Enhancement of data analysis and visualization through high and ultra-high resolution displays

通过高分辨率和超高分辨率显示器增强数据分析和可视化

• 批准号: 478966-2015
• 负责人: Maurer, Frank
• 金额: $ 1.82万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=579870
• 关键词: Enhancement; data; analysis; visualization; through

Data analysis and collection is a well-cited growing global problem. Our industry partner, Evaluate Energy/CanOils, collects data from over 770,000 oil wells across Alberta, British Columbia and Saskatchewan. Each well has 100+ columns of information. Data is divided by province and all gets updated monthly and stored on multiple servers. In short, they are tasked to look at unwieldy and exhaustive datasets and further, make time-sensitive decisions and recommendations to their clients based on this data. Essential questions for our partner include what wells are performing the best; geospatially, where are the wells performing the best; and why are they performing so well? What inhibits these answers is a lack of understanding to easily data mine for historic patterns and how to effectively & efficiently visualize & interactively explore the data for various user groups to forecast future well-production trends.

数据分析和收集是一个被广泛引用的日益严重的全球性问题。我们的行业合作伙伴，评估 能源/CanOils收集了阿尔伯塔、不列颠哥伦比亚省和萨斯喀彻温省77万多口威尔斯油井的数据。 每个井有100+列的信息。数据按省份划分，每月更新， 存储在多个服务器上。简而言之，他们的任务是查看笨重和详尽的数据集，并进一步， 根据这些数据向客户做出时间敏感的决策和建议。基本问题 我们的合作伙伴包括哪些威尔斯井表现最好;地理空间上，哪里的威尔斯井表现最好; 为什么他们表现得这么好？阻碍这些答案的是缺乏对数据的理解 挖掘历史模式，以及如何有效地可视化和交互式地探索数据， 不同的用户群体预测未来的油井生产趋势。