Money laundering, knowledge bases, NLP, game economics, online tracking, product suggestions and twitter. Want to do any of the above? Those all are solved by graphs, and this talk will show how and which database to choose for each problem.
From graph theory through the history of computing and how it affected database design, to why relational databases aren’t about relations. Next, a look at how diverse the current graph database market is and what obvious and not so obvious problems are solved by graphs. We will see how to launder money, suggest products, give answers to NLP tasks, build a knowledge base, balance a game economy and model mixed concept domains. A short introduction to Neo4j’s query language, Cypher, will show the main concepts of querying graph data. Then, by use of the same datasets in both relational and graph databases will compare syntactic clarity and database performance.
This talk will show few things:
- relational databases are not graph databases (although the “relational” part in the name could suggest that) in fact they are the opposite
- diversity of graph databases and the fact that graph databases are designed to solve specific problems at what problems graph modeling shines the most
- the idea behind Neo4j’s Cypher as an example how to ask questions in a graph database
- where to use graph databases and where NOT to I also give a more advanced talk strictly about Neo4j - Neo4j in practice - everything that is left when the hype is over