Why and how to scrape geospatial data from the webPromptCloud
We highlight various use cases of geospatial data along with its ability to augment existing data for robust insights. It also covers some of the prominent sources of data collection.
Data Overload: How much data are we creating?Planetech USA
By 2020:
- The amount of digital data created annually will be around 44 zettabytes, as more devices are connected to the internet and more content is uploaded.
- However, we currently only analyze around 0.5% of the data we create each year, leaving most of it unused.
- This gap between data creation and analysis raises questions about how to better extract meaningful information and insights from the vast troves of raw data being generated every day.
HxRefactored - Jawbone - Andrew Rosenthal HxRefactored
The document outlines five strategies for building a winning data platform as presented by Andrew Rosenthal from Jawbone. The strategies are: 1) Mobilize the platform by enabling standalone value and subsidizing early adopters. 2) Go open at the right time once rapid growth and innovation have occurred. 3) Liberate the data by making it available to users and developers while maintaining ownership. 4) Provide a stable and predictable platform with high uptime and shared expectations. 5) Enable monetization through affiliate programs, complementary products/services, and increasing spend and share of spend.
Research says digital data will grow to 2.75 zettabytes in 2012 and rocket toward nearly 8 zettabytes by 2015. How are we creating, replicating, saving, mining, and analyzing all of this data? What does our data driven reality of today tell us about the future?
Hercule: Journalist Platform to Find Breaking News and Fight Fake OnesOntotext
Hercule: a platform to help journalists detect emerging news topics, check their veracity, track an event as it unfolds and find the various angles in a story as it develops.
Waze @Google is a Big Data company.
We use data and complex analytics to gain insights and make decisions on a daily basis.
This presentations includes teasers and ideas for you based on real use cases from Waze
NTAP consultant Madhu Lakshmanan's presentation about using GIS mapping for targeting and evaluation. Delivered for CERA's Techniques for Targeting Populations webinar
(June 18, 2009): http://www.legalhotlines.org/webinars/targeting.htm.
Why and how to scrape geospatial data from the webPromptCloud
We highlight various use cases of geospatial data along with its ability to augment existing data for robust insights. It also covers some of the prominent sources of data collection.
Data Overload: How much data are we creating?Planetech USA
By 2020:
- The amount of digital data created annually will be around 44 zettabytes, as more devices are connected to the internet and more content is uploaded.
- However, we currently only analyze around 0.5% of the data we create each year, leaving most of it unused.
- This gap between data creation and analysis raises questions about how to better extract meaningful information and insights from the vast troves of raw data being generated every day.
HxRefactored - Jawbone - Andrew Rosenthal HxRefactored
The document outlines five strategies for building a winning data platform as presented by Andrew Rosenthal from Jawbone. The strategies are: 1) Mobilize the platform by enabling standalone value and subsidizing early adopters. 2) Go open at the right time once rapid growth and innovation have occurred. 3) Liberate the data by making it available to users and developers while maintaining ownership. 4) Provide a stable and predictable platform with high uptime and shared expectations. 5) Enable monetization through affiliate programs, complementary products/services, and increasing spend and share of spend.
Research says digital data will grow to 2.75 zettabytes in 2012 and rocket toward nearly 8 zettabytes by 2015. How are we creating, replicating, saving, mining, and analyzing all of this data? What does our data driven reality of today tell us about the future?
Hercule: Journalist Platform to Find Breaking News and Fight Fake OnesOntotext
Hercule: a platform to help journalists detect emerging news topics, check their veracity, track an event as it unfolds and find the various angles in a story as it develops.
Waze @Google is a Big Data company.
We use data and complex analytics to gain insights and make decisions on a daily basis.
This presentations includes teasers and ideas for you based on real use cases from Waze
NTAP consultant Madhu Lakshmanan's presentation about using GIS mapping for targeting and evaluation. Delivered for CERA's Techniques for Targeting Populations webinar
(June 18, 2009): http://www.legalhotlines.org/webinars/targeting.htm.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
AI WORLD: I-World: EIS Global Innovation Platform: BIG Knowledge World vs. BI...Azamat Abdoullaev
Future World Projects
Global Intelligence Platform
Smart World
Smart Nation
Smart Cities Global Initiative
Smart Superpower Projects
Big Data and Big Knowledge, etc.
Dati: La "quinta" rivoluzione dell'information technology - intervento di Mario Rasetti, Fondazione ISI, al Lunch Seminar "Big Data e Internet of Things" del 29 giugno 2015, organizzato dal CSI-Piemonte
Event detection in twitter using text and image fusioncsandit
In this paper, we describe an accurate and effective event detection method to detect events from
Twitter stream. It detects events using visual information as well as textual information to improve
the performance of the mining. It monitors Twitter stream to pick up tweets having texts and photos
and stores them into database. Then it applies mining algorithm to detect the event. Firstly, it detects
event based on text only by using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Secondly, it detects the event based on
image only by using visual features including histogram of oriented gradients (HOG) descriptors,
grey-level co-occurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn)
classification is used in the detection. Finally, the final decision of the event detection is made based
on the reliabilities of text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.93, comparing with 0.89 with texts only, and 0.86
with images only.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
MapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
This document contains the schedule for a data science event taking place on November 29th from 14:00-18:30 in room NAB314. The schedule includes 15-minute presentations on topics like big data practices, data as a design tool, gamification and crowd-sourcing, understanding game play behavior, big data and disasters, ethical challenges in data science, values in digital relations and prosperity theology, legible machine learning, data science applications in interdisciplinary research, and an MSc in data science program. There will also be coffee and discussion/drinks at the end.
In this contribution, we develop an accurate and effective event detection method to detect events from a
Twitter stream, which uses visual and textual information to improve the performance of the mining
process. The method monitors a Twitter stream to pick up tweets having texts and images and stores them
into a database. This is followed by applying a mining algorithm to detect an event. The procedure starts
with detecting events based on text only by using the feature of the bag-of-words which is calculated using
the term frequency-inverse document frequency (TF-IDF) method. Then it detects the event based on image
only by using visual features including histogram of oriented gradients (HOG) descriptors, grey-level cooccurrence
matrix (GLCM), and color histogram. K nearest neighbours (Knn) classification is used in the
detection. The final decision of the event detection is made based on the reliabilities of text only detection
and image only detection. The experiment result showed that the proposed method achieved high accuracy
of 0.94, comparing with 0.89 with texts only, and 0.86 with images only.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular social media platform. Social media analytics helps make informed decisions based on people's needs and opinions. This information, when properly perceived provides valuable insights into different domains, such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet) algorithms. The experiments use different data processing steps including trigrams, without trigrams, hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags helps improve the topic inference results with a better coherence score.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
International Journal of Computer Science, Engineering and Applications (IJCSEA)IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
This primer - or "Big Data 101" specifically for the international development and humanitarian communities - explains the concepts behind using Big Data for social good in easy-to-understand language. Published by the United Nations' Global Pulse initiative, which is exploring how new, digital data sources and real-time analytics technologies can help policymakers understand human well-being and emerging vulnerabilities in real-time. www.unglobalpulse.org
This document discusses huge data and data mining. It defines huge data and notes that huge amounts of data are being created daily from sources like social media, sensors, and digital content. It discusses some key aspects of huge data including that it can be structured or unstructured, comes from decentralized sources, and has complexity in relationships within the data. The 3Vs of huge data are also defined as volume, variety, and velocity. The document states that data mining techniques can be used to extract useful insights from huge data by discovering patterns and relationships within large datasets.
This document provides a review of techniques, tools, and platforms for analyzing social media data. It discusses the types of social media data and formats available, as well as tools for accessing, cleaning, analyzing, and visualizing social media data. Some key challenges of social media research are the restricted access to comprehensive data sources, lack of tools for in-depth analysis without programming, and need for large data storage and computing facilities to support research at scale. The document provides a methodology and critique of current approaches and outlines requirements to better support social media research.
Work/Technology 2050: Scenarios and Actions (Dubai talk)Jerome Glenn
The Millennium Project conducted a three-year global study on the future of work and technology called the Work/Technology 2050 Global Study. The study involved over 1,300 pages and used 37 different futures methods. It developed three scenarios for how work and technology could evolve by 2050: a mixed scenario, a political/economic turmoil scenario, and a self-actualization scenario. National workshops were held to discuss long-term strategies. This resulted in 93 proposed actions that were assessed in the areas of education, government, business, culture, and science/technology. The study explored how emerging technologies could profoundly impact work and the need for new economic and social systems to address issues like unemployment.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
AI WORLD: I-World: EIS Global Innovation Platform: BIG Knowledge World vs. BI...Azamat Abdoullaev
Future World Projects
Global Intelligence Platform
Smart World
Smart Nation
Smart Cities Global Initiative
Smart Superpower Projects
Big Data and Big Knowledge, etc.
Dati: La "quinta" rivoluzione dell'information technology - intervento di Mario Rasetti, Fondazione ISI, al Lunch Seminar "Big Data e Internet of Things" del 29 giugno 2015, organizzato dal CSI-Piemonte
Event detection in twitter using text and image fusioncsandit
In this paper, we describe an accurate and effective event detection method to detect events from
Twitter stream. It detects events using visual information as well as textual information to improve
the performance of the mining. It monitors Twitter stream to pick up tweets having texts and photos
and stores them into database. Then it applies mining algorithm to detect the event. Firstly, it detects
event based on text only by using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Secondly, it detects the event based on
image only by using visual features including histogram of oriented gradients (HOG) descriptors,
grey-level co-occurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn)
classification is used in the detection. Finally, the final decision of the event detection is made based
on the reliabilities of text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.93, comparing with 0.89 with texts only, and 0.86
with images only.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
MapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
This document contains the schedule for a data science event taking place on November 29th from 14:00-18:30 in room NAB314. The schedule includes 15-minute presentations on topics like big data practices, data as a design tool, gamification and crowd-sourcing, understanding game play behavior, big data and disasters, ethical challenges in data science, values in digital relations and prosperity theology, legible machine learning, data science applications in interdisciplinary research, and an MSc in data science program. There will also be coffee and discussion/drinks at the end.
In this contribution, we develop an accurate and effective event detection method to detect events from a
Twitter stream, which uses visual and textual information to improve the performance of the mining
process. The method monitors a Twitter stream to pick up tweets having texts and images and stores them
into a database. This is followed by applying a mining algorithm to detect an event. The procedure starts
with detecting events based on text only by using the feature of the bag-of-words which is calculated using
the term frequency-inverse document frequency (TF-IDF) method. Then it detects the event based on image
only by using visual features including histogram of oriented gradients (HOG) descriptors, grey-level cooccurrence
matrix (GLCM), and color histogram. K nearest neighbours (Knn) classification is used in the
detection. The final decision of the event detection is made based on the reliabilities of text only detection
and image only detection. The experiment result showed that the proposed method achieved high accuracy
of 0.94, comparing with 0.89 with texts only, and 0.86 with images only.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular social media platform. Social media analytics helps make informed decisions based on people's needs and opinions. This information, when properly perceived provides valuable insights into different domains, such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet) algorithms. The experiments use different data processing steps including trigrams, without trigrams, hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags helps improve the topic inference results with a better coherence score.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
International Journal of Computer Science, Engineering and Applications (IJCSEA)IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
This primer - or "Big Data 101" specifically for the international development and humanitarian communities - explains the concepts behind using Big Data for social good in easy-to-understand language. Published by the United Nations' Global Pulse initiative, which is exploring how new, digital data sources and real-time analytics technologies can help policymakers understand human well-being and emerging vulnerabilities in real-time. www.unglobalpulse.org
This document discusses huge data and data mining. It defines huge data and notes that huge amounts of data are being created daily from sources like social media, sensors, and digital content. It discusses some key aspects of huge data including that it can be structured or unstructured, comes from decentralized sources, and has complexity in relationships within the data. The 3Vs of huge data are also defined as volume, variety, and velocity. The document states that data mining techniques can be used to extract useful insights from huge data by discovering patterns and relationships within large datasets.
This document provides a review of techniques, tools, and platforms for analyzing social media data. It discusses the types of social media data and formats available, as well as tools for accessing, cleaning, analyzing, and visualizing social media data. Some key challenges of social media research are the restricted access to comprehensive data sources, lack of tools for in-depth analysis without programming, and need for large data storage and computing facilities to support research at scale. The document provides a methodology and critique of current approaches and outlines requirements to better support social media research.
Work/Technology 2050: Scenarios and Actions (Dubai talk)Jerome Glenn
The Millennium Project conducted a three-year global study on the future of work and technology called the Work/Technology 2050 Global Study. The study involved over 1,300 pages and used 37 different futures methods. It developed three scenarios for how work and technology could evolve by 2050: a mixed scenario, a political/economic turmoil scenario, and a self-actualization scenario. National workshops were held to discuss long-term strategies. This resulted in 93 proposed actions that were assessed in the areas of education, government, business, culture, and science/technology. The study explored how emerging technologies could profoundly impact work and the need for new economic and social systems to address issues like unemployment.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
2. What is GDELT ?
The Global Database of Events, Language, and Tone (GDELT):is a single massive
network that captures what's happening around the world, what its context is and
who's involved, and how the world is feeling about it.
An open real-time indexed global dataset covering the world’s events, emotions,
locations , images and narratives as they happen in the world in a live data stream
and also it includes historical data from 1979 to the present.
GDELT takes advantage of the full power of today’s cloud data source that spans
news, television, social media, google, news agencies, images, books, academic
literature and even the open web itself; all from 65 languages; codifying millions of
themes and thousands of emotions; and exploiting algorithms from simple keyword
matches to massive statistical models to deep learning approaches.
3. Data representation
he majority of work on news analysis has focused on textual news, but With the help deep-
learning algorithms, more than half a million images per day are cataloged, identifying
objects and activities, logos, text, facial sentiment and even image-based geolocation.
GDELT offers raw CSV files of all computed metadata beside Graphs.
Plus it offers a variety of tools and services to allow you to visualize, explore, and export
both the GDELT Event Database and the GDELT Global Knowledge Graph.
1.2 billion location mentions in a 1.5TB table can be aligned with their corresponding
narratives and exported to a cloud mapping platform in less than 60 seconds.
1.4 million photographs from 200 million news articles, millions of books can be sentiment
mined at 340 million words per second.
4.
5.
6.
7. How and where to use the data
You have the world unfold .. What do you want to do ?what are you thinking
about?
JUST Write a standard SQL query .. even the most complex queries returning
in near real-time.
8.
9. Mostly used in economic , environmental,
biomedical, political research ,awareness
and reactions studies and analysis.
Brilliant in conflict study like in crisis respond studies.
For example : in Syrian crisis ..
many complex variables ,events, reactions can all be
Represented in one useful graph that can
be extended to cover all “what if” cases.
Where and how to use the data
12. GDELT makes extensive use of Google Cloud Platform, using Google Compute
Engine to run its production systems.
GDELT creates a wide space of data to explore ..representing the whole world
information of people, organizations, locations, themes, and emotions underlying
events, offers opportunities to understand and interact with our world in new
ways.