> For the complete documentation index, see [llms.txt](https://docs.eseye.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eseye.com/api-reference/connectivity-metrics/correlate-the-datasets.md). # Correlate the datasets The three datasets (authentication, accounting and traffic flow) can be used together to provide a complete view of a device’s connectivity activity or separately to answer specific operational questions. #### Correlation keys You can typically correlate records using **IMSI and timestamp**. The IMSI identifies the SIM across all three datasets, while the timestamp aligns records within the same period of activity. #### How the records connect The three datasets describe consecutive stages of a device’s connectivity activity: 1. Review **authentication records** to confirm whether the device attached successfully and identify rejected attempts or repeated reconnects. 2. Review **accounting records** to determine whether a session was established, how long it remained active and how much data it transferred. 3. Review **traffic flow (NetFlow) records** to identify where the data went while the session was active. For example, repeated authentication rejections, short accounting sessions and few traffic flow records may indicate an unstable connection. A successful attachment, a long session and heavy traffic to an unexpected destination may point to a different issue. Correlating the datasets makes these patterns easier to distinguish. #### Example investigation Consider a device reported as consuming more data than expected. Accounting records confirm the amount of data consumed and show when the relevant sessions occurred. Authentication records for the same IMSI and time period show whether the device was reconnecting repeatedly, which may indicate instability rather than expected usage. Traffic flow records then identify the destinations that received the data, helping you distinguish expected application traffic from unexpected communication. Together, the records provide a complete, time-aligned view of the device’s connectivity behaviour. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.eseye.com/api-reference/connectivity-metrics/correlate-the-datasets.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.