The document discusses the objectives and units of the CS8091 / Big Data Analytics course, which include understanding fundamental concepts of big data, HDFS, MapReduce, clustering, classification, association analysis, and recommendation systems. It also covers sources of big data, data structures, current analytical architectures, drivers of big data, and the emerging big data ecosystem approach to analytics using data devices, collectors, aggregators, and users.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
March Towards Big Data - Big Data Implementation, Migration, Ingestion, Manag...Experfy
Gartner, IBM, Accenture and many others have asserted that 80% or more of the world’s information is unstructured – and inherently hard to analyze. What does that mean? And what is required to extract insight from unstructured data?
Unstructured data is infinitely variable in quality and format, because it is produced by humans who can be fastidious, unpredictable, ill-informed, or even cynical, but always unique, not standard in any way. Recent advances in natural language processing provides the notion that unstructured content can be included in data analysis.
Serious growth and value companies are committed to data. The exponential growth of Big Data has posed major challenges in data governance and data analysis. Good data governance is pivotal for business growth.
Therefore, it is of paramount importance to slice and dice Big Data that addresses data governance and data analysis issues. In order to support high quality business decision making, it is important to fully harness the potential of Big Data by implementing proper Data Migration, Data Ingestion, Data Management, Data Analysis, Data Visualization and Data Virtualization tools.
Check it out: https://www.experfy.com/training/courses/march-towards-big-data-big-data-implementation-migration-ingestion-management-visualization
This document is about Data Warehouse Tools such as:
OLAP (On – line Analytical Processing)
OLTP (On – Line Transaction Processing)
Business Intelligence
Driving Force
Data Mart
Meta Data
This article useful for anyone who want to introduce with Big Data and how oracle architecture Big Data solution using Oracle Big Data Cloud solutions .
International Journal of Database Management Systems (IJDBMS)ijfcst journal
The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field, and establishing new collaborations in these areas.
International Journal of Database Management Systems (IJDBMS)ijfcst journal
The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field, and establishing new collaborations in these areas.
International Journal of Database Management Systems (IJDBMS)MiajackB
The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field, and establishing new collaborations in these areas.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdf
CS8091_BDA_Unit_I_Analytical_Architecture
1. CS8091 / Big Data Analytics
III Year / VI Semester
2. Objectives
To study the basic Big Data and analytics concepts,
HDFS and MapReduce.
To learn the fundamentals of Clustering and
Classification.
To understand the fundamental concepts of
Association and different types of Recommendation
System.
To learn about stream computing and study various
case studies.
To have an introductory knowledge about NoSQL
Data Management and Visualization.
3. Unit I - INTRODUCTION TO BIG
DATA
Evolution of Big data - Best Practices for Big data
Analytics - Big data characteristics - Validating - The
Promotion of the Value of Big Data - Big Data Use
Cases- Characteristics of Big Data Applications -
Perception and Quantification of Value -Understanding
Big Data Storage - A General Overview of High-
Performance Architecture - HDFS - MapReduce and
YARN - Map Reduce Programming Model.
4. DATA
The quantities, characters, or symbols on which
operations are performed by a computer, which may be stored
and transmitted in the form of electrical signals and recorded on
magnetic, optical, or mechanical recording media.
5. BIG DATA
Big Data is also data but with a huge size. Big Data is a
term used to describe a collection of data that is huge in size and
yet growing exponentially with time.
“Big Data” is data whose scale, diversity, and complexity require
new architecture, techniques, algorithms, and analytics to
manage it and extract value and hidden knowledge from it…
6. BIG DATA
Units of Memory-
Byte
Kilo Byte
Mega Byte
Giga Byte
Tera Byte
Peta Byte
Exa Byte
Zetta Byte
Yotta Byte
8. BIG DATA - Sources
Primary sources of Big Data
Social data:
Likes,
Tweets & Retweets,
Comments,
Video Uploads, and general media
9. BIG DATA - Sources
Primary sources of Big Data
Machine data:
Industrial equipment,
sensors that are installed in machinery,
web logs which track user behavior
Sensors such as medical devices, smart meters,
road cameras, satellites, games
10. BIG DATA - Sources
Primary sources of Big Data
Transactional data:
Invoices,
Payment orders,
Storage records,
Delivery receipts
12. BIG DATA – Data Structures
Structured data:
Data containing a defined data type, format, and
structure.
13. BIG DATA – Data Structures
Semi-structured data:
Semi-structured data is information that does not
reside in a relational database but that have some
organizational properties that make it easier to
analyze.
Example: XML Data
14. BIG DATA – Data Structures
Quasi-structured data:
It consists of textual data with erratic data formats,
and can be formatted with effort, software tools,
and time. An example of quasi-structured data is
the data about which webpages a user visited and
in what order.
15. BIG DATA – Data Structures
Quasi-structured data:
16. BIG DATA – Data Structures
Unstructured data:
Data that has no inherent structure, which may
include text documents, PDFs, images, and video.
17. BIG DATA – Data Structures
A clickstream that can be parsed and mined by
data scientists to discover usage patterns and
uncover relationships among clicks and areas
of interest on a website or group of sites.
18. Types of Data Repositories, from an
Analyst Perspective
Data Repository Characteristics
Spreadsheets and
data marts
Spreadsheets and low-volume
databases for recordkeeping
Analyst depends on data extracts
19. Types of Data Repositories, from an
Analyst Perspective
Data Repository Characteristics
Data Warehouses Centralized data containers in a purpose-built
space
Supports BI and reporting, but restricts robust
analyses
Analyst dependent on IT and DBAs for data access
and schema changes
Analysts must spend significant time to get
aggregated and disaggregated data extracts from
multiple sources.
20. Types of Data Repositories, from an
Analyst Perspective
Data Repository Characteristics
Analytic Sandbox
(workspaces)
Data assets gathered from multiple sources and
technologies for analysis
Enables flexible, high-performance analysis in a
nonproduction environment; can leverage in-
database processing
Reduces costs and risks associated with data
replication into “shadow” file systems
“Analyst owned” rather than “DBA owned”
21. State of the Practice in Analytics
Business Driver Examples
Optimize business operations Sales, pricing, profitability,
efficiency
Identify business risk Customer churn, fraud, default
Predict new business
opportunities
Upsell, cross-sell, best new
customer prospects
Comply with laws or regulatory
requirements
Anti-Money Laundering, Fair
Lending, Basel II-III,
Sarbanes-Oxley (SOX)
23. BI Versus Data Science
BI systems make it easy to answer questions
related to:
Quarter-to-date revenue,
Progress toward quarterly targets, and
Understand how much of a given product was sold
in a prior quarter or year
24. BI Versus Data Science
Data Science tends to use disaggregated data
in a
more forward-looking,
exploratory way,
focusing on analyzing the present and enabling
informed decisions about the future.
25. BI Versus Data Science
BI problems tend to require highly structured
data organized in rows and columns for
accurate reporting,
Data Science projects tend to use many types
of data sources, including large or
unconventional datasets
26. Current Analytical Architecture
Most organizations still have data warehouses
that provide excellent support for traditional
reporting and
simple data analysis activities but
unfortunately have a more difficult time
supporting more robust analyses.
28. Current Analytical Architecture
For data sources to be loaded into the data
warehouse, data needs to be well understood,
structured, and normalized with the
appropriate data type definitions.
29. Current Analytical Architecture
Although this kind of centralization enables
security, backup, and failover of highly critical
data,
it also means that data typically must go through
significant preprocessing and checkpoints before
it can enter this sort of controlled environment
30. Current Analytical Architecture
As a result of this level of control on the
EDW, additional local systems may emerge in
the form of departmental warehouses and local
data marts that business users create to
accommodate their need for flexible analysis.
31. Current Analytical Architecture
Once in the data warehouse, data is read by
additional applications across the enterprise for
BI and reporting purposes.
These are high-priority operational processes
getting critical data feeds from the data
warehouses and repositories
32. Current Analytical Architecture
Analysts create data extracts from the EDW to
analyze data offline in R or other local
analytical tools.
33. Current Analytical Architecture
Because new data sources slowly accumulate
in the EDW due to the rigorous validation and
data structuring process, data is slow to move
into the EDW, and the data schema is slow to
change.
34. Current Analytical Architecture
Departmental data warehouses may have been
originally designed for a specific purpose and
set of business needs, some of which may be
forced into existing schemas to enable BI and
the creation of OLAP cubes for analysis and
reporting.
36. Drivers of Big Data
The data now comes from multiple sources,
such as these:
Medical information, such as genomic sequencing
and diagnostic imaging
Photos and video footage uploaded to the World
Wide Web.
37. Drivers of Big Data
The data now comes from multiple sources,
such as these:
Video surveillance, such as the thousands of video
cameras spread across a city
Mobile devices, which provide geospatial location
data of the users, as well as metadata about text
messages, phone calls, and application usage on
smart phones.
38. Drivers of Big Data
The data now comes from multiple sources,
such as these:
Smart devices, which provide sensor-based
collection of information from smart electric grids,
smart buildings, and many other public and
industry infrastructures
Nontraditional IT devices, including the use of
radio-frequency identification (RFID) readers,
GPS navigation systems, and seismic processing.
40. Emerging Big Data Ecosystem and a
New Approach to Analytics
Data devices
“Sensornet” gather data from multiple locations
and continuously generate new data about this
data.
The video game provider captures data about the
skill and levels attained by the player.
41. Emerging Big Data Ecosystem and a
New Approach to Analytics
Data devices
As a consequence, the game provider can fine-
tune the difficulty of the game, suggest other
related games that would most likely interest the
user, and offer additional equipment and
enhancements for the character based on the user’s
age, gender, and interests.
42. Emerging Big Data Ecosystem and a
New Approach to Analytics
Data collectors
Retail stores tracking the path a customer takes
through their store while pushing a shopping cart
with an RFID chip so they can gauge which
products get the most foot traffic using geospatial
data collected from the RFID chips
43. Emerging Big Data Ecosystem and a
New Approach to Analytics
Data aggregators
Organizations compile data from the devices and usage
patterns collected by government agencies, retail stores,
and websites.
In turn, they can choose to transform and package the data
as products to sell to list brokers, who may want to
generate marketing lists of people who may be good targets
for specific ad campaigns.
44. Emerging Big Data Ecosystem and a
New Approach to Analytics
Data users and buyers
These groups directly benefit from the data collected and
aggregated by others within the data value chain.