An exploratory analysis on the Effects of severe weather on health and economics in the US

Find out what the most popular places yelpers check-in to with YelpSí, a visualization tool that let’s you explore Yelpers past daily check-in activity across different cities in the US and Europe. It was built with Shiny, using the Yelp Dataset Challenge academic dataset.

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. Likewise, it is also very common for us to need some kind of search capability on our system, so that we or our users can find relevant information. This is what we’re going to do. Having UIMA annotations with information on what directors and actors are mentioned in a review, we want to be able to search for reviews that mention specific actors, directors, or just search for reviews that do mention screenwriters. ...

The art of writing was invented, I suppose, so that we could communicate with the future, i.e. record the past and present, and in the process create history. Software programs, on the other hand, are written for the purpose of defining the future. One that is meant to be interpreted by machines. The tapping of a keypad turns a blank page into a blueprint. It starts with one file, and can quickly grow larger. Combined, several blueprints become packages. They contain little logical engravings of our expressed intentions. Aggregated, these are translated into binary compatible entities, ready to be executed by little machines living within larger, more complex ones. ...

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. During the first few decades though, the focus was set on structured data. ...

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. What these data sets would have in common is the identity of individuals in our society, assuming of course, they were from the same locality or country. In these scenarios, in order to run a cross-domain analysis we need to find ways to connect the data in a meaningful way, to uncover new information, or discover relationships. We could do that to answer questions like “What percent of the married people own property vs those that don’t”, or more interestingly “who is recently married, and bought property near area X while changing jobs”. ...

I travelled recently, and while I waited at a terminal for my connecting flight, I noticed something intriguing about air travelling: Airline. Let’s start with definitions. Airline is the language used by airlines to communicate with their customers. As an investigator/scientist at heart, I could not help but to take it upon myself the selfless duty of documenting this obscure language. And so I paid close attention throughout my trip, and made an effort to document and analyse it as much as I could, and I now share with the world my efforts in translating some of the most important terms in Airline into plain English. ...

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. ...

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. Here we go! ...

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. ...