Graph

In a post where I talked about modelling data in graphs, I stated that graphs can be used to model different domains, and discover connections. In this post, we’ll do an exercise in using multi-domain graphs to find relationships across them. An institution can have data from multiple sources, in different formats, with different structure and information. Some of that data, though, can be related. For example, we can have public data on civil marriages, employment records, and land/property ownership titles. What these data sets would have in common is the identity of individuals in our society, assuming of course, they were from the same locality or country. In these scenarios, in order to run a cross-domain analysis we need to find ways to connect the data in a meaningful way, to uncover new information, or discover relationships. We could do that to answer questions like “What percent of the married people own property vs those that don’t”, or more interestingly “who is recently married, and bought property near area X while changing jobs”. ...

In recent posts, I wrote about data stores, specifically, about choosing the right one for the right problem domain. I also wrote about modelling data in graphs, with Neo4j. I the last post, on modelling graphs, I promised to discuss how we can get good performance in Neo4j. I will be addressing that in this post, by presenting 12 steps you can follow to attain high performance when using Neo4j, especially in a large data volume setting. ...

On an early post, I described a non-exhaustive taxonomy of data store types, as well as the types of problem domains each one was best suited for. On this post, I will address some approaches to modelling data in graph data stores, particularly with Neo4j. Graph data stores have been increasingly adopted over the past couple of years in several business domains, ranging from logistics to bio-informatics. Their power lies in their ability to model complex networks and tree structures, with data points ranging from hundreds to millions of nodes and edges. ...