Data Processing and Emerging Technologies Quiz

Introduction to Emerging Technologies

• Emerging technologies are technologies currently developing or expected within 5-10 years, often with significant social or economic impact.
• Technological evolution describes radical societal transformation through technological advancements.
• Technology, originally referring to grammar, has come to mean the science of mechanical and industrial arts.
• Evolution is the process of gradual change.

List of Currently Available Emerging Technologies

• Artificial Intelligence
• Blockchain
• DevOps
• Augmented Reality and Virtual Reality
• Big Data
• Internet of Things (IoT)
• Robotic Process Automation (RPA)
• Intelligent Apps (I-Apps)
• Cloud Computing

The Industrial Revolution (IR)

• The Industrial Revolution transitioned to new manufacturing processes beginning in Great Britain in the late 1770s.
• It involved shifting from using tools to making products to using sources like coal to power machinery in factories.
• The manufacturing of goods moved from small shops to large factories.

Types of Industries

• Primary industry: raw material extraction (mining, farming, fishing)
• Secondary industry: manufacturing (cars, steel)
• Tertiary industry: service provision (teaching, nursing)
• Quaternary industry: research and development (IT)

Industrial Revolutions (IR 1.0-4.0)

• IR 1.0 (1760): transition from hand production methods to machines, use of mechanization(steam and hydraulic power), and development of machine tools.
• IR 2.0 (1870): introduction of electrical power and telephones, focusing on mass production.
• IR 3.0 (1950): transition to digital electronics and mass production of logic circuits (e.g. computers, handphones), also introduced e-commerce.
• IR 4.0 (2016): advancements in various technologies encompassing robotics, Internet of Things (IoT), additive manufacturing, and autonomous vehicles; a system monitored by algorithms and communication via IoT.

Important Inventions of the Industrial Revolution

• Transportation: Steam engine, railroad, diesel engine, airplane
• Communication: Telegraph, transatlantic cable, phonograph, telephone
• Industry: Cotton gin, sewing machine, electric lights

Industrial Revolutions' Impact on Society

• Industrial revolutions fundamentally changed and transferred the world around us into modern society.
• The steam engine marked the beginning of a new era of science and mass production.
• The rise of digital technology, coupled with smart and autonomous systems fueled by data and machine learning, characterized the most recent revolution.

Role of Data in Emerging Technologies

• Data is considered the new oil (strategic asset) and the driving force behind the future of science, technology, and the economy.

Enabling Devices and Network (Programmable Devices)

• Digital electronic systems are composed of memory (storing data), microprocessors (executing instructions), and logic (device functionalities).
• Networks connect computers and other devices for data sharing.
• Programmable devices incorporate field programmable logic devices (FPGAs).

List of Programmable Devices

• Actel’s
• Achronix Speedster SPD60
• Lattice Semiconductor’s ECP3
• Silicon Blue Technologies
• Xmos Semiconductor
• Cypress Semiconductor’s programmable system on chip (PSoC) family

Service Enabling Devices (SEDs)

• Modems
• Routers
• Switches
• Conferencing equipment
• Network appliances (NIDs and SIDs)
• Hosting equipment and servers
• Traditional channel service units (CSUs)

Human-Machine Interaction (HMI)

• HMI describes communications and interactions between humans and machines via user interfaces.
• Human-computer interaction (HCI) involves users, computers, and the way they interact. Its goal is improved user-friendliness.

Disciplines Contributing to HCI

• Cognitive psychology: (limitations, information processing, and performance)
• Computer science: (graphics, technology, prototyping, and user interface management systems)
• Linguistics
• Engineering and design
• Artificial intelligence
• Human factors

Emerging Technology Trends (2019)

• 5G Networks
• Artificial Intelligence (AI)
• Autonomous Devices
• Blockchain
• Augmented Analytics
• Digital Twins
• Enhanced Edge Computing
• Immersive Experiences in Smart Spaces

Data Science - Chapter Two

• Data science defines the role of data scientists, differentiating data and information, describing the data processing life cycle, describing the data value chain, understanding big data, and the Hadoop ecosystem components.
• Data science involves multifaceted methods, processes, algorithms, and systems tailored for extracting insights from both structured and unstructured data. Data scientists possess strong quantitative skills (statistics and algebra) and programming knowledge focused on warehousing, data mining, and modelling.
• Data is raw unprocessed facts, whereas information is processed and meaningful data.
• Characteristics of useful processed data: timely, accurate, and complete.
• Data Processing Cycle (Input → Processing → Output).

Data Types

• Data types in programming specify how data is used. Examples include integers, booleans, characters, floating-point numbers, and alphanumeric strings.
• Structured data is organized, using a formal structure, like a database or spreadsheet.
• Semi-structured data has some organization but not as formalized as structured data. They utilize tags or markers to separate data elements. JSON and XML are classic examples.
• Unstructured data lacks a formal schema or structure, like text files, audio, and video.

Metadata

• Metadata is data about data, describing existing data, such as file creation date, or location data.

Data Value Chain

• Data value chain involves data acquisition, analysis, curation, storage, and usage.

Data Acquisition (DAQ)

• Gathering, filtering, and cleaning data before storing it in a data warehouse.

Data Analysis

• Processing data using analytical and statistical tools to discover useful insights. Includes the process of exploring, transforming, and modeling data.

Data Curation

• Managing and preserving data, its trustworthiness, discoverability, and reusability.

Data Storage

• Recording data using mediums. Relational Database Management Systems (RDBMS) with their ACID properties are used for this purpose.

Basic Concepts of Big Data

• Big data consists of massive and complex data sets, difficult to process with traditional database tools.
• Big data characteristics include: volume (massive amounts of data), velocity (data in motion), variety (different forms of data), and veracity (data quality and trustworthiness).

Clustered Computing

• Employing clusters of computers to handle the storage and processing of big data (resource pooling, high availability, scalability).

Hadoop and its Ecosystem

• Hadoop is an open-source framework for distributed processing of massive data sets across clusters. Its key features include: economical, reliable, scalable (horizontally and vertically), and flexible.
• Hadoop ecosystem: HDFS, YARN, MapReduce, Spark, PIG, HIVE, HBase, and machine learning frameworks (Mahout, Spark MLLib) are crucial components.

Big Data Life Cycle with Hadoop

• The cycle includes ingestion of data from various sources, data processing in storage, computing and analyzing data using processing frameworks, and visualizing results.