Sessions are meant to represent continuous streams of user activity. In web, it is common to define a session as ending when a period of 30 minutes passes and no event takes place. Nonetheless, the idea of dividing user activity into discrete blocks of continuous activity is a very useful one. In general we prefer grouping events around specific end goals i. In spite of our reservations, many people including many of our users still sessionize their data.
An interesting alternative to sessions, common amongst mobile games companies, is to aggregate data by user by day. For example, based on that entire history, we might:.
In all the above examples we were combining series of events that occur for individual users and grouping them in ways that summarize continuous streams of activities, where those streams were:. A common example of this type of analysis is attribution modeling. There is a large set of attribution models that are fed with data that describes:.
There are a number of ways we can model attribution data. Many approaches require data in the format shown above: where we aggregate a continuous stream of user activity from a first marketing touch forwards, including any details about any subsequent marketing touches and conversion events.
Once the data has been aggregated as above, it is generally straightforward to:. The hard work of unpicking the sequence of events and applying business logic and using that to perform complex aggregations over the event-level data has already been done, delivering a data set that is easy to work with.
To use the modeled data for analysis, only simple types of aggregation over our higher level entities macro events, workflows, sessions and users is required. It is worth noting that for most analyses we are not working with just one modeled data set: we typically join two or more together.
This was the first in a series of blog posts and recipes on event data modeling. This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful. We use cookies for the website to work as intended.
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An introduction to event data modeling Yali Sassoon. State diagram do not show the flow of data within the system. An example of simple microwave oven to illustrate an event driven modeling. Actually real microwave ovens are more complex then this system but this simple system is easier to understand.
State diagram of a microwave oven. In UML rounded rectangle represent state of a system. The labeled arrows represent the stimuli that caused the transition from one state to another.
Filled circle represent a start and end states of the system. State and stimuli for the microwave oven. State Description. Waiting The oven is waiting for input. The display show current time. Half power The oven power is set to watts. Full power The oven power is set to watts. Set time The cooking time is set to the user input time. The display show. Enabled Oven operation is enabled.
Interior oven light is off. Display shows. Disabled Oven operation is disabled for safety. Interior light is on. Operation Oven in operation, Interior light is on. Display shows the timer. On completion of cooking the buzzer is sounded for. We will introduce the most common architectures, their qualities, and tradeoffs.
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