Slides from Trey's opening presentation for the South Big Data Hub's Text Data Analysis Panel on December 8th, 2016. Trey provided a quick introduction to Apache Solr, described how companies are using Solr to power relevant search in industry, and provided a glimpse on where the industry is heading with regard to implementing more intelligent and relevant semantic search.
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
South Big Data Hub: Text Data Analysis Panel
1. Text Data Analysis Panel: South Big Data Hub
Trey Grainger
SVP of Engineering, Lucidworks
2. Trey Grainger
SVP of Engineering
• Previously Director of Engineering @ CareerBuilder
• MBA, Management of Technology – Georgia Tech
• BA, Computer Science, Business, & Philosophy – Furman University
• Information Retrieval & Web Search - Stanford University
Other fun projects:
• Co-author of Solr in Action, plus numerous research papers
• Frequent conference speaker
• Founder of Celiaccess.com, the gluten-free search engine
• Lucene/Solr contributor
About Me
6. Lucidworks enables Search-Driven Everything
Data Acquisition
Indexing & Streaming
Smart Access API
Recommendations &
Alerts
Analytics & InsightsExtreme Relevancy
CUSTOMER
SERVICE
RESEARCH
PORTAL
DIGITAL
CONTENT
CUSTOMER
INSIGHTS
FRAUD
SURVEILLANCE
ONLINE
RETAIL
• Access all your data in a
number of ways from one
place.
• Secure storage and
processing from Solr and
Spark.
• Acquire data from any source
with pre-built connectors and
adapters.
Machine learning and
advanced analytics turn all
of your apps into intelligent
data-driven applications.
21. The Three C’s
Content:
Keywords and other features in your documents
Collaboration:
How other’s have chosen to interact with your system
Context:
Available information about your users and their intent
Reflected Intelligence
“Leveraging previous data and interactions to improve how
new data and interactions should be interpreted”
23. ● Recommendation Algorithms
● Building user profiles from past searches, clicks, and other actions
● Identifying correlations between keywords/phrases
● Building out automatically-generated ontologies from content and queries
● Determining relevancy judgements (precision, recall, nDCG, etc.) from click
logs
● Learning to Rank - using relevancy judgements and machine learning to train
a relevance model
● Discovering misspellings, synonyms, acronyms, and related keywords
● Disambiguation of keyword phrases with multiple meanings
● Learning what’s important in your content
Examples of Reflected Intelligence