Modeling Time Series Data with Neo4j

I’ve been receiving many questions recently at trainings and meetups regarding how to effectively model time series data with use cases ranging from hour level precision to microsecond level “How granular do I really need to make this to efficiently work with and expose the time-based data being analyzed?” and “Do I need to generate all time nodes down to the desired precision level?” The balance that needs to be considered is the initialization and maintainability of all the time series nodes versus the dynamic creation as time series events require their existence and the impact the missing nodes may or may not have when querying time series events by various date and time ranges.

precision. In assessing the various approaches possible, I landed on a tree structure as the model that best fit the problem. The two key questions I found myself asking as I went through the process of building the time tree to connect the time series events were,

Originating the Time Tree

I ultimately decided that it would be most effective to create the hour, minute and second level nodes only when needed to connect an event into a day. So I expanded on the work done by Mark Needham in his post Neo4j: Cypher – Creating a time tree down to the day. The main modeling change in this step was to use a single CONTAINS relationship going from the higher tree toward the lower tree level to simplify movement up and down the entire tree through the use of depth-based pattern matching. Additionally I concluded that Read More......