I have attended the IEEE Big Data 16 conference in Washington DC. I thank my company for sponsoring the trip. The conference included a special symposium dedicated to manufacturing. The symposium hosted some participants of the Bosch Production Line Performance competition from Kaggle.

2016 IEEE International Conference on Big Data kicked off today in Washington, DC. Share highlights w/ hashtag #IEEEBigData16 & we’ll RT!

— IEEE Big Data (@ieeebigdata) December 5, 2016

I'll list here a few notes I took during the conference.

• Streaming Processing. I heard about the most popular architectures nowadays, and I highly recommend reading the blog posts by the authors of such architectures:
  • Lambda architecture
  • Kappa architecture
• K-Spectral Centroid. The K-Spectral Centroid algorithm clusters time series by their shape, and finds the most representative shape (the cluster centroid) for each cluster.
• K-D Tree partition: an algorithm for space partitioning.
• Database Decay. Interesting keynote by Michael Stonebraker. Shortly, large applications often share a centralized database used by different groups of a company. The DBA point of view:
  • High Risk. When changing a DB schema, I need to find applications all around in the company and update them accordingly (do I have budget for that?).
  • Low Risk. No change in schema, I do a workaround in data.
  • Claim. DBA want to lower the risk. --> no change in schema --> ER diagram diverges from reality --> database decay.
  • At some point, a total rewrite is the only way forward.
  • If you work in analytics getting data from operational DB, you realize data is getting more and more dirty.
• PMML Scoring Engine. Max Ferguson introduced what a Predictive Model Markup Language (PMML) is. Basically, if you train a model and want to share it in a different application, PMML is a standard that defines how models should be stored as an XML.
• Uncertainty in RFs. Random Forests can express uncertainty. One just needs to look at distribution of predictions among the decision trees of the model.
• Bosch. Rumi Ghosh introduced the data science team at Bosch.
  • Insight from production plants: plant managers prefer interpretable models (logistic regression or decision tree) over black box models.
  • Research directions:
  • Root cause analysis (via Bayesian inference)
  • Class imbalance
• 3 Approaches in Kaggle Competition. Bohdan Pavlyshenko gave a talk on the three approaches he explored during the Kaggle competition about failure detection:
  • Pure machine learning approach. 2-Levels of model ensembling, a pure black-box.
  • Generalized Linear Model with Lasso regularization. Informative about feature impact.
  • Bayesian model in BUGS. It enables to obtain the estimate of the probability distribution for each coefficient.
• FTLR. Follow the regularized leader: a feature engineering method used to convert all categorical feature into one numerical feature.
• CRF. Conditional Random Fields is a class of predictive models used when the dataset is represented as a graph. Each node is a sample with a vector X and a target variable y.