house cluster. The experimental results show that our solution can achieve over an order of magnitude of time saving for the SPARQL query evaluation compared to the state-of-art MapReduce-based solutions.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115101964","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544851
Jiajun Liu, Zi Huang, Hong Cheng, Yueguo Chen, Heng Tao Shen, Yanchun Zhang
Supported by the technical advances and the commercial success of GPS-enabled mobile devices, geo-tagged photos have drawn plenteous attention in research community. The explosive growth of geo-tagged photos enables many large-scale applications, such as location-based photo browsing, landmark recognition, etc. Meanwhile, as the number of geo-tagged photos continues to climb, new challenges are brought to various applications. The existence of massive near-duplicate geo-tagged photos jeopardizes the effective presentation for the above applications. A new dimension in the search and presentation of geo-tagged photos is urgently demanded. In this paper, we devise a location visualization framework to efficiently retrieve and present diverse views captured within a local proximity. Novel photos, in terms of capture locations and visual content, are identified and returned in response to a query location for diverse visualization. For real-time response and good scalability, a new Hybrid Index structure which integrates R-tree and Geographic Grid is proposed to quickly identify the Maximal Near-duplicate Photo Groups (MNPG) in the query proximity. The most novel photos from different groups are then returned to generate diverse views on the location. Extensive experiments on synthetic and real-life photo datasets prove the novelty and efficiency of our methods.
{"title":"Presenting diverse location views with real-time near-duplicate photo elimination","authors":"Jiajun Liu, Zi Huang, Hong Cheng, Yueguo Chen, Heng Tao Shen, Yanchun Zhang","doi":"10.1109/ICDE.2013.6544851","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544851","url":null,"abstract":"Supported by the technical advances and the commercial success of GPS-enabled mobile devices, geo-tagged photos have drawn plenteous attention in research community. The explosive growth of geo-tagged photos enables many large-scale applications, such as location-based photo browsing, landmark recognition, etc. Meanwhile, as the number of geo-tagged photos continues to climb, new challenges are brought to various applications. The existence of massive near-duplicate geo-tagged photos jeopardizes the effective presentation for the above applications. A new dimension in the search and presentation of geo-tagged photos is urgently demanded. In this paper, we devise a location visualization framework to efficiently retrieve and present diverse views captured within a local proximity. Novel photos, in terms of capture locations and visual content, are identified and returned in response to a query location for diverse visualization. For real-time response and good scalability, a new Hybrid Index structure which integrates R-tree and Geographic Grid is proposed to quickly identify the Maximal Near-duplicate Photo Groups (MNPG) in the query proximity. The most novel photos from different groups are then returned to generate diverse views on the location. Extensive experiments on synthetic and real-life photo datasets prove the novelty and efficiency of our methods.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130034552","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544840
Can Wang, Zhong She, Longbing Cao
Clustering ensemble is a powerful approach for improving the accuracy and stability of individual (base) clustering algorithms. Most of the existing clustering ensemble methods obtain the final solutions by assuming that base clusterings perform independently with one another and all objects are independent too. However, in real-world data sources, objects are more or less associated in terms of certain coupling relationships. Base clusterings trained on the source data are complementary to one another since each of them may only capture some specific rather than full picture of the data. In this paper, we discuss the problem of explicating the dependency between base clusterings and between objects in clustering ensembles, and propose a framework for coupled clustering ensembles (CCE). CCE not only considers but also integrates the coupling relationships between base clusterings and between objects. Specifically, we involve both the intra-coupling within one base clustering (i.e., cluster label frequency distribution) and the inter-coupling between different base clusterings (i.e., cluster label co-occurrence dependency). Furthermore, we engage both the intra-coupling between two objects in terms of the base clustering aggregation and the inter-coupling among other objects in terms of neighborhood relationship. This is the first work which explicitly addresses the dependency between base clusterings and between objects, verified by the application of such couplings in three types of consensus functions: clustering-based, object-based and cluster-based. Substantial experiments on synthetic and UCI data sets demonstrate that the CCE framework can effectively capture the interactions embedded in base clusterings and objects with higher clustering accuracy and stability compared to several state-of-the-art techniques, which is also supported by statistical analysis.
{"title":"Coupled clustering ensemble: Incorporating coupling relationships both between base clusterings and objects","authors":"Can Wang, Zhong She, Longbing Cao","doi":"10.1109/ICDE.2013.6544840","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544840","url":null,"abstract":"Clustering ensemble is a powerful approach for improving the accuracy and stability of individual (base) clustering algorithms. Most of the existing clustering ensemble methods obtain the final solutions by assuming that base clusterings perform independently with one another and all objects are independent too. However, in real-world data sources, objects are more or less associated in terms of certain coupling relationships. Base clusterings trained on the source data are complementary to one another since each of them may only capture some specific rather than full picture of the data. In this paper, we discuss the problem of explicating the dependency between base clusterings and between objects in clustering ensembles, and propose a framework for coupled clustering ensembles (CCE). CCE not only considers but also integrates the coupling relationships between base clusterings and between objects. Specifically, we involve both the intra-coupling within one base clustering (i.e., cluster label frequency distribution) and the inter-coupling between different base clusterings (i.e., cluster label co-occurrence dependency). Furthermore, we engage both the intra-coupling between two objects in terms of the base clustering aggregation and the inter-coupling among other objects in terms of neighborhood relationship. This is the first work which explicitly addresses the dependency between base clusterings and between objects, verified by the application of such couplings in three types of consensus functions: clustering-based, object-based and cluster-based. Substantial experiments on synthetic and UCI data sets demonstrate that the CCE framework can effectively capture the interactions embedded in base clusterings and objects with higher clustering accuracy and stability compared to several state-of-the-art techniques, which is also supported by statistical analysis.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114344510","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544878
Mianwei Zhou, Hongning Wang, K. Chang
In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent information relevant to the desired relation. To explore useful information from such noisy redundancy, we abstract the task as a distantly supervised ranking problem - based on coarse entity-level annotations, deriving a relation-specific ranking function for the purpose of online searching. As the key challenge, without detailed snippet-level annotations, we have to learn an entity ranking function that can effectively filter noise; furthermore, the ranking function should also be online executable. We develop Pattern-based Filter Network (PFNet), a novel probabilistic graphical model, as our solution. To balance the accuracy and efficiency requirements, PFNet selects a limited size of indicative patterns to filter noisy snippets, and inverted indexes are utilized to retrieve required features. Experiments on the large scale CuleWeb09 data set for six different relations confirm the effectiveness of the proposed PFNet model, which outperforms five state-of-the-art relational entity ranking methods.
{"title":"Learning to rank from distant supervision: Exploiting noisy redundancy for relational entity search","authors":"Mianwei Zhou, Hongning Wang, K. Chang","doi":"10.1109/ICDE.2013.6544878","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544878","url":null,"abstract":"In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent information relevant to the desired relation. To explore useful information from such noisy redundancy, we abstract the task as a distantly supervised ranking problem - based on coarse entity-level annotations, deriving a relation-specific ranking function for the purpose of online searching. As the key challenge, without detailed snippet-level annotations, we have to learn an entity ranking function that can effectively filter noise; furthermore, the ranking function should also be online executable. We develop Pattern-based Filter Network (PFNet), a novel probabilistic graphical model, as our solution. To balance the accuracy and efficiency requirements, PFNet selects a limited size of indicative patterns to filter noisy snippets, and inverted indexes are utilized to retrieve required features. Experiments on the large scale CuleWeb09 data set for six different relations confirm the effectiveness of the proposed PFNet model, which outperforms five state-of-the-art relational entity ranking methods.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114877542","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544832
Jiwon Seo, Stephen D. Guo, M. Lam
With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. We evaluated SociaLite by running eight graph algorithms (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) on two real-life social graphs, Live-Journal and Last.fm. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50% for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, giving up only 16% for the largest benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to make social network queries easily and efficiently.
{"title":"SociaLite: Datalog extensions for efficient social network analysis","authors":"Jiwon Seo, Stephen D. Guo, M. Lam","doi":"10.1109/ICDE.2013.6544832","DOIUrl":"https://doi.org/10.1109/ICDE.2013.6544832","url":null,"abstract":"With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. We evaluated SociaLite by running eight graph algorithms (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) on two real-life social graphs, Live-Journal and Last.fm. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50% for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, giving up only 16% for the largest benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to make social network queries easily and efficiently.","PeriodicalId":399979,"journal":{"name":"2013 IEEE 29th International Conference on Data Engineering (ICDE)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115108399","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}
Pub Date : 2013-04-08DOI: 10.1109/ICDE.2013.6544867
Yinghui Wu, Shengqi Yang, Xifeng Yan
Subgraph querying has been applied in a variety of emerging applications. Traditional subgraph querying based on subgraph isomorphism requires identical label matching, which is often too restrictive to capture the matches that are semantically close to the query graphs. This paper extends subgraph querying to identify semantically related matches by leveraging ontology information. (1) We introduce the ontology-based subgraph querying, which revises subgraph isomorphism by mapping a query to semantically related subgraphs in terms of a given ontology graph. We introduce a metric to measure the similarity of the matches. Based on the metric, we introduce an optimization problem to find top K best matches. (2) We provide a filtering-and-verification framework to identify (top-K) matches for ontology-based subgraph queries. The framework efficiently extracts a small subgraph of the data graph from an ontology index, and further computes the matches by only accessing the extracted subgraph. (3) In addition, we show that the ontology index can be efficiently updated upon the changes to the data graphs, enabling the framework to cope with dynamic data graphs. (4) We experimentally verify the effectiveness and efficiency of our framework using both synthetic and real life graphs, comparing with traditional subgraph querying methods.
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