MongoDB World 2016 Giant Ideas Stage eBook

Check out the organizations that participated in the Giant Ideas Stage at MongoDB World 2016!

1. GIANT IDEAS
STAGE

2. Beet Analytics Technology is committed to providing state of
the art diagnostic and analytical tools to accelerate problem
solving for operations facing complex assembly and automation
challenges. Beet’s software and consulting services provide
manufacturing engineers and automation specialists with the
expertise and technology to improve identifying and reduce
production downtime and to achieve significant productivity gains
during the assembly and automation process.
For more information, check out the website at www.beetllc.com
1. Beet Analytics Technology
Cappius is a Digital Business Transformation company
focusing on renovating enterprise business by leveraging Big
Data Analytics, Internet of Things (IoT), Mobile First and Cloud
technologies. With our innovative solutions like Customer
Analytics, Enterprise Speech Analytics, Fraud Detection &
SalesForecasting, we deliver practical insights to support data
analysis.
2. Cappius
Hackolade is a software built to visually model MongoDB
schemas, create the blueprint of applications, and facilitate the
dialog between analysts, architects, designers, developers, and
DBAs. The cross-platform desktop application (Windows, Mac,
or Linux) assists in the design and documentation of physical
models, leveraging the power of JSON and MongoDB, with a
particular focus on intuitive use and flexibility.
3. Hackolade
Happiest Minds enables Digital Transformation for enterprises
and technology providers by delivering seamless customer
experience, business efficiency and actionable insights
through an integrated set of disruptive technologies: big data
analytics, Internet of things, mobility, cloud, security, unified
communication, etc. Headquartered in Bangalore, India, Happiest
Minds has operation in the US, UK, Singapore, Australia and has
secured US $52.5 million Series-A funding.
4. Happiest Minds
Kineviz, Inc. is the leader in cutting-edge human interfaces for
data visualization that unite art and humanity with technology.
Kineviz creates immersive experiences that are personal,
intuitive, and engaging to the senses resulting in faster discovery,
understanding, and action.
For more information, check out the website at www.kineviz.com
7. Kineviz
Pivotal transforms how the world builds software. Pivotal
combines the Silicon Valley state of mind, modern approach,
and infrastructure with organizations’ core expertise and values.
We enable the leading companies in the world to innovate by
employing an approach focused on building—not buying—
software. Our methodology is about evolving, in both development
and innovation, and our culture is empowering. Our team uses
agile and lean approaches to teach next­generation developers
to create and build new solutions. We optimize for change so
enterprises can move at start­ up speeds and with greater business
agility.
10. Pivotal
Wipro Ltd. (NYSE:WIT) is a leading information technology,
consulting and business process services
company that delivers solutions to enable its clients do business
better. Wipro delivers winning business outcomes through its
deep industry experience and a 360 degree view of “Business
through Technology.”
For more information, please visit www.wipro.com
11. Wipro
Loopd, Inc. provides real intelligence to corporate event
marketers. With Loopd, marketers learn how people interact with
each other, with the company, and with the company’s products.
The Loopd relational analytics gathers real data so that marketers
can educate attendees effectively, optimize venue layouts, create
engaging communities, and define real ROI. Unlike stand alone
event apps, beacon systems or traditional lead retrieval systems,
Loopd combines the three essential components for a corporate
event marketer: a mobile app, a wearable system, and rich
analytics. Loopd is the industry’s only bi-directional solution
that enables the exchange of contact information and content
automatically for partners and attendees.
For more information, check out the website at www.loopd.com
8. Loopd
Meshfire is an social media collaboration tool to help you set up
your campaigns into missions, assign tasks to team members
and listen to your audience. Meshfire distills years of social media
expertise into a virtual team member who suggests tasks, tracks
changes and predicts trends to help you grow and engage your
community. Meshfire is a subscription online service that you can
use anywhere: desktop, iPad, or smart phone.
For more information, check out the website at www.meshfire.
com
9. Meshfire
hiQ is a cloud-based people analytics SaaS platform that helps
protect your most valuable asset, your people. Immediately
pinpoint who is at risk and where to invest. With hiQ, it’s easy to
use data science and machine learning on internal and external
data to drive higher impact people decisions, no matter how far
along you are in your People Analytics journey. The world’s top
brands rely on hiQ to understand the Employee Lifetime value and
reduce turnover and save millions of dollars in employee attrition.
For more information, check out the website at www.hiqlabs.com
5. hiQ
Infusion helps enterprises deploy digital solutions that transform
their business. We rapidly create quality and innovative products
and platforms through a blend of smart software engineering,
design and digital strategy.
6. Infusion

3. Beet Analytics Technology

4. Presented to: MongoDB World
The information depicted or described herein are exclusive property of BEET, llc. and are submitted in confidence. Permission
to use or reproduce in any way this proprietary information is expressly withheld.
MongoDB World Presentation
Providing unprecedented visibility into
manufacturing process

5. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
Manufacturing Problem
1 minute lost of production time
= up to $22,000 lost revenue
* The 2006 study by Nielsen Research is based on 101 manufacturing executives in the automotive industry

6. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
Reason –Workers voices can not be heard
1920 Assembly Line 2015 Assembly Line
THEN NOW
Lots of talking No talking

7. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
The Solution -ENVISION
Throughput
Profit
Efficiency
Downtime
Collect
“Big Data”
Visualization / Collaboration
Results

8. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
ENVISIONbrings factory automation to life
By collecting the vital details of the automation ENVISIONbecomes its EKG
Machine Heartbeat -ENVISIONHuman Heartbeat -EKG

9. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
ENVISIONobtains and displays millions of factory
motions in seconds
ENVISIONData Collector /
Application Server
Programmable
Logic Controller
End User
Interface
“Big Data”
(All automated motions)
Patented
Patented

10. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
ENVISIONanswers the main factory questions
A Factory
Automation Heartbeat–A cycle sequence
What is wrong? -(Live)
What could go wrong? -(Predictive)
Where is the “hidden factory”? -(Capacity lost)
1
2
2
3
3
1

11. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
Case Study –Deep Dive
Profit
$920,000/Day
46 More
/Day
$20,000
Before ENVISION
5 Years, Total 2500
Shifts
4 Months
After ENVISION
Today w/
ENVISION
Average Throughput
(Engine/Shift)
140 152 163
Target Throughput (Engine/Shift) 176 176 176
Average Equipment Efficiency 79% 86% 93%
ROI in ONEDay!

12. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
Technical Challenges We Face
Slide #9
SQL Based
Data
Processing
Performance
Issue
Incomplete and
Greatly Delayed

13. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
Why We Selected MongoDB
Slide #10

14. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
MongoDB + ENVISION
Slide #11
MongoDB
based Data
Processing

15. Presented to: MongoDB World (C) Beet LLC. All rights reserved.
Thank You!
Slide #12
www.beet.com

16. Cappius

17. ConfidentialInformation www.cappius.com
Enterprise Speech Analytics
Actionable insights from customer interactions in a Service Center
Presented by
Name: Surya Putchala
Title: Head, Big Data Analytics

18. ConfidentialInformation www.cappius.com
1.Customer experience: overview
2.Need for the Solution
3.Enterprise Speech Analytics Solution
4.Architecture
5.Features
6.Benefits
2
Agenda

19. ConfidentialInformation www.cappius.com 3
Customer experience: overview
Business
Intelligence
CRM
Intelligence
Social Media
Intelligence
Market
Intelligence
Enhanced
Consumer
Experience
Transactional
Intelligence
Customer
Demographics
Market/Product
Trends, Recalls,
Feedback
Consumer
Sentiment
Customer Insights
With big data and analytics you
can combine all information to
extract insight in real-time and
create an actionable view of
each customer to craft an
exceptional
experience
§Need to integrate multi-Intelligence data
§Increasingly, more intelligence data is
unstructured in text formats, video and
audio.
Traditional
Approach
Enhanced
Approach
Service call
In store, in person
touch points
Moments
of truth

20. ConfidentialInformation www.cappius.com 4
Why Speech Analysis?
In a Service Call center, the audio call data is archived for reference and not mined for customer
interactions and extract value from them. It is exhausting to hear the tapes, hence this source of rich
data is routinely ignored.
Often this data is never accessed, unless there is a special situation such as an escalation or a dispute.
Most information is hidden and should be mustered from the customer call data. Since, it is Audio,
many Enterprises give it the least preference.
The efficiencies will further improve
by knowing the trending call center
enquiries and deploying the right
person for answering the right
issue.
Customer service will enhance
tremendously by knowing the
moods of the customer and intensity
of conversation which will allows
taking necessary actions to provide
better service for the customer.
1 2

21. ConfidentialInformation www.cappius.com
Archival Analysis
ThisSolutionisapplicableincasessuchas:
1.identificationofthetrendinginquiries
2.identifyingcommonpainpointsandimprove
understandingofacustomerbehavior.
Theseleverscouldpre-emptactionsthatwillresult
inpreventingcustomerdissatisfactionandincrease
customerdelight;achievingasuperiorcontact
centerperformance.
5
Enterprise Speech Analytics Solution
Synchronous Analysis
Theprocess:
1.Capturethevoicestreambyapplyingvarious
textandaudioprocessingtechniques
2.Understandthesentimentsaswellasmoodof
theconversationforacustomerservicecall.
Thisisaccomplishedreal-timeandcontinuously
monitoredandtracked;whichallowstheservice
centertheabilitytoprovidesuperiorcustomer
engagement,handlesituationsattherighttimeto
mitigateattritionandescalations.

22. ConfidentialInformation www.cappius.com
Solution Architecture
Google
Web
Speech API
Transcript
Polarity
Trends Ticker
Mood
§Voice data/ Analysis
§Transcriptedtext/Summary
Beyond
Verbal API
Streaming
§Data Collection
§Data Processing
NLP
§Sentiment
§Mood
Real time
Call Signal
Archived
Audio
Apache Storm
Machine
Learning
Real-time
Mood
TranscriptedText
Analytics
Scoring
§Service Rep Scoring
§Outlier detection
§Session benchmarking
§Trending Topics
§Floor Analysis
§Problem Resolution Analysis
§NPS –Net Promoter Score
§Customer Traffic Analysis
Persist real-time data as well as
run predictive Analytics
Customer Sentiment tracking at pre-configured intervals (default 15 sec)
Knowledgebase
6
Core Engine
Visualization
Call Conversation
Voice
Expression
Audio Extract
from Video

23. ConfidentialInformation www.cappius.com
Features
Interprets voice to text (on the fly or from the archives)
§Accent aware speech to text conversion
§Summary and Conclusions of Call sessions
Polarity Deciphering the Feelings and Meaning of a conversation
Mood Analysis Analyze mood of the customer in real-time
Knowledgebase
§Two-way voice data (Customer and Support Executive)
§Results of voice analysis (quantitative and qualitative)
§TranscriptedText
§Polarity, Sentiment, Word clout
§Summary and conclusions data needed for Analytics
Service Rep Scoring
Evaluation Score how well executive handled with customer
§Call Forwarding to most appropriate Staff
§Help to Identified Most Efficient Employee to take up the calls
Effective Handling
§Real-time suggestions for customer executive
§Optimization of the responses in Real-time
§Trending issues, best relevant answer
§Audio sniffer with configurable “key“ words (used for escalations)
Transcription
7

24. ConfidentialInformation www.cappius.com
Speech Analyzer in Action
8

25. ConfidentialInformation www.cappius.com
Analytic Dashboards
Service Rep Scoring
Customer Call Analysis
Topic mining and trending
Average Response Time
Top 10 Customer Service Representatives
Top 100 Customers by # Inbound Calls
Average Number of Outbound Calls to Sell a Product
Change in Customer Satisfaction Rating
Variance in Call Volume by Customer Segment
Forecasted Call Volume
Average Call Length
Call Type (Sales, Service) as % of Total Inquiries
Session Benchmarking
Floor Analysis
Customer Experience
Customer Segmentation
Customer Mood, Sentiment, Satisfaction
Net Promoter Score
Identification of Common Concerns
Trending Topics
Queuing Analysis
Statistical Analysis
Ranking and Scoring
Document Similarity
Anomaly Detection
Customer Segmentation
Anomaly Detection
Analytical Themes

26. ConfidentialInformation www.cappius.com
§Improve the customer experience
§Improve service quality
§Reduce operating expenses and save money
§Revenue enhancement with up-sell and cross-sell
§Reduce Customer Attrition
Benefits
10

27. ConfidentialInformation www.cappius.com
Thank You
Contact us,
www.cappius.com
[email protected]

28. Visual Modeling
of Dynamic Schemas
MongoDB World -Giant Ideas Stage
Pascal Desmarets

30. Performance
Denormalization

31. Source: Forrester Research, Jan 2015

34. Introducing:

35. Demo time!
•Use case 1: start from scratch
•Use case 2: reverse-engineering

37. Thank you!

38. Happiest Minds Value Proposition

39. 2 © Happiest Minds –Confidential
Our Business
Digital Transformationfor enterprises and technology providers leveraging an integrated set of disruptive technologies
Big Data & Analytics Mobility
Security
Cloud Social Computing Unified Communications
BPM, Workflow
Business
Integration
IoT
Digital
Enterprise

40. 3 © Happiest Minds –Confidential
BigData & Information ManagementServices
Service Offering
Visualizations
Strategy Definition
Architectural Consulting
Capacity Planning
Performance Engineering
Platform Support
Platform Engineering
Provisioning & Automation
Big Data &
Information
Management Services
Advisory
Transformation
Managed

41. 4 © Happiest Minds –Confidential
SOLUTIONS, PLATFORMS & TOOKITS

42. 5 © Happiest Minds –Confidential
Data Capture
Data Filter &
Transform
Analyze Visualize Act
Social
Networking
/
Sharing
News
Aggregators
Other Data
Sources:
•Government
Sources
•Private Data
•Others
Get Data >
Enrich Data >
Transfer Data
Raw Data
Text mining for:
•Sentiment Analysis
•Entity Extraction
•Event Extraction
•Event Classification
•Prominence Analysis
Analysis for :
•Profile co relation
•Identity location
Visualized Data
Enriched Data Analyzed Data
Visualization to
showcase:
•Intuitive
dashboard
showing event
graph, maps and
charts
•Alerts and flags
•Network link
graphs for easy
understanding and
analysis
Empower Resource
to Act:
•Case Management
•Communication
and collaboration
•Human
Intelligence
•Agent handling
Intelligence Out Of Unstructured Open Source Intelligence
OSINT Data Resources
Intelligence Tradecraft
Keyword
Search
Cyber Intelligence Platform

43. 6 © Happiest Minds –Confidential
MIDAS Service Platform
•Supports Multiple Protocols & Devices
•Event Hub & Notification
•Service APIs for collaboration
•Better Security for APIs and Devices
•Real time Analytics (Including Edge Devices)
•Supports Enterprise Integration and mash ups
for better smarter insight
•Service Monitoring
•Ecosystem applications (infrastructure) for
maximum benefits
•Microservice Architecture
•Distributed & Scalable Platform
•Thing Center
•Supports built-on-top (Vertical Solutions)

44. 7 © Happiest Minds –Confidential
Anomaly Detection –Made SIMPLE

45. 8 © Happiest Minds –Confidential
Anomaly Detection –Our Approach
CSV
HDFS
API
DATA
INGESTION
BIG DATA : MongoDB, SPARK
FEEDBACK : VALIDATE OUTLIERS
CUSTOMIZE ALGORITHM
SELECT ALGORITHM
SELECT FEATURES
Expose
Outlier
Score
ALGORITHMS
STATISTICS MACHINE LEARNIG NEURAL NETWORKS
ADMINITRATION USER INTERFACE

46. 9 © Happiest Minds –Confidential
R2M
Seamless
migration from
an RDBMS to
MongoDB
Enabling better
performance,
scalability, ease
of management
Operational
reliability with
built-in
monitoring APIs
Complete and
Custom
Migration option
Customized
recommendation
of New NoSQL
Data Model
Option to change
the data model
before migration
to MongoDB
Preview of
Collection sample
before migration
Automation of
the MongoDB
cluster setup
Desktop based
GUI Tool for easy
table selection
and relation
mapping

47. 10 © Happiest Minds –Confidential
CASE STUDY

48. 11 © Happiest Minds –Confidential
Voice To Text Analytics
•A multinational corporation based in India with
revenue of $US 33 billion.
•Involved in Steel, Energy and Infrastructure services
•Operation in 29 countries, employing over 60
thousand people.
•The pilot is for Oil and Gas division of the company.

49. 12 © Happiest Minds –Confidential
Business Requirement
•Whattransactions happened with whomand when
•Regulatory requirement: Communication logs must be
kept and reviewed by Auditors
•Primary use case: Text Analytics on Email, Chat and
Audio Data Combined to spot deceitful transactions

50. 13 © Happiest Minds –Confidential
The Business Problem
•No single view of all communications happened through email, chat
and voice.
•Auditors review process was a daunting task as they need to read
through numerous email and chat files and need to listen to audio files
to qualify a transaction as ‘clean’.
•Huge dependencyon people maintaining these files systems.
•No support for any scientific reasoning to back the findings of the
Auditors.
•Brand Reputation was at risk.

51. 14 © Happiest Minds –Confidential
What was achieved
•The results were staggering!
•RAAD: Completed pilot development in three weeks, which otherwise
would have taken couple of months.
•Performance: The application was responding to user queries within
50 millisecond window. MongoDB enabled low-latency queries across
thousands of documents.

52. 15 © Happiest Minds –Confidential
Business Benefits
•Provided single view of all communications per transaction.
•Auditor’s evaluation time brought down from 2 weeks to 1 day.
•Saved around 300 man hrs., which consumed for manual
consolidation of data from email, chat and audio servers.
•Text Analytics application offered new insights like deeper
understanding of supplier market which was not possible before.

53. Thank you
www.happiestminds.com

54. hiQ Labs
The Global Standard for People Analytics
Founded 2013
San Francisco-based
B-round led by Vayner Capital
www.hiqlabs.com

55. What We Do
●Apply data science to Human Resource problems
●Example: Risk of Attrition
●Data sources: internal and external

56. Say What?
Once more, this time in tech-eze:
●Ingest data
○Structured data through API’s
○Unstructured data from public sources
○Customer data
●Process Data
○Mostly Python code, written by data scientists
■It’s got a bit of R in it
●Deliver Data
○Push data to web stack

57. Why We Chose MongoDB
We needed a platform that:
●Fit well into our R&D tool chain
○Exploratory work in iPython& Jupyter
○Data loosely structured; schema in flux
○Answer: pymongo, mongoengine
●Could quickly scale up to production
○MongoDB mature enough to trust
○Missing piece: distributed processing
○Answer: rolled our own --MongoO

58. Why Reinvent The Wheel?
Existing wheels are complicated
●Apache Spark -native Java/Hadoop
○Python: streaming module
○MongoDB: mongo connector
○Strict functional map/reduce paradigm
●Simple solution, native Mongo/Python
○Didn’t exist
○So we wrote one

59. Introducing MongoO
Simple Distributed MongoDB Mapper
●MongoDB
○Works directly on collections
○uses MongoDB to track and monitor jobs
●Python
○import mongoo
○pymongo and mongoengine (ORM)
●FOSS
○Free & Open Source Software
○Permissive license (no copyleft)

60. Simple Example
Fits on one slide!
import pymongo
from mongoo import mmap
db = pymongo.MongoClient().test
for i in range(10):
db.mongoo_in.save({'_id': i})
def func(source):
return {'_id': source['_id'] * 10}
ret = mmap(func, "mongoo_in", "mongoo_out")

61. Simple Example
Output:
for doc in ret.find():
print doc
{u'_id': 0}
{u'_id': 10}
{u'_id': 20}
{u'_id': 30}
{u'_id': 40}
{u'_id': 50}
{u'_id': 60}
{u'_id': 70}
{u'_id': 80}
{u'_id': 90}

62. github.com/hiqlabs/mongoo

63. Make it RealHave a Vision Make it Beautiful
IoTand Big Data
Building modern applications with
MongoDB and Azure in the real world

64. Introduction and Bios
Challenge, Solution, Value proposition
Underlying Technology Patterns
Architecture
Relevant MongoDB and Azure information
Stories From the Trenches
Today’s Agenda

65. Infusion Confidential –Not For Distribution
We rapidly create innovative
products and platforms through
a blend of smart software
engineering, design, and
digital strategy
Infusion helps
enterprises deploy
digital solutions that
transformtheir
business
600+Employees
16+Years
Innovation Center
Innovation Center
At A Glance
Houston
Raleigh
NYC
Innovation Center
Toronto London
Wrocław
Kraków

66. Infusion Confidential –Not For Distribution
Solving Key Challenges
Business
Insights
Application
Modernization
Customer
Experience
Website
Development
and CMS
Mobile
Development
Cloud
Architecture
Digital
Installations
Data
Enablement
Enterprise
Platform
Development
Digital Strategy
& Design
Managed
Services
Emerging
Technologies
Digital
Talent Solutions
Our Services
Enterprise
Productivity

67. Infusion Confidential –Not For Distribution
The Next Wave
Data Sciences + IoT,
“The Internet of Things”
Virtual + Augmented Reality
Artificial Intelligence
+ Machine Learning
Emerging Technologies

68. Partner Awards

69. Infusion Confidential –Not For Distribution
Our Expertise
Craig Talosi
Practice Lead,
Business Insights
Rob Ringham
Practice Lead,
Mobile Development
Jeremy Bibby
Practice Lead,
Devices
Peter Kuhtey
Practice Lead,
Web Development
David Christensen
Practice Lead,
Cloud Enablement
Enterprise/Data Architect, strategist,
integration expert, software
engineer/developer, MongoDB
consultant + solution architect.
Occasionaleaster egg placer.
Ryan Chase
Director of Technology and Strategy,
Infusion (UK)

70. Challenge / Solution
Challenge Solution
•IoT == many devices and lots of data
•I want to get good value out of this data
via analytics and application
modernization
•Agility
I willpresentone possiblesolutionto this
problem. Thisisa referencearchitecturethat
canbe applied to thesekindof challenges.
The technologieschosenfor thisinstanceof the
referencearchitectureincludeMongoDB, Azure,
Hadoop, PowerBI.
A fewreasonsI likethissolution:
•Uses tech I’m familiar with
•Integration is pretty straightforward–allows
us to swap components in/out as needed.

71. Infusion Confidential –Not For Distribution
Value Proposition
Data Ingestion& Storage
•Abilityto easily ingest data from
IoT devices, providing quick and
easy ways to store large amounts
and data and perform
transformations on that data as
needed.
App Modernization
•Ease of presenting
application optimized data
sets allows rapid and agile
creation of modern
applications.
Data Visualization
•Data can be visualized
via the BI tools that
you use today and/or
new BI tools.

72. Domain Driven Design
Model
Bounded Context
•the idea is actually pretty simple. lets
come up with a ubiquitous language to
described our domain (i.e., world), based
on the language of that domain.
•for example:
•that’s a chair.
•that’s an account.
•that’s a client.
•he has an address.
•etc.
•we use this language and domain model
as input for other design.
•ddddefines aggregate roots and
bounded contexts (which closely relates
to a micro service). these are the words
we use to define the boundaries in our
world/domain.
Overview
Micro Service
•a small application that represents
some logic, function, orbehaviour.
•typically based on some model (I
prefer these to be domain models
-i.e. DDD based approach).
Hexagonal Architecture
•something you can wrap your
model in so you can put it inside
of any hosting environment
•might have a UI or a JMS/REST
endpoint
DOMAIN
DRIVEN
DESIGN

73. •Event Driven Architecture
11
Model
Bounded Context
Model
Bounded Context
Model
Bounded Context Bounded Context
BROKER FABRIC
Event Store
Pub Pub Pub PubSubSubSub
Model
Sub
EDA
Complex Event Processing
CEP
Pub Sub
CQRS
Overview
EVENT
DRIVEN
ARCHITECTURE

74. Events are True ImmutableDataPoints
EVENT DRIVEN
ARCHITECTURE
1
2
Immutable data is factual and is true based on point in time
(“forever true”).
12-09-1990
03-08-1998
06-07-2003
07-09-2008
08-09-
2011customerHasNewDependent(name..)EVENT
customerHasNewDependent(name..)EVENT
customerMarried(lastName..)
EVENT
customerMoved(addr1, addr2 ..)EVENT
customerHasNewDependent(name..)
EVENT
customerCreated(name, addr1 ..)
EVENT
03-08-1998

75. So …
1
3
IoT DDD EDA “big data”≈ + +
(for the purposes of this discussion + proposed solution)

76. Infusion Confidential –Not For Distribution
High Level Architecture
•Two main kinds of data:
•events
•application data
•We use the event data to
enrich our application data.
•We use the data to quickly
build/enhance applications.
•We use PowerBIor
other analytics tools to
get additional insights
into our data.

77. Infusion Confidential –Not For Distribution
MongoDB Specifics
•Use MongoDB BI connector to connect PowerBIand other analytics/dashboards tools
•Use MongoDB Hadoop connector to connect MongoDB to big data clusters
(Hadoop/Spark).

78. Infusion Confidential –Not For Distribution
Azure + IoTSpecifics
•Different devices need
different types of
gateways:
•Field gateways for
simple devices and/or
specific security
concerns.
•Protocol gateways for
more sophisticated
devices
•Event stores can be
done in Azure or in
MongoDB
•We prefer server side
discovery but this is not
a hard/fast rule.

79. MongoDB Deployment Options
1
7
Some quick thoughts –full details are beyond the scope of this
presentation.
Use Ops Manager to
deploy into cloud VMs
We recommend
MongoDB Enterprise
Edition for this
This may change in
the future
•Be careful with micro-
shardingin MongoDB 3.x –
you should use container
groups to avoid memory
issues.
•Third party SaaS providers
that use the community
edition (e.g. MongoLabs)
are not recommended for
this use case.
•There are rumoursabout
a new MongoDB
provided SaaS option
with an option to use
Enterprise Edition –that
will be awesome!

80. Infusion Confidential –Not For Distribution
The Full Picture
TECHNICAL
ARCHITECTURE

81. Stories From the Trenches
1
9
Some things we have learned from implementing this with our clients.
You can do your analytics and
aggregations in MongoDBor in
your big data ecosystem –decide
per use case, it’s not 1 size fits all
MongoDB-Hadoop
connector is a tool -you
need to decide how to use it
Empower data usage for
apps and analytics
•Time series data modelling is a bit
different in MongoDB vs HDFS.
•Realtimejobs are different
•Understand compute/memory
needs of MongoDBconnectors
(Hadoop& BI)
•Give analysts dedicated reporting
clusters
•Create use case optimized data
views to accelerate application
development.
•Use the MongoDBBI connector to
hook up your existing dashboards
and visualization tools.
•MongoDB aggregation pipeline for
simple aggregations/rollups
•Send more complex analytics to
your Hadoop/Spark cluster
•Understand your compute/memory
needs.
•Shard your MongoDB cluster if you
need more memory.

82. Confidentiality
The information (data) contained on all sheets of this document/quotation constitutes confidential information of InfusionDevLLC or its affiliates (collectively
hereinafter “Infusion”) and is provided for evaluation purposes only. In consideration of receipt of this document, the recipient agrees to maintain such
information in confidence and to not reproduce or otherwise disclose this information to any person outside the group directly responsible for evaluation of
its contents, unless otherwise authorized by Infusion in writing. There is no obligation to maintain the confidentiality of any such information that was known
to recipient without restriction before receipt of this document as evidenced by written business records; which becomes publicly known through no fault of
recipient; or which is rightfully received by recipient from a third party without restriction.
This document includes information about current Infusion sales and service programs that may be enhanced or discontinued at Infusion’s sole discretion.
Infusion has endeavored to include in this document the materials that are believed to be reliable and relevant for the purpose of recipient’s evaluation.
Neither Infusion nor its representatives make any warranties as to the accuracy or completeness of the information. Accordingly,this document is provided for
information purposes only in the hope that Infusion may be considered to receive your business. Neither Infusion nor its representatives shall have any liability
to the recipient or any of its representatives resulting from the use of the information provided. Only a mutually agreed-upon written definitive agreement,
signed by the authorized representatives of the parties, shall be binding on Infusion or its affiliates.
The term “solution” in the context of this proposal is defined as the products and services proposed herein. Since additionalinformation may be required
from you in order to develop the appropriate configuration for your project, the term “solution” does not imply that those services as proposed are
guaranteed to, or will, meet your requirements.
The use of the terms “partner” or “partnership” in this proposal does not imply a formal, legal, or contractual partnership, butrather a mutually beneficial
relationship arising from the teamwork between the parties.
Unless otherwise agreed in writing, pricing estimates are valid for 60 days from date of submission of this proposal.
© Copyright 2016 infusion

84. Data Analytics

in VR
Weidong Yang Ph. D.
Kineviz, Inc.

85. VR + Data Viz
VR is physical, intuitive
and viscerally
understandable

86. VR Tech Stack
•WebVR
•HTC Vive (Room size VR)
•NodeJS+OpenCL
•Server-side GPU computation

87. WebVR Advantages
•GPU Performance
•Real time collaboration
•Platform independence
•Can scale

88. Bridge Between Reality and Data
MR = Mixed Reality

89. MR Advantage
VR no longer limited to single user POV

90. MR Applications
Life Sciences & Bioinformatics
Defense/Security
Marketing
Fraud and Crime Detection

91. Kineviz - Bringing Art to Technology
Kineviz has a team of engineers, artists and scientists. We are looking for partners
to develop this exciting new technology for real world problems
kineviz.com
[email protected]

110. Elias Israel, CEO
[email protected]
Artificial Intelligence For Social Media
8 June 2016
Meshfire finds you the people, conversations and opportunities you’re
missing in the flood of Social Media.

111. THE PROBLEM

112. ITS GETTING WORSE

113. Teams overwhelmed
The Ideal
✔Brand voice consistency
✔Effective community engagement
✔Complete reporting
The Painful Reality
✗Overworked
✗Losing opportunities
✗Reporting sporadically
The Community Manager “Army of One”
Managing 3-20 regional or national brands online
60% OF MAJOR BRANDS HAVE SOCIAL
MEDIA TEAMS OF ONLY 1 TO 3 PEOPLE.

114. OUR SOLUTION

115. OUR TASKBOARD

116. OUR CHALLENGE
Turn Amber Into This (“Ember”)

117. WHAT KIND OF AI?
Expert
Systems
Fuzzy Logic
Knowledge
Management
Programming
Instructions
Neural Networks
Machine
Learning
Statistical
Analysis
Learning Examples
BOTH!

118. HOW WE DO IT
•Tracking over 15 million Twitter users
•Inserting and processing more than 35 million
tweets daily
•Over 120GB of total data
•Real-time analysis of Twitter user relationships
•Real-time analysis of tweets as they happen

119. Customer Quotes
“Until Meshfire came along, I spent more time catching up and responding
to missed messages. With Meshfire, I get instant notifications.”
–Karl Kovacs, Social Media PM, HP
“I caught a hashtag local arts orgs were using for a tweet-chat they forgot to
tell us about. […] I would not have caught it without Meshfire, for sure.”
–Amie Simon, Social Media Producer, EMP Museum
“Meshfire does the thinking for us. We're now joining the right
conversations, and there’s no time wasted managing social media.”
–Sharon Herzog, VP Marketing, Sex Wax

120. USER Base GROWING
Partners
Earned Media
Meshfire customers have a combined
audience exceeding
15.4Million Twitter Users

121. SCUF GAMING
•1,006,826+ Twitter followers
•200+ YouTube and
Pro-Affiliates
•150+ Million Subscribers
•10k+ new followers each week
“As we’ve grown ScufGaming, Smart Tools
become critical to our continued success.
Meshfire is extremely valuable for controlled
management and growth of our online
community.”
Duncan Ironmonger
CEO & Co-Founder of ScufGaming

122. Elias Israel, CEO
[email protected]
Artificial Intelligence For Social Media
8 June 2016
Meshfire finds you the people, conversations and opportunities you’re
missing in the flood of Social Media.

123. Transforming How the World Builds
Software
Ian Andrews
VP Products
@IanAndrewsDC
MallikaIyer
Principal Software Architect
@cloudfoundryart

124. Implementing
—
New methodologies to
influence the software
development culture of Silicon
Valley’s most influential
Internet companies
Discovering
—
An agile, rapid iteration, test-
driven approach to software
development
Accelerating
—
The digital transformation of
the world’s largest companies
with a modern software
development methodology and
modern cloud platform
Rob Mee Paul Maritz
Scott Yara Bill Cook
Founded
Transforming
—
The world’s largest companies
into cloud native software
companies

125. CASE STUDY: GENERAL ELECTRIC
The engine behind
GE Predix

126. CASE STUDY: DAIMLER
The engine behind the
Mercedes-Benz connected car

127. 5
Operating
System
Cloud API
Container Orchestration
Google AWS Azure VMW Openstack
Multiple
Languages
Microservices
Support
Services
Marketplace
Spring CloudSpring Boot
DEVELOPMENT
Native
User
Provided
Partner
App Deployment
& Management
Availability
Visibility &
Administration
CI/CD Tools,
ID, Security
Health,
Metrics,
Patching
Apps &
Platform
Dashboards
OPERATIONS
Exploratory
Questions:
•What are the Ops
team goals?
•Main priorities that
the team is working
on?
•Is multi cloud of
interest?
•Any PaaS/cloud
platforms being
used/evaluated
today?

128. Everything to Deploy and Manage the App
6
4. Health
management
2. Metrics
3. Log
Aggregation
1. Roles and
Policy
5. Security
and
Isolation
7. Scaling
6. Blue-
Green
deployment

129. Services prepackaged for simple consumption
7
•Easy accessibility
through Marketplace
•Instant Provisioning and
full lifecycle managed
•Bind to apps through
easy to use interface
•Common access control
and audit trails across
services
MySQL
Session state
caching
GemFire
Single Sign-On
Jenkins
Enterprise
RabbitMQ
ConfigServer
Service
Directory
Circuit Breaker
Redis
DataStax
Cassandra
AND MORE
Services Marketplace

130. Running MongoDB
on
Pivotal Cloud Foundry

131. > cf
marketplace
> cf
create-
service
> cf bind-
service
> cf
unbind-
service
> cf
delete-
service
CCDB
Servic
e
Broke
r
Service
Plans
(single
node,
single-
replica-set,
sharded,
etc...)
IaaS
Services APIRouter
Cloud
Controller
Fetch
Catalog
Provision
De-Provision
Create
Binding
Delete
Binding
On Demand
VM
Creation…
VM Deletion...
On-Demand Service Broker Workflow

132. MongoDB -On Demand Service as
a Pivotal Cloud FoundryTile
-Provision the IAAS resources during service
instance creation
-Everything packaged into a “tile” that runs on
Pivotal Cloud Foundry on any IAAS

133. Demo

134. Let’s build something great

135. 1
How to Monitor and Troubleshoot Modern Day Apps
with Sumo Logic
Lavanya Shastri, Product Manager, Sumo Logic
Sam Weaver, Product Manager, MongoDB

136. 2Sumo Logic
Confidential
2
Agenda
•About Sumo Logic
•Demo
Optimizing slow queries
Deployment health
Security
•Q&A

137. 3
About Sumo Logic
•Cloud Native Machine Data Analytics
•6+ years
•100 petabytes of data processed daily
•1000 customers
•10,000 users
•Multi-tenant architecture, scales on demand

138. 4
Q&A

139. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL1
Insights driven Customer Experience
Aggregating multi-channel information to create superior customer experiences
Chandra Surbhat, VP & Global Head,
Digital Technologies, Wipro
Prasad Pillalamarri, Domain Consultant,
DCxMPlatform, Wipro

140. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL2
The Age of the Customer
Source: Forrester Research & other reports
95% 85% 55%
95%ofdissatisfiedcustomers
tellothersabouttheirbad
experience
By2020,85%ofcustomer
relationshipwillbewithout
humaninteraction.
55%ofconsumersare
willingtopaymorefora
guaranteed good
experience.

141. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL3
Insights Driven Experience across Customer Journey
IoT and APIs
TOUCH POINTS
MARKETING NEXT-GEN COMMERCE CUSTOMER SERVICE
PROCESSCONTENT
MOBILITYWEB
USER EXPERIENCE
CUSTOMER
LIFE CYCLE
LOYALTYPURCHASECONSIDERATIONFAMILIARITYAWARENESS SERVICE
On Premise & Cloud
Analytics and Insights

142. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL4
Insights are the DNA of Success…
Provides Competitive Edge through Consumer Insights
Drives Customer Centric Growth
Creates Personalized Customer Experiences
Enables Faster Decision making, Reduced Cost,
& quicker launch of new Products and Services

143. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL5
Driving Value -Converting Insights into Action
Digital Customer Experience Management (DCxM)
Stitches information and weaves digital fabric for a superior customer experience

144. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL6
Experience-as-a-Service
Experience-
as-a-Service
Relevant
Content
Demand
Generation
Representative
Reviews
Doculytics
Digital
Hiring
Competition
Analysis
Digital
Self-care
Loyalty
Gamification
Createanengagingexperience
basedonrealtimedata,analytics
andrelevantcontext
Automatetheresumescreening
processandrecommendbest
profilesfortheJob
Measureandmetertheneedfor
everyproductfeatureinthearea
ofnewproductideas/innovation
Movetrafficfromassistedtoun-
assistedchannelsbyproviding
managednavigationsandsolution
snippets
Relevancyenginetoensuremost
relevantinformationisextracted
andtoboostthesearchrelevancy
foronlineconversions
Summaryofreviewsthatextracts
insightsonpricing,promotion,
churnandcompetition
HelpprovidesmartOCRfor
contractmanagement, credit
extensions,mortgageadvisory&
documentclassification
Provide cross channel
conversationsbyclusteringclient
interestsfortargetedcontent
delivery

145. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL7
Business Benefits
Lead Generation and Demand Metering
by targeting relevant customers and improve
online conversion
INCREASED REVENUES
Offer Personalized Campaigns and relevant
experience across digital channels, leading to
improved Loyalty and Qualified Referrals
PERSONALIZED CUSTOMER EXPERIENCE
Streamlined and efficient processes through
digitizing document driven operations
PROCESS DIGITIZATION
Incorporate Customer Intelligence through
Social Listening in designing products,
pricing, promotions
PRODUCT INNOVATION

146. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL8
Machine
Learning
OCR
Natural
Language
Processing
Information
Extraction
Services on MongoDB
MongoDB helps in aggregating unstructured
information with higher computational
flexibility to drive insights in real time
Unsupervised
Algorithm

147. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL9
Why MongoDB ?
0
Embeddeddocuments,transformationthroughmap-reduce,derived
valuesandvalidationframeworkshelpsourceandstorehighvolume,and
agooddesign
Schema less design helps to inject data from any channel, format into the system
seamlessly & develop application layer with “Separation of Concern” principle.

148. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL10
Why MongoDB ?
0
Out-of-the-box JSON documents help in connecting data into the
dashboard through AJAX calls that filter data on the front end
Providesadvancedsearchcapabilitieswhichisnotpossiblewith
traditionalsearchtools
CloudandopensourceplatformencompassingmodulesincludingNLP,text
analytics,OCR,InformationExtraction,MachineLearningandAnalytics

149. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL11
Launching DCxM3.0

150. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL12
Chandra Surbhat
@Surbhat
Visit us @ Wipro Booth# 7
THANK YOU
Prasad Pillalamarri
@PPillalamarri

151. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL13
Appendix

152. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL14
DCxM 3.0 –Document Classification

153. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL15
DCxM 3.0 –Document Classification

154. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL16
DCxM 3.0 –Demand Generation

155. © 2016 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL17
DCxM 3.0 –Demand Generation