Real-time Mining of Dynamic, Fast Evolving Data

实时挖掘动态、快速变化的数据

• 批准号: 261294-2013
• 负责人: Viktor, Herna
• 金额: $ 1.09万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547711
• 关键词: Real; time; Mining; Dynamic; Fast

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 In this case, good approximations are often better than full-fledged, but outdated models.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.

社会面临的最大挑战之一是理解目前正在生产的大量流数据。数据的爆炸式爆炸使计算机科学界挑战了新算法，以描述，探索和建模这些快速发展的存储库。 迫切需要新颖的解决方案来检测交通拥堵，分析智能手机使用模式，跟踪商品在线销售的趋势，用于移动人群感测或追踪社交网络中的想法，观点和运动的传播。 数据流挖掘是一个活跃的研究领域，开发了解决这些挑战的技术解决方案。尽管现有算法取得了一些成功，但仍有许多研究挑战。在该领域工作的大多数研究人员都将注意力集中在挖掘同步流上，那里存在恒定的信息流，并且数据属性集保持固定，而不是解决当今数据流的异步和动态性质。由于此类数据可能会异步到达，尤其是在分布式环境中，因此需要新的解决方案来选择数据以及构建模型。此外，这些庞大的快速发展的存储库需要近乎固有的自适应解决方案，提供解释当前数据的模型，并可以用于预测近期的未来。在这种情况下，良好的近似值通常比全面的模型好，但模型过时。 本文提出的研究计划致力于开发新颖的数据挖掘解决方案，以帮助我们理解这种动态发展，异步数据流中目前正在发生的事情。在我们提出的工作中，我们创建了能够生成及时模型的各种技术的水库。连续选择并提供给用户的最佳模型。我们提出的解决方案提供了对数据当前状态的近乎确切的，准确的解释，从而帮助决策者预测在不久的将来期望的趋势。