Ajit jaokar slides

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Getting started in maths:

http://www.opengardensblog.futuretext.com/archives/2016/01/what-is-the-best-way-

for-getting-started-in-statistics-for-programmersdata-science.html

Lipstick Robot(Deep learning)

http://www.opengardensblog.futuretext.com/archives/2016/02/the-lipstick-robot-a-

great-way-to-explain-deep-learning.html

Evolution of Deep learning models:

http://www.opengardensblog.futuretext.com/archives/2015/07/evolution-of-deep-

learning-models.html

http://www.opengardensblog.futuretext.com/archives/2016/01/data-science-for-

internet-of-things-practitioner-course-march-2016.html

http://www.opengardensblog.futuretext.com/archives/2016/01/what-is-the-best-way-for-getting-started-in-statistics-for-programmersdata-science.html

























http://www.opengardensblog.futuretext.com/archives/2016/02/the-lipstick-robot-a-great-way-to-explain-deep-learning.html





















http://www.opengardensblog.futuretext.com/archives/2015/07/evolution-of-deep-learning-models.html










http://www.opengardensblog.futuretext.com/wp-content/uploads/2016/01/dsiot-futuretext-banner-300x100.jpg


Ajit Jaokar

Roadmap and Big Picture


Ajit Jaokar

-

Data Science for IoT @Oxford Uni + UPM(Smart cities) + Online Next book part of Stanford Uni course In 2015, Ajit was included in 16 Top Data Science bloggers on Data Science Central, Top 100 blogs on KDnuggets and Top 50 people to follow on Twitter by IoT central for IoT. World Economic Forum Spoken at MWC(5 times), CEBIT, CTIA, Web 2.0, CNN, BBC, Oxford Uni, Uni St Gallen, European Parliament. @feynlabs – teaching kids Computer Science. Adivsory – Connected Liverpool www.opengardensblog.futuretext.com

http://www.opengardensblog.futuretext.com/


Data Science for Internet of Things – practitioner course – March

2016

Now running in it’s second batch ..

Welcome to the world’s first course that helps you to become a

Data Scientist for the Internet Of Things ..


http://www.opengardensblog.futuretext.com/wp-content/uploads/2015/09/ajit-jaokar-DS.jpg

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Ajit Jaokar

The Big Picture – The Data Science and IoT landscape

Copyright : Futuretext Ltd. London

Internet of Things

CNN,

RNN

Data Lake Event

Based

analysis

Rules/

Workflow

Edge

Processing

Engine Rules/

Workflow

Alerts

Trigger s

Actions Cloud / Data Lake Edge Device

Event

Collector

Predictive Alerts

Stream Processing System

Event

Store

Analytics

Model Build Model

HDFS

Batch Processing System

Validate

Event

Sequence

CNN,

RNN

Data Lake Event

Based

analysis

CEP

CEP

CEP


Ajit Jaokar

INTERNET OF THINGS


As the term Internet of Things implies (IOT) – IOT is about Smart objects For an object (say a chair) to be ‘smart’ it must have three things - An Identity (to be uniquely identifiable – via iPv6) - A communication mechanism(i.e. a radio) and - A set of sensors / actuators + Physical context(ex location) Social context + Decisions at the ‘edge’ ex with sensor fusion and even in offline mode Workflow – (IFTTT) often also at the edge – Thus, IOT is all about Data .. IoT != M2M (M2M is a subset of IoT)


Ajit Jaokar

Many of the consumer IOT cases will happen with iBeacon in the next two years


Ajit Jaokar

And 5G will provide the WAN connectivity 5G - Source – Ericsson


Closed Loop Message –

Response System

Senso

rs Rules/

Workflow

Edge Processor

Rules/

Workflow

Analytic Workbench: Operational

Investigative, Predictive Analytics

and Machine Learning

Possible

Specialized Store

Enterprise Apps:

ERP, CRM, and

other enterprise

apps

Alerts

Trigger

Actions Cloud Based

Central Repository

Source: http://events.linuxfoundation.org/sites/events/files/slides/EdgeProcessing-

allseenalliance_4x3_template_24sept2014.pdf

http://events.linuxfoundation.org/sites/events/files/slides/EdgeProcessing-allseenalliance_4x3_template_24sept2014.pdf




12

iOt relates to Automation in three key areas based on Sensing and Predicting

a) Move from exception handling to patterns of exceptions over time.(are

some exceptions occurring repeatedly? Do I need to redsign my product, Is that a

new product?) –

b) Move from optimization to disruption – ownership to rental ship (Where are all

these dynamic assets?)

c) Move to self learning: Robotics: From assembly line to self learning

robots(Boston Dynamics), autonomous helicopters


Machines generate Data - Types of Big Data Status Data almost everything will have a status data. This will create vast amounts of data – much of it will be summarized at the ‘edge’ Location Data: Almost everything will have location data even if that location is static. Things will be in transit (where is my product/car etc etc) Machines taking action: Thermostat is automatically reduced Actionable Data: Data in human actionable form – workflow – IFTTT Machines learning by themselves in areas where there are no ‘rules’ – Most interesting space – best example is Deep Learning


Data Science for IoT: The role of hardware in analytics

Processing at the Edge (which Cisco and others have called Fog Computing). Alternately, we see entirely new classes of hardware specifically involved in Data Science for IoT(such as synapse chip for Deep learning)

http://www.cisco.com/web/about/ac50/ac207/crc_new/university/RFP/rfp13078.html

http://www.theguardian.com/technology/2014/dec/09/synapse-ibm-neural-computing-chip


Edge computing


Different Data Formats

POS data

Social media

External feeds

Payments

Log data

Telephone

conversations

RFID Scans

Events

Emails

Sensors

Free-form text

Geospatial

Audio

Still images/videos

Transactions

Call center notes

Adapted from Ravi Kalakota PhD


IoT Reference Stack

Portal Dashboard

API

Manageme

nt Event Processing and Analytics

Aggregation / Bus Layer

ESB and Message Broker

Devices

Communications

MQTT / HTTP/COAP

Devic

e M

gr

Ide

nti

ty &

Acc

ess M

an

ag

em

en

t

Pro

toco

ls

Sta

nd

ard

s

Industrial Internet Consumer Governance

Smart

Grid

Manufacturi

ng

Logistic&

Transpor

tation

Robotics Connecte

d Car Wearabl

es Health

Public

Safety

Smart

Cities Retail


Multiple Protocols of IOT

HTTP/ REST, MQTT, COAP, etc

TCP, UDP IPV6, IPV6 w 6LOWPAN, etc

Wireless (802.15.4, Wifi, BLE,

etc.)

Higher layer protocols ‒ Application

‒ Transport

‒ Network

Higher layer protocols ‒ Link layer










Ajit Jaokar

MACHINE LEARNING


What is Machine Learning? Mitchell's Machine Learning Tom Mitchell in his book Machine Learning “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.”

formally: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Think of it as a design tool where we need to understand: What data to collect for the experience (E) What decisions the software needs to make (T) and How we will evaluate its results (P).

A programmers perspective: Machine Learning involves: a) Training of a model from data b) Predicts/ Extrapolates a decision c) Against a performance measure.


Technique Applicability Algorithms

Classification

Most commonly used

technique for predicting a

specific outcome such as

response / no-response, high /

medium / low-value

customer, likely to buy / not

buy.

Logistic Regression —classic

statistical technique but now

available inside the Oracle

Database and supports text

and transactional data

Naive Bayes —Fast, simple,

commonly applicable

Support Vector Machine—

Next generation, supports text

and wide data

Decision Tree —Popular,

provides human-readable

rules

Source: Oracle


Regression

Technique for predicting

a continuous numerical

outcome such as customer

lifetime value, house

value, process yield rates.

Multiple Regression —

classic statistical

technique but now

available inside the

Oracle Database and

supports text and

transactional data

Support Vector Machine

—Next generation,

supports text and wide

data

Attribute Importance

Ranks attributes

according to strength of

relationship with target

attribute. Use cases

include finding factors

most associated with

customers who respond to

an offer, factors most

associated with healthy

patients.

Minimum Description

Length—Considers each

attribute as a simple

predictive model of the

target class

Source: Oracle


Anomaly Detection

Identifies unusual or

suspicious cases based on

deviation from the norm.

Common examples include

health care fraud, expense

report fraud, and tax

compliance.

One-Class Support Vector

Machine —Trains on

"normal" cases to flag

unusual cases

Clustering

Useful for exploring data and

finding natural groupings.

Members of a cluster are

more like each other than

they are like members of a

different cluster. Common

examples include finding

new customer segments, and

life sciences discovery.

Enhanced K-Means—

Supports text mining,

hierarchical clustering,

distance based

Orthogonal Partitioning

Clustering—Hierarchical

clustering, density based

Expectation Maximization—

Clustering technique that

performs well in mixed data

(dense and sparse) data

mining problems.

Source: Oracle


Association

Finds rules associated with

frequently co-occuring

items, used for market

basket analysis, cross-sell,

root cause analysis. Useful

for product bundling, in-

store placement, and defect

analysis.

Apriori—Industry standard

for market basket analysis

Feature Selection and Extraction

Produces new attributes as

linear combination of

existing attributes.

Applicable for text data,

latent semantic analysis,

data compression, data

decomposition and

projection, and pattern

recognition.

Non-negative Matrix

Factorization—Next

generation, maps the

original data into the new

set of attributes

Principal Components

Analysis (PCA)—creates

new fewer composite

attributes that respresent

all the attributes.

Singular Vector

Decomposition—

established feature

extraction method that has

a wide range of

applications.

Source: Oracle


Ajit Jaokar

KEY CONCEPTS – DATA SCIENCE AND IOT Deep learning

Big Data

Complex event Processing

Streaming


Ajit Jaokar

DEEP LEARNING


Internet of Things

CNN,

RNN

Data Lake Event

Based

analysis

Rules/

Workflow

Edge

Processing

Engine Rules/

Workflow

Alerts

Trigger s


Event

Collector

Predictive Alerts


Event

Store

Analytics

Model Build Model

HDFS


Validate

Event

Sequence

CNN,

RNN

Data Lake Event

Based

analysis

CEP

CEP

CEP


And its coming to mobile phones!

.

http://venturebeat.com/wp-content/uploads/2015/03/IMG_8336.jpg


In a groundbreaking paper published today in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level. What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.

It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.

But by playing lots and lots of games many times over, the computer learnt first how to play, and then how to play well.

http://dx.doi.org/10.1038/nature14236

http://deepmind.com/


Deep Learning and Feature learning Deep Learning can be hence seen as a more complete, hierarchical and a ‘bottom up’ way for feature extraction and without human intervention. Source: ELEG 5040 Advanced Topics on Signal Processing (Introduction to Deep Learning) by Xiaogang Wang



Ajit Jaokar

Big Data – Hadoop, Spark


Internet of Things

CNN,

RNN

Data Lake Event

Based

analysis

Rules/

Workflow

Edge

Processing

Engine Rules/

Workflow

Alerts

Trigger s


Event

Collector

Predictive Alerts


Event

Store

Analytics

Model Build Model

HDFS


Validate

Event

Sequence

CNN,

RNN

Data Lake Event

Based

analysis

CEP

CEP

CEP


Diagram courtesy of Mark Grover.


HDFS Databa

se Dashbo

ards

Kafka

Flume

HDFS ZeroM

Q Twitter

Spark

Streaming

Spark streaming https://spark.apache.org/docs/0.9.0/img/streaming-arch.png

https://spark.apache.org/docs/0.9.0/img/streaming-arch.png





Optional Storage

And Queries

Real-time

Feeds

Stream Processing Application

Alerts

Actions

Memory

Disk

Source: The 8 Requirements of Real-Time Stream Processing

By Michael Stonebraker et al


Kafka

Producers

Brokers

Consumers

Front End Front End Front End Service

Hadoop

Clusters

Security

systems

Real-time

monitorin

g

Other

consumer

service

Data

warehous

e


NoSql

HDFS Data

Sources

Stream Processing Architecture based on Apache Spark Adapted from

http://ingest.tips/2015/06/24/real-time-analytics-with-kafka-and-spark-streaming/

















Ajit Jaokar

Complex Event Processing


Internet of Things

CNN,

RNN

Data Lake Event

Based

analysis

Rules/

Workflow

Edge

Processing

Engine Rules/

Workflow

Alerts

Trigger s


Event

Collector

Predictive Alerts


Event

Store

Analytics

Model Build Model

HDFS


Validate

Event

Sequence

CNN,

RNN

Data Lake Event

Based

analysis

CEP

CEP

CEP


For example: • Complex event processing involves combining outputs of multiple

sensors and inferring events from readings even when the event is not directly observed by a specific sensor. For Complex event processing, we also need to add statistical models such as likelihood, confidence and probability using techniques like Bayesian networks, neural networks, Dempster-Shafer methods, kalman filters etc (ex care home – image Guardian)

http://en.wikipedia.org/wiki/Complex_event_processing

http://en.wikipedia.org/wiki/Complex_event_processing

http://biorobotics.ri.cmu.edu/papers/sbp_papers/integrated3/kleeman_kalman_basics.pdf


Quaternions

Heading Pitch, roll and

yaw Linear

acceleration Gravity

Sensor fusion

algorithm

Inputs Outputs

3 –axis earth magnetic field

3 –axis linear acceleration

3 –axis angular rate Source: ST microsystems


Ajit Jaokar

Methodology for Data Science for IoT


Creating an open methodology for Internet of Things (IoT) Analytics: Data science for Internet of Things January 9, 2016 By ajit Leave a Comment

http://www.opengardensblog.futuretext.com/archives/author/ajit

http://www.opengardensblog.futuretext.com/archives/2016/01/creating-an-open-methodology-for-internet-of-things-iot-analytics-data-science-for-internet-of-things.html

http://www.opengardensblog.futuretext.com/wp-content/uploads/2016/01/IoT-analytics-methodology.jpg


There is no specific methodology to solve Data Science for IoT (IoT

Analytics) problems.

This leads to some initial questions:

Should there be a distinct methodology to solve Data Science problems for

IoT?

Are IoT problems for Data Science unique enough to warrant a specific

approach?

What existing methodologies should we draw upon?

On one hand , A Data Science for IoT problem is a typical Data Science

problem. On the other hand, there are some unique considerations to IoT –

for example in the use of Hardware, High Data volumes, Use of

CEP(Complex event processing), impact of verticals(like automotive),

Impact of streaming data etc.


Background and inspiration

Some initial background:

Data mining has well known methodologies such as Crisp DM. Hilary Mason

and others have also proposed specific methodologies for Data Science .

Kaggle problems have a specific approach to solving them . With techniques

like PFA(Portable format for Analytics) provide a way of formalizing and

moving Analytics models.

All these strategies also apply to IoT. IoT itself has methodologies like Ignite

IoT – but these do not cover IoT analytics in detail.

A methodology for IoT analytics(Data Science for IoT) should cover the

unique aspects of each step in Data Science. For example: It is more than

the choice of the model family. The choice of the model family (ANN, SVM,

Trees, etc) is only one of the many choices to make – Others include :

http://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html

http://cdn.oreillystatic.com/en/assets/1/event/54/Practical Problems and Solutions for Machine Learning Presentation.pdf

http://machinelearningmastery.com/discover-the-methodology-and-mindset-of-a-kaggle-master-an-interview-with-diogo-ferreira/

http://www.kdnuggets.com/2016/01/portable-format-analytics-models-production.html

http://www.kdnuggets.com/2016/01/portable-format-analytics-models-production.html

https://projects.eclipse.org/proposals/igniteiot

https://projects.eclipse.org/proposals/igniteiot


a) Choice of the model structure – optimisation methodology (CV,

Bootstrap, etc)

b) Choice of the model parameter optimisation algorithm (joint gradients

vs. conjugate gradients )

c) Preprocessing of the data (centring, reduction, functional reduction, log-

transform, etc.)

d) How to deal with missing data (case deletion, imputation, etc.)

e) How to detect and deal with suspect data (distance-based outlier

detection, density-based, etc.)

f) How to choose relevant features (filters, wrappers, embedded method ?)

g) How to measure prediction performances (mean square error, mean

absolute error, misclassification rate, lift, precision/recall, etc.)

source Methodology and standards for data analysis with machine learning

tools Damien Fran¸cois ∗

https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2008-4.pdf




The methodology could also cover -

Exploratory analysis of data

Hypothesis testing (“Given a sample and an apparent effect, what is the

probability of seeing such an effect by chance?” )

and other ideas ..

Who?

Ajit Jaokar – futuretext

Jean-Jacques (JJ) Bernard, management & technology consultant

Shiva soleimani – student - Isfahan university

http://www.analyticsvidhya.com/blog/2014/08/baby-steps-python-performing-exploratory-analysis-python/



Data Science for Internet of Things – practitioner course – March

2016

Now running in it’s second batch ..

Welcome to the world’s first course that helps you to become a

Data Scientist for the Internet Of Things ..


http://www.opengardensblog.futuretext.com/wp-content/uploads/2015/09/ajit-jaokar-DS.jpg

http://www.opengardensblog.futuretext.com/wp-content/uploads/2015/09/sumit.png

http://www.opengardensblog.futuretext.com/wp-content/uploads/2015/09/paul-DS.jpg


Weekly schedule Concepts

Week 0 March 15 Orientation, introductions, Personal learning plans, Platform

signup

Week 1 mar 21 Foundations:An analytics Driven Organization – IoT and

Machine Learning - Data Science for IoT – Unique

characteristics – Data Science for IoT – why now?

Mar 28 Machine Learning concepts Deep Learning concepts

Apr 4 An introduction to IoT (Internet of Things)

Apr 11 IoT platforms – From sensor to Cloud

Apr 18 Concepts of Big Data Part One

Apr 25 Concepts of Big Data Part Two

May 2 Market drivers for IoT

May 9 Choosing a model – what technique to Use?

May 16 Use Cases and IoT datasets (these will continue throughout

the course)

May 23 Time series and NoSQL databases


May 30 Streaming analytics part One

June 6 Streaming analytics part two

June 13 Deep learning part one

June 20 Deep learning part two

June 2 7 Machine learning algorithms – part one

July 4 Machine learning algorithms – part two

July 11 Mathematical foundations – part one

July 18 Mathematical foundations – part two

July To Dec 31 Project

Contact us at [email protected] to signup



Programming

Week 0 Mar 15 Orientation, introductions, Personal

learning plans, Platform signup

Week 1 mar 21

Mar 28

Apr 4 Intro to R, Installations, Basics of R

Apr 11

Apr 18 Data Frames in R & Tabular Data

Apr 25

May 2 Data Processing & Data Visualization in R

May 9

May 16 Scala basics

May 23

May 30 Spark batch processing I


June 6

June 13 Spark Batch Processing II

June 20

June 2 7 Spark SQL

July 4

July 11 Spark Streaming

July 18

July To Dec 31 Projects

Contact us at [email protected] to signup



@ajitjaokar

[email protected]




A Reference Architecture for the Internet of Things Daniel Karzel, Hannelore Marginean, Tuan-Si Tran adapted from defined by IoT-A The IoT interconnects the Things in order to exchange information to fulfill tasks for the users. Ideas of fridges communicating not only with your smart-phone, but with the producer's server farm or an energy power plant will soon become reality. Terminology: • Thing: An object of our everyday life placed in our everyday

environment. A thing can be a car, fridge but can also be abstracted to a complete house or city depending on the use case.

• Device: A sensor, actuator or tag. Usually the device is part of a thing. The thing processes the devices’ context information and communicates selected information to other things. Furthermore, the thing can pass actions to actuators.

• Interoperability and Integration components • Context aware components • Middleware components(load balancing etc) • Security

http://www.infoq.com/author/Daniel-Karzel,-Hannelore-Marginean,-Tuan~Si-Tran









http://www.iot-a.eu/public/public-documents/d1.5/at_download/file





Anind K. Dey’s context toolkit. The context toolkit was designed on an application level, as it was designed for Geographical Information Systems (GIS). In the IoT we have to extend the context toolkit towards the intercommunication between things. However, the basic idea of goal, context information and resulting actions remains in the IoT world.

http://contexttoolkit.sourceforge.net/


In the IoT world we don’t only define the goal on the user level (i.e. by application), but things themselves can work towards certain goals without actively including the user. In the end the devices still serve the user but they act autonomously in the background – which is exactly the idea of ubiquitous computing. Context defines the state of an environment (usually the user’s environment) in a certain place at a certain time. The context model usually distinguishes between context elements and context situation. Context elements define specific context, usually on the device level. A context element can be for example a temperature value at a certain time and location.

http://www.ubiq.com/hypertext/weiser/UbiHome.html



Location and time are context elements themselves, but they play a special role as they are needed to locate sensor values in space and time. Without knowing where and when a temperature was measured the temperature does not help much for making conclusions. The context situation is an aggregation of context elements. The context situation is thus a view on the environment in a certain location at a certain time. Similarly to the context model you can also define an action model that defines what things can trigger (e.g. open a window, take a photo). Actions can only be triggered with the combination of context information (e.g. a context situation) and defined goals. Goals are usually depicted as rules of a rule engine (e.g. IF temperature > 25* THEN open window).




Consists of 6 layers. Besides these layers there are two “cross-section-layers” that affect all other layers, namely “Security” and “Management”.


The device integration layer connects all the different device types and consumes device measurements as well as it communicates actions (on device level). This layer can be seen as a translator that speaks many languages. The output of the sensors and tags depends on the protocol they implement. The input of the actuators is also defined by the protocol they implement.


The device management is in charge of taking device registrations and sensor-measurements from the device integration layer. Furthermore it communicates status changes for actuators down to the device integration layer. The device integration layer then just validates that the status change (i.e. the action) is conform with the actuator and then translates the status change to the actuator.


The data management can be seen as a central database that holds all data of a “thing”, but this is only one possible implementation. For larger things within the system (e.g. a device life-cycle monitoring system collecting data from other things) data management might be a data warehouse or even a complete data farm. The implementation of the data management layer thus strongly depends on the use-case for the specific thing.


The context management defines the central business logic and is responsible for six tasks: 1. Define the goals of the thing. 2. Consume the context situation(s) of other things 3. Produce the (own) context situation of the thing. 4. Evaluate the (own) context situation towards the goal. 5. Trigger actions that help to fulfill the goal according to the evaluated rules. 6. Publish context situations for other things.


According to these tasks we can divide the context management into eight components as shown below.


Rule Engine & Artificial Intelligence (AI): Define and manage all of the rules necessary for context evaluation. This includes the goal (which is basically as set of rules) as well as rules for creating the context situation and actions. Context Situation Integration Module: Listens to context situations of other things and integrates the incoming context situations. Action Integration Module: Incoming actions of other things are evaluated and passed on to the device management layer by this component. Rules have to be considered, that define in which situations an action received from another thing can be passed on for triggering an actuator. Context Situation Creator Module: Collects data from the system and builds the context situation(s). This can also be driven by rules. Action Creator Module: Similar to the context situation creator module, action objects have to be created once triggered during rule evaluation.


Context Situation Publisher Module: Provide context situations to the thing integration layer. According to the sophistication level of the implementation the context situation publisher can provide a set of context situations for different things that are subscribed or one context situation for everybody. The context situation publisher module has to take care of data permission levels towards other things. Only trusted other things should receive selected context information. Furthermore this module has to take care of defining the context situation schemas that are communicated to other things that want to subscribe. The schema is used to evaluate whether a thing is capable of communicating with another thing. Action Publisher Module: Similar to the context situation publisher module this module is responsible to communicate actions to the thing integration layer to be communicated to other things. Additionally the action schema(s) are managed by this component.


Context Evaluation Module: Evaluates the rules using the (current) context situation and triggers actions that are communicated down to the devices or to the action creator module. The action creator module in turn passes the created actions to the action publisher that communicates the actions to other things. One way to simply evaluate rules is to build decision trees from the rules defined by the rule engine. The concrete architecture and complexity of offered functionality strongly depends on the use case for the thing under development. Especially the rule engine & artificial intelligence component might not have to be very sophisticated for less intelligent things (e.g. a fridge). For things that collect context information from other systems these components will, however, be very sophisticated. Higher sophistication can be for example data science and data mining techniques.


The thing integration layer is responsible for finding other things and communicating with them. Once two things found each other they have to undergo a registration mechanism. The thing integration layer has to evaluate if the communication with the thing to be partnered with is possible. For this purpose the context situation and/or action schemata have to be compared. These are provided by the context management layer. If the schema-match is evaluated positively, the thing can notify the other thing upon new context situation or action creation. The context situations and actions to be communicated to other things are provided by the context management layer. The thing registration can be done in a central component or by the thing itself (e.g. auto-discovery network scan).



The application integration layer connects the user to the thing. Applications that are (directly) on top of the architecture are located here. The application integration can be seen as a service layer, or even as a simple UI on top of the stack. The concrete implementation of the layer depends on the use case.


@ajitjaokar

[email protected]



Ajit jaokar slides

Data & Analytics