19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)最新文献_第2页

Efficient Approximation of Spatial Network Queries using the M-Tree with Road Network Embedding 基于道路网络嵌入的m树空间网络查询的高效逼近

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.11

K. Shaw, Elias Ioup, J. Sample, M. Abdelguerfi, Olivier Tabone

{"title":"Efficient Approximation of Spatial Network Queries using the M-Tree with Road Network Embedding","authors":"K. Shaw, Elias Ioup, J. Sample, M. Abdelguerfi, Olivier Tabone","doi":"10.1109/SSDBM.2007.11","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.11","url":null,"abstract":"Spatial networks, such as road systems, operate differently from normal geospatial systems because objects are constrained to locations on the network. Performing queries on spatial networks demands entirely different solutions. Most spatial queries make use of an R-Tree to process them efficiently. The M-Tree is a data tree index which is capable of indexing data in any metric space. The M-Tree index can replace the R-Tree index for spatial network queries, such as range and KNN queries. The difficulty is that the M-Tree is only as efficient as the distance algorithm used on the underlying objects. Most network distance algorithms, such as A*, are too slow to allow the M-Tree to operate efficiently on spatial networks. The truncated road network embedding (tRNE) maps the network into a higher dimensional space where any LP metric can be used to efficiently compute an accurate approximation of network distance. The M-Tree combined with tRNE creates an efficient index structure for computing spatial network queries. The M-Tree substantially outperforms network expansion, the most popular method of computing spatial network queries, when performing spatial network KNN and range queries.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125414061","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

On Exploring Complex Relationships of Correlation Clusters 探讨相关簇的复杂关系

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.21

Elke Achtert, C. Böhm, H. Kriegel, Peer Kröger, A. Zimek

引用次数: 62

Managing Scientific Data: New Challenges for Database Research 管理科学数据:数据库研究的新挑战

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.18

M. Winslett

引用次数: 0

MonetDB/SQL Meets SkyServer: the Challenges of a Scientific Database MonetDB/SQL遇上SkyServer:科学数据库的挑战

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.19

M. Ivanova, N. Nes, R. Goncalves, M. Kersten

引用次数: 60

Effective Summarization of Multi-Dimensional Data Streams for Historical Stream Mining 面向历史流挖掘的多维数据流的有效汇总

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.32

Samer Nassar, J. Sander

引用次数: 9

Efficient Indexing of Heterogeneous Data Streams with Automatic Performance Configurations 具有自动性能配置的异构数据流的高效索引

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.33

K. Pu, Ying Zhu

引用次数: 4

On Efficient Processing of Subspace Skyline Queries on High Dimensional Data 高维数据上子空间Skyline查询的高效处理

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.20

Wen Jin, A. Tung, M. Ester, Jiawei Han

{"title":"On Efficient Processing of Subspace Skyline Queries on High Dimensional Data","authors":"Wen Jin, A. Tung, M. Ester, Jiawei Han","doi":"10.1109/SSDBM.2007.20","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.20","url":null,"abstract":"Recent studies on efficiently answering subspace skyline queries can be separated into two approaches. The first focused on pre-materializing a set of skylines points in various subspaces while the second focus on dynamically answering the queries by using a set of anchors to prune off skyline points through spatial reasoning. Despite effort to compress the pre-materialized subspace skylines through removal of redundancy, the storage space for the first approach remain exponential in the number of dimensions. The query time for the second approach on the other hand also grow substantially for data with higher dimensionality where the pruning power of anchors become much weaker. In this paper, we propose methods for answering subspace skyline query on high dimensional data such that both prematerialization storage and query time can be moderated. We propose novel notions of maximal partial-dominating space, maximal partial-dominated space and the maximal equality space between pairs of skyline objects in the full space and use these concepts as the foundation for answering subspace skyline queries for high dimensional data. Query processing involves mostly simple pruning operations while skyline computation is done only on a small subset of candidate skyline points in the subspace. We also develop a random sampling method to compute the subspace skyline in an on-line fashion. Extensive experiments have been conducted and demonstrated the efficiency and effectiveness of our methods.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"49 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130293608","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: 34

Incorporating Uncertainty Metrics into a General-Purpose Data Integration System 将不确定性度量纳入通用数据集成系统

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.36

Brenton Louie, L. Detwiler, Nilesh N. Dalvi, Ron Shaker, P. Tarczy-Hornoch, Dan Suciu

引用次数: 19

Window-Oblivious Join: A Data-Driven Memory Management Scheme for Stream Join 窗口无关连接:一种数据驱动的流连接内存管理方案

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.43

Ji Wu, K. Tan, Yongluan Zhou

引用次数: 8

Adaptive Wavelet Density Estimators over Data Streams 数据流上的自适应小波密度估计

19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) Pub Date : 2007-07-09 DOI: 10.1109/SSDBM.2007.28

C. Heinz, B. Seeger

{"title":"Adaptive Wavelet Density Estimators over Data Streams","authors":"C. Heinz, B. Seeger","doi":"10.1109/SSDBM.2007.28","DOIUrl":"https://doi.org/10.1109/SSDBM.2007.28","url":null,"abstract":"A variety of scientific and commercial applications requires an immediate analysis of transient data streams. Many approaches for analyzing data share the property that an estimation of the underlying data distribution is used as a fundamental building block. To estimate the density of a continuous data distribution, wavelet density estimation, a technique from the area of nonparametric statistics, is very appealing as it is theoretically well-founded and practically approved. For that reason, its application to data streams is highly promising; it provides a convenient way to analyze the characteristics of a stream. However, the heavy computational cost of wavelet density estimators renders their direct application to the streaming scenario impossible. In this work, we tackle this problem and present a novel approach to adaptive wavelet density estimators over data streams. Not only do our estimators meet the rigid processing requirements for data streams, they also adapt to changing system resources in a well-defined manner. A thorough experimental evaluation demonstrates the efficacy of our wavelet density estimators and shows their superiority to competing kernel- and histogram-based estimators.","PeriodicalId":122925,"journal":{"name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115215124","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: 13