Back in 2011-2012 I put a lot of time and energy into creating a simple and sleek JSON API framework for quick intelligence prototyping; an API capable of managing JSON objects, and performing a lot of smart computing tasks. Fast forward to 2016, I decided to open source the codebase, sharing it with the world because I believe this framework, although a bit outdated by now, still has the potential to help others.
SQLpie™ is an open source API framework that uses all sorts of SQL statements to creatively perform all kinds of computing tasks (thus, SQLpie). With SQLpie, developers can store JSON objects in a SQL database and run a lot of information retrieval and machine learning tasks on the data, covering areas such as: Text Classification, Text Summarization, Collaborative Filtering (item recommendation and similarity), Boolean/Vector Search, Document Matching, TagClouds, etc… The project is 100% written in Python and runs on top of a MySQL database.
The SQLpie project went after a lot of big challenges, and although I do not advocate that it includes the best implementations to handle all of those tasks, I believe the combined effort can help people quickly prototype new ideas, and hopefully, create new and awesome products.
Its API services can help developers with the following type of questions:
• How can one store JSON documents? (answer: documents services)
• How can one keep track of document relationships? (answer: observations services)
• What documents exist for query Q? (answer: indexing and search services)
• What documents are located near location L? (answer: geosearch service)
• What top keyphrases and keywords relate to query Q? (answer: tagcloud search service)
• What are the key sentences, entities, and terms associated with document D? (answer: summarization service)
• What documents are similar (or relate) to document D? (answer: document matching service)
• Will user U like document D? (answer: classification service)
• How likely is user U to like document D? (answer: classification service)
• What documents is user U likely to love based on user data? (answer: recommendation service)
• What other users have a document taste similar to user U? (answer: similarity service)
~ Andre Lessa
Let’s start with the kind of question you are likely to ask yourself the first time you come across something new.
“What do I need a Benchmarking Engine for?”
A possible short answer is this: To efficiently and automatically identify opportunities for business performance improvement, customer/vendor satisfaction, and revenue generation.
Now for a more comprehensive answer… Continue reading
My thoughts about this article: “Meet Ross, the IBM Watson-Powered Lawyer“:
Interesting concept. In the demo, I see a Natural Language parser for the user’s query, a search engine capable of indexing a ton of documents, and a recommendation engine that updates document scores based on the user’s pos/neg feedback… should be an interesting project to re-create a similar demo without Watson.
Today is a great day.
Our technology is really cool. It takes structured data (think big spreadsheets!) and finds insights that are hidden in plain sight. But not just that, it ALSO writes them up in perfect English, just like if a real person had analyzed the data and written a report about it.
To get the word out about the technology we created an application that leverages US College data. All the insights were created using an automated process. How much insight data has the software generated for this first application? Well, think something equivalent to 30 “Moby Dick”, or 65 “The Hobbit”, or 80 “Philosopher’s Stone” books.
Check it all out at OnlyBoth.com
~ Andre Lessa (@lessaworld)