This document discusses enterprise search solutions and describes Searchbox. It summarizes that Searchbox leverages the Apache Solr search technology and offers plugins, a search framework, and search-as-a-service. Enterprise search differs from web search in focusing on productivity within an organization using heterogeneous structured, semi-structured, and unstructured data from various sources. Searchbox aims to provide an iterative tool to derive value from big data by enabling information retrieval and mining of both big and small data sources.

1. ENTERPRISE SEARCH
by jonathan.rey@searchbox.com

2. ENTERPRISE SEARCH
PANORAMA
IBM
Dassault Systems
Open source
Google
Commercial solutions (all acquired in the past 5 years)

3. WHAT IS SEARCHBOX?
Searchbox leverages Apache Solr Technology and:
Offers various Solr plugins
Offers a Search Framework which can be used to
develop custom search engines tied to business
needs
Search-as-a-Service (On the cloud)

4. ENTERPRISE SEARCH VS.
WEB SEARCH
Productivity
Heterogeneous data
False negative / False positive
Structured, semi-structured, unstructured data

5. PRODUCTIVITY
Web search is
driven by the
engine.

6. HETEROGENEOUS DATA
Intra/extranet
Websites
CMS
Filesystem
Repository (Files, XML)

7. FALSE NEGATIVES ARE A
KILLER
On the web it’s ok not to
find a specific document
Not an option within a
company
Real time indexing
Liability concerns
Compliance (Why this
result?)

8. ENTERPRISE DATA IS...
Structured
Semi-structured
Unstructured
Web Search today is “semi-structured”
and somewhat consistent.

9. BUSINESS-CENTRIC
DATA
Data Normalization
Adaptation to
business needs
=> Goal: Productivity
gain
Linkedin is a great
“enterprise search”
example

10. WHAT IS BIG DATA?
Distributed & disparate data from several sources
Structured - semi structured - non structured
Big data & machine learning
Enhance existing unstructured data (tagging, entity
extraction, summarization)
Content curation

11. FROM BIG TO SMALL
DATA STACK
Scalable Backend infrastructure & archiving
Information Retrieval
Analysis / Discovery
Visualization
Sharepoint, Cassandra, Hadoop, Oracle, SAP, MangoDB, ...
Solr, Lucene, Elastic Search, Business Warehouse,
SAP BW, ...
Searchbox backend
Searchbox
frontend
Big Data
Small Data

12. OUR APPROACH TO
CONVERGENCE
- Index
- Crawl
- Fields
- Metadata
- Facets
- Filters
- More Like This
- Search Framework
- Presets
- Templating
- Tagging
- Summarization
- Sorting
Connect
Discover
Lift /
Enhance
Specialize

13. CONCLUSION
Working with Big data is expensive and time
consuming
Requires high level of expertise in multiple fields
(Networking, Programming, ML, NLP, Mathematics,
Statistics, ...)
Information Retrieval / mining can serve as an
iterative tool to leverage value from big data

14. SEARCHBOX FOR BIG
DATA
Data centric (Machine learning based enhancements)
Solr storage (Solr 4.x as scalable key-value store)
Hosted Solr Cluster with sharding and replication
Iterative process
Guided administration panel
Human friendly as opposed to CLI

15. “Sort by”
“Clickable
tags”
Range
facets with
histogram
Searchbox demo on
http://pubmed.searchbox.com
Search with
autocomplete