Columnstore and B+ tree – are hybrid physical designs important?

Columnstore and B+ tree - are hybrid physical designs important? Dziedzic et al., SIGMOD'18 Earlier this week we looked at the design of column stores and their advantages for analytic workloads. What should you do though if you have a mixed workload including transaction processing, decision support, and operational analytics? Microsoft SQL Server supports hybrid … Continue reading Columnstore and B+ tree – are hybrid physical designs important?

The design and implementation of modern column-oriented database systems

The design and implementation of modern column-oriented database systems Abadi et al., Foundations and trends in databases, 2012 I came here by following the references in the Smoke paper we looked at earlier this week. "The design and implementation of modern column-oriented database systems" is a longer piece at 87 pages, but it’s good value-for-time. … Continue reading The design and implementation of modern column-oriented database systems

Smoke: fine-grained lineage at interactive speed

Smoke: fine-grained lineage at interactive speed Psallidas et al., VLDB'18 Data lineage connects the input and output data items of a computation. Given a set of output records, a backward lineage query selects a subset of the output records and asks "which input records contributed to these results?" A forward lineage query selects a subset … Continue reading Smoke: fine-grained lineage at interactive speed

Same-different problems strain convolutional neural networks

Same-different problems strain convolutional neural networks Ricci et al., arXiv 2018 Since we’ve been looking at the idea of adding structured representations and relational reasoning to deep learning systems, I thought it would be interesting to finish off the week with an example of a problem that seems to require it: detecting whether objects in … Continue reading Same-different problems strain convolutional neural networks

Relational inductive biases, deep learning, and graph networks

Relational inductive biases, deep learning, and graph networks Battaglia et al., arXiv'18 Earlier this week we saw the argument that causal reasoning (where most of the interesting questions lie!) requires more than just associational machine learning. Structural causal models have at their core a graph of entities and relationships between them. Today we’ll be looking … Continue reading Relational inductive biases, deep learning, and graph networks

The seven tools of causal inference with reflections on machine learning

The seven tools of causal inference with reflections on machine learning Pearl, CACM 2018 With thanks to @osmandros for sending me a link to this paper on twitter. In this technical report Judea Pearl reflects on some of the limitations of machine learning systems that are based solely on statistical interpretation of data. To understand … Continue reading The seven tools of causal inference with reflections on machine learning

An empirical analysis of anonymity in Zcash

An empirical analysis of anonymity in Zcash Kappos et al., USENIX Security'18 As we’ve seen before, in practice Bitcoin offers little in the way of anonymity. Zcash on the other hand was carefully designed with privacy in mind. It offers strong theoretical guarantees concerning privacy. So in theory users of Zcash can remain anonymous. In … Continue reading An empirical analysis of anonymity in Zcash