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Data in Motion - tech-intro-for-paris-hackathon

  1. Thierry Gruszka Senior Technology Manager 4th Nov. 2015 Workshop Cisco DevNet Hackathon Data in Motion - DMo
  2. DATA !? Wisdow Knowledge Information Data • Je ferais bien de m’arrêter Control • Je conduis et le feu tricolore vers lequel je me dirige passe au 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,, v2.0Raw DMo
  3. © 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
  4. 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
  5. Examples and Use Cases
  6. 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
  7. 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
  8. 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
  9. 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.
  10. 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
  11. Data in Motion Architecture
  12. 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
  13. • 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 RESTful communication paradigm then sends this rule. This is the main publish part. How does it works…
  14. Data in Motion is a native application in Cisco IOx IOS + IOx SDK Virtual Machine Linux OS Data in Motion +IOx Application Management Control Plane Data Plane
  15. Hands On
  16. Data in Motion Policy / Rules A 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 (01) 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 (01) 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)
  17. More information on Data in Motion •