Mingwang Tang, Feifei Li, J. M. Phillips, Jeffrey Jestes
In distributed data management, a primary concern is monitoring the distributed data and generating an alarm when a user specified constraint is violated. A particular useful instance is the threshold based constraint, which is commonly known as the distributed threshold monitoring problem [4], [16], [19], [29]. This work extends this useful and fundamental study to distributed probabilistic data that emerge in a lot of applications, where uncertainty naturally exists when massive amounts of data are produced at multiple sources in distributed, networked locations. Examples include distributed observing stations, large sensor fields, geographically separate scientific institutes/units and many more. When dealing with probabilistic data, there are two thresholds involved, the score and the probability thresholds. One must monitor both simultaneously, as such, techniques developed for deterministic data are no longer directly applicable. This work presents a comprehensive study to this problem. Our algorithms have significantly outperformed the baseline method in terms of both the communication cost (number of messages and bytes) and the running time, as shown by an extensive experimental evaluation using several, real large datasets.
{"title":"Efficient Threshold Monitoring for Distributed Probabilistic Data","authors":"Mingwang Tang, Feifei Li, J. M. Phillips, Jeffrey Jestes","doi":"10.1109/ICDE.2012.34","DOIUrl":"https://doi.org/10.1109/ICDE.2012.34","url":null,"abstract":"In distributed data management, a primary concern is monitoring the distributed data and generating an alarm when a user specified constraint is violated. A particular useful instance is the threshold based constraint, which is commonly known as the distributed threshold monitoring problem [4], [16], [19], [29]. This work extends this useful and fundamental study to distributed probabilistic data that emerge in a lot of applications, where uncertainty naturally exists when massive amounts of data are produced at multiple sources in distributed, networked locations. Examples include distributed observing stations, large sensor fields, geographically separate scientific institutes/units and many more. When dealing with probabilistic data, there are two thresholds involved, the score and the probability thresholds. One must monitor both simultaneously, as such, techniques developed for deterministic data are no longer directly applicable. This work presents a comprehensive study to this problem. Our algorithms have significantly outperformed the baseline method in terms of both the communication cost (number of messages and bytes) and the running time, as shown by an extensive experimental evaluation using several, real large datasets.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117246786","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}
Pit Fender, G. Moerkotte, Thomas Neumann, Viktor Leis
Finding the optimal execution order of join operations is a crucial task of today's cost-based query optimizers. There are two approaches to identify the best plan: bottom-up and top-down join enumeration. For both optimization strategies efficient algorithms have been published. However, only the top-down approach allows for branch-and-bound pruning. Two pruning techniques can be found in the literature. We add six new ones. Combined, they improve performance roughly by an average factor of 2 - 5. Even more important, our techniques improve the worst case by two orders of magnitude. Additionally, we introduce a new, very efficient, and easy to implement top-down join enumeration algorithm. This algorithm, together with our improved pruning techniques, yields a performance which is by an average factor of 6 - 9 higher than the performance of the original top-down enumeration algorithm with the original pruning methods.
{"title":"Effective and Robust Pruning for Top-Down Join Enumeration Algorithms","authors":"Pit Fender, G. Moerkotte, Thomas Neumann, Viktor Leis","doi":"10.1109/ICDE.2012.27","DOIUrl":"https://doi.org/10.1109/ICDE.2012.27","url":null,"abstract":"Finding the optimal execution order of join operations is a crucial task of today's cost-based query optimizers. There are two approaches to identify the best plan: bottom-up and top-down join enumeration. For both optimization strategies efficient algorithms have been published. However, only the top-down approach allows for branch-and-bound pruning. Two pruning techniques can be found in the literature. We add six new ones. Combined, they improve performance roughly by an average factor of 2 - 5. Even more important, our techniques improve the worst case by two orders of magnitude. Additionally, we introduce a new, very efficient, and easy to implement top-down join enumeration algorithm. This algorithm, together with our improved pruning techniques, yields a performance which is by an average factor of 6 - 9 higher than the performance of the original top-down enumeration algorithm with the original pruning methods.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449293","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}
The skyline of a multidimensional point set consists of the points that are not dominated by other points. In a scenario where product features are represented by multidimensional points, the skyline points may be viewed as representing competitive products. A product provider may wish to upgrade uncompetitive products to become competitive, but wants to take into account the upgrading cost. We study the top-k product upgrading problem. Given a set P of competitor products, a set T of products that are candidates for upgrade, and an upgrading cost function f that applies to T, the problem is to return the k products in T that can be upgraded to not be dominated by any products in P at the lowest cost. This problem is non-trivial due to not only the large data set sizes, but also to the many possibilities for upgrading a product. We identify and provide solutions for the different options for upgrading an uncompetitive product, and combine the solutions into a single solution. We also propose a spatial join-based solution that assumes P and T are indexed by an R-tree. Given a set of products in the same R-tree node, we derive three lower bounds on their upgrading costs. These bounds are employed by the join approach to prune upgrade candidates with uncompetitive upgrade costs. Empirical studies with synthetic and real data show that the join approach is efficient and scalable.
{"title":"Upgrading Uncompetitive Products Economically","authors":"Hua Lu, Christian S. Jensen","doi":"10.1109/ICDE.2012.92","DOIUrl":"https://doi.org/10.1109/ICDE.2012.92","url":null,"abstract":"The skyline of a multidimensional point set consists of the points that are not dominated by other points. In a scenario where product features are represented by multidimensional points, the skyline points may be viewed as representing competitive products. A product provider may wish to upgrade uncompetitive products to become competitive, but wants to take into account the upgrading cost. We study the top-k product upgrading problem. Given a set P of competitor products, a set T of products that are candidates for upgrade, and an upgrading cost function f that applies to T, the problem is to return the k products in T that can be upgraded to not be dominated by any products in P at the lowest cost. This problem is non-trivial due to not only the large data set sizes, but also to the many possibilities for upgrading a product. We identify and provide solutions for the different options for upgrading an uncompetitive product, and combine the solutions into a single solution. We also propose a spatial join-based solution that assumes P and T are indexed by an R-tree. Given a set of products in the same R-tree node, we derive three lower bounds on their upgrading costs. These bounds are employed by the join approach to prune upgrade candidates with uncompetitive upgrade costs. Empirical studies with synthetic and real data show that the join approach is efficient and scalable.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"1156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365457","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}
Top-k queries have gained considerable attention as an effective means for narrowing down the overwhelming amount of data. This paper studies the problem of constructing an indexing structure that efficiently supports top-k queries for varying scoring functions and retrieval sizes. The existing work can be categorized into three classes: list-, layer-, and view-based approaches. This paper focuses on the layer-based approach, pre-materializing tuples into consecutive multiple layers. The layer-based index enables us to return top-k answers efficiently by restricting access to tuples in the k layers. However, we observe that the number of tuples accessed in each layer can be reduced further. For this purpose, we propose a dual-resolution layer structure. Specifically, we iteratively build coarse-level layers using skylines, and divide each coarse-level layer into fine-level sub layers using convex skylines. The dual-resolution layer is able to leverage not only the dominance relationship between coarse-level layers, named for all-dominance, but also a relaxed dominance relationship between fine-level sub layers, named exists-dominance. Our extensive evaluation results demonstrate that our proposed method significantly reduces the number of tuples accessed than the state-of-the-art methods.
{"title":"Efficient Dual-Resolution Layer Indexing for Top-k Queries","authors":"Jongwuk Lee, Hyunsouk Cho, Seung-won Hwang","doi":"10.1109/ICDE.2012.73","DOIUrl":"https://doi.org/10.1109/ICDE.2012.73","url":null,"abstract":"Top-k queries have gained considerable attention as an effective means for narrowing down the overwhelming amount of data. This paper studies the problem of constructing an indexing structure that efficiently supports top-k queries for varying scoring functions and retrieval sizes. The existing work can be categorized into three classes: list-, layer-, and view-based approaches. This paper focuses on the layer-based approach, pre-materializing tuples into consecutive multiple layers. The layer-based index enables us to return top-k answers efficiently by restricting access to tuples in the k layers. However, we observe that the number of tuples accessed in each layer can be reduced further. For this purpose, we propose a dual-resolution layer structure. Specifically, we iteratively build coarse-level layers using skylines, and divide each coarse-level layer into fine-level sub layers using convex skylines. The dual-resolution layer is able to leverage not only the dominance relationship between coarse-level layers, named for all-dominance, but also a relaxed dominance relationship between fine-level sub layers, named exists-dominance. Our extensive evaluation results demonstrate that our proposed method significantly reduces the number of tuples accessed than the state-of-the-art methods.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127197591","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}
The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.
{"title":"Reducing Uncertainty of Low-Sampling-Rate Trajectories","authors":"Kai Zheng, Yu Zheng, Xing Xie, Xiaofang Zhou","doi":"10.1109/icde.2012.42","DOIUrl":"https://doi.org/10.1109/icde.2012.42","url":null,"abstract":"The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133086060","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}
The studies of protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) are important bioinformatics topics. High-resolution (length<;10) TF-TFBS binding cores are discovered by expensive and time-consuming 3D structure experiments. Recent association rule mining approaches on low-resolution binding sequences (TF length>;490) are shown promising in identifying accurate binding cores without using any 3D structures. While the current association rule mining method on this problem addresses exact sequences only, the most recent ad hoc method for approximation does not establish any formal model and is limited by experimentally known patterns. As biological mutations are common, it is desirable to formally extend the exact model into an approximate one. In this paper, we formalize the problem of mining approximate protein-DNA association rules from sequence data and propose a novel efficient algorithm to predict protein-DNA binding cores. Our two-phase algorithm first constructs two compact intermediate structures called frequent sequence tree (FS-Tree) and frequent sequence class tree (FSCTree). Approximate association rules are efficiently generated from the structures and bioinformatics concepts (position weight matrix and information content) are further employed to prune meaningless rules. Experimental results on real data show the performance and applicability of the proposed algorithm.
{"title":"Predicting Approximate Protein-DNA Binding Cores Using Association Rule Mining","authors":"Po-Yuen Wong, Tak-Ming Chan, M. Wong, K. Leung","doi":"10.1109/ICDE.2012.86","DOIUrl":"https://doi.org/10.1109/ICDE.2012.86","url":null,"abstract":"The studies of protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) are important bioinformatics topics. High-resolution (length<;10) TF-TFBS binding cores are discovered by expensive and time-consuming 3D structure experiments. Recent association rule mining approaches on low-resolution binding sequences (TF length>;490) are shown promising in identifying accurate binding cores without using any 3D structures. While the current association rule mining method on this problem addresses exact sequences only, the most recent ad hoc method for approximation does not establish any formal model and is limited by experimentally known patterns. As biological mutations are common, it is desirable to formally extend the exact model into an approximate one. In this paper, we formalize the problem of mining approximate protein-DNA association rules from sequence data and propose a novel efficient algorithm to predict protein-DNA binding cores. Our two-phase algorithm first constructs two compact intermediate structures called frequent sequence tree (FS-Tree) and frequent sequence class tree (FSCTree). Approximate association rules are efficiently generated from the structures and bioinformatics concepts (position weight matrix and information content) are further employed to prune meaningless rules. Experimental results on real data show the performance and applicability of the proposed algorithm.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124265540","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}
Dictionary-based entity extraction has attracted much attention from the database community recently, which locates sub strings in a document into predefined entities (e.g., person names or locations). To improve extraction recall, a recent trend is to provide approximate matching between sub strings of the document and entities by tolerating minor errors. In this paper we study dictionary-based approximate entity extraction with edit-distance constraints. Existing methods have several limitations. First, they need to tune many parameters to achieve high performance. Second, they are inefficient for large edit-distance thresholds. We propose a trie-based method to address these problems. We first partition each entity into a set of segments, and then use a trie structure to index segments. To extract similar entities, we search segments from the document, and extend the matching segments in both entities and the document to find similar pairs. We develop an extension-based method to efficiently find similar string pairs by extending the matching segments. We optimize our partition scheme and select the best partition strategy to improve the extraction performance. Experimental results show that our method achieves much higher performance compared with state-of-the-art studies.
{"title":"An Efficient Trie-based Method for Approximate Entity Extraction with Edit-Distance Constraints","authors":"Dong Deng, Guoliang Li, Jianhua Feng","doi":"10.1109/ICDE.2012.29","DOIUrl":"https://doi.org/10.1109/ICDE.2012.29","url":null,"abstract":"Dictionary-based entity extraction has attracted much attention from the database community recently, which locates sub strings in a document into predefined entities (e.g., person names or locations). To improve extraction recall, a recent trend is to provide approximate matching between sub strings of the document and entities by tolerating minor errors. In this paper we study dictionary-based approximate entity extraction with edit-distance constraints. Existing methods have several limitations. First, they need to tune many parameters to achieve high performance. Second, they are inefficient for large edit-distance thresholds. We propose a trie-based method to address these problems. We first partition each entity into a set of segments, and then use a trie structure to index segments. To extract similar entities, we search segments from the document, and extend the matching segments in both entities and the document to find similar pairs. We develop an extension-based method to efficiently find similar string pairs by extending the matching segments. We optimize our partition scheme and select the best partition strategy to improve the extraction performance. Experimental results show that our method achieves much higher performance compared with state-of-the-art studies.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125069197","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}
The efficient processing of large collections of Boolean expressions plays a central role in major data intensive applications ranging from user-centric processing and personalization to real-time data analysis. Emerging applications such as computational advertising and selective information dissemination demand determining and presenting to an end-user only the most relevant content that is both user-consumable and suitable for limited screen real estate of target devices. To retrieve the most relevant content, we present BE*-Tree, a novel indexing data structure designed for effective hierarchical top-k pattern matching, which as its by-product also reduces the operational cost of processing millions of patterns. To further reduce processing cost, BE*-Tree employs an adaptive and non-rigid space-cutting technique designed to efficiently index Boolean expressions over a high-dimensional continuous space. At the core of BE*-Tree lie two innovative ideas: (1) a bi-directional tree expansion build as a top-down (data and space clustering) and a bottom-up growths (space clustering), which together enable indexing only non-empty continuous sub-spaces, and (2) an overlap-free splitting strategy. Finally, the performance of BE*-Tree is proven through a comprehensive experimental comparison against state-of-the-art index structures for matching Boolean expressions.
{"title":"Relevance Matters: Capitalizing on Less (Top-k Matching in Publish/Subscribe)","authors":"Mohammad Sadoghi, H. Jacobsen","doi":"10.1109/ICDE.2012.38","DOIUrl":"https://doi.org/10.1109/ICDE.2012.38","url":null,"abstract":"The efficient processing of large collections of Boolean expressions plays a central role in major data intensive applications ranging from user-centric processing and personalization to real-time data analysis. Emerging applications such as computational advertising and selective information dissemination demand determining and presenting to an end-user only the most relevant content that is both user-consumable and suitable for limited screen real estate of target devices. To retrieve the most relevant content, we present BE*-Tree, a novel indexing data structure designed for effective hierarchical top-k pattern matching, which as its by-product also reduces the operational cost of processing millions of patterns. To further reduce processing cost, BE*-Tree employs an adaptive and non-rigid space-cutting technique designed to efficiently index Boolean expressions over a high-dimensional continuous space. At the core of BE*-Tree lie two innovative ideas: (1) a bi-directional tree expansion build as a top-down (data and space clustering) and a bottom-up growths (space clustering), which together enable indexing only non-empty continuous sub-spaces, and (2) an overlap-free splitting strategy. Finally, the performance of BE*-Tree is proven through a comprehensive experimental comparison against state-of-the-art index structures for matching Boolean expressions.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123581426","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}
Changjiu Jin, S. Bhowmick, Byron Choi, Shuigeng Zhou
In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI to filter irrelevant matches and prefetch partial query results [8]. Our first attempt at implementing this vision, called GBLENDER [8], shows significant improvement in system response time (SRT) for sub graph containment queries. However, GBLENDER suffers from two key drawbacks, namely inability to handle visual sub graph similarity queries and inefficient support for visual query modification, limiting its usage in practical environment. In this paper, we propose a novel algorithm called PRAGUE (Practical visu Al Graph QUery Blender), that addresses these limitations by exploiting a novel data structure called spindle-shaped graphs (SPIG). A SPIG succinctly records various information related to the set of super graphs of a newly added edge in the visual query fragment. Specifically, PRAGUE realizes a unified visual framework to support SPIG-based processing of modification-efficient sub graph containment and similarity queries. Extensive experiments on real-world and synthetic datasets demonstrate effectiveness of PRAGUE.
在之前的一篇论文中，我们提出了一种新的图形查询处理范式的愿景，在这种范式中，可视化查询图不是在构建之后才进行处理，而是利用GUI提供的延迟来过滤不相关的匹配并预取部分查询结果，从而将可视化查询的制定和处理交织在一起[8]。我们实现这一愿景的第一次尝试，称为GBLENDER[8]，显示了子图包含查询在系统响应时间(SRT)方面的显著改进。然而，GBLENDER有两个主要缺点，即无法处理可视化子图相似性查询和对可视化查询修改的低效支持，限制了它在实际环境中的使用。在本文中，我们提出了一种名为PRAGUE (Practical visual Al Graph QUery Blender)的新算法，该算法通过利用一种名为纺锤形图(SPIG)的新型数据结构来解决这些限制。SPIG简洁地记录了与视觉查询片段中新增边的超图集相关的各种信息。具体来说，PRAGUE实现了一个统一的可视化框架，以支持基于spig的修改高效子图包含和相似性查询的处理。在真实世界和合成数据集上进行的大量实验证明了PRAGUE的有效性。
{"title":"PRAGUE: Towards Blending Practical Visual Subgraph Query Formulation and Query Processing","authors":"Changjiu Jin, S. Bhowmick, Byron Choi, Shuigeng Zhou","doi":"10.1109/ICDE.2012.49","DOIUrl":"https://doi.org/10.1109/ICDE.2012.49","url":null,"abstract":"In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI to filter irrelevant matches and prefetch partial query results [8]. Our first attempt at implementing this vision, called GBLENDER [8], shows significant improvement in system response time (SRT) for sub graph containment queries. However, GBLENDER suffers from two key drawbacks, namely inability to handle visual sub graph similarity queries and inefficient support for visual query modification, limiting its usage in practical environment. In this paper, we propose a novel algorithm called PRAGUE (Practical visu Al Graph QUery Blender), that addresses these limitations by exploiting a novel data structure called spindle-shaped graphs (SPIG). A SPIG succinctly records various information related to the set of super graphs of a newly added edge in the visual query fragment. Specifically, PRAGUE realizes a unified visual framework to support SPIG-based processing of modification-efficient sub graph containment and similarity queries. Extensive experiments on real-world and synthetic datasets demonstrate effectiveness of PRAGUE.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121210230","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}
In recent years, many new applications, such as sensor network monitoring and moving object search, show a growing amount of importance of uncertain data management and mining. In this paper, we study the problem of discovering threshold-based frequent closed item sets over probabilistic data. Frequent item set mining over probabilistic database has attracted much attention recently. However, existing solutions may lead an exponential number of results due to the downward closure property over probabilistic data. Moreover, it is hard to directly extend the successful experiences from mining exact data to a probabilistic environment due to the inherent uncertainty of data. Thus, in order to obtain a reasonable result set with small size, we study discovering frequent closed item sets over probabilistic data. We prove that even a sub-problem of this problem, computing the frequent closed probability of an item set, is #P-Hard. Therefore, we develop an efficient mining algorithm based on depth-first search strategy to obtain all probabilistic frequent closed item sets. To reduce the search space and avoid redundant computation, we further design several probabilistic pruning and bounding techniques. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.
{"title":"Discovering Threshold-based Frequent Closed Itemsets over Probabilistic Data","authors":"Yongxin Tong, Lei Chen, Bolin Ding","doi":"10.1109/ICDE.2012.51","DOIUrl":"https://doi.org/10.1109/ICDE.2012.51","url":null,"abstract":"In recent years, many new applications, such as sensor network monitoring and moving object search, show a growing amount of importance of uncertain data management and mining. In this paper, we study the problem of discovering threshold-based frequent closed item sets over probabilistic data. Frequent item set mining over probabilistic database has attracted much attention recently. However, existing solutions may lead an exponential number of results due to the downward closure property over probabilistic data. Moreover, it is hard to directly extend the successful experiences from mining exact data to a probabilistic environment due to the inherent uncertainty of data. Thus, in order to obtain a reasonable result set with small size, we study discovering frequent closed item sets over probabilistic data. We prove that even a sub-problem of this problem, computing the frequent closed probability of an item set, is #P-Hard. Therefore, we develop an efficient mining algorithm based on depth-first search strategy to obtain all probabilistic frequent closed item sets. To reduce the search space and avoid redundant computation, we further design several probabilistic pruning and bounding techniques. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079838","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}
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