This talk was given at the IIPC General Assembly in Paris in May 2014. It introduces the distributed, parallel extraction framework provided by the Web Data Commons project. The framework is public accessible and tailored for the Amazon Web Service Stack. Besides the presentation includes an excerpt of datasets which were extracted from over 100 TB of crawling data and are as well available at http://webdatacommons.org.
An introduction deck for the Web of Data to my team, including basic semantic web, Linked Open Data, primer, and then DBpedia, Linked Data Integration Framework (LDIF), Common Crawl Database, Web Data Commons.
Building a Scalable Web Crawler with Hadoop by Ahad Rana from CommonCrawl
Ahad Rana, engineer at CommonCrawl, will go over CommonCrawl’s extensive use of Hadoop to fulfill their mission of building an open, and accessible Web-Scale crawl. He will discuss their Hadoop data processing pipeline, including their PageRank implementation, describe techniques they use to optimize Hadoop, discuss the design of their URL Metadata service, and conclude with details on how you can leverage the crawl (using Hadoop) today.
AWS Public Data Sets: How to Stage Petabytes of Data for Analysis in AWS (WPS...Amazon Web Services
AWS hosts a variety of public data sets that anyone can access for free. Previously, large data sets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without downloading or storing it themselves. In this session, the AWS Open Data Team shares tips and tricks, patterns and anti-patterns, and tools to help you effectively stage your data for analysis in the cloud.
Have you ever been curious as to how widely Google Analytics is used across the web? Stop pondering, start coding! In this presentation, Stephen discusses how he used the Common Crawl dataset to perform wide scale analysis over billions of web pages and what this means for privacy on the web at large.
An introduction deck for the Web of Data to my team, including basic semantic web, Linked Open Data, primer, and then DBpedia, Linked Data Integration Framework (LDIF), Common Crawl Database, Web Data Commons.
Building a Scalable Web Crawler with Hadoop by Ahad Rana from CommonCrawl
Ahad Rana, engineer at CommonCrawl, will go over CommonCrawl’s extensive use of Hadoop to fulfill their mission of building an open, and accessible Web-Scale crawl. He will discuss their Hadoop data processing pipeline, including their PageRank implementation, describe techniques they use to optimize Hadoop, discuss the design of their URL Metadata service, and conclude with details on how you can leverage the crawl (using Hadoop) today.
AWS Public Data Sets: How to Stage Petabytes of Data for Analysis in AWS (WPS...Amazon Web Services
AWS hosts a variety of public data sets that anyone can access for free. Previously, large data sets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without downloading or storing it themselves. In this session, the AWS Open Data Team shares tips and tricks, patterns and anti-patterns, and tools to help you effectively stage your data for analysis in the cloud.
Have you ever been curious as to how widely Google Analytics is used across the web? Stop pondering, start coding! In this presentation, Stephen discusses how he used the Common Crawl dataset to perform wide scale analysis over billions of web pages and what this means for privacy on the web at large.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
In this Introduction to Apache Sqoop the following topics are covered:
1. Why Sqoop
2. What is Sqoop
3. How Sqoop Works
4. Importing and Exporting Data using Sqoop
5. Data Import in Hive and HBase with Sqoop
6. Sqoop and NoSql data store i.e. MongoDB
7. Resources
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
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?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Introduction to web scraping from static and Ajax generated web pages with Python, using urllib, BeautifulSoup, and Selenium. The slides are from a talk given at Vancouver PyLadies meetup on March 7, 2016.
Introduction: This workshop will provide a hands-on introduction to Apache Spark using the HDP Sandbox on students’ personal machines.
Format: A short introductory lecture about Apache Spark components used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache Spark. This lab will use the following Spark and Apache Hadoop components: Spark, Spark SQL, Apache Hadoop HDFS, Apache Hadoop YARN, Apache ORC, and Apache Ambari User Views. You will learn how to move data into HDFS using Spark APIs, create Apache Hive tables, explore the data with Spark and Spark SQL, transform the data and then issue some SQL queries.
Pre-requisites: Registrants must bring a laptop that can run the Hortonworks Data Cloud.
Speaker:
Robert Hryniewicz, Developer Advocate, Hortonworks
BDT204 Awesome Applications of Open Data - AWS re: Invent 2012Amazon Web Services
Dive into the world of big data as we discuss how open, public datasets can be harnessed using the AWS cloud. With a lot of large data collections (such as the 1000 Genomes Project and the Common Crawl), join this session to find out how you can process billions of web pages and trillions of genes to find new insights into society.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
In this Introduction to Apache Sqoop the following topics are covered:
1. Why Sqoop
2. What is Sqoop
3. How Sqoop Works
4. Importing and Exporting Data using Sqoop
5. Data Import in Hive and HBase with Sqoop
6. Sqoop and NoSql data store i.e. MongoDB
7. Resources
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
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?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Introduction to web scraping from static and Ajax generated web pages with Python, using urllib, BeautifulSoup, and Selenium. The slides are from a talk given at Vancouver PyLadies meetup on March 7, 2016.
Introduction: This workshop will provide a hands-on introduction to Apache Spark using the HDP Sandbox on students’ personal machines.
Format: A short introductory lecture about Apache Spark components used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache Spark. This lab will use the following Spark and Apache Hadoop components: Spark, Spark SQL, Apache Hadoop HDFS, Apache Hadoop YARN, Apache ORC, and Apache Ambari User Views. You will learn how to move data into HDFS using Spark APIs, create Apache Hive tables, explore the data with Spark and Spark SQL, transform the data and then issue some SQL queries.
Pre-requisites: Registrants must bring a laptop that can run the Hortonworks Data Cloud.
Speaker:
Robert Hryniewicz, Developer Advocate, Hortonworks
BDT204 Awesome Applications of Open Data - AWS re: Invent 2012Amazon Web Services
Dive into the world of big data as we discuss how open, public datasets can be harnessed using the AWS cloud. With a lot of large data collections (such as the 1000 Genomes Project and the Common Crawl), join this session to find out how you can process billions of web pages and trillions of genes to find new insights into society.
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary ...Robert Meusel
Promoted by major search engines, schema.org has become a widely adopted standard for marking up structured data in HTML web pages. In this paper, we use a series of largescale Web crawls to analyze the evolution and adoption of schema.org over time. The availability of data from dierent points in time for both the schema and the websites deploying data allows for a new kind of empirical analysis of standards adoption, which has not been possible before. To conduct our analysis, we compare dierent versions of the schema.org vocabulary to the data that was deployed on hundreds of thousands of Web pages at dierent points in time. We measure both top-down adoption (i.e., the extent to which changes in the schema are adopted by data providers) as well as bottom-up evolution (i.e., the extent to which the actually deployed data drives changes in the schema). Our empirical analysis shows that both processes can be observed.
The future is here. Virtual Reality (VR) and Augmented Reality (AR) are now in fact a reality. According to Statistics, the revenue of virtual reality products is projected to reach 4.6 billion U.S. dollars by the end of 2017.
SEJ’s Chief Social Media Strategist, Brent Csutoras shares updates about both the technology and the medium, along with the companies that are already utilizing Altered Realities in their marketing campaigns.
The Switchabalizer - our journey from spell checker to homophone corrrecterCommonCrawl
Presentation given at Open Data Bay Area by Oskar Singer on using Common Crawl and NLP techniques to improve grammar and spelling correction, specifically homophones.
Clickstream Data Warehouse - Turning clicks into customersAlbert Hui
As web is becoming a main channel for reaching customers and prospects, Clickstream data generated by websites has become another important enterprise data source, like other traditional business data sources, like store transactions, CRM data, call center’s logs etc. As simple as it sounds for recording every click a customer made, Clickstream data actually offers a wide range of opportunities for modelling user behaviour, gaining valuable customer insights. This is definitely a data source which has been under utilized. However, benefits also come with a problem. Amazon records 5 Billion clicks a day and the whole US generates 400 Billion clicks, equivalent to 3.4 Petabytes a day. This immense volume has given enterprises and their IT professionals a big data problem before they can fully utilize this insight-rich data source.
This presentation will use big data technology to help solve this big data problem; the presenter will explain everything about Clickstream data, like benefits, challenges and the solution. The end-to-end solution will include proposed data architecture, ETL, and various machine learning algorithms. A real world successful example will also be presented for audience to better grasp the concept and its applications. Sample codes and demo will also be presented for audience to apply in their respective areas.
The presentation will describe methods for discovering interesting and actionable patterns in log files for security management without specifically knowing what you are looking for. This approach is different from "classic" log analysis and it allows gaining an insight into insider attacks and other advanced intrusions, which are extremely hard to discover with other methods. Specifically, I will demonstrate how data mining can be used as a source of ideas for designing future log analysis techniques, that will help uncover the coming threats. The important part of the presentation will be the demonstration how the above methods worked in a real-life environment.
Open Data is a global trend. This presentation describes part of the trend, focuses on one of the most advanced initiatives - Dublinked.ie in Ireland and provides guidance on how to get started.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Data migration at a petabyte scale is now a simple service from AWS. You can easily move large volumes of data from onsite environments to the cloud, or quickly get started with the cloud as a backup target using data transfer services, like AWS Snowball or AWS Storage Gateway. Learn about the various data migration options available to you and understand which one is the right fit for your requirements.
Search Joins with the Web - ICDT2014 Invited LectureChris Bizer
The talk will discuss the concept of Search Joins. A Search Join is a join operation which extends a local table with additional attributes based on the large corpus of structured data that is published on the Web in various formats. The challenge for Search Joins is to decide which Web tables to join with the local table in order to deliver high-quality results. Search joins are useful in various application scenarios. They allow for example a local table about cities to be extended with an attribute containing the average temperature of each city for manual inspection. They also allow tables to be extended with large sets of additional attributes as a basis for data mining, for instance to identify factors that might explain why the inhabitants of one city claim to be happier than the inhabitants of another.
In the talk, Christian Bizer will draw a theoretical framework for Search Joins and will highlight how recent developments in the context of Linked Data, RDFa and Microdata publishing, public data repositories as well as crowd-sourcing integration knowledge contribute to the feasibility of Search Joins in an increasing number of topical domains.
Applications of xml, semantic web or linked data in Library/Information Servi...Nurhazman Abdul Aziz
Applications of XML, Semantic Web & Linked Data in Library/Information Services & Skills needed by System Librarians.
H6716 (Internet & Web Technologies) & K6224 (Internet Technologies & Applications)
Semester 2 – 2011/2012
Hazman Aziz, Librarian (Library Technology & Systems)
Amirrudin Dahlan, Senior IT Specialist (Center for IT & Services)
Nanyang Technological University
This presentation is the culmination of my detail to the E-Government Office in the US Office of Management and Budget and the work I did to evolve and mature initiatives like recovery.gov and data.gov.
Presentation about - Semantic Web - Overview -Semantic Web
Web of Data, Giant Global Graph, Data Web, Web 3.0, Linked Data Web, Semantic Data Web, Enterprise Information Web, HTML, CSS,
For Biodiversity Informatics workshop in Stockholm, Friday September 13. Describing some of the tools in the mx system for mx; a collaborative web-based content management system for evolutionary systematists, particularly those working on descriptive taxonomy.
Yoder, M.J., Dole, K., Seltmann, K., and Deans, A. 2006-Present. Mx, a collaborative web based
content management for biological systematists.
Strategies for integrating semantic and blockchain technologiesHéctor Ugarte
Semantic Blockchain is the use of Semantic web standards on blockchain based systems. The standards promote common data formats and exchange protocols on the blockchain, making used of the Resource Description Framework (RDF).
Ontology BLONDiE for Bitcoin and Ethereum.
Research how to extract data from Ethereum.
Research how to store RDF data on Ethereum.
Prototype DeSCA: Ethereum application.
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.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
3. Slide 3
Hyperlink Graphs
Knowledge about the structure of the Web can be used to
improve crawling strategies, to help SEO experts or to
understand social phenomena.
4. Slide 4
HTML-embedded Data on the Web
Several million websites semantically markup the content of
their HTML pages.
Markup Syntaxes
Microformats
RDFa
Microdata
Data snippets
within info boxes
5. Slide 5
Relational HTML Tables
HTML Tables over semi-structured data which can be used to
build up or extend knowledge bases as DBPedia.
• Cafarella, et al.: WebTables: Exploring the Power of Tables on the Web. VLDB 2008.
In a corpus of 14B raw
tables, 154M are „good“
relations (1.1%)
6. Slide 6
The Web Data Commons Project
Has developed an Amazon-based framework for extracting data
from large web crawls
Capable to run on any cloud infrastructure
Has applied this framework to the Common Crawl data
Adaptable to other crawls
Results and framework are publicly available
http://webdatacommons.org
Goal: Offer an easy-to-use, cost efficient, distributed
extraction framework for large web crawls, as well as
datasets extracted out of the crawls.
8. Slide 8
Extraction Worker
AWS S3
AWS S3
WDC Extractor
.(w)arc
Worker
Filter
output
Worker:
• Written in Java
• Process one page at
once
• Independent from
other files and
workers
Download file
Upload output file
Filter:
• Reduce Runtime
• Mime-Type filter
• Regex detection of
content or meta-
information
Worker
9. Slide 9
Web Data Commons – Extraction Framework
Written in Java
Mainly tailored for Amazon Web Services
Fault tolerant and cheap
300 USD to extract 17 billion RDF statements from 44 TB
Easy customizable
Only worker has to be adapted
Worker is a single process method processing one file each time
Scaling is automated by the framework
Access Open Source Code:
https://www.assembla.com/code/commondata/
Alternative: Hadoop Version, which can run on any Hadoop
cluster without Amazon Web Services.
10. Slide 10
Extracted Datasets
Hyperlink Graph
HTML-embedded Data
Relational HTML Tables
Hyperlink Graph
HTML-embedded Data
Relational HTML Tables
11. Slide 11
Hyperlink Graph
Extracted from the Common Crawl 2012 Dataset
Over 3.5 billion pages connected by over 128 billion links
Graph files: 386 GB
http://webdatacommons.org/hyperlinkgraph/
http://wwwranking.webdatacommons.org/
12. Slide 12
Hyperlink Graph
Degrees do not follow a power-law
Detection of Spam pages
Further insights:
WWW‘14: Graph Structure in the Web – Revisited (Meusel et al.)
WebSci‘14: The Graph Structure of the Web aggregated by Pay-Level Domain (Lehmberg et al.)
Discovery of evolutions in the global structure of the World
Wide Web.
13. Slide 13
Hyperlink Graph
Discovery of important and interesting sites using different
popularity rankings or website categorization libraries
Websites connected by at least ½ Million Links
14. Slide 14
HTML-embedded Data
More and more Websites semantically
markup the content of their HTML pages.
Markup Syntaxes
RDFa
Microformats
Microdata
15. Slide 15
Websites containing Structured Data (2013)
1.8 million websites (PLDs) out of 12.8 million
provide Microformat, Microdata or RDFa data (13.9%)
585 million of the 2.2 billion pages contain
Microformat, Microdata or RDFa data (26.3%).
Web Data Commons - Microformat, Microdata, RDFa Corpus
17 billion RDF triples from Common Crawl 2013
Next release will be in winter 2014
http://webdatacommons.org/structureddata/
16. Slide 16
Top Classes Microdata (2013)
• schema = Schema.org
• dv = Google‘s
Rich Snippet Vocabulary
17. Slide 17
HTML Tables
• Cafarella, et al.: WebTables: Exploring the Power of Tables on the Web. VLDB 2008.
• Crestan, Pantel: Web-Scale Table Census and Classification. WSDM 2011.
In corpus of 14B raw tables, 154M are “good” relations (1.1%).
Cafarella (2008)
Classification Precision: 70-80%
18. Slide 18
WDC - Web Tables Corpus
Large corpus of relational Web tables for public download
Extracted from Common Crawl 2012 (3.3 billion pages)
147 million relational tables
selected out of 11.2 B raw tables (1.3%)
download includes the HTML pages of the tables (1TB zipped)
Table Statistics
Heterogeneity: Very high.
http://webdatacommons.org/webtables/
Min Max Average Median
Attributes 2 2,368 3.49 3
Data Rows 1 70,068 12.41 6
19. Slide 19
Attribute Statistics
28,000,000 different attribute labels
WDC - Web Tables Corpus
Attribute #Tables
name 4,600,000
price 3,700,000
date 2,700,000
artist 2,100,000
location 1,200,000
year 1,000,000
manufacturer 375,000
counrty 340,000
isbn 99,000
area 95,000
population 86,000
Subject Attribute Values
1.74 billion rows
253,000,000 different subject labels
Value #Rows
usa 135,000
germany 91,000
greece 42,000
new york 59,000
london 37,000
athens 11,000
david beckham 3,000
ronaldinho 1,200
oliver kahn 710
twist shout 2,000
yellow submarine 1,400
20. Slide 20
Conclusion
Three factors are necessary to work with web-scale data:
Thanks to Common Crawl, this data is available
Like Amazon or other on-demand cloud-services
The Web Data Commons Framework, or standard tools like Pig
Cost evaluation on task-base, but the WDC framework has turned
out to be cheaper
Availability of Crawls
Availability of cheap, easy-to-use infrastructures
Easy to adopt scalable extraction frameworks
21. Slide 21
Questions
Please visit our website: www.webdatacommons.org
Data and Framework are available as free download
Web Data Commons is supported by: