Data mining and OLAP over sequential data

研究人员处理越来越大的分布式数据集。文学研究人员可以使用数字格式的大多数经典作品。网上有大量的生物数据可以免费获取。诸如文本或时间序列之类的顺序数据很常见，并且已被数据库研究社区确定为一个至关重要的领域。使用用户驱动技术在tb级的时间序列或文本中挖掘模式是一个重要的开放问题。我们希望为用户提供工具，以便他们可以交互地将顺序数据聚合到视图中，就像联机分析处理（OLAP）一样。要做到这一点，我们需要在数据上很好地适应层次结构，以便支持向下钻取和向上卷取查询。我们需要稳健的时间序列索引。最后，我们需要一个好的顺序数据库模型：我们应该补充现有的信息检索工具还是扩展当前的数据库？在任何一种情况下，数据量都是如此之大，以至于我们需要好的策略来预聚合数据。在OLAP中，属性值按星型模式层次结构（商店/城市/省、客户ID/年龄组）聚合。单词可以聚合成短语、句子和段落，也可以通过下义组合（car/vehicle），但词义消歧存在问题。时间序列的等效方法是分割。分割将数据按照一个简单的模型（如常数、线性、二次、单峰、单调、凸）近似地划分为多个区间。分割是一个分层的过程，因为区间可以进一步细分。近年来，在时间扭曲索引方面取得了一些进展。在比较两个时间序列时，时间扭曲局部加速或减慢一个时间序列中的时间，以计算鲁棒的相似性度量。

Researchers work over increasingly large distributed data sets. Literary researchers have access to most classical works in digital format. There are terabytes of biological data freely available on the Web. Sequential data such as text or time series is common and has been identified as an area of critical importance  by the database research community.Mining patterns in terabytes of time series or  text with user-driven techniques, is an important open problem. We want to provide tools to users so that they can interactively aggregate sequential data into views, as in On-Line Analytical Processing (OLAP). To do so, we need well adapted hierarchies over the data so that drill-down and roll-up queries can be supported. We need robust time series indexing. Finally, we need a good model for sequential databases: should we complement existing information retrieval tools or extend current databases? In either case, the amount of data is so large that we need good strategies to preaggregate the data.In  OLAP, attribute values  are aggregated by star schema hierarchies (store/city/province, client ID/age group). Words can be aggregated in phrases, sentences and paragraphs or by hyponymy (car/vehicle) though there are issues with word sense disambiguation. An equivalent for time series is segmentation. Segmentation divides the data into intervals where it behaves approximately according to a simple model (e.g. constant, linear, quadratic, unimodal, monotonic, convex). Segmentation is  a hierarchical process since intervals can be further subdivided.Recently, some progress has been made in time warping indexing.  When comparing two time series, time warping locally accelerates or slows time in one time series to compute robust similarity measures.