High performance time series data analysis and pattern discovery

高性能时间序列数据分析和模式发现

• 批准号: 250351-2010
• 负责人: ShiriVarnaamkhaasti, Nematollaah
• 金额: $ 1.09万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=455686
• 关键词: performance; time; series; data; analysis

This research is motivated by the increasing demand for high performance time series data management and analysis techniques. A time series is a sequence of values of a variable measured often at regular time intervals, e.g., yearly, monthly, weekly, daily, and hourly. Advances in technology in development of automatic sensors and devices for data collection and storage have provided us with huge amount of time series that need to be processed to extract the real benefits buried in the data. Time series appear in a wide range of numerous applications in finance, meteorology, telecommunication and wireless networks, security, etc., in which the data is analyzed and searched for similarity matching, correlation queries, and pattern discovery. Two major challenges in management of single and multiple, large scale time series in such applications include modeling and processing, where modeling aims at describing the data and providing insights into its generation mechanisms while processing aims at prediction and forecasting. Time series data has two important characteristics, namely high dimensional and temporal, that affect the performance, explained as follows. Each time point of a time series can be viewed as a dimension, and hence a time series of n values is a point in an n-dimensional space. Processing points in a high dimensional space is extremely difficult, noting that n is large in practice. The temporal property of time series implies that consecutive and near by data values are similar to each other within some predictable threshold. This implies possibility of redundancy and hence opportunity for reduction in dimension and space. This primarily provides storage efficiency, however, its real benefit is time reduction. Storage and time efficient techniques are crucial in success of time series applications. The literature is vast and rich on such techniques for handling Gigabyte or close to Terabyte time series. This research attempts to raise the bar even higher to surpass handling Terabyte data towards Petascale time series. The theoretical and practical developments in this research will advance the state-of-the-art and technology for high performance time series data management.

这项研究的动机是不断增长的需求，高性能的时间序列数据管理和分析技术。时间序列是经常以规则的时间间隔测量的变量的值的序列，例如，每年、每月、每周、每日和每小时。自动传感器和数据收集和存储设备的发展技术的进步为我们提供了大量的时间序列，需要进行处理，以提取隐藏在数据中的真实的好处。时间序列出现在金融、气象、电信和无线网络、安全等众多应用中，其中分析和搜索数据以进行相似性匹配、相关性查询和模式发现。 在此类应用中，管理单个和多个大规模时间序列的两个主要挑战包括建模和处理，其中建模旨在描述数据并提供对其生成机制的见解，而处理旨在预测和预报。时间序列数据有两个重要的特征，即高维和时态，这两个特征影响性能，解释如下。时间序列的每个时间点都可以被看作是一个维度，因此n个值的时间序列是n维空间中的一个点。在高维空间中处理点是非常困难的，注意到n在实践中很大。时间序列的时间属性意味着连续的和邻近的数据值在某个可预测的阈值内彼此相似。这意味着冗余的可能性，因此有机会减少尺寸和空间。这主要提供存储效率，然而，其真实的好处是时间减少。 存储和时间有效的技术是成功的时间序列应用的关键。文献是巨大的，丰富的技术，用于处理兆字节或接近兆字节的时间序列。这项研究试图将标准提高到更高的水平，以超越处理兆兆字节数据的时间序列。本研究的理论和实践发展将推动高性能时间序列数据管理的发展。