Thierry Gruszka

Senior Technology Manager

4th Nov. 2015

Workshop Cisco DevNet Hackathon

Data in Motion - DMo

DATA !?

Wisdow

Knowledge

Information

Data

• Je ferais bien de m’arrêter Control

• Je conduis et le feu tricolorevers lequel je me dirige passeau rouge

Context

• Le feu tricolore à l’Angle sud de la rue Tom et de l’avenue Jerry vient de passer au Rouge

Meaning

• Rouge, 192.234.235.245.678, v2.0Raw

DMo

© 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 3

• Data in Motion is an IoT software product that runs in the network to transform raw data from sensors and endpoints into actionable information.

• Data in Motion enables to build scalable IoT solutions

Data in Motion Overview

Cisco Confidential 4© 2013-2014 Cisco and/or its affiliates. All rights reserved.

Data in Motion at the Edge

Input Data

Store raw data or filtered data for general data management

Analytics

Cloud and Data Centers

Generate Actionable Events and learn new rules

Cache raw data or abstracted information

(e.g. indexed data)

Data in Motion:

Analyze First,

Optional Store

Input Data

<XML>

Rules can express:

Predicates and Filters

Data / Information

conversion

Summarization

Pattern Matching

Categorization &

Classification

Event Trigger analysis

Notifications

</XML>

sensor Router/Switch

Traditional Data

Management:

Store First,

Analyze later

Examples and Use Cases

Cisco Confidential 7© 2013-2014 Cisco and/or its affiliates. All rights reserved.

Mining

+ +

• Data reduction and summarization

• Event triggered Analysis

• Edge data subscription model

• Predicates

• Policy driven

• Categorization and classification (indexed)

• Content re-purposing

• Data understanding at the edge

• Programmability at the edge

• Connectivity

• Multiprotocol

• micro-CDN (store & forward)

1 2

VEHICLE WEIGHT: 08 TONS

GROSS WEIGHT: 16 TONS

Customer: Anglo American

Use case: Track truck pressure tires for load monitoring

Targeted Platform: 819H

Software Equipped: Data in Motion

Release Date: November 2013

Cisco Confidential 8© 2013-2014 Cisco and/or its affiliates. All rights reserved.

Smart Agriculture

HUMIDITY: 40%

TEMPERATURE: 82F

ACTION: SPRINKLER

ACTION: SPRINKLER

ACTION: SPRINKLER

• Content re-purposing

• Data understanding at the edge

• Programmability at the edge

• Connectivity

• Multiprotocol

• micro-CDN (store & forward)

Customer: University Space Research Association (USRA) for USAID

Use case: Frost Detection for Crop Management in Third World USAID Programs

Targeted Platform: UCS-E/C and CGR 1K

Software Equipped: Data in Motion

Release Date: April 2014

1

Cisco Confidential 9© 2013-2014 Cisco and/or its affiliates. All rights reserved.

Monitoring

Actual data is sent only

when system is at fault

Event is detected right

at the edge

EVENT: LEAKAGECONTAINER 107

Pressure : 2psi

Humidity: 14%

Temperature: 35F

Use Case with Event Notification (Surveillance)

Supporting various data Sources:

webcams, files with Data in

Motion.

Two major search capabilities

Searching people or objects

example: Search people carrying

a backpack and having short hair.

Searching scenes

example: Two people carrying

backpack within the same view

of a camera. One of them is

wearing black shirt and the other

is wearing white shirt.

Train jubatus with annotated training

data set

Data in Motion

…

Automatically add tags

using Machine Learning.

Search tags with temporal

Information. Full text search

Is also supported.

video analysis system

Jubatus learns which tags to set

for each person or object.

All you have to do is to provide

annotated data.

This system allows users to search

people or objects in their video

flexibly by using Machine Learning

and a search engine.

Example Use-case with video

• Purpose

• Annotate people’s appearance and behaviors

• Detect anomalies and make search index

• Application

• Alarm for crimes and suspicious behaviors

• Help investigating criminals on the run

• Search and locate suspects by characteristics

• Advantage

• No need to monitoring by human eye

• Instant search by characteristics tags

• No need to check all videos for massive hours

• Purpose

• Annotate customers’ appearance and behaviors

• Estimate their profile and intention in detail

• Application

• Detect unseen demands to serve

• Analyze POS data with detailed categorization

• Optimize items, layout and shopping process

• Advantage

• More precise and dynamic than analyzing only

POS and membership information

(1) Surveillance (2) In-store behavior analysis

Data in Motion Architecture

Data in Motion Data Sheet

Data in Motion plane

Data (Packets)

Data Acquisition & Transformation

Information

Rules/Patterns

Data to Information Capabilities• Event Detection & Aggregation

• Rule-Based Data Normalization

• Dynamic Sensors Polling

• Unstructured Data Understanding

• Data & Information Caching

• μ-CDN (Controlled Distribution)

• Pub-Sub API (Eclipse IDE)

Supported Platforms• UCS-E/Blade

• CGR-1K

• C8xx with Iox Packaging

Use Cases• Data Reduction and

Compression

• Sensor Virtualization and

Plug & Play

• The API interfaces with the user's programing environment. The user writes a software program that specifies what data s/he is interested in.

• The API helps the user translate rules in open standard JSON format encapsulated as a REST message that can be understood by the API.

• A key part is the format of the JSON messages used to express a rule. The API to the edge device of interest using a RESTfulcommunication paradigm then sends this rule. This is the main publish part.

How does it works…

Data in Motion is a native application in Cisco IOx

IOS +IOx SDK

Virtual Machine

Linux OSData in Motion+IOx

Application

Management

Control Plane Data Plane

Hands On

Data in Motion Policy / RulesA true Real time transaction with a Model Definition

• Dynamic Data Definition involve the relationship of three simple concepts

• Pattern Extraction real time content indexing

• Condition Rule Engine to query over index & algebraically

• Action Many, including data transformation and engaging network

connectivity

• Ultimately this breaks down into data understanding and of:

D3

Meta (1)

D3_Id, Context_ID, Processing Method (Timer, Cache)

Network (01)

Filterby: (protocol {tcp/ip, UDP}

Source/Dest IP, Source/Dest Port (multiple ANDed)

Decode: (variable A=first 8 Bits, var B=next 16 bits, etc….)

Application (01)

Filterby:

Protocol: http

Field: content-type:json, etc.

Content

Example: variable Temperature>56

Action (>1)

Type: Primitive

payload

Header

Type: Procedure

FetchData

Gpsupdate()

syslog

Type: Timed

FetchData

Gpsupdate()

syslog

• Network Meta Data • Application • Content

• Action(s)

More information on Data in Motion

• https://developer.cisco.com/site/data-in-motion/