III-COR-Small: Collaborative Research: Time Series Subsequence Matching for Content-based Access in Very Large Multimedia Databases

在哼唱查询系统中，用户唱一段旋律，计算机识别出包含该旋律的歌曲。在手语识别系统中，手语用户在手语内容的视频数据库中搜索特定手语的出现。这是两个示例应用程序，其中用户希望在给定查询序列的时间序列数据库中检索最佳匹配的连续性。该项目正在开发使用流行的动态时间弯曲（DTW）距离测量在大型时间序列数据库中进行有效子序列匹配的方法。嵌入正在设计，部分转换的子序列匹配问题，更易于管理的问题，在向量空间中的相似性搜索。这种转换允许利用矢量索引和度量索引方法的全部武器库来加速子序列匹配。所提出的方法将适用于各种各样的时间序列域，包括，例如，股票市场建模、地震活动分析和基于传感器的健康监测。为了展示该研究的商业、社会和教育影响，该项目将产生三个演示系统：一个通过哼唱进行查询的系统，一个通过关键字进行手写文档搜索的系统，以及一个标志识别系统。研究结果正在被整合到这些系统中，以便在存在大量数据的情况下实现高效检索。为这三个系统创建和传播大型真实世界数据集将是该项目的另一项贡献。

In a query-by-humming system, a user sings part of a melody and the computer identifies the songs that contain the melody. In a sign spotting system, a sign language user searches for occurrences of specific signs in a video database of sign language content. These are two example applications where users want to retrieve the best matching subsequences in a time series database given a query sequence. This project is developing methods for efficient subsequence matching in large time-series databases using the popular Dynamic Time Warping (DTW) distance measure. Embeddings are being designed that partially convert the subsequence matching problem into the much more manageable problem of similarity search in a vector space. This conversion allows leveraging the full arsenal of vector indexing and metric indexing methods for speeding up subsequence matching. The proposed methods will be applicable in a wide variety of time series domains, including, e.g., stock market modeling, seismic activity analysis, and sensor-based health monitoring. To showcase the commercial, social, and educational impact of the research, the project will produce three demonstration systems: a query-by-humming system, a handwritten document search-by-keyword system, and a sign spotting system. The results of the research are being integrated into these systems to achieve efficient retrieval in the presence of large amounts of data. The creation and dissemination of large, real-world datasets for these three systems will be an additional contribution of the project.