Stream Analytics for Diverse Applications

拟议计划的长期目标是开发下一代工具和算法，以分析为各种应用收集的流和传感器数据，如海洋监测、人类慢性疾病管理和智能城市。在所有这些应用程序中，生成的数据流都很长，带有时间戳，通常是空间地理编码，并且通常捕获数值量，例如温度、血压等。然而，数据流并不局限于数字数量；疾病管理和智慧城市应用的文本流被纳入并研究在拟议的计划中。在未来5年，我们提出了四个重点领域：(A)无线传感器网络的聚合查询处理；(B)文本流的主题建模和情感提取；（三）异常值检测与解释；(D)基于前缀的预测。关于焦点区域(A)，当存在跟踪移动物体的传感器无线网络时，我们将解决传感器网络的一个更复杂的设置。我们将为对象重复数据删除开发新的可扩展边缘计算方案，为聚合的“连续”查询提供精确和近似的答案。我们还计划用group-by子句扩展这些查询。文本流是数据流的一种特殊情况。对于慢性疾病管理和智慧城市监测来说，两类分析技术都是有价值的，它们是主题建模和情感跟踪。我们将研究如何使用本体有效地执行主题建模。我们将推进最先进的情绪分析，以检测患者或公民的情绪（如愤怒、焦虑或抑郁）随时间的波动，并进行话语连贯分析，以识别可能的精神功能障碍和谵妄的发作。无论数据流是数字还是文本，对于许多监控应用程序来说，给定长时间的“正常”或“后台活动”，最有趣的分析任务之一就是识别“异常”发生时的事件。我们将研究如何使异常点检测在时空轨迹上具有可扩展性，以及如何为检测到的异常点或异常点组提供可能的解释。对于长时空轨迹的另一个重要分析任务是基于轨迹的较早时间点或前缀来预测轨迹的结果（例如以类标签的形式）。准确的基于前缀的预测有许多应用，包括早期干预。对于区域(D)，我们将为数字流和文本流开发新的可扩展方法。我们还将研究前缀长度和预测精度之间的权衡。最后但并非最不重要的是，拟议计划的一个关键组成部分是在三个应用领域内紧密整合和验证新方法：cfi资助的海洋监测项目，cihr资助的远程监测患者队列和智慧城市倡议。

The long-term objective of the proposed program is to develop next-generation tools and algorithms to analyze stream and sensor data collected for diverse applications, such as ocean monitoring, human chronic disease management, and smart cities. In all these applications, the data streams generated are long, time-stamped, and often geo-coded spatially, and typically capture numeric quantities, such as temperature, blood pressure, etc. However, data streams are not restricted to numeric quantities; text streams for disease management and smart cities applications are included and studied in the proposed program. For the next 5 years, we propose four focal areas: (A) aggregate query processing for wireless sensor networks; (B) topic modeling and sentiment extraction for text streams; (C) outlier detection and explanations; and (D) prefix based forecasting. Regarding focal area (A), we will tackle one of the more complex settings for sensor networks when there is a wireless network of sensors tracking moving objects. We will develop novel scalable edge-computing schemes for object de-duplication to provide exact and approximate answers for aggregate "continuous" queries. We also plan to extend those queries with group-by clauses. Text streams is are a special case of data streams. For both chronic disease management and smart cities monitoring, two classes of analytic techniques are valuable, which are topic modeling and sentiment tracking. We will study how topic modeling can be performed effectively with ontologies. We will advance state-of-the-art sentiment analysis to detect the fluctuation of patients' or citizens' affects (such as anger, anxiety or depression) over time, and to conduct discourse coherence analysis to identify possible onsets of mental dysfunction and delirium. Whether a data stream is numeric or text, for many monitoring applications, given long periods of "normalcy" or "background activities", one of the most interesting analytic tasks is to identify events when "abnormalities" occur. We will study how to make outlier detection scalable for spatio-temporal trajectories, and how to provide possible explanations to detected outliers or groups of outliers. Another important analytic task for long spatio-temporal trajectories is to predict the outcome (such as in the form of a class label) of the trajectory based on the earlier time points, or the prefix, of the trajectory. Accurate prefix based forecasting has numerous applications, including earlier intervention. For area (D), we will develop novel scalable methods for both numeric and text streams. We will also study the tradeoff between the length of the prefix and prediction accuracy. Last but not least, a key component of the proposed program is the tight integration and validation of the new methods within three application domains: a CFI-funded ocean monitoring project , a CIHR-funded tele-monitoring patient cohort, and a smart city initiative.