EFFICIENT MACHINE LEARNING METHODS FOR BIG DATA ANALYTICS

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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.**