Spheres of influence for more effective viral marketing

July 15, 2016November 11, 2019 ~ Adrian Colyer ~ 1 Comment

Spheres of influence for more effective viral marketing Mehmood et al. SIGMOD ’16 In viral marketing the idea is to spread awareness of a brand or campaign by exploiting pre-existing social networks. The received wisdom is that by targeting a few influential individuals, they will be able to spread your marketing message to a large ... Continue Reading

DBSherlock: A performance diagnostic tool for transactional databases

July 14, 2016November 11, 2019 ~ Adrian Colyer ~ 5 Comments

DBSherlock: A performance diagnostic tool for transactional databases Yoon et al. SIGMOD ’16 …tens of thousands of concurrent transactions competing for the same resources (e.g. CPU, disk I/O, memory) can create highly non-linear and counter-intuitive effects on database performance. If you’re a DBA responsible for figuring out what’s going on, this presents quite a challenge. ... Continue Reading

Ambry: LinkedIn’s scalable geo-distributed object store

July 13, 2016 ~ Adrian Colyer ~ 2 Comments

Ambry: LinkedIn’s scalable geo-distributed object store Noghabi et al. SIGMOD ’16 Ambry is LinkedIn’s blob store, designed to handle the demands of a modern social network: Hundreds of millions of users continually upload and view billions of diverse massive media objects, from photos and videos to documents. These large media objects, called blobs, are uploaded ... Continue Reading

Goods: organizing Google’s datasets

July 12, 2016 ~ Adrian Colyer ~ 9 Comments

Goods: organizing Google’s datasets Havely et al. SIGMOD 2016 You can (try and) build a data cathedral. Or you can build a data bazaar. By data cathedral I’m referring to a centralised Enterprise Data Management solution that everyone in the company buys into and pays homage to, making a pilgrimage to the EDM every time ... Continue Reading

Realtime data processing at Facebook

July 11, 2016 ~ Adrian Colyer ~ 4 Comments

Realtime Data Processing at Facebook Chen et al. SIGMOD 2016 ‘Realtime Data Processing at Facebook’ provides us with a great high-level overview of the systems Facebook have built to support real-time workloads. At the heart of the paper is a set of five key design decisions for building such systems, together with an explanation of ... Continue Reading

Ten challenges in highly-interactive dialog systems

July 8, 2016 ~ Adrian Colyer ~ Leave a comment

Ten challenges in highly-interactive dialog systems Ward &De Vault AAAI 2015 It’s time to end our look into the technology behind chatbots and dialog systems - there’s a huge crop of SIGMOD 2016 papers waiting to be explored for starters! To end the mini-series, today I’ve chosen a 2015 position paper from Ward and De ... Continue Reading

Multi-domain dialog state tracking using recurrent neural networks

July 7, 2016 ~ Adrian Colyer ~ Leave a comment

Multi-domain dialog state tracking using recurrent neural networks a Mrksic et al. 2015 Suppose you want to build a chatbot for a domain in which you don’t have much data yet. What can you do to bootstrap your dialog state tracking system? In today’s paper the authors show how to transfer knowledge across domains, and ... Continue Reading

Machine learning for dialog state tracking: a review

July 6, 2016 ~ Adrian Colyer ~ 1 Comment

Machine learning for dialog state tracking: a review Henderson MLSLP 2015 Today we turn our attention to the task of figuring out, potentially over multiple interactions with a bot, what it is the user is requesting the bot to do. This task goes by the name of Dialog State Tracking, and it’s something that Matthew ... Continue Reading

End-to-end learning of semantic role labeling using recurrent neural networks

July 5, 2016 ~ Adrian Colyer ~ 2 Comments

End-to-end learning of semantic role labeling using recurrent neural networks Zhou & Xu International joint conference on Natural Language Processing, 2015 Collobert’s 2011 paper that we looked at yesterday represented a turning point in NLP in which they achieved state of the art performance on part-of-speech tagging (POS), chunking, and named entity recognition (NER) using ... Continue Reading

Natural language understanding (almost) from scratch

July 4, 2016 ~ Adrian Colyer ~ 4 Comments

Natural language understanding (almost) from scratch Collobert et al. Journal of Machine Learning Research 2011 Having spent much of last week looking at non-goal driven dialogue systems trained end-to-end, today it’s time to turn our attention to some of the building blocks of natural language processing that a chatbot can take advantage of if you’re ... Continue Reading

the morning paper

a random walk through Computer Science research, by Adrian Colyer
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