Graph databases are a fantastic tool for modelling connected data no matter what sort of graph store you choose.
When it comes to modelling people often forget that graphs change over time, and just store what amounts to one person's arbitrary view of the data.
Even if you timestamp everything, it's easy to overlook the fact that it's not only the data that changes over time, but our view of the timeline itself.
This talk will cover the dark art of modelling the change of a graph over time. Covering several common solutions such as the Entity-State model and Discrete Time Dynamic Graphs, their limitations, and how you can handle that awkward second time dimension.
If you choose to attend this talk you will come away with a solid understanding of:
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Dexter is a Big Data specialist who started his professional life making EPOS systems before moving into the world of Big Data in the advertising industry at VisualDNA.
He has a passion for scalable and reliable systems and can often be found tinkering with Hadoop internals or exploring new technologies.
As data becomes ever more interconnected, he has delved into graph data and how it can be best exploited at extreme scale.
Achievements include engineering a linearly scalable solution for ID linking in Janus and creating one of the fastest Neo4J bulk loading systems around.
He currently works in the quantitative research sector with G-Research. dealing with dirty data and squeezing as much speed as possible out of Spark.
Outside of technical achievements, he has founded and grown possibly the largest company Riichi Mahjong group in Europe, including arranging several sponsored Mahjong tournaments.
When not evangelising Mahjong, he lives with his wife and cat and enjoys reading, films, computer games, hiking, scuba diving, comedy, food and is always up for a coffee.