2016 March 25

• Processing JSON with dask.bag

  Often times, I am dealing with JSONL files, though panda’s DataFrame is great (and blaze to certain extend), however it is offering too much for the job. Most of the received data is in the form of structured text and I do all sorts of work with them. For example checking for consistency, doing replace based on values of other columns, stripping whitespace etc.

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Random Posts

• Visualization of my profile, in (not quite) RDF way

  After a miserable trip back to academic world, I finally re-gained the courage to get back to job-market. For the time spent in university, I spent quite some time reading about Semantic Web and RDF. Then I thought, I should have published more in this format in future. However, that didn’t really happen, mostly because I am too lazy.

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• Collaborative Tagging & Folksonomy

  Folksonomy is a neologism of two words, ’folk’ and ’taxonomy’ which describes conceptual structures created by users ^{[4, 5]}. A folksonomy is a set of unstructured collaborative usage of tags for content classification and knowledge representation that is popularized by Web 2.0 and social applications ^{[1, 5]}. Unlike taxonomy that is commonly used to organize resources to form a category hierarchy, folksonomy is non-hierarchical and non-exclusive ^[3]. Both content hierarchy and folksonomy can be used together to better content classification.

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• Recommender System Classification

  Had a discussion with my secondary supervisor and it turned out pretty bad because I wasn’t fully prepared and he was rushing to somewhere else for a meeting. So I am jotting down a brief summary (read: highly based on personal/subjective feelings/opinions) of my readings here to help organize things before the followup meeting that is taking place next week.

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• More Interviewing Questions

  Just survived a job interview, so I should probably celebrate this despite the outcome. Well, considering I was off the job market for a couple of years, I probably has all the reason to be nervous. Anyway, like most geeky serious job interview, there are a test given by the company to the attendees.

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• Approximate Neighbour Search in Multiple Dimensions

  In the previous post, I re-implemented Annoy in 2D with some linear algebra maths. Then I spent some time going through some tutorial on vectors, and expanded the script to handle data in 3D and more. So instead of finding gradient, the perpendicular line in the middle of two points, I construct a plane, and find the distance between it and points to construct the tree.

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