IBM Watson harnessed for data discovery

Lucidworks is tapping into the IBM Watson Developer Cloud platform for its Fusion platform, an application framework that helps developers to create enterprise discovery applications so companies can understand their data and take action on insights.

Today’s knowledge workers face an avalanche of data and documents. Lucidworks’ Fusion is an application framework for creating powerful enterprise discovery apps that help organisations access their information to make better, data-driven decisions.

By integrating Watson, Lucidworks says Fusion can deliver insights within seconds and can process massive amounts of structured and multi-structured data in context, including voice, text, numerical, and spatial data. Fusion’s platform upgrade with Watson reduces the work and time it takes to create enterprise discovery apps from months of in-house development to only weeks with Watson.

“Watson is a powerful platform for companies and developers to build collaboration solutions and data analysis apps,” said Will Hayes, Chief Executive Officer of Lucidworks.

“We’re bringing next-generation capabilities to enterprise discovery by embedding cognitive computing technology like Watson into our Fusion platform to transform how customers create discovery applications for today’s data-rich, fast-paced work environment.”

Fusion applies Watson’s machine learning capabilities to an organisation’s unique and proprietary mix of structured and unstructured data so each app gets smarter over time by learning to deliver better answers to users with each query. Fusion also integrates several Watson services such as Retrieve and Rank, Speech to Text, Natural Language Classifier, and AlchemyLanguage to bolster the platform’s performance by making it easier to interact naturally with the platform and improving the relevance of query results for enterprise users.

Watson APIs at Your Fingertips: Enrich your search app with Watson’s capabilities as data and documents are indexed and as queries are received by the application and a set of search results are returned to the end user.

Retrieve and Rank: Find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data. Built on top of Apache Solr, developers load their data into the service, train a machine learning model based on known relevant results, then leverage this model to provide improved results to their end users based on their question or query.

Speech to Text: Use machine intelligence to combine information about grammar and language structure with knowledge of the composition of an audio signal to generate accurate transcriptions. Transcription of incoming audio is continuously sent back to the client with minimal delay and corrected as more speech is heard.

Natural Language Classifier: Create natural language interfaces for your applications without needing a background in machine learning or statistical algorithms. The service interprets the intent behind text and returns a corresponding classification with associated confidence levels.

AlchemyLanguage: Analyze your content with sophisticated natural language processing techniques including entity extraction, sentiment analysis, emotion analysis, keyword extraction, microformat parsing, and more.