IOT, CONNECTED CARS & BIG DATA ANALYTICS Subramaniam Ganesan, - - PowerPoint PPT Presentation

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IOT, CONNECTED CARS & BIG DATA ANALYTICS Subramaniam Ganesan, - - PowerPoint PPT Presentation

Jan 11, 2024 463 likes •735 views

CENTER FOR DATA SCIENCE SCIENCE Strength in Numbers AND BIG D BIG DATA ANALYTICS IOT, CONNECTED CARS & BIG DATA ANALYTICS Subramaniam Ganesan, School of Engineering and Computer Science Vijayan Sugumaran, Ravi kattre and other

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CENTER FOR

DATA SCIENCE SCIENCE

AND

BIG D BIG DATA

ANALYTICS

Strength in Numbers

IOT, CONNECTED CARS & BIG DATA ANALYTICS

Subramaniam Ganesan, School of Engineering and Computer Science Vijayan Sugumaran, Ravi kattre and other members of the Center

  • Dec. 1, 2016

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

What’s the Internet of Things

From any time ,any place connectivity for anyone, we will now have connectivity for anything!

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The Internet of Things, refers to a wireless network between

  • bjects, and internet.
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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

IIOT- Industrial Internet of Things

Energy, health care, automotive, manufacturing Industries are viewing at IIOT. Here Robots, sensors, machines in a plant etc are connected as IIOT. Industrial Ethernet, WiFi, Bluetooth mesh network …..

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Sensor technology

Wireless sensor technology play a pivotal role in bridging the gap between the physical and virtual worlds, and enabling things to respond to changes in their physical environment. Sensors collect data from their environment, generating information and raising awareness about context. Example: sensors in an electronic jacket can collect information

about changes in external temperature and the parameters of the jacket can be adjusted accordingly

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

A connected car

It is a car that is equipped with internet access, and usually also with a wireless local area network. This allows the car to share internet access to other devices both inside and outside the vehicle. A connected car is connected to Internet, other cars and infrastructure.

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Vehicle-to-Infrastructure Communication

  • We want to know where vehicles are, what they’re doing
  • Many sensors are already in the field/car to do this
  • With V to I, we wish to communicate the hazardous road conditions

and about approaching vehicles.

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

How it Works

  • Transmit data from the vehicle
  • Data from GPS, accelerometers, magnetometers, or

in-vehicle sensors

  • Transmit to other vehicles or roadside equipment using
  • Cellular, Bluetooth, WiMAX, Wi-Fi, DSRC

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Potential of Connected Vehicles

  • Three ways to connect:

1) Vehicle-to-vehicle:

  • For Crash avoidance
  • Broadcast your vehicle speed etc to other vehicles

2) Vehicle-to-infrastructure:

  • Incident detection
  • Weather/ice detection

3) Infrastructure-to-vehicle

  • Broadcast traffic signal timing
  • Dynamic re-routing

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Automotive Sensor Net

  • A network of sensors like multiple radars and camera in automobile

help in lane sensing, object, and hazard identification.

  • Safety applications include adaptive cruise control, pre-crash

prediction, active head-rest, tire pressure monitoring, rain sensors to adjust braking, multiple airbag.

  • Fusion of multiple sensors.

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Technical Challenges

  • Development of new types of smart-sensors for different

applications

  • Development of low cost sensors with more functionality, small size,

and low power consumption.

  • Integration of sensors in the application or system
  • Sensor Maintenance:

Self diagnosing Self healing Self calibrating Self correcting

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Imagine the opportunities to use real-time data from the vehicle. Complex analytical models running in the cloud or even on board the vehicle can predict service events and notify the driver. In real time, drivers could be notified of a defect in the vehicle or maintenance issue. Volvo Truck is doing exactly that, and more. It strives to provide service and maintenance before a breakdown. Volvo monitors quality and product warranties, analyzing more than 100 parameters to predict the wear on a component, identify abnormal events and speed up the diagnostics of incidents affecting the vehicle.

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Location-based offers: traffic, weather, parking, gas and charging station locations are used to communicate with a person in the environment. It can be used to pass information and marketing details

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Location Based Analysis and Service

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and

  • ther useful business information.

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

What is Data Mining?

  • Discovery of useful, possibly unexpected, patterns in

data

  • Non-trivial extraction of implicit, previously unknown

and potentially useful information from data

  • Exploration & analysis, by automatic or

semi-automatic means, of large quantities of data in

  • rder to discover meaningful patterns

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Data Mining Tasks

  • Classification [Predictive]
  • Clustering [Descriptive]
  • Association Rule Discovery [Descriptive]
  • Sequential Pattern Discovery [Descriptive]
  • Regression [Predictive]
  • Deviation Detection [Predictive]
  • Collaborative Filter [Predictive]

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

MapReduce Model DAG Model Graph Model BSP/Collective Model Storm Twister For Iterations/ Learning For Streaming For Query S4 Drill Hadoop MPI Dryad/ DryadLINQ Pig/PigLatin Spark Shark Spark Streaming MRQL Hive Tez Giraph Hama GraphLab Harp GraphX HaLoop Samza The World

  • f Big Data

Tools Stratosphere Reef

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Layered Architecture (Upper)

  • NA – Non Apache projects
  • Green layers are Apache/Commercial

Cloud (light) to HPC (darker) integration layers

Orchestration & Workflow Oozie, ODE, Airavata and OODT (Tools)

NA: Pegasus, Kepler, Swift, Taverna, Trident, ActiveBPEL, BioKepler, Galaxy

Data Analytics Libraries: Machine Learning

Mahout , MLlib , MLbase CompLearn (NA)

Linear Algebra

Scalapack, PetSc (NA)

Statistics, Bioinformatics

R, Bioconductor (NA)

Imagery

ImageJ (NA)

MRQL

(SQL on Hadoop, Hama, Spark)

Hive

(SQL on Hadoop)

Pig

(Procedural Language)

Shark

(SQL on Spark, NA)

Hcatalog Interfaces Impala (NA) Cloudera (SQL on Hbase) Swazall

(Log Files Google NA)

High Level (Integrated) Systems for Data Processing Parallel Horizontally Scalable Data Processing Giraph ~Pregel Tez

(DAG)

Spark

(Iterative MR)

Storm S4 Yahoo Samza LinkedIn Hama

(BSP)

Hadoop (Map Reduce) Pegasus

  • n Hadoop

(NA)

NA:Twister

Stratosphere Iterative MR

Graph Batch Stream Pub/Sub Messaging Netty (NA)/ZeroMQ (NA)/ActiveMQ/Qpid/Kafka ABDS Inter-process Communication Hadoop, Spark Communications MPI (NA) & Reductions Harp Collectives (NA) HPC Inter-process Communication

Cross Cutting Capabilities

Distributed Coordination: ZooKeeper, JGroups Message Protocols: Thrift, Protobuf (NA) Security & Privacy Monitoring: Ambari, Ganglia, Nagios, Inca (NA)

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Layered Architecture (Lower)

  • NA – Non Apache projects
  • Green layers are Apache/Commercial

Cloud (light) to HPC (darker) integration layers

In memory distributed databases/caches: GORA (general object from NoSQL), Memcached

(NA), Redis(NA) (key value), Hazelcast (NA), Ehcache (NA); Mesos, Yarn, Helix, Llama(Cloudera) Condor, Moab, Slurm, Torque(NA) …….. ABDS Cluster Resource Management HPC Cluster Resource Management ABDS File Systems User Level HPC File Systems (NA) HDFS, Swift, Ceph FUSE(NA) Gluster, Lustre, GPFS, GFFS Object Stores POSIX Interface Distributed, Parallel, Federated iRODS(NA) Interoperability Layer Whirr / JClouds OCCI CDMI (NA) DevOps/Cloud Deployment Puppet/Chef/Boto/CloudMesh(NA)

Cross Cutting Capabilities

Distributed Coordination: ZooKeeper, JGroups Message Protocols: Thrift, Protobuf (NA) Security & Privacy Monitoring: Ambari, Ganglia, Nagios, Inca (NA)

SQL

MySQL

(NA)

SciDB

(NA) Arrays,

R,Python

Phoenix

(SQL on HBase)

UIMA (Entities) (Watson) Tika

(Content)

Extraction Tools

Cassandra

(DHT)

NoSQL: Column HBase

(Data on HDFS)

Accumulo

(Data on HDFS)

Solandra

(Solr+ Cassandra) +Document

Azure Table

NoSQL: Document

MongoDB

(NA)

CouchDB Lucene Solr Riak ~Dynamo NoSQL: Key Value (all NA) Dynamo Amazon Voldemort ~Dynamo Berkeley DB Neo4J

Java Gnu (NA)

NoSQL: General Graph

RYA RDF on Accumulo NoSQL: TripleStore RDF SparkQL AllegroGraph Commercial Sesame (NA) Yarcdata

Commercial (NA)

Jena ORM Object Relational Mapping: Hibernate(NA), OpenJPA and JDBC Standard

File Management

IaaS System Manager Open Source Commercial Clouds OpenStack, OpenNebula, Eucalyptus, CloudStack, vCloud, Amazon, Azure, Google Bare Metal Data Transport BitTorrent, HTTP, FTP, SSH Globus Online (GridFTP)

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

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  • We work on Data Mining and Algorithms development.
  • We mine the Data collected from connected cars for

Condition based maintenance (CBM) and predictive maintenance.

  • CBM is useful for Military Vehicles diagnostics and

preventive maintenance based the sensor data before failure. A few PhD thesis, MS thesis, and journal articles have been completed on CBM and related areas

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

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  • We develop sensors for health monitoring.
  • Develop techniques to collect health data
  • f a Vehicle driver
  • Develop algorithms to analyze the health

data and alert Hospital.

Big Data and Health Sensor Monitoring We have published papers and written project proposals on health data monitoring

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

Our Expertise in Data center.

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Data Center has multi disciplinary expertise including, Statistical and data analysis, bio data intelligence etc. We bring expertise in Sensor, wireless communication, data base architecture, real time processing etc.

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CENTER FOR DATA SC SCIENC IENCE AND BIG BIG DATA A ANALYTICS

Strength in Numbers

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We will have 4 and 8 credits Engineering and Computer Science graduate and undergraduate student projects and independent studies done in the center. Regular Interaction with the Center faculty will result in good quality projects. Interaction with the Industry and Government agencies through the Data Center will provide wide opportunities to work on projects relevant to community.

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CENTER FOR

DATA SCIENCE SCIENCE

AND

BIG D BIG DATA

ANALYTICS

Strength in Numbers

Questions?

Thanks ganesan@Oakland.edu

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