Effective and Efficient Data Analysis Techniques for Emerging Data Intensive Applications

适用于新兴数据密集型应用程序的有效且高效的数据分析技术

• 批准号: 250508-2013
• 负责人: Alhajj, Reda
• 金额: $ 1.09万
• 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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594464
• 关键词: Effective; Efficient; Data; Analysis; Techniques

Recent developments in the technology from handheld devices to sensors to surveillance to microarrays to the wide availability of the Web (e.g., social media, social networking, internet of things, click stream, etc.) allow for electronically capturing, collecting and maintaining huge volumes of structured and unstructured data leading to big data repositories. Example data sources include logs and transactions related to information retrieval via Web access, to e-commerce such as click streams and retail purchases, on-going events such as weather and stock market status, social interactions such as social media and social networking, monitoring systems such as traffic control, fraud detection, and homeland security, and so on. Such data is recognized as a valuable resource for knowledge discovery leading to effective decision making. Yet this can happen only if these large volumes of data can be processed and analyzed effectively, which is increasingly problematic to do by conventional means. Sometimes the data could be skewed where the number of data instances is small compared to the huge number of features like having hundreds of samples and thousands of genes; this could be fixed by data enrichment. In addition, real data generally suffers from various other interrelated problems that require dealing with dimensionality reduction, missing values and noise, scalability under limited computing power (i.e., using commonly available computers), data modeling and integration to uncover specific semantics (e.g., capturing and monitoring behavior and trend in social media data to allow for better recommendations), etc. These problems cannot be treated in isolation and hence there is a need for an integrated framework capable of handling any combination of these problems when present in the data.

从手持设备到传感器，监视到微阵列，再到网络的广泛可用性（例如社交媒体，社交网络，物联网，点击流等）的最新发展，可以通过电子方式捕获，收集，并维持大量的结构化和未结构化的数据，从而导致大数据相互依赖。示例数据源包括通过Web访问信息检索与信息检索有关的日志和交易，例如点击流和零售购买，例如天气和股票市场状况，例如社交媒体和社交网络，例如交通控制，欺诈检测以及诸如社交媒体和社交网络，例如，欺诈检测以及诸如社交互动，等等。这些数据被认为是知识发现的宝贵资源，从而导致有效的决策。然而，只有在可以有效地处理和分析这些大量数据时，这种情况才会发生，这越来越有问题地通过常规手段来做。有时，与拥有数百个样本和数千个基因（数百个样品和数千个基因）相比，数据实例数量少，数据可能会偏斜。这可以通过数据丰富来解决。此外，实际数据通常遇到其他各种相互关联的问题，这些问题需要处理尺寸降低，缺失价值和噪声，在有限的计算能力下的可伸缩性（即使用常见的计算机），数据建模和集成以发现特定的语义学（例如，捕获和监视的趋势，都可以在社交媒体中捕获和监控范围，以造成更好的建议和HE的趋势等。数据中存在这些问题的组合。