信息化应用系统用户活跃度分析数据

原先绩效数据需要建设单位截图提供佐证，存在造假的风险，通过本次数据采集后，可以通过算法模型自动计算出用户当日访问人次、当日访问人数、当月访问人数、当日新增用户数、当月新增用户数、累计用户数、当日活跃率、当月活跃率等数据，为信息化项目的使用情况提供数据支撑，提高绩效评价指标的客观性和公平性，最终为财政和信息化项目主管部门对信息化项目的下年度经费保障提供指导。1、数据采集：利用日志采集anget插件安装到各个信息化应用系统的数据库中，定时采集应用产生的日志信息中的系统标识（系统ID）、用户标识（账号ID）和登录时间。2、数据处理：由于anget插件具备数据质量校验功能， 不符合质量的数据不会采集，因此数据质量均符合日志规范。3、数据展示：（1）当日访问人次：采集当日用户登录系统后产生的日志数据中的的用户标识，并不去重复值计算累计登录过系统的账号数量即为当日访问人次。（2）当日访问人数：采集当日用户登录系统后产生的日志数据中的的用户标识，并去掉重复值计算累计登录过系统的账号数量即为当日访问人数。（3）当日新增用户数访问人数：采集当日用户登录系统后产生的日志数据中的的用户标识（账号数据），并和历史采集的用户标识进行比对，如历史未出现的用户标识累计值即为当日新增用户数访问人数。（4）累计用户数：截至到当日内有登录系统的用户标识总数（去重处理）（5）当日活跃率：当日访问人数/累计用户数

Originally, performance data required construction entities to submit screenshots as supporting documentation, posing a risk of fraudulent data. Following the implementation of this data collection project, algorithmic models can automatically compute metrics including daily visit times, daily active users, monthly active users, daily new users, monthly new users, cumulative user count, daily active rate, monthly active rate, etc. These metrics provide data-backed insights into the usage of informatization projects, improving the objectivity and fairness of performance evaluation metrics, and ultimately providing guidance for financial and informatization project authorities when allocating annual funding for informatization projects.
1. Data Collection: Deploy the log collection agent plugin into the databases of various informatization application systems, and regularly collect system identification (system ID), user identification (account ID), and login time from the log records generated by the applications.
2. Data Processing: Since the agent plugin is equipped with a data quality validation function, data that fails to meet quality standards will not be collected, ensuring that all collected data complies with log specifications.
3. Data Display:
(1) Daily Visit Times: Count all user identifications from the log records generated when users log in to the system on the current day without deduplication; the cumulative count of these identifications constitutes the daily visit times.
(2) Daily Active Users (DAU): Count user identifications from the log records generated when users log in to the system on the current day, deduplicate the identifications, and the resulting cumulative count is the daily active users.
(3) Daily New Users: Compare the user identifications (account data) from the log records generated when users log in to the system on the current day with the historically collected user identifications. The cumulative count of user identifications that have not appeared in historical records is the daily new users.
(4) Cumulative User Count: The total number of unique user identifications that have logged in to the system up to the current day (after deduplication processing)
(5) Daily Active Rate: Daily Active Users / Cumulative User Count

搜集汇总

数据集介绍

特点

该数据集为信息化应用系统用户活跃度分析数据，包含多个用户活跃度指标，每日更新，用于支持信息化项目的绩效评价和经费保障决策。