clickstream data

Click Focus on the following points 1. Request server path --------- url 2. Click the id. 3. method used to send requests to the server: get, post, and file upload 4. header information when sending a request to the server 5. next click 6. Click result 7. Click starttime. 8. Click the endtime. 9. clickstreamcontext 10. Click delay (lazy) The overall design oom is roughly as follows: The above design is based on interfaces. Through our xml, It is parsed and encapsulated into the

Clickstream (click Stream) is the track on which users continue to access the site. As we all know, each visit to the site contains a series of click Action Behavior, these click Behavior data constitute the Clickstream data (click on the Stream), which represents the user to browse the entire process of the site. At p

structure diagram of off-line analysis system The overall architecture of the entire offline analysis is to use Flume to collect log files from the FTP server and store them on the Hadoop HDFS file system, then clean the log file with the MapReduce of Hadoop, and finally use HIVE to build the Data warehouse for offline analysis. Task scheduling is done using Shell scripts, and of course you can try some automated task scheduling tools, such as Azkab

) and velocity (velocity). Speed refers to data flow and very fast data, data accumulation or low latency when entering the data warehouse, so that people can make decisions more quickly (or even automatically). Data flow is really a big problem, but for me, the variability

differentiation and positioning, combined with the user archive site recommendation function. The entire Adobe Marketing Cloud covers three environments: data generation, analysis, and application, with powerful integration capabilities. At the same time, Marketing Cloud supports data integration in multiple ways: Adobe Insight provides multi-channel, online, and offline integration products, and can impro

collect information from other sources, including mobile applications, sensors, websites, clickstream data, and social media activities. The data can be converted into products. It is not easy to collect and analyze large amounts of data, especially unstructured data. Curr

Perform a clickstream analysis of the company's website. Analyze the factors that cause the server to fail. Capture and analyze the sequence of activities during outpatient visits in order to develop best practices around general activities. Sequential analysis and cluster analysis algorithms Find a group of common items in a transaction Use Market Basket analysis to determine produc

Using the Docker process, we need to look at the data generated in the container, and between the container and the container, the container and the host before the data sharing, backup and other operations, where the data management of the container. The management of data currently provides the following two ways:#数据

1Course PlanMenu Data ManagementRights Data ManagementRole Data ManagementUser Data Managementin the Realm in the dynamic query user rights, RolesS Hiro integrated in Ehcache Cache Permission Data2Menu Data Additions2.1 using combotree parent menu item

Sometimes we need to perform a large-scale data test and insert a large amount of data into the database. There are three points to consider: [Protect existing data] This has two purposes: 1. We only want to test the inserted data. 2. After the test, we need to delete the data