英语学习打卡第20天

For the purpose of our interpretation of the data, we can consider the degree to which someone repeatedly buys a product to be indicative of whether or not they like that product. But how might we define "live in a neighborhood"? Well, it turns out that geospatial coordinates are very conveniently coordinates is called an R-Tree. An R-Tree is a graph-like index that describes bounded boxes around geographies. Using such a structure we can describe overlapping hierarchies of locations. For example, we can represent the fact that London is the UK, and that the postal code SW111BD is in Battersea, which is a district in London, which is southeastern England, which, in turn, is in Great Britain. And because UK postal codes are fine-grained, we can use that boundary to target people with somewhat similar tastes.

Such pattern-matching queries are extremely difficult to write in SQL, and laborious to write against aggregate stores, and in both cases they tend to perform very poorly. Graph databases, on the other hand, are optimized for precisely these types of traversals and pattern-matching queries, providing in many cases millisecond responses. Moreover, most graph databases provide a query language suited to expressing graph constructs and graph queries. In the next chapter, we'll look at Cypher, which is a pattern-matching language tuned to the way we tend to describe graphs using diagrams.

We can use our example graph to make recommendations to users, but we can also use it to benefit the seller. For example, given certain buying patterns (products, cost of typical order, and so on), we can establish whether a particular transaction is potentially fraudulent. Patterns outside of the norm for a given user can easily be detected in a graph and then flagged for further attention (using well-known similarity measures from the graph data-mining literature), thus reducing the risk for the seller.

From the data practitioner's point of view, it's clear that the graph database is the best technology for dealing with complex, variably structured, densely connected data - that is, with datasets so sophisticated they are unwieldy when treated in any form other than a graph.

Summary

In this chapter we've seen how connectedness in relational databases and NOSQL data stores requires developers to implement data processing in the application layer, and contrasted that with graph databases, where connectedness is a first-class citizen, In the next chapter, we look in more detail at the topic of graph modeling.