Getting knowledge graph semantics and definitions right. The Year of the Graph Newsletter Vol. 6, October 2018
Getting knowledge graph semantics and definitions right, semantic web standards used in the real world, by Google no less, and ArangoDB, Azure CosmosDB, Neo4j and TigerGraph announcing new versions. By now you probably know that knowledge graphs are in Gartner’s Hype Cycle. But how does one actually define a knowledge graph? My take on ZDNet. […]
Read More →Processing time series data: What are the options?
Get your data from everywhere you can, anytime you can, they said, so you did. Now, you have a series of data points through time (a time series) in your hands, and you don't know what to do with it? Worry not, because there's a bunch of options.
Read More →Knowledge graphs beyond the hype: Getting knowledge in and out of graphs and databases
What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.
Read More →The past, present, and future of streaming: Flink, Spark, and the gang
Reactive, real-time applications require real-time, eventful data flows. This is the premise on which a number of streaming frameworks have proliferated. The latest milestone was adding ACID capabilities, so let us take stock of where we are in this journey down the stream — or river.
Read More →Artifictional Intelligence: is the Singularity or the Surrender the real threat to humanity?
Artificial intelligence is one of those things: overhyped and yet mystical, the realm of experts and yet something everyone is inclined to have an opinion on. Harry Collins is no AI expert, and yet he seems to get it in a way we could only wish more experts did. Collins is a sociologist. In his book “Artifictional […]
Read More →Google can now search for datasets. First research, then the world?
Did you ever need data on a topic you wanted to research, and had a hard time finding it? Wish you could just Google it? Well, now you can do that.
Read More →Knowledge graphs in Gartner’s hype cycle. The Year of the Graph Newsletter Vol. 5, September 2018
Knowledge graphs in Gartner’s hype cycle, machine learning extensions and visual tools for graph databases, Ethereum analytics with RDF, Using Gremlin with R, SPARQL, and Spring, graph database research wins best paper award in VLDB, and benchmarking AWS Neptune. Not bad for a typical summer vacation month such as August. This edition of the Year […]
Read More →The web as a database: The biggest knowledge graph ever
Imagine you could get the entire web in a database, and structure it. Then you would be able to get answers to complex questions in seconds by querying, rather than searching. This is what Diffbot promises.
Read More →AI chips for big data and machine learning: Hard choices in the cloud and on-premise
How can GPUs and FPGAs help with data-intensive tasks such as operations, analytics, and machine learning, and what are the options?
Read More →Data-driven disaster relief: Measuring the impact of emergency response
With natural disasters picking up in frequency and intensity, the role of NGOs in disaster relief is picking up as well. A key requirement for all NGOs is transparency, and applying data-driven techniques may help.
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