Veritas: shared verifiable databases and tables in the cloud Allen et al., CIDR'19 Two (or more) parties want to transact based on the sharing of information (e.g. current offers). In order to have trust in the system and provide a foundation for resolving disputes, we’d like a tamperproof and immutable audit log of all shared … Continue reading Veritas: shared verifiable databases and tables in the cloud
Month: January 2019
The case for network-accelerated query processing
The case for network-accelerated query processing Lerner et al., CIDR'19 Datastores continue to advance on a number of fronts. Some of those that come to mind are adapting to faster networks (e.g. ‘FARM: Fast Remote Memory’) and persistent memory (see e.g. ‘Let’s talk about storage and recovery methods for non-volatile memory database systems’), deeply integrating … Continue reading The case for network-accelerated query processing
Programming paradigms for dummies: what every programmer should know
Programming paradigms for dummies: what every programmer should know Peter Van Roy, 2009 We’ll get back to CIDR’19 next week, but chasing the thread starting with the Data Continuum paper led me to this book chapter by Peter Van Roy mapping out the space of programming language designs. (Thanks to TuringTest for posting a reference … Continue reading Programming paradigms for dummies: what every programmer should know
The data calculator: data structure design and cost synthesis from first principles and learned cost models
The Data Calculator: data structure design and cost synthesis from first principles and learned cost models Idreos et al., SIGMOD'18 This paper preceded the work on data continuums that we looked at last time, and takes a more general look at interactive and semi-automated design of data structures. A data structure here is defined as … Continue reading The data calculator: data structure design and cost synthesis from first principles and learned cost models
Design continuums and the path toward self-designing key-value stores that know and learn
Design continuums and the path toward self-designing key-value stores that know and learn Idreos et al., CIDR'19 We’ve seen systems that help to select the best data structure from a pre-defined set of choices (e.g. ‘Darwinian data structure selection’), systems that synthesise data structure implementations given an abstract specification (‘Generalized data structure synthesis’), systems that … Continue reading Design continuums and the path toward self-designing key-value stores that know and learn
Towards a hands-free query optimizer through deep learning
Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR'19 Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisation— today’s paper choice picks up, looking exclusively at the potential to apply learning (in this case deep reinforcement learning) to build a better optimiser. Why reinforcement learning? … Continue reading Towards a hands-free query optimizer through deep learning
SageDB: a learned database system
SageDB: a learned database system Kraska et al., CIDR'19 About this time last year, a paper entitled ‘The case for learned index structures’ (part I, part II) generated a lot of excitement and debate. Today’s paper choice builds on that foundation, putting forward a vision where learned models pervade every aspect of a database system. … Continue reading SageDB: a learned database system
Serverless computing: one step forward, two steps back
Serverless computing: one step forward, two steps back Hellerstein et al., CIDR'19 The biennial Conference on Innovative Data Systems Research has come round again. Today’s paper choice is sure to generate some healthy debate, and it’s a good set of questions to spend some time thinking over as we head into 2019: Where do you … Continue reading Serverless computing: one step forward, two steps back
Unsupervised learning of artistic styles with archetypal style analysis
Unsupervised learning of artistic styles with archetypal style analysis Wynen et al., NeurIPS'18 I’ve always enjoyed following work on artistic style transfer. The visual nature makes it easy to gain an appreciation for what is going on and the results are very impressive. It also something that’s been unfolding within the timespan of The Morning … Continue reading Unsupervised learning of artistic styles with archetypal style analysis
Neural Ordinary Differential Equations
Neural ordinary differential equations Chen et al., NeurIPS'18 ‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for me!), but in the spirit of ‘deliberate practice’ that doesn’t mean there isn’t something to be gained from trying to understand as much as possible. … Continue reading Neural Ordinary Differential Equations
the morning paper 