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

数据是知识发现的宝贵资源，有助于制定有效的决策。它的收集、存储和处理使用了各种技术，从简单的、传统的和手动的到复杂的、自动化的和先进的。事实上，从手持设备到传感器，再到监控，再到微阵列，再到Web 2.0等技术的最新发展，允许以电子方式捕获和收集海量数据，从而形成大数据仓库。大数据的特点是实例数量大，具有多样性和维度高的特点。这样的数据类似于蕴藏着大量宝贵资源的地下资源。最受欢迎的数据来源是社交网络平台和社交媒体门户网站。可以观察这些来源，以获取对基于兴趣和需求的各种发现有价值的数据，例如，情感检测、犯罪和恐怖网络分析、流行病、风险、安全和救援、观点、影响和经济趋势等。 不幸的是，传统的数据处理技术不能更好地理解数据，因此无法产生有价值的金块。一般而言，改进的或新的技术可以通过最大化从各种来源(如社交网络平台和社交媒体门户)填充的(小的或大的)数据储存库的好处来回答与理解数据的基础和应用有关的研究问题。基础问题涵盖各种和高维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)。在此提案中成功解决这些基础问题将服务于应用问题，这些问题可能揭示与人们的情感相关的有价值的发现，以及这如何与快乐、悲伤等联系在一起，识别潜在的罪犯和恐怖分子，导致早期预警，处理风险，灾难情况下的安全和救援，观察和识别观点的变化，有影响力的人，与某些疾病相关的讨论，其潜在的传播和风险等。 为了解决这些问题等，将完成以下相互关联的任务，作为提议的研究计划的组成部分：数据捕获和整合、不完整/缺失的价值发现、用于动态数据储存库的有效分析的可扩展技术、以及通过研究创新技术来捕获和分析趋势、影响和行为，目的是建立能够替代人类观察者并很好地扩展大型和动态资源和网络的自动化模型，对于人类观察者来说，在动态环境中大规模覆盖的范围即使是可能的，也是昂贵的。为了解决这些任务，我们将开发、扩展和整合来自数据挖掘、机器学习和网络分析的各种技术。

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.