What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
This document is the first deliverable of the Lean Big Data work package 7 (WP7). The main goal of the package 7 is to provide the use cases applications that will be used to validate the Lean Big Data platform. To this end, an analysis of requirement of each use case will be provided in the scope.This analysis will be used as basis for the description of the evaluation, benchmarking and validation of the Lean Big Data platform.
This deliverable comprises the analysis of requirements for the following case of study provided in the context of Lean Big Data: Data Centre monitoring Case Study, Electronic Alignment of Direct Debit transactions Case Study, Social Network-based Area surveillance Case Study and Targeted Advertisement Case Study.
Use of big data technologies in capital marketsInfosys
What concerns capital market firms today is not the increase in data, but the volume of overall unstructured data. Capital market firms invest heavily in Big Data technologies despite the implementation costs involved. This article discusses the key transformations that capital market firms are undergoing to handle big data, drivers for use of big data technology in capital markets and relevant use cases.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The volume, velocity and variety of data available is almost unthinkable. 90% of the world’s data is less than 2 years old, we are able analyze less than 5% of it and 80% of what people generally are looking at is less than 6 weeks old. Harnessing this data for effective decision making is a goal for organizations worldwide and has created a 50Billion dollar industry to provide tools and consulting.
Even before “Big Data” Purchasing groups were swimming in data and struggled to put it to effective use. The success of Strategic Sourcing methodology had the effect of also identifying and standardizing the types and format of information that can be used to drive improvement.
This discussion will connect how big data sources and methodology can be used to develop specific and relevant spend analytics. Also presented will be an illustration of how you can use data and tools you already have - to get immediate results and make you better prepared to evaluate the need for more powerful analytic tools.
Finally will conclude with comments on how Big Data along with other disruptive digital trends will create a new required skill sets for Purchasing and Supply Chain Professionals and are transform how operate all ready.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Welcome to big data use case course. In this course we will talk about what is big data? Who are using it and at the end we will share the lessons learnt from the early adopters. Big Data is an umbrella term used to refer the technology behind collecting and analyzing large volume of data at a fast speed. In last few years, number of devices and services customers use, have increased multi fold. As customers are using more of every thing, they are creating more data. By inter connecting these data, you can know your customer better and provide a better service. Big Data helps you in storing and connecting these data.
Bigdata analysis in supply chain managmentKushal Shah
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Societal Impact of Applied Data Science on the Big Data StackStealth Project
Data availability should ideally improve accountability and decision processes. Armed with evidence of data science working across multiple domains from healthcare analytics to internet advertising big data is enabling changes in society, one application at a time. This talk will have two parts. We will first present a data scientist's overview of different technologies in use today and their utility.
Then we will do a deep-dive on specific implementation and challenges we addressed while working with multiple partners in the healthcare industry on real-world healthcare data. We will discuss and demonstrate prototypes of our solutions for cost prediction and risk-of-readmission care management, and how we leveraged big data machine learning frameworks. We will end with an open conversation about challenges in verticals other than healthcare and provide an overview of ongoing efforts for social good at the University of Washington Center for Data Science; each a story in its own.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
This document is the first deliverable of the Lean Big Data work package 7 (WP7). The main goal of the package 7 is to provide the use cases applications that will be used to validate the Lean Big Data platform. To this end, an analysis of requirement of each use case will be provided in the scope.This analysis will be used as basis for the description of the evaluation, benchmarking and validation of the Lean Big Data platform.
This deliverable comprises the analysis of requirements for the following case of study provided in the context of Lean Big Data: Data Centre monitoring Case Study, Electronic Alignment of Direct Debit transactions Case Study, Social Network-based Area surveillance Case Study and Targeted Advertisement Case Study.
Use of big data technologies in capital marketsInfosys
What concerns capital market firms today is not the increase in data, but the volume of overall unstructured data. Capital market firms invest heavily in Big Data technologies despite the implementation costs involved. This article discusses the key transformations that capital market firms are undergoing to handle big data, drivers for use of big data technology in capital markets and relevant use cases.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The volume, velocity and variety of data available is almost unthinkable. 90% of the world’s data is less than 2 years old, we are able analyze less than 5% of it and 80% of what people generally are looking at is less than 6 weeks old. Harnessing this data for effective decision making is a goal for organizations worldwide and has created a 50Billion dollar industry to provide tools and consulting.
Even before “Big Data” Purchasing groups were swimming in data and struggled to put it to effective use. The success of Strategic Sourcing methodology had the effect of also identifying and standardizing the types and format of information that can be used to drive improvement.
This discussion will connect how big data sources and methodology can be used to develop specific and relevant spend analytics. Also presented will be an illustration of how you can use data and tools you already have - to get immediate results and make you better prepared to evaluate the need for more powerful analytic tools.
Finally will conclude with comments on how Big Data along with other disruptive digital trends will create a new required skill sets for Purchasing and Supply Chain Professionals and are transform how operate all ready.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Welcome to big data use case course. In this course we will talk about what is big data? Who are using it and at the end we will share the lessons learnt from the early adopters. Big Data is an umbrella term used to refer the technology behind collecting and analyzing large volume of data at a fast speed. In last few years, number of devices and services customers use, have increased multi fold. As customers are using more of every thing, they are creating more data. By inter connecting these data, you can know your customer better and provide a better service. Big Data helps you in storing and connecting these data.
Bigdata analysis in supply chain managmentKushal Shah
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Societal Impact of Applied Data Science on the Big Data StackStealth Project
Data availability should ideally improve accountability and decision processes. Armed with evidence of data science working across multiple domains from healthcare analytics to internet advertising big data is enabling changes in society, one application at a time. This talk will have two parts. We will first present a data scientist's overview of different technologies in use today and their utility.
Then we will do a deep-dive on specific implementation and challenges we addressed while working with multiple partners in the healthcare industry on real-world healthcare data. We will discuss and demonstrate prototypes of our solutions for cost prediction and risk-of-readmission care management, and how we leveraged big data machine learning frameworks. We will end with an open conversation about challenges in verticals other than healthcare and provide an overview of ongoing efforts for social good at the University of Washington Center for Data Science; each a story in its own.
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.
A technical Introduction to Big Data AnalyticsPethuru Raj PhD
This presentation gives the details about the sources for big data, the value of big data, what to do with big data, the platforms, the infrastructures and the architectures for big data analytics
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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.
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.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
4. - 4-
Big Data Overview: Table of Contents
Big Data
Overview
Data Growth Definition
Big Data v.s.
Relational
Data
Its Value
Big Data
Benefit
Big Data
Usage
Challenges
5. - 5-
Big Data Overview: Data Growth
Storage capacity increases 23% on
average annually
End the ability to store all the
available information
0/通用格式
19/通用格式
9/通用格式
29/通用格式
18/通用格式
6/通用格式
26/通用格式
15/通用格式
Exabytes
Years
Data Storage Growth
0/通用格式
8/通用格式
18/通用格式
24/通用格式
3/通用格式
11/通用格式
18/通用格式
28/通用格式
6/通用格式
15/通用格式
Exabytes Years
Data Storage Growth
Exponential growth during a decade
starts from 2010
6. - 6-
Big Data Overview: Definition
Gartner Definition(2012): "Big data is high volume,
high velocity, and/or high variety information assets
that require new forms of processing to enable
enhanced decision making, insight discovery and
process optimization."
7. - 7-
Big Data Overview: Big Data V.S. Relational Data
Application Relation-Based Data Big Data
Data processing
Single-computer
platform that scales with
better CPUs, centralized
processing.
Cluster platforms that
scale to thousands of
nodes, distributed
process.
Data management
Relational database
(SQL), centralized
storage.
Non-relational
databases that manage
varied data types and
formats (NoSQL),
distributed storage.
Analytics
Batched, descriptive,
centralized.
Real-time, predictive
and prescriptive,
distributed analytics.
8. - 8-
Big Data Overview: Its Value 1/3
Several classes of company
heading the revenue
chart($11.59 billion)
broad-portfolio tech giants
(IBM, HP, Oracle, EMC)
leading software houses
(Teradata, SAP, Microsoft)
professional services
companies (PwC, Accenture)
Source: Wikibon, Big Data
Vendor Revenue and Market
Forecast 2012-2017
Source: http://www.zdnet.com/big-data-an-overview_p2-7000020785/
9. - 9-
Big Data Overview: Its Value 2/3
Pure play: vendors who
derive 100 percent of
their revenue from this
market
Source: Wikibon, Big Data
Vendor Revenue and
Market Forecast 2012-
2017
Source: http://www.zdnet.com/big-data-an-overview_p2-7000020785/
10. - 10-
Big Data Overview: Its Value 3/3
Source: Worldwide Big Data Technologies and
Services: 2012-2015 Forecast (IDC, 2012)
IDC: Big data will become a
$17 billion business by
2015($23.8 billion by
2016)
Big data storage will
account for 6.8% of the
entire worldwide storage
market by 2015
Source: http://www.zdnet.com/big-data-an-overview_p2-7000020785/
11. - 11-
Big Data Overview: Big Data Benefits
Business benefits received by implementing an effective Big Data
methodology. The survey is based on 1153 responses from 325 respondents
12. - 12-
Big Data Overview: Big Data Usage 1/2
E-Commerce and Market Intelligence
– Recommender system
– Social media monitoring and analysis
– Crowd-sourcing systems
– Social and virtual games
E-Government and Politics 2.0
– Ubiquitous government services
– Equal access and public services
– Citizen engagement
Science & Technology
– S&T innovation
– Hypothesis testing
– Knowledge discovery
Smart Health and Wellbeing
– Human and plant genomics
– Healthcare decision support
– Patient community analysis
Security and Public Safety
– Crime analysis
– Computational criminology
– Terrorism informatics
– Open-source intelligence
– Cyber security
13. - 13-
Big Data Overview: Big Data Usage 2/2
Survey of European companies from Steria's Business Intelligence Maturity Audit (biMA)
14. - 14-
Big Data Overview: Challenges 1/2
Main challenges between Big Data and companies. The survey is based on
1153 responses from 325 respondents
15. - 15-
Big Data Overview: Challenges 2/2
A Survey of European
companies from Steria's
Business Intelligence Maturity
Audit (biMA)
Technical
– 38% has data quality
problem
– A lack of data
governance; no master
data management
system(38%)
Organizational
– 72% has no BI strategy;
70% has no BI governance
– 7% grades big data as
relevant
Source: http://www.steria.com/uk/media-centre/press-releases/press-releases/article/survey-suggests-only-7-
of-european-companies-rate-big-data-as-very-relevant-to-their-business/
17. - 17-
Big Data, DW & BI: Table of Contents
Big Data,
DW & BI
Evolution Techniques Cost
Best
Practices
18. - 18-
BI Evolution
Key Characteristics
Gartner BI Platforms Core
Capabilities
Gartner Hype Cycle
BI&A 1.0
-DBMS-based, structured content.
-RDBMS & data warehousing.
-ETL & OLAP.
-Dashboards & scorecards.
-Data mining & statistical analysis.
-Ad hoc query & search-based BI
-Reporting, dashboards &
scorecards
-OLAP
-Interactive visualization
-Predictive modeling & data mining.
-Column-based DBMS
-In-memory DBMS
-Real-time decision
-Data mining workbenches
BI&A 2.0
Web-based, unstructured content
-Information retrieval and
extraction
-Opinion mining
-Question answering
-Web analytics and web
intelligence
-Social media analytics
-Social network analysis
-Spatial-temporal analysis
-Information semantic
services
-Natural language question
answering
-Content & text analytics
BI&A 3.0
Mobile and sensor-based content
-Location-aware analysis
-Person-centered analysis
-Context-relevant analysis
-Mobile visualization & HCI
-Mobile BI
BI and Analytics: evolution and characteristics
19. - 19-
Big Data Overview: Techniques 1/2
A/B Testing
A technique in which a control group is compared with a
variety of test groups in order to determine what treatments
will improve a given objective. An example application is
determining what copy text, layouts, images, or colors will
improve conversion rates on an e-commerce Web site. Big
Data enables huge numbers of tests to be executed and
analyzed.
Cluster Analysis
A statistical method aimed to classify an huge data set and
in particular to identify a common behavior.
Classification
Classification. A set of techniques to identify the categories
in which new data points belong, based on a training set
containing data points that have already been categorized.
Data Mining
A set of techniques and technologies with the purpose to
extract patterns from large datasets through the combination
of methods following statistics and algorithms. These
techniques include association rule learning, cluster analysis,
classification, and regression.
McKinsey Global Institute in 2011 provided a list of the top 10 common
techniques applicable across a range of industries, particularly in response to
the need to analyze new amounts of data and their combination.
List of the top 10 techniques which require Big data(1/2)
20. - 20-
Big Data Overview: Techniques 2/2
McKinsey Global Institute in 2011 provided a list of the top 10 common
techniques applicable across a range of industries, particularly in response to
the need to analyze new amounts of data and their combination.
List of the top 10 techniques which require Big data(2/2)
Network analysis
A set of techniques used to characterize relationships among discrete
nodes in a graph or a network. In social network analysis, connections
between individuals in a community or organization are analyzed.
Predictive modeling
A set of techniques in which a mathematical model is created or
chosen to best predict the probability of an outcome.
Sentiment analysis
Application of natural language processing and other analytic
techniques to identify and extract subjective information from source text
material.
Statistics
The science of the collection, organization, and interpretation of data,
including the design of surveys and experiments. Statistical techniques
are often used to understand the relationships between all the variables.
Visualization
Techniques used to create images, diagrams or animations, usually
integrated in more complex dashboards.
21. - 21-
Big Data: Cost 1/2
ESG (Enterprise Strategy Group) provides an analysis on the costs of Big Data, in
particular a comparison between a “build” and “buy” solution.
Item Cost Notes
Servers $400,000
@$22k each; enterprise class with dual
power supplies, 36TB of serial attached
SCSI (SAS) storage, 48-64 gigabytes
memory, 1 rack
Server support $60,000 @15% of server cost
Switches $15,000
3 @ $5k for InfiniBand; in older network
switches will run at least 3x the costs of
InfiniBand
Distribution/systems
management software
$90,000 Cloudera: 18 nodes @ $5k each
Integration $100,000 Licenses and dedicated hardware
Information
Management Tools
$20,000 320 hours @ $100/hour human cost
Node Configuration
and Implementation
$16,000
8 hours/node, 20 nodes = 160 hours,
$100/hour
Build Project Costs $733,000
Those project items where a "buy" option
exists
Build Versus Buy Elements (Using Build Pricing)
22. - 22-
Big Data: Cost 2/2
ESG (Enterprise Strategy Group) provides an analysis on the costs of Big Data, in
particular a comparison between a “build” and “buy” solution.
Build Versus Buy Elements (Using Buy Pricing)
Item Cost Notes
Build Total $733,000
Buy (Oracle Big Data
Appliance)
$450,000
Cost of Oracle Big Data
Appliance for same
infrastructure and tasks
costs (list)
Buy (Oracle Big Data
Appliance) Savings
$283,000
Not lifecycle costs, just
for initial project
ESG Estimated Savings
~39%
Oracle Big Data
Appliance lowers costs
versus do-it-yourself
23. - 23-
Big Data: Best Practices 1/3
First of all, however, we need to focus on some considerations on when is suitable
to use Big Data technologies
Analyze a huge quantity of data not only structured but also semi-structured and
unstructured from a wide variety of resources;
All of the data gathered must be analyzed against a sample or in another case,
sampling of data is not as effective as the analysis made upon a large amount of
data;
Iterative and explorative analysis when business measures on the data are not
determined a priori;
Solving information and business challenges that are not properly addressed by a
traditional relational database approach.
24. - 24-
Big Data: Best Practices 2/3
The best practices that we are going to describe regard both the
management aspects and the organizational and technological ones.
Muting the HiPPOs: the highest-paid person opinions are those on which
depend the most important decisions on how to retrieve and analyze data.
Today these people rely too much on intuition and experience rather than
the pure rationality of data so there is the need to transform this behavior;
Start with initiative that led to customer-centric outcome. It is very
important for those organization that are customer oriented to begin with
customer analytics that enable better services as a result of a deep
understand of customers needs and future behaviors;
Develop an enterprise schema that include the vision, the strategies and the
requirements for Big Data and is useful to align the business users need
and the implementation roadmap of information technologies;
In order to achieve near-term results is crucial the adoption of a pragmatic
approach, starting from the most logical and cost-effective place to look for
insight that is within the enterprise;
25. - 25-
Big Data: Best Practices 3/3
Big Data Analytics effectiveness strictly depends on analytical skills and analytics tools.
So the enterprises should invest in acquiring both tools and skills;
The Big Data strategy and the business analytics should encompass an evaluation of the
decision-making processes of the organization as well as an evaluation on the groups
and types of decision makers;
Try to uncover new metrics, key performance indicators and new analytics technique to
lock at new and existing data in a different way in order to find new opportunity. This
could require setting up a separate Big Data team with the purpose of experiment and
innovate;
The final goal of a Big Data project is not the collection of much data as possible but the
support of the concrete business needs and provide new reliable information to decision
makers;
Only one technology cannot meet all the Big Data requirements. The presence of
different workloads, data types, and user types should be served by the most suitable
technology. For example, Hadoop could be the best choice for a large-scale Web log
analysis but is not suitable for a real-time streaming at all. Multiple Big Data technologies
must coexist and address use cases for which they are optimized.
27. - 27-
Big Data Market Definition
IDC(2012) defines
the big data
market as an
aggregation of
storage, server,
networking,
software, and
services market
segments, each
with several sub-
segments.
Big Data Technology Stack
28. - 28-
Big Data Market Segments
Services
– business consulting, business process
outsourcing, plus IT projectbased
services, IT outsourcing, and IT support,
and training services related to Big Data
implementations
Infrastructure
– External storage systems
– Servers(including internal storage,
memory, network cards) and supporting
system software as well as spending for
self-built servers by large cloud service
providers
– Datacenter networking infrastructure
used in support of Big Data server and
storage infrastructure
Softwares
– Data organization and management
software, including parallel and
distributed file systems and others
– Analytics and discovery software,
including search engines used for Big
Data applications, data mining, text
mining, rich media analysis, data
visualization, and others
29. - 29-
Big Data Market Analysis
Marketsandmarkets
– Big Data Market By Types (Hardware; Software;
Services; BDaaS - HaaS; Analytics; Visualization as
Service); By Software (Hadoop, Big Data Analytics
and Databases, System Software (IMDB, IMC):
Worldwide Forecasts & Analysis (2013 – 2018)
32. - 32-
Hadoop: Overview
Master Node
Hadoop Overview
Slave Node1 Slave Node K Slave Node N
...... ......
Storage
Computing
Storage
Computing
Storage
Computing
HDFS
Map-Reduce
The Apache Hadoop software library is a framework that allows for the
distributed processing of large data sets across clusters of computers
using simple programming models
– Open source
– Scalable
– Distributed
Master Node controls everything!
34. - 34-
Hadoop: HDFS Structure
Name Node Metadata
HDFS Structure
Data Node1 Data Node K Data Node N
…....
..
…....
..
1
22
3
1
22
3
1
22
3
File
Name node controls almost everything about storage
Large files are partitioned into chunks and stored across multiple nodes
File chunks are replicated to mitigate the node failure problems
42. - 42-
Hadoop Ecosystem: HBase
HDFS
– Structured/semi-
structure/unstructure
d data
– Write only once, read
many
Hbase is an open-
source, distributed,
versioned, column-
oriented store
modeled after
Google's Bigtable
Column based database. It
supports
– Insert
– Delete
– Update
46. - 46-
Hadoop Ecosystem: Pig
Hadoop
– A lot of java codes in
case of analyzing
– No scripting
Pig is a platform for analyzing large
data sets that consists of a high-
level language for expressing data
analysis programs
Pig generates and compiles a
Map/Reduce program(s) on the fly.
47. - 47-
Hadoop Ecosystem: Pig Sample Scripts
RawInput = LOAD '$INPUT' USING
com.contextweb.pig.CWHeaderLoader('$RESOURCES/schema/wide.xml');
input = foreach RawInput GENERATE ContextCategoryId as Category,
DefLevelId , TagId, URL,Impressions;
defFilter = FILTER input BY (DefLevelId == 8) or (DefLevelId == 12);
GroupedInput = GROUP defFilter BY (Category, TagId, URL);
result = FOREACH GroupedInput GENERATE group,
SUM(input.Impressions) as Impressions;
STORE result INTO '$OUTPUT' USING
com.contextweb.pig.CWHeaderStore();
48. - 48-
Hadoop Ecosystem: Hive
Hive is a data warehouse infrastructure built on top of hadoop
Supports analysis of large datasets stored in Hadoop compatible file systems like
HDFS and Amazon S3 file system
Provides SQL-Like query language called HiveSQL
Provides index to accelerate queries
49. - 49-
Hadoop Ecosystem: HiveSQL
DML
– Select
DDL
– SHOW TABLES
– CREATE TABLE
– ALTER TABLE
– DROP TABLE
51. - 51-
Mahout: Overview
A scalable machine
learning library built on
Hadoop, written in java
Driven by Ng et al.’s
paper “MapReduce for
Machine Learning on
Multicore”
54. - 54-
Mobility Analyzer: A Show Case
HANA DB
CSV Files
Sequence Files
Mahout
Clusterdump
Cluster Info.
Cluster Info.
HANA DB
Site Data Flow Modules
CSVConverter
ImportClusterInfo
ExportTweetsInfoLocal
Hadoop
Local
Run.sh