Concept Drift: Notes for the practicioner

October 20, 2018

In this article, I share notes on handling concept drift for machine learning models. Introduction Concept drift occurs in an online supervised learning setting, when the relationship between the input data X and output data y is altered to the extent that a model mapping X to y can no longer do so with the same efficacy. In online supervised learning, there are three types of drift that can occur: (1) feature drift, i. ... Read more

Understanding Data

April 3, 2018

Rich Metadata on Data I have been learning many things about dealing with data at a large scale. At first, I kept using the term quality to describe the state of data. However, it quickly became clear that the term had various dimensions to it, and it could not summarise the issues one can observe. I have come to use the expression understanding data instead because (1) it captures the state I wish to describe and (2) speaks the scientific and functional purposes of that state. ... Read more

Indexing UIMA Annotated Docs, with Solr

January 7, 2016

In this post, I’m going to walk you through the process of indexing UIMA annotated documents with Solr. In a previous post, Finding Movie Starts, I demonstrated how we can use UIMA to find and tag structured information in unstructured data. In most scenarios, once we have that data we extracted, we want to be able to query it. To do this, we can put our data into a data store, be it an RDMS, document store, graph, or other. ... Read more

Finding Movie Stars: Named Entity Recognition with UIMA & OpenNLP

November 26, 2015

In this post, we are going to use text analysis tools UIMA and OpenNLP to identify film personas, like directors and screenwriters, from a corpus of movie reviews. Warning: Working knowledge of Java is necessary for completing this guide. Estimated required time: ~60-90 minutes Overview of Natural Language Processing Since the 70s, experts and businesses had realised the potential that exists in gathering, storing, and processing information about their operations to add and create new value for their organisation and their customers. ... Read more

Connecting Data: multi-domain graphs

September 9, 2015

In a post where I talked about modelling data in graphs, I stated that graphs can be used to model different domains, and discover connections. In this post, we’ll do an exercise in using multi-domain graphs to find relationships across them. An institution can have data from multiple sources, in different formats, with different structure and information. Some of that data, though, can be related. For example, we can have public data on civil marriages, employment records, and land/property ownership titles. ... Read more

12 Steps for a Performant Graph, with Neo4j

July 16, 2015

In recent posts, I wrote about data stores, specifically, about choosing the right one for the right problem domain. I also wrote about modelling data in graphs, with Neo4j. I the last post, on modelling graphs, I promised to discuss how we can get good performance in Neo4j. I will be addressing that in this post, by presenting 12 steps you can follow to attain high performance when using Neo4j, especially in a large data volume setting. ... Read more

HTTP Status Codes Explained: A Daily Life Translation

July 15, 2015

If you browse the web, I’m willing to bet you’ve encountered of an HTTP status code at some point in time. A dreadful 404 when the page is missing; 301/302 when you’re redirected to another page; or a good old 200 when you actually get to see the page. Well, I decided to do a translation of the meaning of some of most common HTTP codes into examples that non-techies can possibly relate to. ... Read more

Modelling Graphs, with Neo4j

June 8, 2015

On an early post, I described a non-exhaustive taxonomy of data store types, as well as the types of problem domains each one was best suited for. On this post, I will address some approaches to modelling data in graph data stores, particularly with Neo4j. Graph data stores have been increasingly adopted over the past couple of years in several business domains, ranging from logistics to bio-informatics. Their power lies in their ability to model complex networks and tree structures, with data points ranging from hundreds to millions of nodes and edges. ... Read more

Data Store Types, and their Modelling Use Cases

June 2, 2015

This post will list a non-exhaustive taxonomy of data store types, and outline how they can be used to model different problem domains. Data Modelling In database design, modelling can be defined as the process of mapping the entities and events from a particular domain, into a representational format that can be stored into a database. The goal is to be able to answer relevant questions with data once it’s stored. ... Read more