EFFICIENT MACHINE LEARNING METHODS FOR BIG DATA ANALYTICS

目前，大数据在科学和工业中发挥着关键作用。大数据涉及来自多个来源的大量复杂且不断增长的数据集。对能够探索大量数据并为未来行动提取有用信息或知识的方法的需求日益增长。目前大量可用的数据提供了一个前所未有的机会来提取信息，并回答一些以前被认为是无形的问题。然而，要揭示大数据分析的巨大潜力，必须应对一些挑战。****最近的机器学习研究声称是关于大数据的，实际上使用的是低维的平稳数据，其中的决策是基于二进制概念的。大数据通常是非结构化的，数据来源多样化，数据量持续增长。除了数据量和复杂性之外，还必须考虑另外两个特征：数据的多样性和速度。术语volume是数据集的大小，velocity表示数据输入和输出的速度，variety描述数据类型和来源的范围。****因此，目前的大数据分析方法与承诺之间存在很大差距。有几位作者声称要处理大数据分析，但他们仅限于“在当前存储的数据上构建模型，并使用这些模型对新数据进行预测”。虽然它们能够处理大量非结构化数据，但在处理异构和动态变化的数据时却遇到了麻烦。如果数据的特征发生变化，学习模型将无法将观察到的数据与正确的概念联系起来。****我们研究计划的主要目标是通过提出和开发高效的大数据分析机器学习方法来填补这一空白，这些方法不仅可以处理大量数据，还可以处理大数据固有的速度和多样性。为了实现这一目标，我们的研究项目有两个主要目标：(i)开发适应非结构化和异构数据的学习方法（数量和种类）；（ii）开发非平稳数据[速度]的自适应学习方法。****这个研究项目是为大数据分析应用程序的发展铺平道路的基础，这些应用程序不仅限于数据量，而且还涉及大数据的多样性和速度。参加该研究项目的学生将成为数据科学方面的专家，并将有资格为科学界和加拿大的工业和企业传播知识的进步

Currently, big data plays a key role in science and in industry. Big data concern large volume of complex and growing datasets coming from multiple sources. There is a growing demand for approaches that are able to explore large volumes of data and extract useful information or knowledge for future actions. This large amount of data currently available provides an unprecedented opportunity to extract information and to answer several questions that were previously considered intangible. However, several challenges must be met to unveil the great potential of big data analytics.****Recent studies in machine learning that claim to be on big data actually used stationary data of low dimensionality where the decisions are taken on binary concepts. Big data is commonly unstructured, the data comes from diversified sources and the volume of data grows continuously. Besides the volume and the complexity of data, two other characteristics must be taken into account: data variety and velocity. The term volume is the size of dataset, velocity indicates the speed of data in and out, and variety describes the range of data types and sources. ****Therefore, there is a big gap between what has been delivered by current big data analytics approaches and the promises. There are several authors that claim to deal with big data analytics but they are limited to “built models on current stored data and use such models to predict on new data”. While they are able to handle large volumes of unstructured data, they have trouble to deal with heterogeneous and dynamically changing data. If the characteristics of data change, the learned model will fail to relate the observed data to a correct concept.****The main goal of our research program is to fill such a gap by proposing and developing efficient machine learning methods for big data analytics that cope not only with the large volume of data, but also with velocity and variety inherent of big data. To achieve such a goal, our research program is structured along two main objectives: (i) development of learning methods adapted to unstructured and heterogeneous data on stationary data [volume and variety]; (ii) development of adaptive learning methods for non-stationary data [velocity].****This research program is fundamental to pave the way to the development of big data analytics applications that go beyond the data volume but also deal with the variety and the velocity of big data. The students enrolled in this research program will become specialists in data science and will be highly qualified to disseminate the advances in knowledge for the scientific community and Canadian industries and businesses.**