Cross-Domain Data Analytics

由于网络技术、社交媒体以及移动和传感器设备的发展，每天生成和记录的数据量正在以前所未有的速度增长。这种增长导致了海量数据集的产生，如果分析得当，这些数据集可以提供有价值的见解和知识。*数据分析旨在使用统计学、机器学习、数据挖掘和其他领域的技术从数据中发现有用的信息。由于收集的数据集的大小和多样性，今天进行数据分析是具有挑战性的。首先，分析来自单个领域的数据通常是不够的；必须用来自其他来源的数据来补充单个领域的数据。例如，顾客的历史购买记录可以通过天气和社交媒体信息来丰富，以创建更丰富的购物习惯分析。此外，数据的巨大规模对现有的分析算法提出了挑战，必须对其进行调整，甚至重新设计，以将现代运行时环境考虑在内。最后，可视化大型数据集和分析过程的进展对用户来说是必不可少的，但目前的工具并不完全支持。*本研究计划旨在通过设计一个能够处理大型异类数据集的综合架构来推动跨域数据分析领域的发展，并为用户提供丰富的、交互式的分析过程可视化。因此，四个主要方向将是这一研究计划的重点。首先，它将研究跨域融合技术，目的是整合来自不同领域的数据和分析过程，并提高获得的见解的质量和多样性。其次，该项目将研究迁移、集成和强化学习范例，使它们能够处理大型跨域数据集。第三，它将专注于创建一个统一的抽象和伴随的运行时平台，该平台无缝融合不同的学习过程，并对大规模数据进行操作。最后，该计划将开发新的可视化技术来展示在学习过程中归纳的知识以及学习过程本身。*拟议的研究计划不仅将推动数据分析研究的边界，还将帮助有兴趣从大规模跨域数据中获得洞察力的组织和个人。通过扩大数据分析的影响，该计划将帮助加拿大公司创造竞争优势，并将使加拿大在这一新兴领域处于领先地位。

The amount of data generated and recorded every day is growing at an unprecedented rate as a result of developments in Web technologies, social media, and mobile and sensor devices. This growth has led to massive data sets, which if properly analyzed, can provide valuable insights and knowledge. ******Data analytics aim to uncover useful information from data using techniques from statistics, machine learning, data mining, and other areas. Due to the size and diversity of the collected data sets, conducting data analytics today is challenging. First, it is typically insufficient to analyze data from a single domain; single domain data must be supplemented with data from other sources. For instance, customers' historical purchase records can be enriched with weather and social media information to create a richer analysis of shopping habits. Moreover, the sheer size of data poses challenges to existing analytics algorithms, which must be adapted or even redesigned to take modern run-time environments into consideration. Finally, visualizing large data sets and the progress of analytics processes is essential to users, but is not entirely supported by current tools.******This research program aims to advance the field of cross-domain data analytics by devising a comprehensive architecture that can handle large heterogeneous data sets and provides users with rich and interactive visualizations of the analysis process. Hence, four main directions will be the focus of this research program. First, it will investigate cross-domain fusion techniques with the aim of integrating data and analytics processes from different domains and improving the quality and diversity of insights obtained. Second, this program will research transfer, ensemble, and reinforcement learning paradigms to enable them to work with large cross-domain data sets. Third, it will focus on creating a unified abstraction and accompanying run-time platform that seamlessly fuses different learning processes and operates on large-scale data. Finally, this program will develop novel visualization techniques to present the knowledge induced during learning as well as the learning process itself.******The proposed research program is expected not only to push the boundaries of data analytics research but also to assist organizations and individuals that are interested in obtaining insights from large-scale cross-domain data. By broadening the impact of data analytics, this program will assist Canadian companies to create a competitive advantage and will place Canada in a leadership position in this emerging field.