Department of ICT, University of Agder, 4879 Grimstad, Norway.
IEEE Trans Cybern. 2013 Jun;43(3):1118-30. doi: 10.1109/TSMCB.2012.2224339.
Discovering and tracking of spatiotemporal patterns in noisy sequences of events are difficult tasks that have become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalities increase as event sharing expands into larger areas of one's life. Ironically, instead of being helpful, an excessive number of event notifications can quickly render the functionality of event sharing to be obtrusive. Indeed, any notification of events that provides redundant information to the application/user can be seen to be an unnecessary distraction. In this paper, we introduce a new scheme for discovering and tracking noisy spatiotemporal event patterns, with the purpose of suppressing reoccurring patterns, while discerning novel events. Our scheme is based on maintaining a collection of hypotheses, each one conjecturing a specific spatiotemporal event pattern. A dedicated learning automaton (LA)--the spatiotemporal pattern LA (STPLA)--is associated with each hypothesis. By processing events as they unfold, we attempt to infer the correctness of each hypothesis through a real-time guided random walk. Consequently, the scheme that we present is computationally efficient, with a minimal memory footprint. Furthermore, it is ergodic, allowing adaptation. Empirical results involving extensive simulations demonstrate the superior convergence and adaptation speed of STPLA, as well as an ability to operate successfully with noise, including both the erroneous inclusion and omission of events. An empirical comparison study was performed and confirms the superiority of our scheme compared to a similar state-of-the-art approach. In particular, the robustness of the STPLA to inclusion as well as to omission noise constitutes a unique property compared to other related approaches. In addition, the results included, which involve the so-called " presence sharing" application, are both promising and, in our opinion, impressive. It is thus our opinion that the proposed STPLA scheme is, in general, ideal for improving the usefulness of event notification and sharing systems, since it is capable of significantly, robustly, and adaptively suppressing redundant information.
发现和跟踪嘈杂事件序列中的时空模式是具有挑战性的任务，由于无处不在的计算技术的进步，如基于社区的社交网络应用程序，这些任务变得越来越重要。这类应用程序的核心活动包括事件的共享和通知，并且随着事件共享扩展到人们生活的更大领域，这些功能的重要性和有用性会增加。具有讽刺意味的是，大量的事件通知不仅没有帮助，反而会迅速使事件共享功能变得令人讨厌。实际上，任何向应用程序/用户提供冗余信息的事件通知都可以被视为不必要的干扰。在本文中，我们引入了一种新的方案来发现和跟踪嘈杂的时空事件模式，目的是抑制重复出现的模式，同时识别新的事件。我们的方案基于维护一组假设，每个假设都推测出一个特定的时空事件模式。与每个假设相关联的是一个专门的学习自动机（LA）——时空模式 LA（STPLA）。通过处理事件的展开，我们试图通过实时引导的随机游走来推断每个假设的正确性。因此，我们提出的方案具有计算效率高、内存占用最小的特点。此外，它是遍历的，允许自适应。涉及广泛模拟的实验结果表明，STPLA 具有优越的收敛和自适应速度，并且能够成功地处理噪声，包括错误地包含和遗漏事件。进行了实证比较研究，证实了我们的方案相对于类似的最先进方法具有优越性。特别是，STPLA 对包含和遗漏噪声的鲁棒性与其他相关方法相比具有独特的性质。此外，包括所谓的“存在共享”应用程序在内的结果是有希望的，在我们看来，令人印象深刻。因此，我们认为，所提出的 STPLA 方案通常非常适合提高事件通知和共享系统的有用性，因为它能够显著、稳健和自适应地抑制冗余信息。
