Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.最新文献_第3页

Modeling and language support for the management of pattern-bases 模式基管理的建模和语言支持

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.54

Manolis Terrovitis, Panos Vassiliadis, Spiros Skiadopoulos, E. Bertino, B. Catania, Anna Maddalena

引用次数: 41

Knowledge discovery from databases on the semantic Web 语义Web上的数据库知识发现

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.45

B. Scotney, S. McClean

引用次数: 7

Kepler: an extensible system for design and execution of scientific workflows 开普勒:一个可扩展的系统，用于设计和执行科学工作流程

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.44

I. Altintas, Chad Berkley, Efrat Jaeger, Matthew B. Jones, Bertram Ludäscher, S. Mock

{"title":"Kepler: an extensible system for design and execution of scientific workflows","authors":"I. Altintas, Chad Berkley, Efrat Jaeger, Matthew B. Jones, Bertram Ludäscher, S. Mock","doi":"10.1109/SSDBM.2004.44","DOIUrl":"https://doi.org/10.1109/SSDBM.2004.44","url":null,"abstract":"Most scientists conduct analyses and run models in several different software and hardware environments, mentally coordinating the export and import of data from one environment to another. The Kepler scientific workflow system provides domain scientists with an easy-to-use yet powerful system for capturing scientific workflows (SWFs). SWFs are a formalization of the ad-hoc process that a scientist may go through to get from raw data to publishable results. Kepler attempts to streamline the workflow creation and execution process so that scientists can design, execute, monitor, re-run, and communicate analytical procedures repeatedly with minimal effort. Kepler is unique in that it seamlessly combines high-level workflow design with execution and runtime interaction, access to local and remote data, and local and remote service invocation. SWFs are superficially similar to business process workflows but have several challenges not present in the business workflow scenario. For example, they often operate on large, complex and heterogeneous data, can be computationally intensive and produce complex derived data products that may be archived for use in reparameterized runs or other workflows. Moreover, unlike business workflows, SWFs are often dataflow-oriented as witnessed by a number of recent academic systems (e.g., DiscoveryNet, Taverna and Triana) and commercial systems (Scitegic/Pipeline-Pilot, Inforsense). In a sense, SWFs are often closer to signal-processing and data streaming applications than they are to control-oriented business workflow applications.","PeriodicalId":383615,"journal":{"name":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129706543","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: 1023

Multiscale classification of moving objects trajectories 运动物体轨迹的多尺度分类

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.55

C. Mouza, P. Rigaux

引用次数: 22

Hierarchical stream aggregates: querying nested stream sessions 分层流聚合:查询嵌套流会话

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.40

Damianos Chatziantoniou, A. Anagnostopoulos

引用次数: 5

BASS: approximate search on large string databases BASS:在大型字符串数据库上的近似搜索

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.20

Jiong Yang, Wei Wang, Philip S. Yu

{"title":"BASS: approximate search on large string databases","authors":"Jiong Yang, Wei Wang, Philip S. Yu","doi":"10.1109/SSDBM.2004.20","DOIUrl":"https://doi.org/10.1109/SSDBM.2004.20","url":null,"abstract":"In this paper, we study the problem on how to build an index structure for large string databases to efficiently support various types of string matching without the necessity of mapping the substrings to a numerical space (e.g., string B-tree and MRS-index) nor the restriction of in-memory practice (e.g., suffix tree and suffix array). Towards this goal, we propose a new indexing scheme, BASS-tree, to efficiently support general approximate substring match (in terms of certain symbol substitutions and misalignments) in sublinear time on a large string database. The key idea behind the design is that all positions in each string are grouped recursively into a fully balanced tree according to the similarities of the subsequent segments starting at those positions. Each node is labeled with a regular expression that describes the commonality of the substrings indexed through the subtree. Any search can then be properly directed to the portion in the database with a high potential of matching quickly. With the BASS-tree in place, wild card(s) in the query pattern can also be handled in a seamless way. In addition, search of a long pattern can be decomposed into a series of searches of short segments followed by a process to join the results. It has been demonstrated in our experiments that the potential performance improvement brought by BASS-tree is in an order of magnitude over alternative methods.","PeriodicalId":383615,"journal":{"name":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126053445","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: 2

Merging R-trees 合并r - tree

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.50

Vasilis Vasaitis, A. Nanopoulos, Panayiotis Bozanis

引用次数: 1

Discovery of serial episodes from streams of events 从事件流中发现系列情节

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.30

T. Mielikainen

引用次数: 1

Mining deviants in time series data streams 挖掘时间序列数据流中的偏差

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.51

S. Muthukrishnan, R. Shah, J. Vitter

{"title":"Mining deviants in time series data streams","authors":"S. Muthukrishnan, R. Shah, J. Vitter","doi":"10.1109/SSDBM.2004.51","DOIUrl":"https://doi.org/10.1109/SSDBM.2004.51","url":null,"abstract":"One of the central tasks in managing, monitoring and mining data streams is that of identifying outliers. There is a long history of study of various outliers in statistics and databases, and a recent focus on mining outliers in data streams. Here, we adopt the notion of \"deviants\" from Jagadish et al. (1999) as outliers. Deviants are based on one of the most fundamental statistical concept of standard deviation (or variance). Formally, deviants are defined based on a representation sparsity metric, i.e., deviants are values whose removal from the dataset leads to an improved compressed representation of the remaining items. Thus, deviants are not global maxima/minima, but rather these are appropriate local aberrations. Deviants are known to be of great mining value in time series databases. We present first-known algorithms for identifying deviants on massive data streams. Our algorithms monitor streams using very small space (polylogarithmic in data size) and are able to quickly find deviants at any instant, as the data stream evolves over time. For all versions of this problem - uni- vs multivariate time series, optimal vs near-optimal vs heuristic solutions, offline vs streaming - our algorithms have the same framework of maintaining a hierarchical set of candidate deviants that are updated as the time series data gets progressively revealed. We show experimentally using real network traffic data (SNMP aggregate time series) as well as synthetic data that our algorithm is remarkably accurate in determining the deviants.","PeriodicalId":383615,"journal":{"name":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122660265","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: 56

Spatial join for high-resolution objects 高分辨率对象的空间连接

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004. Pub Date : 2004-06-21 DOI: 10.1109/SSDBM.2004.64

H. Kriegel, Peter Kunath, M. Pfeifle, M. Renz

{"title":"Spatial join for high-resolution objects","authors":"H. Kriegel, Peter Kunath, M. Pfeifle, M. Renz","doi":"10.1109/SSDBM.2004.64","DOIUrl":"https://doi.org/10.1109/SSDBM.2004.64","url":null,"abstract":"Modern database applications including computer-aided design (CAD), medical imaging, molecular biology, or multimedia information systems impose new requirements on efficient spatial query processing. One of the most common query types in spatial database management systems is the spatial join. In this paper, we investigate spatial join processing for two sets of very complex spatial objects. We present an approach that is based on a fast filter step performing the spatial join on simple primitives which conservatively approximate the objects. Our main attention is focused on the problem how to generate approximations adequate for high-resolution objects. In this paper, we introduce gray approximations as a general concept which helps to range between replicating and nonreplicating object approximations. The key idea of our approach is to build these replications based on statistical information taking the data distribution of the respective join-partner relation into account. Furthermore, our approach uses compression techniques for the effective storage and retrieval of the decomposed spatial objects. We demonstrate the benefits of our new method for the spatial intersection join on high resolution data. The experimental evaluation on real-world test data points out that our new concept accelerates the spatial intersection join considerably.","PeriodicalId":383615,"journal":{"name":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123337810","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