探索大规模数据的策略

J. JáJá
{"title":"探索大规模数据的策略","authors":"J. JáJá","doi":"10.1109/ISPAN.2004.1300447","DOIUrl":null,"url":null,"abstract":"Summary form only given. We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.","PeriodicalId":198404,"journal":{"name":"7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2004-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Strategies for exploring large scale data\",\"authors\":\"J. JáJá\",\"doi\":\"10.1109/ISPAN.2004.1300447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.\",\"PeriodicalId\":198404,\"journal\":{\"name\":\"7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPAN.2004.1300447\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPAN.2004.1300447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

只提供摘要形式。我们考虑查询大规模多维时间序列数据的问题,以发现感兴趣的事件,测试和验证假设,或将时间模式与特定事件相关联。这种类型的数据目前主导着大多数其他类型的可用数据,并且考虑到收集业务、科学、人口统计和模拟数据的时间序列的当前趋势,将来很可能变得更加普遍。交互式地探索这些集合的能力,即使是在粗略的层次上,对于发现这些集合中嵌入的信息和知识将是至关重要的。我们开发了索引技术和搜索算法来有效地处理多维时间序列数据的时间范围值查询。我们的索引使用线性空间数据结构,这使得在I/O时间内处理查询本质上与处理单个时间片相同,假设处理器的可用性是时间窗口的对数函数。当多维对象数量较少时,给出了一种具有可证明的几乎最优渐近界的数据结构。这些技术在串行或并行处理方面明显优于标准技术,并通过广泛的实验结果进行评估,证实了其优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategies for exploring large scale data
Summary form only given. We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信