Stream Analytics for Diverse Applications

该计划的长期目标是开发下一代工具和算法，以分析为海洋监测、人类慢性病管理和智慧城市等不同应用收集的流和传感器数据。在所有这些应用中，生成的数据流都很长，带有时间戳，并且通常在空间上进行地理编码，并且通常捕获数字量，例如温度，血压等。在接下来的5年里，我们提出了四个重点领域：（A）聚合查询处理的无线传感器网络;（B）主题建模和情感提取的文本流;（C）离群检测和解释;和（D）基于前缀的预测。 关于重点领域（A），我们将解决传感器网络的一个更复杂的设置，当有一个无线网络的传感器跟踪移动物体。我们将开发新的可扩展的边缘计算方案的对象重复数据删除，以提供聚合的“连续”查询的准确和近似的答案。我们还计划使用group-by子句扩展这些查询。 文本流是数据流的一种特殊情况。对于慢性病管理和智慧城市监测，有两类分析技术是有价值的，即主题建模和情感跟踪。我们将研究如何主题建模可以有效地进行本体。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，并进行话语连贯性分析，以确定精神功能障碍和谵妄的可能发作。 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“正常状态”或“后台活动”，最有趣的分析任务之一是在“异常”发生时识别事件。我们将研究如何使离群点检测可扩展的时空轨迹，以及如何提供可能的解释检测离群点或离群点组。 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）。基于前缀的准确预测具有许多应用，包括早期干预。对于区域（D），我们将为数字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对新方法进行紧密集成和验证：一个由CFI资助的海洋监测项目，一个由CIHR资助的远程监测患者队列，以及一个智慧城市计划。

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.