Mining interesting patterns from big data

从大数据中挖掘有趣的模式

• 批准号: RGPIN-2017-06206
• 负责人: Leung, CarsonKaiSang
• 金额: $ 1.68万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679333
• 关键词: Mining; interesting; patterns; big; data

In the current era of big data, high volumes of a wide variety of valuable data of different veracity (e.g., imprecise or uncertain data like sensor data and medical lab test results, in which the contents are uncertain due to factors like inherited measurement inaccuracies or sampling frequency) can be easily generated or collected at a high velocity. Consequently, we are drowning in data but starving for knowledge. In order to be able to make sense of these data, data science solutions for big data management and big data mining (which discovers implicit, previously unknown e efficient, user-friendly, and powerful data science system for mining interesting patterns from big data. Specifically, I plan to (1) adapt the developed algorithms to take into account the user preference and push these user constraints inside the mining process so that the resulting algorithms only output interesting patterns and no post-processing step is needed; (2) mine (3) further improve performance so as to provide users with real-time responses by exploring other optimizations and techniques (e.g., Apache Spark, Scala) and further reduce the memory requirements (e.g., by adapting Bigtable); (4) explore other quantitative (5) explore real-life applications (e.g., mining social networks, Web, telecommunication data, agricultural data, meteorological data, news feed, tweets, blogs) and find other interesting patterns such as trends, sequences and (6) develop data visualization This, in turn, helps users to enrich their knowledge so that they could promptly take appropriate actions or to make the best (business or military) decision, which would consequently have significant positive impacts on the improvement of human life and the benefits to Canadian economy as well as national defense

在当前的大数据时代，大量的各种不同准确性的有价值的数据（例如，不精确或不确定的数据，如传感器数据和医学实验室测试结果，其中的内容由于诸如固有的测量不准确性或采样频率之类的因素而不确定）可以被容易地生成或高速收集。因此，我们淹没在数据中，却渴望知识。为了能够理解这些数据，用于大数据管理和大数据挖掘的数据科学解决方案（发现隐含的，以前未知的，高效的，用户友好的，强大的数据科学系统，用于从大数据中挖掘有趣的模式。具体来说，我计划（1）调整开发的算法，以考虑用户偏好，并将这些用户约束推到挖掘过程中，以便所得算法只输出有趣的模式，不需要后处理步骤;（2）挖掘（3）进一步提高性能，以便通过探索其他优化和技术（例如，Apache Spark、Scala），并进一步降低内存需求（例如，通过调整Bigtable）;（4）探索其他定量（5）探索现实生活中的应用（例如，挖掘社交网络、Web、电信数据、农业数据、气象数据、新闻源、推文、博客），并找到其他有趣的模式，如趋势、序列和（6）开发数据可视化，这反过来又有助于用户丰富他们的知识，以便他们能够及时采取适当的行动或做出最好的选择。（商业或军事）决定，这将对改善人类生活和加拿大经济以及国防利益产生重大积极影响。