分布式数据存储平台用户活跃度数据
收藏资源简介：
本数据集的应用场景如下：1.产品改进决策：通过了解分布式数据存储平台的用户活跃度数据，有助于指导产品团队对平台进行改进。2.营销效果评估：本数据的用户活跃度是长期积累的，可用于公司评估分布式数据存储平台的市场营销效果。3.行业分析：行业分析者可参考本数据来分析数据存储行业的用户行为和市场趋势，为整个行业提供宏观的视角。4.投资决策：本数据可为投资者评估数据存储平台的市场受欢迎情况提供参考，从而做出明智的投资决策。5.政策制定：对于政府部门而言，用户活跃度数据可以其作为了解数据存储行业市场运行状况的一个指标，帮助制定相关政策和监管措施。1.数据采集和预处理：（1）从公司运营的分布式数据存储平台的日志中，收集日期、当日访问次数A、当日访问人数B、当日数据操作（增删改查）功能使用次数C、当日数据下载功能使用次数D、当日数据共享功能使用次数E、当日数据标注与注释功能使用次数F、当日数据备份与恢复功能使用次数G、当日数据版本管理功能使用次数H、当日数据访问权限管理功能使用次数I、当日数据收藏功能使用次数J、当日数据订阅与通知功能使用次数K等数据。（2）对收集的数据进行清洗，检查并去除异常数据点。 2.设置事件权重：（1）将原始数据字段划分为2个组别，A-B为一组，记为X组，C-K为一组，记为Y组。（2）X组、Y组的权重值均设置为100。（3）X组中，A与B的权重值均设置为50。（3）Y组中，C、D、E、H、G、I、F、J、K的权重值分别设置为25、20、15、10、8、7、6、5、4；权重值综合考虑功能的使用频率和重要性确定。 3.计算当日用户活跃度：当日用户活跃度=[（A*50+B*50）+（C*25+D*20+E*15+H*10+G*8+I*7+F*6+J*5+K*4）]/200。
Application scenarios of this dataset are as follows: 1. Product improvement decision-making: By acquiring user activity data of the distributed data storage platform, it can help guide the product team to optimize and improve the platform. 2. Marketing effect evaluation: The user activity data in this dataset is accumulated over a long period, which can be used by the company to assess the marketing effectiveness of the distributed data storage platform. 3. Industry analysis: Industry analysts can refer to this dataset to analyze user behaviors and market trends in the data storage industry, providing a macro-level perspective for the entire industry. 4. Investment decision-making: This dataset can provide references for investors to evaluate the market popularity of data storage platforms, so as to make well-informed investment decisions. 5. Policy formulation: For government departments, user activity data can serve as an indicator to understand the market operation status of the data storage industry, assisting in the formulation of relevant policies and regulatory measures. Data collection and preprocessing: (1) Collect data including date, daily access count A, daily number of unique visitors B, daily usage count of CRUD (create, delete, update, query) data functions C, daily usage count of data download function D, daily usage count of data sharing function E, daily usage count of data annotation and commenting function F, daily usage count of data backup and recovery function G, daily usage count of data version management function H, daily usage count of data access permission management function I, daily usage count of data collection function J, and daily usage count of data subscription and notification function K from the logs of the distributed data storage platform operated by the company. (2) Clean the collected data, check and eliminate abnormal data points. Setting event weights: (1) Divide the original data fields into two groups: Group X consists of A and B, and Group Y consists of C-K. (2) Set the total weight values of both Group X and Group Y to 100. (3) For Group X, the weight values of A and B are both set to 50. (4) For Group Y, the weight values of C, D, E, H, G, I, F, J, K are set to 25, 20, 15, 10, 8, 7, 6, 5, 4 respectively; the weight values are determined by comprehensively considering the usage frequency and importance of each function. Calculating daily user activity: Daily user activity = [(A*50 + B*50) + (C*25 + D*20 + E*15 + H*10 + G*8 + I*7 + F*6 + J*5 + K*4)] / 200.
