AI, Cloud Computing, Big Data & IoT - Key to a Smarter Future

AI, Cloud Computing, Big Data & IoT

Introduction

In the ever-evolving landscape of technology, certain trends emerge as pivotal drivers shaping the future. Among these, Artificial Intelligence (AI), Cloud Computing, Big Data, and the Internet of Things (IoT) stand out as transformative forces, propelling societies towards a smarter and more interconnected future. These technologies are not only revolutionizing industries but also reshaping how we live, work, and interact with the world around us. From enhancing efficiency in businesses to optimizing resource utilization in cities, their impact is profound and multifaceted. This blog explores the synergistic potential of AI, Cloud Computing, Big Data, and IoT, illuminating their roles as the linchpins of innovation in the digital age. Join us as we delve into the intricate web of possibilities woven by these cutting-edge technologies, unraveling the roadmap to a smarter, more efficient, and interconnected tomorrow.

Artificial Intelligence (AI)

Artificial intelligence (AI) is a combination of two words: 'Artificial', indicating 'not real', and 'intelligence', referring to the demonstration of intelligent behavior.

AI, or Artificial Intelligence, is a branch of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. These tasks include problem-solving, learning, understanding natural language, and recognizing patterns. AI technologies encompass machine learning, where algorithms learn from data to improve their performance over time, and deep learning, which mimics the neural networks of the human brain to process vast amounts of information. AI finds applications across various sectors, from autonomous vehicles and virtual assistants to healthcare diagnostics and financial analysis. As AI continues to advance, it holds the promise of revolutionizing industries, augmenting human capabilities, and shaping the future of technology and society.

Different type of AI

Different Type of AI

Narrow AI

Artificial Narrow Intelligence (ANI), commonly referred to as Weak AI, is essential for voice assistants such as Siri, Alexa, and Google Assistant. These intelligent systems are programmed or trained to execute particular tasks or resolve specific problems, without the need for a general-purpose design.

General AI

AGI, also known as strong AI, is programmed to handle tasks that are intricate and closely resemble human capabilities. This concept is still theoretical, as it requires machines to comprehend and execute a diverse range of tasks based on their accumulated experiences. AGI is intended to replicate human intelligence by enabling systems to reason and think in a similar manner to humans.

Super AI

The concept of artificial super intelligence (ASI) carries immense implications for mankind, including the possibility of its complete annihilation. While this idea may resemble a narrative extracted from the pages of a science fiction novel, it does hold some validity: ASI denotes a system where machine intelligence surpasses human intellect in every aspect, outperforming humans in all functions.

Major Branches of AI 

Branches of AI

Perceptive system

A system that emulates the sensory perception of a human, including vision, hearing, and tactile sensations 

Vision system

A system designed to capture, store, and manipulate visual images and photographs.

Robotics

 Mechanical and computerized devices that carry out repetitive tasks with exceptional accuracy.

Expert system

A system that stores knowledge and draws inferences based on that knowledge.

Natural language processing

The ability of computers to comprehend and respond to statements and commands given in a "natural" language, such as English.

Neural network

A computer system that can mimic or simulate the functioning of the human brain.

Learning system

A computer system that adapts its functioning or responses to different situations based on feedback received.

History of Artificial Intelligence

The year 1950 witnessed the publication of "Computing Machinery and Intelligence" by Alan Turing. This notable paper endeavors to provide an answer to the thought-provoking question of whether machines have the capacity to engage in thinking. Turing introduces the groundbreaking Turing Test, which serves as a mechanism to assess whether a computer can demonstrate intelligence on par with that of a human, or yield results comparable to human intelligence.

The year 1956 witnessed the birth of the term 'artificial intelligence' when John McCarthy introduced it during the inaugural AI conference held at Dartmouth College. Later in the same year, Allen Newell, J.C. Shaw, and Herbert Simon developed the Logic Theorist, marking the inception of the first-ever functional AI software program.

The year 1967 witnessed the creation of the Mark 1 Perceptron by Frank Rosenblatt, marking the advent of the first computer based on a neural network that acquired knowledge through trial and error.

During the 1980s, neural networks utilizing the backpropagation algorithm gained widespread usage in the realm of artificial intelligence, enabling self-training capabilities.

Fast forward to 1997, IBM's Deep Blue emerged victorious in a chess match against the then world chess champion, Garry Kasparov, not only in the initial encounter but also in the subsequent rematch.

In 2011, IBM Watson emerged victorious against Jeopardy! champions Ken Jennings and Brad Rutter, showcasing its remarkable capabilities. Fast forward to 2015, Baidu's Minwa supercomputer introduced a groundbreaking technology known as a convolutional neural network. This advanced system surpassed human performance in image identification and categorization, exhibiting a level of accuracy that exceeded the average human's abilities.

The year 2016 witnessed DeepMind's AlphaGo program, powered by a deep neural network, emerging victorious over Lee Sodol, the esteemed world champion Go player, in a five-game competition. This triumph is particularly noteworthy considering the immense number of possible moves in the game (surpassing 14.5 trillion after just four moves!). Following this success, Google acquired DeepMind for a reported USD 400 million.

In 2023, the emergence of large language models (LLMs) like ChatGPT brings about a significant transformation in the capabilities of AI and its ability to enhance enterprise value. These advanced generative AI techniques enable deep-learning models to undergo pre-training using extensive quantities of unprocessed and unlabeled data, leading to remarkable advancements in performance.

10 Advantages of using Artificial  Intelligence

• Displaying cognitive abilities
• Absorb lessons from past encounters
• Utilize information gained through experience
• Tackle intricate scenarios
• Find solutions in the absence of key data
• Identify crucial elements
• Respond promptly and accurately to novel circumstances
• Interpret visual representations
• Manage and interpret symbols
• Demonstrate creativity and innovation
• Employ problem-solving strategies

5 Disadvantages of Artificial Intelligence

• Expensive prices
• Lack of innovation
• Joblessness
• Encourage human idleness
• Lack of moral principles
• Cold-hearted
• Stagnation

Expert Systems

Expert System

Expert Systems:- An expert system serves as a computer program engineered to address intricate problems and furnish decision-making capabilities comparable to those of a human expert. Through the utilization of artificial intelligence (AI) technologies, an expert system emulates the judgment and actions of a human or organization possessing expertise in a specific field. This is achieved by extracting knowledge from its knowledge base and applying reasoning and inference rules in response to user queries. A component of AI, the expert system was first developed in 1970, representing a significant advancement in artificial intelligence. By tapping into the knowledge stored in its knowledge base, it effectively resolves complex issues in a manner akin to an expert. By incorporating both facts and heuristics, an expert system aids in decision-making for complex problems, mirroring the approach of a human expert. Its designation as an expert system is derived from its possession of expert knowledge in a particular domain, enabling it to address any complex problem within that domain. Expert systems are tailored for specific domains, such as medicine and science, and are typically designed to complement rather than replace human experts.

6 Characteristics of Expert System:-

Highest Level of Expertise: IT provides the highest level of proficiency, efficiency, and precision.

Reaction Time: It boasts a minimal reaction time, outperforming human experts in solving intricate issues.

Reliable: IT is dependable and free of errors.

Flexible: IT is adaptable to address various problems.

Effective: It is equipped with a strong mechanism to effectively resolve complex issues.

Capable: IT is capable of managing complex problems and providing timely solutions.

Expert System Structure & Components

Expert System

                     

Expert systems are structured based on the knowledge engineers' understanding of knowledge representation methods and intelligent decision-making processes with the help of computer-based systems. To ensure that the computer can efficiently retrieve and utilize heuristic knowledge, the knowledge must be organized in a format that is easily accessible and clearly distinguishes between data, knowledge, and control structures.

3 components of Expert System:

o User Interface

o Inference Engine

o Knowledge Base

(a) Knowledge Base: The knowledge base is a compilation of problem-solving rules, procedures, and intrinsic data that hold relevance to a specific problem domain. It encompasses the problem-solving rules, facts, or intuitive knowledge that a human expert would employ to tackle problems within that particular problem domain. Typically, the knowledge base is stored in the form of if-then rules.

b) User Interface/Working Memory: The given text pertains to data that is specific to the task being discussed. This module functions as the dynamic component of the system. It includes a crucial element called the database. In general, the workspace consists of a set known as the rule base, which comprises a collection of rules to be utilized by the system at a given point in time.

c)Inference Engine: The Inference Engine acts as a standard control mechanism that utilizes the foundational knowledge in the knowledge base to analyze the task-specific data and produce a solution or conclusion. In production systems, inference is executed by recursively applying rules in a chain-like fashion, either moving forward or backward, until a conclusion is ultimately reached.

5 Applications of Expert System:-

1. Within the realm of design and production

It has wide applications in the creation and production of tangible items like camera lenses and vehicles.

2. Within the field of knowledge

These systems are mainly utilized to disseminate pertinent information to users. The two commonly used ES in this field are an advisor and a tax advisor.

3. Within the finance domain

ES systems play a vital role in the finance industry by effectively detecting various forms of fraud and suspicious activities. Additionally, these systems offer valuable insights to bankers, assisting them in making informed decisions regarding loan approvals for businesses.

4. Diagnosis and troubleshooting of different devices

The utilization of ES system is prevalent in the field of medical diagnosis, marking its initial application in this domain. This system plays a crucial role in the diagnosis and troubleshooting of devices.

5. Planning and Scheduling of Projects & Task

Expert systems can also be utilized to plan and schedule specific tasks or Projects in order to accomplish the objectives associated with those tasks.

10 Advantages of using an an expert system: 

• Enhances the quality of decision-making.
• Economical by reducing the need for consulting human experts in issue resolution.
• Delivers quick and reliable solutions to intricate problems within a particular field.
• Acquires and utilizes scarce knowledge effectively.
• Ensures uniformity in addressing recurring problems.
• Retains a substantial amount of information.
• Furnishes prompt and precise responses.
• Offers thorough explanations for decision-making.
• Resolves complex and demanding issues.
• Operates consistently without experiencing fatigue.

5 Disadvantages of Expert System

• Incapable of making decisions in exceptional circumstances.
• The principle of Garbage-in Garbage-out (GIGO) applies here, meaning that if there is an error in the knowledge base, we will inevitably receive incorrect decisions.
• The maintenance expenses are higher.
• Every problem is unique, and expert systems have certain limitations when it comes to solving diverse problems. In such instances, a human expert demonstrates greater creativity.

CLOUD COMPUTING

Cloud Computing

Cloud computing refers to the delivery of computing services over the internet, providing storage, processing power, and software. These services are available on-demand and do not require direct user management. Instead of owning and maintaining physical servers or infrastructure, users can access computing resources hosted by third-party cloud service providers. Cloud computing offers scalability, allowing users to easily adjust resources based on demand. It also offers pay-per-use pricing models, reducing upfront costs, and enables users to access their data and applications from anywhere with an internet connection. Cloud computing encompasses various service models, including IaaS, PaaS, and SaaS, catering to different levels of abstraction and management responsibilities. It has transformed the way organizations deploy, manage, and utilize IT resources, promoting innovation, agility, and cost-efficiency across industries.

What is Cloud?

The term Cloud denotes a Network or Internet. In simpler terms, Cloud is an entity that is located remotely.Cloud has the capability to offer services over a network, whether it is on public networks or private networks like WAN, LAN, or VPN. Applications such as e-mail, web conferencing, and customer relationship management (CRM) all function within the cloud.

What is Cloud Computing?

The concept of Cloud Computing revolves around the online manipulation, configuration, and accessibility of applications. It facilitates the storage of data, infrastructure, and applications on the internet. This comprehensive approach combines software and hardware resources, which are delivered as a network service. Cloud computing operates through virtual shared servers, enabling customers to utilize software, infrastructure, platform, devices, and other resources on a pay-as-you-use basis.

What is Cloud Computing?

Cloud Computing

Cloud Computing Architecture

Cloud Computing Architecture

10 ARCHITECTURE OF CLOUD COMPUTING

Infrastructure Layer:

Physical hardware resources such as servers, storage devices, and networking equipment.

Data centers or server farms where hardware resources are housed. Virtualization technology enables the creation of virtual machines (VMs) to efficiently utilize hardware resources.

Platform Layer:

Provides a platform for developing, deploying, and managing applications.

Includes middleware, development tools, databases, and runtime environments.

Platform as a Service (PaaS) offerings provide ready-to-use development and deployment environments, abstracting underlying infrastructure complexities.

Application Layer:

Software applications accessible over the internet.Software as a Service (SaaS) offerings allow users to access applications hosted in the cloud without needing to install or maintain software locally.

Deployment Models:

Public Cloud: Services are provided over the internet by third-party providers accessible to the general public.

Private Cloud: Infrastructure and services are dedicated to a single organization, providing greater control and security.

Hybrid Cloud: Combination of public and private cloud environments, allowing data and applications to move between them as needed.

Community Cloud: Shared infrastructure and services among organizations with common interests or requirements.

Service Models:

Infrastructure as a Service (IaaS): Provides virtualized computing resources over the internet, including servers, storage, and networking.

Platform as a Service (PaaS): Offers development and deployment tools, middleware, and runtime environments to build, deploy, and manage applications.

Software as a Service (SaaS): Delivers software applications over the internet on a subscription basis, eliminating the need for installation and maintenance.

Management Layer:

Cloud Management Platform (CMP) or Cloud Management Software (CMS) facilitates the management of cloud resources, including provisioning, monitoring, automation, and optimization. Includes tools for resource allocation, performance monitoring, security management, and cost optimization.

Security and Compliance:

Security measures such as encryption, identity and access management (IAM), firewalls, and intrusion detection systems (IDS) safeguard cloud environments.

Compliance with regulations and standards such as GDPR, HIPAA, and PCI DSS ensures data protection and privacy.

Networking:

Networking infrastructure connects cloud components, users, and devices.

Virtual Private Clouds (VPCs), content delivery networks (CDNs), and load balancers optimize performance, reliability, and scalability.

Scalability and Elasticity:

Scalability allows cloud resources to handle varying workloads by adding or removing resources dynamically. Elasticity automatically adjusts resource allocation based on demand, ensuring optimal performance and cost efficiency.

Fault Tolerance and High Availability:

Redundancy, failover mechanisms, and distributed architectures ensure continuous availability and resilience against hardware failures or disruptions.

COMPONENT OF CLOUD COMPUTING ARCHITECTURE

Cloud computing architecture comprises two crucial components: the front end and the back end. The front end is the part that directly interacts with the client, encompassing their network or computer and the applications employed to access the cloud through a user interface, like a web browser. On the other hand, the back end refers to the actual 'cloud' infrastructure, which encompasses a diverse range of computers, servers, and data storage devices.

Working Model OF CLOUD COMPUTING

The cloud computing infrastructure relies on various services and models operating in the background to ensure accessibility and feasibility for end users. The key working models for cloud computing include Deployment Models and Service Models.

Deployment Models

The location of the cloud is determined by the deployment models, which also dictate the type of access. There are four options for accessing the cloud: Public, Private, Hybrid, and Community.

Deployment Models

PUBLIC CLOUD

PUBLIC CLOUD

Public cloud services are accessible to the public by a provider that manages the cloud infrastructure. Typically, major public cloud providers such as Amazon AWS, Microsoft, and Google are responsible for owning and running the infrastructure, providing Internet access to users. Under this setup, customers do not have insight or authority over the physical location of the infrastructure. It is crucial to understand that all users on public clouds utilize the same infrastructure resources with restricted customization options, security measures, and availability differences.

Systems and services are easily accessible to the general public through the Public Cloud. However, it is essential to acknowledge that the public cloud may pose security risks due to its openness, particularly with email platforms.

PRIVATE CLOUD

Private cloud refers to a cloud infrastructure that is specifically allocated to a single organization. This type of cloud environment allows businesses to securely host their applications while effectively managing data security and control, which are often compromised in public cloud settings. Unlike public clouds, private clouds are not shared with other organizations and can be managed either internally or by a trusted third-party. Moreover, they can be hosted either internally within the organization's own infrastructure or externally.

PRIVATE CLOUD

The Private Cloud ensures the availability of systems and services solely within an organization, thereby enhancing security due to its private nature.

HYBRID CLOUD

Hybrid Clouds are created by merging two or more independent clouds (private, community, or public) that maintain their distinct identities while being interconnected to provide the benefits of various deployment models. By utilizing third-party cloud providers within a hybrid cloud framework, you can increase the flexibility of your computing infrastructure. Combining the resources of a public cloud with those of a traditional private cloud allows for efficient management of sudden surges in workload.

HYBRID CLOUD

 The Hybrid Cloud integrates public and private cloud services, with essential operations being conducted on the private cloud and less critical operations on the public cloud.

Service Models

The basis of Cloud Computing is formed by Service Models, which serve as the reference models. These models can be categorized into three essential service models as provided below:

1. Infrastructure as a Service (IaaS)

2. Platform as a Service (PaaS)

3. Software as a Service (SaaS).

1.Infrastructure as a Service (IaaS)

The concept of Infrastructure as a Service (IaaS) involves the delivery of technology infrastructure as a service that can be scaled up or down as needed. IaaS provides users with access to key resources like physical machines, virtual machines, and virtual storage.

•Usually billed based on usage

•Can be coupled with Managed Services for OS and  application support

Platform as a Service (PaaS)

PaaS offers the necessary runtime environment, development and deployment tools, and more. It encompasses all the essential components needed to facilitate the entire process of creating and delivering web applications and services solely through the Internet.

Platform as a Service (PaaS)

Typically applications must be developed with a particular  platform in mind

Software as a Service (SaaS)

SaaS, also known as Software as a Service, is a software delivery methodology that offers software applications as web-based services. This model allows end users to access the software remotely and utilize its functions as a service. SaaS operates in a multi-tenant environment, enabling multiple users to access the software simultaneously. It is highly scalable in terms of architecture and is typically billed based on usage.

Software as a Service (SaaS)

10 ADVANTAGES OF CLOUD COMPUTING

Flexibility: High level of flexibility is present.

Low Cost: Significant cost savings can be achieved by utilizing cloud computing, which removes the need for hardware and software expenses.

Speed & Scales: Purchasing and setting up hardware and software through traditional methods can be time-consuming.

Enhanced Data and Information Management: Centralizing all data results in better organization, simplifying the management process.

Device Flexibility: Our applications and data can be accessed globally, regardless of the system being used.

Expanded Storage Capability: Cloud computing offers the advantage of increased storage capacity, accommodating larger amounts of data than a typical personal computer.

It is simple to grasp and comprehend: As individuals are already familiar with cloud applications such as GMail and Google Docs, anything similar is likely to be easily understood by users.

Seamless Updates: Updating multiple servers is automated, saving companies valuable time and resources.

Tailored Configurations: Cloud computing enables customization of business applications to suit specific needs.

5 DISADVANTAGES OF CLOUD  COMPUTING

1. Reliance: A significant drawback of cloud computing is the user's reliance on the provider.

2. Exposure: Utilizing cloud computing services involves accessing services from distant servers.

3. Necessitates a Stable internet connection: The primary drawback is that cloud computing heavily depends on network connectivity.

4. Confidentiality: Security and privacy pose the most significant worries regarding cloud computing.

5. Transition Challenge: The issue of migration is also a major concern with cloud computing.

Internet of Things (IOT)

Introduction

Internet of Things (IOT)

The Internet of Things (IoT) is an ecosystem that consists of interconnected physical objects or electronics. These objects are embedded with sensors and have the ability to collect and exchange data through the internet. This data exchange is facilitated by the use of an IP address assigned to each physical device and its network connection.

How IoT Works?

The Internet of Things (IoT), also known as the Internet of Everything (IoE), encompasses web-enabled devices that gather, transmit, and respond to data obtained from their surroundings through embedded sensors, processors, and communication hardware. These devices, commonly referred to as "connected" or "smart" devices, are capable of communicating with other related devices, a process known as machine-to-machine (M2M) communication, and taking action based on the information exchanged. While humans can interact with these gadgets to configure them, provide instructions, or access data, the devices primarily operate autonomously without human intervention. The availability of compact mobile components and the constant connectivity of our home and business networks have facilitated the existence of these devices.

How IoT Works?

Life cycle of IOT

Life cycle of IOT

Compilation

Devices and sensors gather data from various locations (such as homes, cars, and offices).

Transmission

Data and events are transmitted through networks to destinations like cloud platforms and data servers.

Interpretation

Information is derived from the data, including generating reports, filtering data, and visualizing the information.

Execution

Actions are taken based on the information and data, such as sending notifications, communicating with other computers, and sending emails.

Components of IoT

Smart Systems and Internet of Things are driven by a combination of :

1) Sensors

2) Connectivity

3) People & Processes

Components of IoT

10 Application of IOT

Application of IOT

Smart Home: IoT devices enable automation and remote control of home appliances, lighting, security cameras, thermostats, and door locks for enhanced convenience, energy efficiency, and security.

Industrial IoT (IIoT): In manufacturing and industrial settings, IoT sensors and devices monitor equipment performance, optimize processes, track inventory, and enable predictive maintenance to improve efficiency and reduce downtime.

Smart Agriculture: IoT sensors collect data on soil moisture, temperature, humidity, and crop health, allowing farmers to optimize irrigation, monitor crop conditions, and increase yields while minimizing water usage and environmental impact.

Healthcare: IoT devices such as wearable fitness trackers, smart medical devices, and remote patient monitoring systems enable continuous health monitoring, personalized treatment plans, and early detection of health issues.

Smart Cities: IoT technology is used in urban infrastructure for traffic management, public transportation systems, waste management, environmental monitoring, and energy efficiency initiatives to create safer, more sustainable cities.

Logistics and Supply Chain Management: IoT sensors provide real-time tracking and monitoring of goods in transit, optimizing logistics operations, reducing theft and loss, and improving inventory management and delivery efficiency.

Environmental Monitoring: IoT sensors monitor air quality, water quality, noise pollution, and other environmental parameters, providing valuable data for environmental protection efforts, urban planning, and public health initiatives.

Retail: IoT-enabled smart shelves, beacons, and RFID tags track inventory levels, analyze customer behavior, personalize shopping experiences, and optimize store layout and product placement to enhance customer satisfaction and increase sales.

Energy Management: Through the utilization of IoT devices and smart meters, Energy Management involves the monitoring of energy consumption, identification of inefficiencies, and implementation of demand-response programs to optimize energy usage, reduce expenses, and advance sustainability goals.

Vehicle Telematics: IoT technology in vehicles enables real-time tracking of vehicle location, performance monitoring, predictive maintenance, and driver behavior analysis for fleet management, insurance purposes, and connected car services.

What is Big Data?

Introduction

Big Data represents a vast collection of information that is constantly increasing in size as time progresses. This data is distinguished by its immense volume and intricate nature, posing difficulties for traditional data management tools to efficiently handle storage and processing tasks.Big data refers to large volumes of structured, semi-structured, and unstructured data generated from various sources such as social media, sensors, mobile devices, and enterprise systems. This data is characterized by its velocity, volume, variety, and veracity, making it challenging to process and analyze using traditional data processing methods. Big data technologies and analytics tools enable organizations to extract valuable insights, patterns, and trends from this data to make informed decisions, improve operations, and drive innovation. By leveraging advanced analytics techniques such as machine learning, data mining, and predictive analytics, big data helps uncover hidden patterns, correlations, and opportunities that can lead to enhanced customer experiences, optimized processes, and competitive advantages across industries.

Examples Of Big Data

Social media posts and interactions.

Sensor data from IoT devices.

E-commerce transaction records.

Healthcare electronic records.

Financial transactions and market data.

GPS and transportation data.

Meteorological and weather data.

Machine-generated data from industrial equipment.

Web traffic and user behavior data.

Government demographic and service data.

Three Characteristics of Big Data 

Three Characteristics of Big Data 

Volume(Data quantity)

In the near future, the widespread use of smart phones, coupled with the vast amount of data they produce and consume, as well as the incorporation of sensors into everyday objects, will lead to the emergence of countless data feeds. These feeds will be constantly updated and will contain diverse information, including environmental data, location details, and various other types of information. Furthermore, social media platforms will continue to be regularly updated with a wide range of content, encompassing videos, pictures, messages, and more.

Velocity(Data Speed)

Clickstreams and ad impressions record user behavior at a rapid pace of millions of events per second. High-frequency stock trading algorithms respond to market changes within microseconds. Data exchange between billions of devices is facilitated by machine to machine processes. Real-time massive log data is generated by infrastructure and sensors. On-line gaming systems accommodate millions of concurrent users, each generating numerous inputs per second.

Variety(Data Types)

Big Data encompasses more than just numerical values, dates, and text strings. It also includes geospatial data, 3D data, audio and video files, as well as unstructured text like log files and social media content. Unlike traditional database systems, which were built for handling smaller amounts of structured data with limited updates, Big Data analysis deals with a variety of data types.

The Structure of Big Data

Structured Data:-

Structured data refers to any information that is capable of being stored, retrieved, and manipulated in a predetermined format. A prime illustration of structured data is an 'Employee' table found within a database.

structured Big Data

unstructured data

Unstructured data refers to a diverse assortment of data that encompasses various types such as plain text files, images, videos, and more. An illustration of unstructured data can be seen in the results obtained from a 'Google Search'.

unstructured Big Data

Semi-structured 

Both structured and unstructured data can be found within semi-structured data. While it may resemble structured data, it lacks a defined table definition in relational DBMS. An instance of semi-structured data can be observed in the form of data stored in an XML file.

Examples Of Semi-structured Data 

Personal data stored in an XML file- 

Semi-structured Big Data

Big data source

source of Big data

• Social media platforms.
• Internet of Things (IoT) devices.
• E-commerce transactions.
• Healthcare records.
• Financial transactions.
• Sensor data.
• Web traffic.
• Government databases.
• Mobile applications.
• Industrial machinery.

10 Application Of Big Data

application of BIG DATA

Personalized Marketing: Big data analytics help companies analyze customer behavior and preferences to create targeted marketing campaigns, personalized recommendations, and tailored promotions.

Healthcare Analytics: Big data is used to analyze electronic health records, medical imaging data, and genomic data to improve patient outcomes, optimize treatments, and accelerate medical research.

Fraud Detection: Financial institutions use big data analytics to detect fraudulent transactions, identify suspicious patterns, and prevent financial fraud and cybercrime.

Predictive Maintenance: Industries leverage big data analytics to monitor equipment performance, predict potential failures, and schedule maintenance proactively to reduce downtime and maintenance costs.

Smart Cities: Big data analytics are used in urban planning, transportation management, and resource optimization to improve public services, reduce traffic congestion, and enhance overall quality of life in cities.

Supply Chain Optimization: Big data analytics help companies optimize inventory management, streamline logistics operations, and identify opportunities for cost reduction and efficiency improvement across the supply chain.

Risk Management: Big data analytics enable organizations to assess and mitigate risks across various domains, including financial risk, cybersecurity risk, and operational risk, through predictive modeling and scenario analysis.

Customer Experience Enhancement: Big data analytics help businesses analyze customer feedback, sentiment, and interactions across different channels to improve products, services, and customer support processes.

Energy Management: Big data analytics are used in energy utilities to optimize energy generation, distribution, and consumption, identify inefficiencies, and promote sustainable energy practices.

Research and Development: Big data analytics facilitate scientific research, drug discovery, and product development by analyzing large datasets, identifying patterns, and generating insights that drive innovation and discovery.

10 disadvantages of Big Data

• Privacy concerns.
• Security risks.
• Data quality issues.
• Costs and complexity.
• Overreliance on data.
• Legal and regulatory compliance.
• Data breach vulnerability.
• Bias and discrimination.
• Data ownership and control.
• Infrastructure limitations.
• Will be so overwhelmed:- 
• Need the right people and solve the right problems
• Costs escalate too fast
• Isn’t necessary to capture 100%
• Many sources of big data  is privacy
• self-regulation
• Legal regulation

10 Advantages/Benefits of Big Data

• Informed decision-making.
• Improved efficiency.
• Enhanced customer experience.
• Competitive advantage.
• Innovation and product development.
• Risk management.
• Real-time insights.
• Personalization and targeting.
• Healthcare outcomes improvement.
• Scientific advancements.

Conclusion

In conclusion , this blog cover a brief description of following topics

AI, Artificial intelligence, Different type of AI, Branches of AI, AI History, Expert System, Components of Expert system, What is Cloud?, What is Cloud Computing?, Cloud Computing Architecture, IOT, How IoT Works?, life cycle of IOT, Components of IOT, BIG data, Characteristics of Big data

In summary, I can say that these topics are related to Fundamental of Computer and very helpful for those who pursuing BCA,PGDCA, DCA ,'O' Level Courses from different universities 

I hope this blog helps you a lot Happy learning....

Frequently Asked Question(FAQ)

What is AI ?

AI, or Artificial Intelligence, is a branch of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence.

what is Expert Systems ?

An expert system serves as a computer program engineered to address intricate problems and furnish decision-making capabilities comparable to those of a human expert.

What is Cloud computing ?

Cloud computing refers to the delivery of computing services over the internet, providing storage, processing power, and software. These services are available on-demand and do not require direct user management.

What is IOT ?

The Internet of Things (IoT) is an ecosystem that consists of interconnected physical objects or electronics. These objects are embedded with sensors and have the ability to collect and exchange data through the internet.

What is Big Data ?

Big data refers to large volumes of structured, semi-structured, and unstructured data generated from various sources such as social media, sensors, mobile devices, and enterprise systems.