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2016, I decided to open source the codebase, sharing it with the world because I believe this framework, although a bit outdated by now, still has the potential to help others.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.