Spatio-Temporal Data Mining and Artificial Intelligence for Computer Animation

计算机动画的时空数据挖掘和人工智能

• 批准号: RGPIN-2014-04598
• 负责人: Hamilton, Howard
• 金额: $ 1.89万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611133
• 关键词: Spatio; Temporal; Data; Mining; Artificial

Applications (apps) running on mobile devices, such as smart phones and tablet computers, are generating massive databases of spatio-temporal data. In spatio-temporal data, a physical location, such as one given by the Global Position System (GPS) of the mobile device, and a time, such as when the user was at that location, are important. For example, if the app shares information about mosquito density, the place and time where a user reports encountering a heavy mosquito infestation are important. In my research, I will develop new techniques for performing data mining on spatio-temporal data and then implement these ideas in software. Data mining refers to an automated process that runs on a computer, typically a larger central computer that the app communicates with. It takes some data as input, finds patterns in the data, and then either displays the results on the screen or passes them to some other software. Although many kinds of patterns might be considered, I will concentrate on two types, called high utility itemsets and interesting cuboids. In this summary, I will describe only the first type. A high utility itemset is a combination of some items that were reported together (such as a combination of grocery items in a purchase or features observed at a city intersection) that is highly useful to the person looking for patterns in the data. Thus, the person who wants to perform data mining decides what properties would make combinations highly useful and then the data mining software searches through the database to find all the combinations that have those properties. In my research, I will look in particular for combinations that happen at some places and some times. A challenge will be to identify how large of a place (it will vary in size for different problems) and how long a duration (it will also vary) should be considered. One can imagine a chain of convenience store using the results of our research to decide which mobile users should be offered which special offers (buy these two items together right now!) and at what places and times. If successful, this line of research will lead to new software products that will be of immediate interest to retailers and advertisers and may also be useful in less commercial crowd-sourced apps. Although it is not closely related, I will also pursue curiosity-driven research on applying Artificial Intelligence techniques to simulating large numbers of animals. For example, biologists have many questions about how humpback whales are able to work together to herd fish into a compact ball surrounded by a sort of curtain of bubbles and then suddenly all surge up together from under the fish and eat them. Since it is difficult and expensive to see these events occurring, we will create an animated simulation of the events running on the computer. We will encode ideas from the biologists about what the whales may be doing and run the simulation to see if they have the expected effect. Since the biologists will be able to see the animated whales doing the actions, they will be able to judge whether their ideas were correct and whether our simulation is correct. The hundreds of thousands of fish that need to be simulated will be a challenge for our programming and we expect to devise some new techniques to allow the simulations to be efficient enough to run at their natural speed. The results of our research will be of interest to biologists, especially those interested in humpback whales but also those interested in other types of animals, such as bats, that form large groups. The techniques may also be useful in new computer games and animated films.

运行在移动的设备（例如智能电话和平板计算机）上的应用程序（app）正在生成大量时空数据的数据库。在时空数据中，物理位置（诸如由移动终端的全球定位系统（GPS）给出的物理位置）和时间（诸如用户何时在该位置）是重要的。例如，如果应用程序共享有关蚊子密度的信息，则用户报告遇到严重蚊子侵扰的地点和时间非常重要。 在我的研究中，我将开发新的技术来对时空数据进行数据挖掘，然后在软件中实现这些想法。数据挖掘是指在计算机上运行的自动化过程，通常是应用程序与之通信的更大的中央计算机。它将一些数据作为输入，在数据中找到模式，然后将结果显示在屏幕上或将其传递给其他软件。尽管可以考虑许多类型的模式，但我将集中讨论两种类型，称为高效用项集和有趣的长方体。在本摘要中，我将仅描述第一种类型。 高效用项目集是一起报告的一些项目的组合（例如购买中的杂货项目或在城市交叉口观察到的特征的组合），其对于在数据中寻找模式的人非常有用。因此，想要执行数据挖掘的人决定哪些属性会使组合非常有用，然后数据挖掘软件搜索数据库以找到具有这些属性的所有组合。在我的研究中，我将特别寻找在某些地方和某些时间发生的组合。一个挑战将是确定一个地方有多大（它的大小因不同问题而异）和多长时间（它也会有所不同）应该被考虑。你可以想象一家连锁便利店使用我们的研究结果来决定应该向哪些移动的用户提供哪些特别优惠（现在就一起购买这两种商品！）在什么时间什么地点如果成功的话，这一系列的研究将带来新的软件产品，这些产品将立即引起零售商和广告商的兴趣，也可能对商业性较低的众包应用程序有用。 虽然这不是密切相关的，我也将追求好奇心驱动的研究应用人工智能技术来模拟大量的动物。例如，生物学家有很多问题关于座头鲸如何能够一起工作，将鱼聚集成一个紧凑的球，周围环绕着一种气泡，然后突然从鱼下面一起涌上来并吃掉它们。由于要看到这些事件的发生是困难和昂贵的，我们将创建一个在计算机上运行的事件的动画模拟。我们将对生物学家关于鲸鱼可能在做什么的想法进行编码，并运行模拟，看看它们是否有预期的效果。由于生物学家将能够看到动画鲸鱼做的动作，他们将能够判断他们的想法是否正确，以及我们的模拟是否正确。需要模拟的数十万条鱼对我们的编程来说将是一个挑战，我们希望设计一些新技术，使模拟能够以自然速度有效运行。我们的研究结果将引起生物学家的兴趣，特别是那些对座头鲸感兴趣的人，以及那些对其他类型的动物（例如蝙蝠）感兴趣的人，这些动物形成了大群体。这些技术也可能在新的电脑游戏和动画电影中有用。