Real-time Mining of Dynamic, Fast Evolving Data

点击翻译按钮获取中文摘要

One of the greatest challenges facing society is to make sense of the vast amount of streaming data that are currently being produced. The resulting explosion of data has challenged the computer science community to develop new algorithms to describe, explore and model these fast evolving repositories. There is an urgent need for novel solutions to detect traffic congestions, to analyze smart phone usage patterns, to track the trends in the online sales of merchandise, for mobile crowd sensing, or to trace the spread of ideas, opinions and movements in social networks. Data stream mining is an active research area, developing technical solutions to address these challenges. Although existing algorithms have had some success, a significant number of research challenges remain. Most researchers working in this domain focus their attention on mining synchronous streams, where there is a constant flow of information and the set of data attributes remain fixed, rather than addressing the asynchronous and dynamic nature of today's data streams. Because such data may arrive asynchronously, especially in distributed environments, new solutions are needed to select the data, as well as to build the models. Further, these massive, fast evolving repositories require near-instant adaptive solutions, providing models that explain the current data and may be used to predict the immediate future. In this case, good approximations are often better than full-fledged, but outdated models. The research program proposed herein is devoted to the development of novel data mining solutions to aid our understanding of what is happening right now in such dynamically evolving, asynchronous data stream. In our proposed work, we create a reservoir of diverse techniques that are able to produce just-in-time models. Continuously, the best models are selected and provided to the users. Our proposed solution provides a near-instant, accurate explanation of the current state of the data, thus aiding decision makers to predict what trends to expect in the near future.