The document discusses the evolution and future of search technologies, focusing on reflected intelligence and the integration of big data analytics. It highlights various use cases, such as social media analysis in telecom and fraud detection in insurance claims, emphasizing the importance of scalable and efficient search platforms like those offered by MapR and Lucidworks. Additionally, it outlines the architectural requirements for building effective search and discovery systems that can leverage machine learning and enhance user interactions.

1. Crowd Sourcing Reflected
Intelligence Using Search and Big
Data
Ted Dunning
Grant Ingersoll
©MapR Technologies - Confidential 1

2. Grant’s Background
 Co-founder:
– LucidWorks – Chief Scientist
– Apache Mahout
 Long time Lucene/Solr committer
 Author: Taming Text
 Background in IR and NLP
– Built CLIR, QA and a variety of other search-based apps
©MapR Technologies - Confidential 2

3. Ted’s Background
 Academia, Startups
– Aptex, MusicMatch, ID Analytics, Veoh
– Big data since before big
 Open source
– since the dark ages before the internet
– Mahout, Zookeeper, Drill
– bought the beer at first HUG
 MapR
– Chief Application Architect
 Founding member of Apache Drill
©MapR Technologies - Confidential 3

4. Agenda
 Intro
 Search Evolution and Search Revolution
 Reflected Intelligence Use Cases
 Building a Next Generation Search and Discovery Platform
– MapR
– LucidWorks
 1+1=3
©MapR Technologies - Confidential 4

5. Search is Dead, Long Live Search
 Search is a system building block Content
– text is only a part of the story
 If the algorithms fit,
use them! Content User
Relationships Interaction
 Embrace fuzziness!
 Scoring features are everywhere Access
©MapR Technologies - Confidential 5

6. Search (R)evolution
 Search use leads to search abuse
– denormalization frees your mind
– scoring is just a sparse matrix multiply
 Lucene/Solr evolution
– non free text usages abound
– many DB-like features
– noSQL before NoSQL was cool
– flexible indexing
– finite State Transducers FTW!
 Scale
 “This ain’t your father’s relevance anymore”
©MapR Technologies - Confidential 6

7. Add (Lots of) Water
 Large-scale analysis is key to reflected intelligence
– correlation analysis
• based on queries, clicks, mouse tracks,
even explicit feedback
• produce clusters, trends, topics, SIP’s Search
– start with engineered knowledge,
refine with user feedback
 Large-scale discovery features
encourage experimentation
 Always test, always enrich! Analytics Discovery
©MapR Technologies - Confidential 7

8. Social Media Analysis in Telecom
 Correlate mobile traffic analysis with social media analysis
– events cause traffic micro-bursts
– participants tweet the events ahead of time
 Deploy operations faster to predict outages and better handle
emergency situations
– high cost bandwidth augmentation can be marshaled as the traffic appears
– anticipation beats reaction
©MapR Technologies - Confidential 8

9. Provenance is 80% of value
 Analysis of social media to determine advertising reach and
response
 In one case the same untargeted advertising was worth 5x if sold
with supporting data.
©MapR Technologies - Confidential 9

10. Claims Analysis
 Goal
– Insurance claims processing and analysis
– fraud analysis
 Method
– Combine free text search with metadata analysis to identify high risk
activities across the country
– Integrate with corporate workflows to detect and fix outliers in customer
relations
 Results
– Questions that took 24-48 hours now take seconds to answer
©MapR Technologies - Confidential 10

11. Virginia Tech - Help the World
 Grab data around crisis
 Search immediately
 Large-scale analysis enriches data to find
ways to improve responses and
understanding
 http://www.ctrnet.net
©MapR Technologies - Confidential 11

12. Bright Planet - Catch the Bad Guys
 Online Drug Counterfeit detection
 Identify commonly used language indicating counterfeits
– you know it when you see it
– and you know you have seen it
 Feed to analyst via search-driven application
– enrich based on analysts feedback
©MapR Technologies - Confidential 12

13. Veoh - Cross Recommendations
 Cross recommendation as search
– with search used to build cross recommendation!
 Recommend content to people who exhibit certain behaviors
(clicks, query terms, other)
 (Ab)use of a search engine
– but not as a search engine for content
– more like a search engine for behavior
©MapR Technologies - Confidential 13

14. What Platform Do You Need?
 Fast, efficient, scalable search
– bulk and near real-time indexing
– handle billions of records with sub-second search and faceting
 Large scale, cost effective storage and processing capabilities
 NLP and machine learning tools that scale to enhance discovery
and analysis
 Integrated log analysis workflows that close the loop between the
raw data and user interactions
©MapR Technologies - Confidential 14

15. Reference Architecture
Access APIs
•View into
Search View Analytic numeric/histo Personalization &
ric data
1 Services Machine Learning
2 Services
Shards 3 N
•Classification
•Recommendation
Document •Documents Classification Models
Discovery & •Users
Enrichment Store In memory
•Logs Replicated
Clustering, classificat
ion, NLP, topic Multi-tenant
identification, searc
h log analysis, user
behavior
Content Acquisition
ETL, batch or near
real-time
Data
• LucidWorks Search
connectors
• Push
©MapR Technologies - Confidential 15

16. MapR
 MapR provides the technology leading Hadoop distribution
– full eco-system distribution
– integrated data platform
– complete solution for data integrity
 MapR clusters also provide tight integration with search
technologies like LucidWorks
– integration is key for effective ops
©MapR Technologies - Confidential 16

17. LucidWorks
 LucidWorks provides the leading packaging of Apache Lucene and
Solr
– build your own, we support
– founded by the most prominent Lucene/Solr experts
 LucidWorks Search
– “Solr++”
• UI, REST API, MapR connectors, relevance tools, much more
 LucidWorks Big Data
– Big Data as a Service
– Integrated LucidWorks Search, Hadoop, machine learning with prebuilt
workflows for many of these tasks
©MapR Technologies - Confidential 17

18. LucidWorks Big Data Architecture
Uniform ReST API
Content Search – Discovery – Analytics System
• LucidWorks Search
Acquisition • Machine Learning Management
(classification, clustering, recommendations)
• Administration
• Natural Language Processing
• Enterprise
• SQL (Hive) Interface
Repository • Provisioning
• Data Workflows (ETL, log analysis, common metrics)
• Extensible
• Social Media • Monitoring
• Databases Big Data Operating System • Configuration
• HDFS • Service Management
• Cloud (S3) • Data Management
• Push • Security
Hadoop/HBase Search Search
Logs Indexes
©MapR Technologies - Confidential 18

19. Easy Wins
 Analyze logs from application stored in MapR
 Seamlessly store search indexes in MapR
– and feed to Pig, Mahout and others
– use mirrors + NFS to directly deploy indexes
 Snapshots make backups a snap
 LucidWorks 2.5 (2013 Q1) easily connects with MapR
©MapR Technologies - Confidential 19

20. 1+1=3
©MapR Technologies - Confidential 20

21. Learn More
 More information
http://www.mapr.com/company/events/lucidworks-12-13-2012
 Vote for this topic for Hadoop Summit EU:
http://bit.ly/128tLQe
 Talk to Ted
@ted_dunning
tdunning@maprtech.com
 Talk to Grant
@gsingers
 MapR and Lucid Works
http://www.mapr.com
http://www.lucidworks.com
©MapR Technologies - Confidential 21