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.

数据是知识发现的宝贵资源，导致有效的决策。它是使用从简单，传统和手动到精致，自动化和高级的各种技术收集和处理的。的确，从手持设备到传感器到微阵列再到Web 2.0及以后的技术的最新发展，可以通过电子方式捕获和收集大量数据，从而导致大数据存储库。大数据的区别是具有大量以高维度和维度为特征的实例。这样的数据类似于巨大的地下储备。最受欢迎的数据来源包括社交网络平台和社交媒体门户。可以注意这些来源以捕获基于兴趣和需求的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，意见，影响，影响力和经济趋势等。一般而言，改进或新技术可以通过从各种来源（例如，社交网络平台和社交媒体门户）中最大化（小或大的）数据存储库来回答与基础有关的研究问题和使数据有意义的应用。基础问题涵盖了具有多样性和高维度，缺失的价值和噪音，捕获，监控和分析行为和趋势等的数据语义的不平衡和动态数据。在意见中，有影响力的研究员，与某些疾病有关的讨论，其潜在的差异，风险等。解决这些问题等，以下相互关联的任务将作为拟议的研究计划的组成部分完成：数据捕获和集成，不完整/缺失的价值发现，可扩展技术，以有效地捕获动态数据的分析和分析技术，以及在趋势上构建和分析的方法，以及趋势，以及趋势，以及趋势，行为分析，并进行分析，并将其构建，并进行趋势，行为构建技术，并进行分析，并将其构建，并在趋势上构建，并进行分析，并将其构建，并在趋势上进行分析，并将其构建，并进行了趋势，以及行为的分析，并将其行为分析。将人类观察者替换为大型，动态的来源和网络，如果人类观察者在动态环境中大规模覆盖，则范围为昂贵的范围。为了解决这些任务，我们将从数据挖掘，机器学习和网络分析中开发，扩展和集成各种技术。