One of the primary advantages of using a graph database is the ability to present the relationships that exist between datasets and files. Much of the data is connected, and graph database use cases are increasingly helping to find and explore these relationships and develop new conclusions. Additionally, graph databases are designed for quick data […]
Property Graphs vs. Knowledge Graphs
One of the greatest strengths of graph databases is their ability to treat “relationships” between the data as being as important as the data itself. They show a visual image of a graph in response to queries. Graph databases are designed to hold data without restricting it to a fixed, predetermined model. As a consequence, […]
2021: Three Game-Changing Data Modeling Perspectives
Click to learn more about author Thomas Frisendal. Common wisdom has it that we humans can only focus on three things at a time. So, I had to cut down my January 2021 list of things of importance in Data Modeling in this new, fine year (I hope)! Looking at them from above, as we […]
Semantic Web and Semantic Technology Trends in 2020
One way or another, it’s all about graphs. And machine learning. And AI. And what their connections to each other are. Welcome to the world of semantic technology in 2020. The year ahead largely picks up the pace on what industry experts predicted would happen in 2019. Graph technologies have finally become mainstream, says Andreas […]
Knowledge Graphs and Data Modeling
Click to learn more about author Thomas Frisendal. Trip Report From Graphorum / Data Architecture Summit 2019 On October 14th thru 17th Chicago hosted the two co-located conferences Graphorum and Data Architecture Summit 2019 by DATAVERSITY®. It was two days of good tutorials and two good days of conference presentations. One thing to think about […]
Modeling Misfit Types: Why Type Inheritance Is Not a Good Fit in Data Models
Click to learn more about author Thomas Frisendal. “Complete Consistence” Drives Temporality, … And What Else? In August I published a blog post called The Future History of Time in Data Models. The short version of that story is that if you aim for “Complete Consistence for Temporal Extensions”, you need to work on the […]
The Future History of Time in Data Models
Click to learn more about author Thomas Frisendal. Timely Concerns in Data Models In June I published a blog post called Timely Concerns in Data Models. In summary the concerns that I mentioned in June were: Roles of time (such as Valid Time, Recorded Time, As-Is vs. As-Of, Read timelines, Time Series), The scope of […]
The History of Time in Data Models
Click to learn more about author Thomas Frisendal. In my last blogpost Timely Concerns in Data Models, we looked at the basic challenges of dealing with time dependencies in Data Modeling. I promised to continue this quest by going over the history of these issues. How well have we actually solved these challenges? So, hop […]
Timely Concerns in Data Models
Click to learn more about author Thomas Frisendal. The Component Parts of Data Models Back in March 2019 I published a post here on DATAVERSITY® titled The Atoms and Molecules of Data Models. The objective was to scope ”a universal set of constituents in data models across the board”. I used this classic data model, […]
Semantic Web and Semantic Technology Trends in 2019
What to expect of Semantic Web and other Semantic Technologies in 2019? Quite a bit. DATAVERSITY® engaged with leaders in the space to get their thoughts on how Semantic Technologies will have an impact on multiple areas. As DATAVERSITY discussed last year, Knowledge Graphs have gotten a lot of attention as a backbone for Machine […]