Adaptive Understanding of Big Data for Smart Systems

智能系统大数据的自适应理解

• 批准号: RGPIN-2020-05588
• 负责人: Zhao, Jiashu
• 金额: $ 1.75万
• 依托单位: Wilfrid Laurier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717709
• 关键词: Adaptive; Understanding; Big; Data; Smart

This work will propose novel approaches for promoting better user experience and higher performance for smart information systems. Billions of people's lives are affected by smart system applications in the areas including searching, recommendation, health, gaming, etc.. When the user interacts with the system, both the user's interests and the system's contents are involving over the time. Therefore, the system's responses should be adaptive to these changes. This study will produce a novel adaptive framework to capture all the necessary information, extract knowledge, and then convert to the optimal outputs. In this process, it is very crucial to thoroughly understand the data in the system. The dynamic nature of the big data comes from the large number of items, users, and their interactions within the system. The characteristics of such big data for a smart system include the following aspects. First, the items in the system update from time to time that a large number of new items are added to the system simultaneously and the existing items are also updated or eliminated. Second, the new users of the system should be treated with proper cold start strategies for better experience. Third, the development of the system, the trend of the environment, the existing users' interests are all kept changing. The anticipated outcomes of this project are: 1) the advancement of theories of generative and predictive approaches including deep learning and reinforcement learning algorithms to understand the big data associated with the smart systems; 2) improved modelling and data mining tools to strengthen the analysis of large data sets with dynamic characteristics; 3) the development of a framework of the smart system with several key components that are able to treat items, users and the environment separately. Meanwhile, these components will communicate with each other and share the discovered knowledge. The proposed approaches will first learn the full information from the existing data in the form of individual entries as well as sequences of interactive actions, and then adapt to generate better future actions. Students will work on the project developing their expertises and ability to work with big data sets collected from real users. Knowledge will be shared in academic conferences and journals, as well as with the industrial stakeholders.

这项工作将提出新的方法来促进智能信息系统更好的用户体验和更高的性能。数十亿人的生活受到搜索、推荐、健康、游戏等领域的智能系统应用的影响。当用户与系统交互时，用户的兴趣和系统的内容都随着时间的推移而涉及。因此，系统的响应应该适应这些变化。这项研究将产生一个新颖的自适应框架来捕获所有必要的信息，提取知识，然后转换为最佳输出。在这个过程中，透彻理解系统中的数据是非常关键的。大数据的动态本质来自于系统内大量的项目、用户及其交互。智能系统大数据的特点包括以下几个方面。首先，系统中的项目不时更新，大量新项目同时添加到系统中，现有项目也被更新或淘汰。其次，应该对系统的新用户采取适当的冷启动策略，以获得更好的体验。第三，系统的发展、环境的趋势、现有用户的利益都在不断变化。该项目的预期成果是：1）推进生成和预测方法理论，包括深度学习和强化学习算法，以理解与智能系统相关的大数据； 2）改进的建模和数据挖掘工具，加强对具有动态特征的大数据集的分析； 3）开发智能系统框架，其中包含能够分别处理物品、用户和环境的几个关键组件。同时，这些组件将相互通信并共享发现的知识。所提出的方法将首先以单个条目以及交互动作序列的形式从现有数据中学习完整信息，然后进行调整以生成更好的未来动作。学生将参与该项目，发展他们的专业知识和处理从真实用户收集的大数据集的能力。知识将在学术会议和期刊以及行业利益相关者中分享。