Blockchain Technology and Its Application in Artificial Intelligence and Machine Learning, Big Data and Data Science

Dr. KOTRAPPA SIRBI
B.E, M.S, M Tech (CSE), Ph.D(CSE), MISTE
PROFESSOR in Computer Science and Engineering (CSE)
KLE DR MSS College of Engineering & Technology
Belgaum, Karnataka
E-mail : dr.s.kotrappa@klescet.ac.in
https://www.klescet.ac.in
Blockchain Applications in Big Data , Data Science and AI/MLT

Agenda
• Blockchain and Its Applications
 Introduction ,Building Blocks and Methodology
 Blockchain Features and Its Applications
• Blockchain and Big Data and Its Applications
• Blockchain and Data Science and Its Applications
• Blockchain and Artificial Intelligence/MLT
and Its Applications
• Conclusions

Introduction to Blockchain
• A blockchain is a decentralized, distributed,
and oftentimes public, digital ledger that is
used to record transactions across many
computers so that any involved record cannot
be altered retroactively, without the
alteration of all subsequent blocks.
• Blockchain use complex / pure mathematical
functions to create a secure and definite
record of who owns what and when.

Blockchain is Revolutionary
• Decentralized TRUST
• From Education to Banking ,
Insurance, Collection of Tax ,
Healthcare, IoT, Social Interactions ,
Governments, Supply Chains etc.,
• Connecting every network of
services like INTERNET.,

• A blockchain , originally block chain , is a growing list of
records, called blocks, that are linked using cryptography .
Each block contains a cryptographic hash of the previous
block, a timestamp, and transaction data (generally
represented as a Merkle tree).
The main chain (black) consists of the
longest series of blocks from the genesis
block (green) to the current block. Orphan
blocks (purple) exist outside of the main
chain.

Merkle Tree
• Merkle trees are a fundamental part
of blockchain technology.
• A merkle tree is a structure that allows for
efficient and secure verification of content in a
large body of data. This structure helps verify
the consistency and content of the data.
• Merkle trees are used by both Bitcoin and
Ethereum etc.,

Building Blocks of Blockchain
• Blockchain is a Technology to establish TRUST
• It consists of blocks
• Blockchain technology takes existing,
proven concepts and merges them
together into a single solution, and
• Networks , nodes , cryptography,
hashing ...

Building Blocks of Blockchain
• We live in networks ., Internet and Mobile…
• Node in any user in a network
 Computer, Mobile, IoT, Laptop etc..,
• Networks are of different types ,
 Centralized (Client-Server), and
 P2P (Peer-to-Peer)

Blockchain Technology and Its Application in Artificial Intelligence and Machine Learning, Big Data and Data Science

Interactions
• Nodes interact with other nodes on the
networks,
• These interactions are called Transactions,
• Message(sms),
• Documents (attachments), and
• Email
• Like…,

Blockchain Features
• Tokenization
• Decentralizated Systems
• Distributed Ledger
• Encryption
• Immutability
• Minting
• Safer and Secure Ecosystem

Blockchain and Its Applications
• What type of applications can we build
using blockchain technologies?
• From a new type of money (i.e.
cryptocurrency) to powering our digital
identity in the internet-of-things, the
blockchain technology application stack
is the engine powering these new
innovations.

Education

• Asset Management: Trade
Processing and Settlement
• Insurance: Claims
processing
• Payments: Cross-Border
Payments
• Unconventional money
lenders/ hard money
lending ,
• Blockchain Internet-of-
Things (IoT)
• Smart Appliances
• Blockchain music
• Blockchain Government
• Public value/ community
• Vested responsibility
• Blockchain Identity
• Passports
• Birth, wedding, and death
certificates
• Personal Identification
• Supply Chain Sensors
• Blockchain Healthcare

• “The blockchain is an incorruptible digital ledger of
economic transactions that can be programmed to
record not just financial transactions but virtually
everything of value.”
• The basic advantages of Blockchain technology are
decentralization, immutability, security, and
transparency. The blockchain technology allows for
verification without having to be dependent on third-
parties. The data structure in a blockchain is append-
only. So, the data cannot be altered or deleted.

• “The blockchain is an incorruptible digital ledger of
economic transactions that can be programmed to
record not just financial transactions but virtually
everything of value.”
• The basic advantages of Blockchain technology are
decentralization, immutability, security, and
transparency.
• The blockchain technology allows for verification
without having to be dependent on third-parties.
• The data structure in a blockchain is append-only. So,
the data cannot be altered or deleted.

&
What It Means to Big Data
• “Arguably the most significant development in
information technology over the past few
years, blockchain has the potential to change
the way that the world approaches big data,
with enhanced security and data quality just
two of the benefits afforded to businesses
using Satoshi Nakamoto’s landmark
technology.”

Importance of Big Data
• Blockchain, as have been noted, is in its
nascent stages though it may not appear
so due to the hype the technology have
gotten in a short period.
• One would expect that as the technology
matures and there are more innovations
around it, more concrete use cases will be
identified and explored — data science
being one area that will benefit from this.

• "Big data" is a field that treats ways to
analyze, systematically extract information
from, or otherwise deal with data sets that
are too large or complex to be dealt with by
traditional data-processing application
software.

5 Blockchain Use Cases in Big Data
• There are at least five specific ways blockchain
data can help data scientists in general.
1. Ensuring Trust (Data Integrity)
2. Preventing Malicious Activities
3. Making Predictions (Predictive Analysis)
4. Real-Time Data Analysis
5. Manage Data Sharing

• Blockchain and Big Data are among the top
emerging technologies tipped to revolutionize
several industries, radically changing the way
businesses and organizations are run. One
might assume that these technologies are
mutually exclusive — each forging unique
paths and applied independent of one
another.

How Blockchain Can Help Big Data?
• If big is the quantity, Maria Weinberger
of Janexter says, blockchain is the
quality.
• This follows the understanding that
blockchain is focused on validating data
while data science or big data involves
making predictions from large amounts
of data.

Big Data Analytics
• Big data analytics is the often complex
process of examining large and
varied data sets, or big data, to uncover
information -- such as hidden patterns,
unknown correlations, market trends and
customer preferences -- that can help
organizations make informed business
decisions.

• A blockchain, originally block chain, is a
growing list of records, called blocks, that are
linked using cryptography. ... By design,
a blockchain is resistant to modification of
the data. It is "an open, distributed ledger
that can record transactions between two
parties efficiently and in a verifiable and
permanent way".

Blockchain and Data Science
• Data science is a multi-disciplinary field
that uses scientific methods, processes,
algorithms and systems to extract
knowledge and insights from structured
and unstructured data.
• Nate Silver referred to data science as a
sexed up term for statistics.

• Blockchain — just like data science — is
gradually transforming the way several
industries operate. And while data science
focuses on harnessing data for proper
administration, blockchain ensures trust of
data by maintaining a decentralized ledger.

How can blockchain disrupt data
science?
• To answer these questions, it will be helpful to get a better
understanding of Blockchain and Data Science separate from one
another.
• What is Blockchain?
• Blockchain is basically a distrusted ledger that records economic
transactions such that they cannot be manipulated.
• The technology came into prominence as a result of the interest in
bitcoin and cryptocurrency in general but has since found relevance
in recording not just cryptocurrency transactions but anything of
value.
• Knowing the capabilities of this emerging technology, developers
and tech enthusiasts have gone to work fashioning out use case
after use case for blockchain.

• High Demand for Blockchain Developers
• The demand for blockchain developers has
swelled in the last few years just as projects
working on different applications of blockchain.
Reports from freelancing platforms like UpWork
have retained blockchain skills as the most
demanded skills. In a similar manner,
professionals in other areas like Legal studies are
said to have a major advantage if they have
blockchain skill — or at least have an
understanding of the technology.

• What is Data Science?
• Data science seeks to extract knowledge and insights
from structured and unstructured data. This field
encompasses statistics, data analysis, machine learning
and other advanced methods used to understand and
analyze actual processes using data.
• Data is often described as the new oil in economic
parlance, reason why leading businesses including the
famed GAFAs (Google, Amazon, Facebook, and Apple)
are in control of loads of data.

• Some common applications of data science is
seen in internet engine protocols, digital
advertisements, and recommender services.
Data analysis, a key aspect of data science, has
been found relevant in the healthcare industry
to track patient treatment and equipment
flow; in travel a gaming to improve consumer
experience; for energy management as well as
many other sectors.

• High Demand for Data Scientists
• There’s also a seemingly insatiable demand for
data scientists who can provide more insights
with data and help solve more problems. This
is even more pronounced when considering
big data, an advanced aspect of data science
which deals with extremely large amounts of
data which cannot be handled by traditional
data processing methods.

• The relationship between Blockchain and
Data Science
• Unlike in areas like Fintech, healthcare and
supply chain where blockchain is now very
familiar, the technology has not been explored
extensively in aspects of data science. To
some, the relationship between the concepts
are unclear if not non-existent.

• For starters, both blockchain and data
science deals with data — data science
analyses data for actionable insights,
while blockchain records and validates
data.
• Both make use of algorithms created to
govern interactions with various data
segments.
• A common theme which you will soon
notice is this, “data science for
prediction; blockchain for data
integrity.”

• Blockchain impact on data
• Data science, just like any technological
advancement has its own challenges and
limitations which when addressed will unleash
its full capabilities. Some major challenges to
data science include inaccessible data, privacy
issues, and dirty data.

• The control of dirty data (or erroneous
information) is one area that blockchain
technology can positively impact the data
science field in no small measure.
• According to 2017 survey of 16,000 data
professionals, the inclusion of dirty data like
duplicate or incorrect data was identified as
the biggest challenge to data science.
• Through decentralized consensus algorithm
and cryptography, blockchain validates data
making it almost impossible to be
manipulated due to the huge amount of
computing power that will be required.

• Again through its decentralized system,
blockchain technology ensures the security and
privacy of data.
• Most data are stored in centralized servers that
are often the target of cyber attackers; the
several reports of hacks and security breaches
goes to show the extent of the threat.
• Blockchain, on the other hand, restores the
control of data to the individuals generating the
data making it an uphill task for cybercriminals
to access and manipulate data on a large scale.

• Blockchain has brought a whole new way of
managing and operating with data — no
longer in a central perspective where all data
should be brought together but a
decentralized manner where data may be
analyzed right off the edges of individual
devices.
• Blockchain integrates with other advanced
technologies, like cloud solutions, Artificial
intelligence (AI) and the Internet of Things
(IoT).

• Furthermore, validated data generated via
blockchain technology comes structured and
complete plus the fact it is immutable .
• Another important area where
blockchain generated data becomes a
boost for big data is in data integrity
since blockchain ascertains the origin of
data though its linked chains.

• That being said, a few challenges have been
raised about its impact in data science
especially in big data which requires
exceptionally large amounts of data to be
handled.
• One concern is that blockchain application in
this regard will be very expensive to pursue.
• This is because data storage on a blockchain is
expensive compared to traditional means.
• Blocks deal with relatively small amounts of
data compared to the large volumes of data
collected per second for big data and other
data analysis tasks.

• How blockchain evolves to address this
concerns and proceed to disrupt the data
science space will be particularly interesting
because, like we have seen, the technology
has huge potential to transform how we
manage and use data.

• A blockchain, originally block chain, is a
growing list of records, called blocks, that are
linked using cryptography. ... By design,
a blockchain is resistant to modification of
the data. It is "an open, distributed ledger that
can record transactions between two parties
efficiently and in a verifiable and permanent
way".

Blockchain and Data Science
• Data science is a multi-disciplinary field that
uses scientific methods, processes, algorithms
and systems to extract knowledge and insights
from structured and unstructured data. ...
Nate Silver referred to data science as a sexed
up term for statistics.

How can blockchain disrupt data
science?
• To answer these questions, it will be helpful to get a better
understanding of Blockchain and Data Science separate
from one another.
• What is Blockchain?
• Blockchain is basically a distrusted ledger that records
economic transactions such that they cannot be
manipulated. The technology came into prominence as a
result of the interest in bitcoin and cryptocurrency in
general but has since found relevance in recording not just
cryptocurrency transactions but anything of value. Knowing
the capabilities of this emerging technology, developers
and tech enthusiasts have gone to work fashioning out use
case after use case for blockchain.

• What is Data Science?
• Data science seeks to extract knowledge and insights from
structured and unstructured data. This field encompasses statistics,
data analysis, machine learning and other advanced methods used
to understand and analyze actual processes using data.
• Data is often described as the new oil in economic parlance, reason
why leading businesses including the famed GAFAs (Google,
Amazon, Facebook, and Apple) are in control of loads of data. Some
common applications of data science is seen in internet engine
protocols, digital advertisements, and recommender services. Data
analysis, a key aspect of data science, has been found relevant in
the healthcare industry to track patient treatment and equipment
flow; in travel a gaming to improve consumer experience; for
energy management as well as many other sectors.

• The relationship between Blockchain and Data Science
• Unlike in areas like Fintech, healthcare and supply chain
where blockchain is now very familiar, the technology has
not been explored extensively in aspects of data science. To
some, the relationship between the concepts are unclear if
not non-existent.
• For starters, both blockchain and data science deals with
data — data science analyses data for actionable insights,
while blockchain records and validates data. Both make use
of algorithms created to govern interactions with various
data segments. A common theme which you will soon
notice is this, “data science for prediction; blockchain for
data integrity.”

• Blockchain impact on data
• Data science, just like any technological advancement has its own
challenges and limitations which when addressed will unleash its
full capabilities. Some major challenges to data science include
inaccessible data, privacy issues, and dirty data.
• The control of dirty data (or erroneous information) is one area that
blockchain technology can positively impact the data science field in
no small measure. According to 2017 survey of 16,000 data
professionals, the inclusion of dirty data like duplicate or incorrect
data was identified as the biggest challenge to data
science. Through decentralized consensus algorithm and
cryptography, blockchain validates data making it almost impossible
to be manipulated due to the huge amount of computing power
that will be required.

• Again through its decentralized system,
blockchain technology ensures the security and
privacy of data. Most data are stored in
centralized servers that are often the target of
cyber attackers; the several reports of hacks and
security breaches goes to show the extent of the
threat. Blockchain, on the other hand, restores
the control of data to the individuals generating
the data making it an uphill task for
cybercriminals to access and manipulate data on
a large scale.

How Blockchain Can Help Big Data?
• If big is the quantity, Maria Weinberger of
Janexter says, blockchain is the quality. This
follows the understanding that blockchain is
focused on validating data while data science
or big data involves making predictions from
large amounts of data.

• Blockchain has brought a whole new way of
managing and operating with data — no
longer in a central perspective where all data
should be brought together but a
decentralized manner where data may be
analyzed right off the edges of individual
devices. Blockchain integrates with other
advanced technologies, like cloud solutions,
Artificial intelligence (AI) and the Internet of
Things (IoT).

• Furthermore, validated data generated via
blockchain technology comes structured and
complete plus the fact it is immutable like we
mentioned earlier. Another important area
where blockchain generated data becomes a
boost for big data is in data integrity since
blockchain ascertains the origin of data
though its linked chains.

Blockchain and Artificial Intelligence/MLT
and Its Applications
• AI and blockchain are proving to be quite
the powerful pair, improving almost
every industry in which they are
implemented.
• Blockchain and artificial intelligence are
combining to upgrade everything from food
supply chain logistics and healthcare record
sharing to media royalties and financial
security.

Blockchain and AI: A Perfect Match
• Blockchain and Artificial Intelligence are
two of the hottest technology trends
right now. Even though the two
technologies have highly different
developing parties and applications,
researchers have been discussing and
exploring their combination .

Blockchain and AI: A Perfect Match
• PwC (PricewaterhouseCoopers)
predicts that by 2030 AI will add up to
$15.7 trillion to the world
economy, and as a result, global GDP
will rise by 14%.
• According to Gartner’s prediction,
business value added by Blockchain
Technology will increase to $3.1 trillion
by 2030.

Blockchain Vs AI
• By definition, a blockchain is a distributed,
decentralized, immutable ledger used to store
encrypted data.
• On the other hand, AI is the “engine” or the “brain”
that will enable analytics and decision making from
the data collected.
• It goes without saying that each technology has its
own individual degree of complexity, but both AI
and blockchain are in situations where they can
benefit from each other, and help one another.

• With both these technologies able to
effect and enact upon data in different
ways, their coming together makes
sense, and it can take the exploitation of
data to new levels.
• At the same time, the integration of
machine learning and AI into
blockchain, and vice versa, can enhance
blockchain’s underlying architecture and
boost AI’s potential.

• Additionally, blockchain can also make
AI more coherent and understandable,
and we can trace and determine why
decisions are made in machine learning.
• Blockchain and its ledger can record all
data and variables that go through a
decision made under machine learning.

• Moreover, AI can boost blockchain
efficiency far better than humans, or
even standard computing can.
• But presently blockchains are currently
run on standard computers proves that
with a lot of processing power needed
to perform even basic tasks.

Applications of AI and Blockchain

Applications of Blockchain and
AI/MLT
1. Smart Computing Power
2. Creating Diverse Data Sets
3. Data Protection
4. Data Monetization
5. Trusting AI Decision Making

Conclusion
• The combination of Blockchain Technology ,
Big Data, Data Science and Artificial
Intelligence, Machine Learning is still a largely
undiscovered area.
• Putting the these technologies together has
the potential to use data in ways never
before thought possible.

• Data is the key ingredient for the
development and enhancement of Big Data,
Data Science and Artificial Intelligence,
Machine Learning algorithms, and
• Blockchain secures this data, and allows us to
audit all intermediary steps ,
• AI takes to draw conclusions from the data
and allows individuals to monetize their
produced data.

References
• A Survey of Blockchain and Its Applications
• A Beginner’s Guide to Big Data and
Blockchain
• How blockchain disrupt will Data Science : 5
Blockchain Use Cases in Big Data
• Blockchain Data Analytics

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