Making Sense of Data by Capturing and Analyzing Various Data Types from Different Sources for Effective Decision Making

通过捕获和分析不同来源的各种数据类型来理解数据，以做出有效的决策

• 批准号: RGPIN-2018-04163
• 负责人: Alhajj, Reda
• 金额: $ 4.08万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749774
• 关键词: Making; Sense; Data; Capturing; Analyzing

Data is a valuable resource for knowledge discovery leading to effective decision making. It is collected stored and processed using variety of techniques from simple, traditional and manual to sophisticated, automated and advanced. Indeed, recent development in technology from handheld devices to sensors to surveillance to microarrays to Web 2.0 and beyond allows for electronically capturing and collecting huge volumes of data leading to big data repositories. Big data is distinguished by having large number of instances characterized by high variety and dimensionality. Such data is analogous to huge underground reserves of valuable resources. Among most popular sources of data are social networking platforms and social media portals. These sources could be watched to capture data valuable for a variety of discoveries based on interest and need, e.g., emotion detection, criminal and terror network analysis, epidemic, risk, safety and rescue, opinion, influence and economic trends, etc.Unfortunately, traditional data processing techniques are not capable of making more sense of data and hence will not be capable of producing valuable nuggets. Generally speaking, improved or new techniques could answer research questions related to foundations and applications of making sense of data by maximizing benefit from (small or big) data repositories populated from various sources, e.g., social networking platforms and social media portals. Foundation questions cover unbalanced and dynamic data with variety and high dimensionality, missing values and noise, data semantics like capturing, monitoring and analysis of behavior and trend, etc. Successfully addressing these foundation questions in this proposal, will serve applications questions that could reveal valuable discoveries related to emotions of people and how this could be linked to happiness, sadness, etc., identifying potential criminals and terrorists leading to early warning, handling risk, safety and rescue in case of disaster, watching and identifying change in opinion, influential fellows, discussions related to certain disease, its potential spread, risk, etc.To address these questions and the like, the following interrelated tasks will be completed as components of the proposed research program: data capturing and integration, incomplete/missing value discovery, scalable techniques for effective analysis of dynamic data repositories, as well as trend, influence and behavior capturing and analysis by researching innovative technologies with the aim to build an automated model capable of substituting human observer and scaling well for large and dynamic sources and networks, a scope costly if at all possible for human observers to cover at large scale in a dynamic environment. To tackle these tasks, we will develop, expand and integrate various techniques from data mining, machine learning and network analysis.

数据是知识发现和有效决策的宝贵资源。它是使用各种技术收集、存储和处理的，从简单、传统和手动到复杂、自动化和先进。事实上，从手持设备到传感器、监控、微阵列、Web 2.0 等技术的最新发展允许以电子方式捕获和收集大量数据，从而形成大数据存储库。大数据的特点是拥有大量具有高度多样性和维度的实例。这些数据类似于地下巨大的宝贵资源储备。最流行的数据来源是社交网络平台和社交媒体门户。可以根据兴趣和需求观察这些来源，以捕获对各种发现有价值的数据，例如情绪检测、犯罪和恐怖网络分析、流行病、风险、安全和救援、舆论、影响力和经济趋势等。不幸的是，传统的数据处理技术无法使数据更有意义，因此无法产生有价值的金块。一般来说，改进的或新技术可以通过最大化来自各种来源（例如社交网络平台和社交媒体门户）的（小型或大型）数据存储库的利益来回答与理解数据的基础和应用相关的研究问题。基础问题涵盖具有多样性和高维性的不平衡和动态数据、缺失值和噪声、数据语义（如行为和趋势的捕获、监控和分析等）。在本提案中成功解决这些基础问题，将服务于应用问题，这些问题可以揭示与人们的情绪相关的有价值的发现，以及如何将其与快乐、悲伤等联系起来，识别潜在的罪犯和恐怖分子，从而进行早期预警，处理风险，发生灾难时的安全和救援，观察和识别变化 为了解决这些问题等，将完成以下相互关联的任务，作为拟议研究计划的组成部分：数据捕获和集成、不完整/缺失值发现、有效分析动态数据存储库的可扩展技术，以及通过研究创新技术进行趋势、影响和行为捕获和分析，旨在建立一个能够 替代人类观察者并很好地扩展大型动态源和网络，如果人类观察者有可能在动态环境中大规模覆盖，那么其范围的成本将会很高。为了解决这些任务，我们将开发、扩展和集成数据挖掘、机器学习和网络分析的各种技术。