2012 IEEE 28th International Conference on Data Engineering最新文献

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.119

Steven Euijong Whang, H. Garcia-Molina

引用次数: 44

On Discovery of Traveling Companions from Streaming Trajectories 从流轨迹中发现旅伴

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.33

L. Tang, Yu Zheng, Jing Yuan, Jiawei Han, Alice Leung, Chih-Chieh Hung, Wen-Chih Peng

{"title":"On Discovery of Traveling Companions from Streaming Trajectories","authors":"L. Tang, Yu Zheng, Jing Yuan, Jiawei Han, Alice Leung, Chih-Chieh Hung, Wen-Chih Peng","doi":"10.1109/ICDE.2012.33","DOIUrl":"https://doi.org/10.1109/ICDE.2012.33","url":null,"abstract":"The advance of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory stream. Such technique has broad applications in the areas of scientific study, transportation management and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the algorithm's efficiency. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A new data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery on trajectory stream. The traveling buddies are micro-groups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. The buddy-based method is an order of magnitude faster than existing methods. It also outperforms other competitors with higher precision and recall in companion discovery.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"27 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":"123093717","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: 163

Integrating Frequent Pattern Mining from Multiple Data Domains for Classification 集成多数据域频繁模式挖掘进行分类

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.63

D. Patel, W. Hsu, M. Lee

引用次数: 8

Towards Preference-aware Relational Databases 面向支持偏好的关系数据库

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.31

Anastasios Arvanitis, G. Koutrika

引用次数: 23

AutoDict: Automated Dictionary Discovery 自动字典发现

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.126

Fei Chiang, Periklis Andritsos, Erkang Zhu, Renée J. Miller

引用次数: 9

Extending Map-Reduce for Efficient Predicate-Based Sampling 基于谓词的高效采样扩展Map-Reduce

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.104

Raman Grover, M. Carey

引用次数: 56

Parameter-Free Determination of Distance Thresholds for Metric Distance Constraints 度量距离约束中距离阈值的无参数确定

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.46

Shaoxu Song, Lei Chen, Hong Cheng

{"title":"Parameter-Free Determination of Distance Thresholds for Metric Distance Constraints","authors":"Shaoxu Song, Lei Chen, Hong Cheng","doi":"10.1109/ICDE.2012.46","DOIUrl":"https://doi.org/10.1109/ICDE.2012.46","url":null,"abstract":"The importance of introducing distance constraints to data dependencies, such as differential dependencies (DDs) [28], has recently been recognized. The metric distance constraints are tolerant to small variations, which enable them apply to wide data quality checking applications, such as detecting data violations. However, the determination of distance thresholds for the metric distance constraints is non-trivial. It often relies on a truth data instance which embeds the distance constraints. To find useful distance threshold patterns from data, there are several guidelines of statistical measures to specify, e.g., support, confidence and dependent quality. Unfortunately, given a data instance, users might not have any knowledge about the data distribution, thus it is very challenging to set the right parameters. In this paper, we study the determination of distance thresholds for metric distance constraints, in a parameter-free style. Specifically, we compute an expected utility based on the statistical measures from the data. According to our analysis as well as experimental verification, distance threshold patterns with higher expected utility could offer better usage in real applications, such as violation detection. We then develop efficient algorithms to determine the distance thresholds having the maximum expected utility. Finally, our extensive experimental evaluation demonstrates the effectiveness and efficiency of the proposed methods.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"1 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":"122136990","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

Multi-query Stream Processing on FPGAs fpga上的多查询流处理

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.39

Mohammad Sadoghi, Rija Javed, Naif Tarafdar, Harsh V. P. Singh, R. Palaniappan, H. Jacobsen

引用次数: 47

An Efficient Graph Indexing Method 一种高效的图索引方法

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.28

Xiaoli Wang, Xiaofeng Ding, A. Tung, Shanshan Ying, Hai Jin

引用次数: 95

Analyzing Query Optimization Process: Portraits of Join Enumeration Algorithms 分析查询优化过程:联接枚举算法的画像

2012 IEEE 28th International Conference on Data Engineering Pub Date : 2012-04-01 DOI: 10.1109/ICDE.2012.132

A. Nica, I. Charlesworth, Maysum Panju

{"title":"Analyzing Query Optimization Process: Portraits of Join Enumeration Algorithms","authors":"A. Nica, I. Charlesworth, Maysum Panju","doi":"10.1109/ICDE.2012.132","DOIUrl":"https://doi.org/10.1109/ICDE.2012.132","url":null,"abstract":"Search spaces generated by query optimizers during the optimization process encapsulate characteristics of the join enumeration algorithms, the cost models, as well as critical decisions made for pruning and choosing the best plan. We demonstrate the Join Enumeration Viewer which is a tool designed for visualizing, mining, and comparing plan search spaces generated by different join enumeration algorithms when optimizing same SQL statement. We have enhanced Sybase SQL Anywhere relational database management system to log, in a very compact format, its search space during an optimization process. Such optimization log can then be analyzed by the Join Enumeration Viewer which internally builds the logical and physical plan graphs representing complete and partial plans considered during the optimization process. The optimization logs also contain statistics of the resource consumption during the query optimization such as optimization time breakdown, for example, for logical join enumeration versus costing physical plans, and memory allocation for different optimization structures. The SQL Anywhere Optimizer implements a highly adaptable, self-managing, search space generation algorithm by having several join enumeration algorithms to choose from, each enhanced with different ordering and pruning techniques. The emphasis of the demonstration will be on comparing and contrasting these join enumeration algorithms by analyzing their optimization logs. The demonstration scenarios will include optimizing SQL statements under various conditions which will exercise different algorithms, pruning and ordering techniques. These search spaces will then be visualized and compared using the Join Enumeration Viewer.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"142 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":"128604366","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: 4