High performance time series data analysis and pattern discovery

本研究的动机是对高性能时间序列数据管理和分析技术日益增长的需求。时间序列是通常以固定的时间间隔（例如，每年、每月、每周、每天和每小时）测量的变量值的序列。随着技术的进步，自动传感器和数据收集和存储设备的发展，为我们提供了大量的时间序列，需要对这些时间序列进行处理，以提取隐藏在数据中的真正好处。时间序列在金融、气象、电信和无线网络、安全等领域有着广泛的应用，对数据进行分析和搜索，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尺度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测。时间序列数据具有影响性能的两个重要特征，即高维和时间，如下所述。时间序列的每个时间点可以看作一个维度，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，注意在实际中n很大。时间序列的时间特性意味着连续和邻近的数据值在一些可预测的阈值内彼此相似。这意味着冗余的可能性，因此有机会减少维度和空间。这主要提供了存储效率，然而，其真正的好处是减少了时间。存储和时间效率技术是时间序列应用成功的关键。关于处理gb或接近tb时间序列的此类技术的文献非常丰富。这项研究试图将标准提高到更高，以超越处理tb数据到Petascale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技术的发展。

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.