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，以促进智能信息系统的更好的用户体验和更高的性能。数十亿人的生活受到智能系统应用的影响，包括搜索，建议，健康，游戏等。因此，系统的响应应适应这些变化。这项研究将产生一个新颖的自适应框架，以捕获所有必要的信息，提取知识，然后转换为最佳输出。在此过程中，彻底了解系统中的数据非常重要。大数据的动态性质来自系统中大量项目，用户及其交互。智能系统此类大数据的特征包括以下方面。首先，系统中的项目不时更新，同时将大量新项目添加到系统中，并且现有项目也会更新或消除。其次，该系统的新用户应采用适当的冷启动策略来治疗，以获得更好的体验。第三，系统的开发，环境的趋势，现有用户的兴趣都在不断变化。该项目的预期结果是：1）生成和预测方法理论的发展，包括深度学习和强化学习算法，以了解与智能系统相关的大数据； 2）改进建模和数据挖掘工具，以增强具有动态特征的大型数据集的分析； 3）开发智能系统的框架，该框架具有几个能够分别处理项目，用户和环境的关键组件。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的形式以及交互式动作的序列从现有数据中学习全部信息，然后适应以产生更好的未来动作。学生将研究该项目，开发他们的专业知识和能力与从真实用户收集的大数据集合作。知识将在学术会议和期刊以及工业利益相关者中共享。