SocCnx11 - Social Analytics - Why Your Biggest Goldmine Is (Probably) Untappedpanagenda
Join us for a unique perspective on social software adoption and how data analytics can help influence implementation and deployment of your enterprise social networking platform of choice, including IBM Connections.
Come and learn about the benefits of pattern analysis and visualizations to unlock more effective operational and strategic decisions. Find out how your biggest untapped gold mine can help transform your organization by re-engaging your workforce across the board, while we share a case study on how data analytics sometimes has the most unexpected outcomes.
A Data Viz Makeover: Approaches for Improving your VisualizationsAmanda Makulec
A joint presentation made at the 2015 USAID Global Health Mini University, introducing key data visualization concepts and setting the stage for two interactive activities on storyboarding for data visualizations and visual best practices for graph and chart design.
Wrangling Big Data in a Small Tech EcosystemShalin Hai-Jew
This work summarizes an early endeavor to explore some big(gish) data from an LMS data portal from flat files. There are some patterns that are identifiable: file types that are most common on the LMS instance, the types of assignments given, time-based patterns for quizzes, types of third-party tool activations (based on LTI), typical computer device configurations for those touching the LMS, features of email conversations shared through the instance, and others. This work includes some creative applications of "survival analysis" (time-to-event analysis) and computational linguistic analysis to textual descriptions. The particular LMS instance is from Kansas State University's Canvas instance; Canvas is created by Instructure. Much is learnable from LMS data portal "flat files," but much is left unexploited, without going to a larger database.
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.Reraser Juan José Calderón
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.R. Department of Computer Science, Christ University, Bangalore, India Department of Computer Science , Jain University, Bangalore, India
BIG DATA ANALYTICS AND E LEARNING IN HIGHER EDUCATIONIJCI JOURNAL
With the advent of internet and communication technology the penetration of e-learning has increased. The
digital data being created by the higher educational institutions is also on ascent. The need for using “Big
Data” platforms to handle, analyse these large amount of data is prime. Many educational institutions are
using analytics to improve their process. Big Data analytics when applied onto teaching learning process
might help in improvising as well as developing new paradigms. Usage of Big Data supported databases
and parallel programming models like MapReduce may facilitate the analysis of the exploding educational
data. This paper focuses on the possible application of Big Data Techniques on educational data.
SocCnx11 - Social Analytics - Why Your Biggest Goldmine Is (Probably) Untappedpanagenda
Join us for a unique perspective on social software adoption and how data analytics can help influence implementation and deployment of your enterprise social networking platform of choice, including IBM Connections.
Come and learn about the benefits of pattern analysis and visualizations to unlock more effective operational and strategic decisions. Find out how your biggest untapped gold mine can help transform your organization by re-engaging your workforce across the board, while we share a case study on how data analytics sometimes has the most unexpected outcomes.
A Data Viz Makeover: Approaches for Improving your VisualizationsAmanda Makulec
A joint presentation made at the 2015 USAID Global Health Mini University, introducing key data visualization concepts and setting the stage for two interactive activities on storyboarding for data visualizations and visual best practices for graph and chart design.
Wrangling Big Data in a Small Tech EcosystemShalin Hai-Jew
This work summarizes an early endeavor to explore some big(gish) data from an LMS data portal from flat files. There are some patterns that are identifiable: file types that are most common on the LMS instance, the types of assignments given, time-based patterns for quizzes, types of third-party tool activations (based on LTI), typical computer device configurations for those touching the LMS, features of email conversations shared through the instance, and others. This work includes some creative applications of "survival analysis" (time-to-event analysis) and computational linguistic analysis to textual descriptions. The particular LMS instance is from Kansas State University's Canvas instance; Canvas is created by Instructure. Much is learnable from LMS data portal "flat files," but much is left unexploited, without going to a larger database.
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.Reraser Juan José Calderón
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.R. Department of Computer Science, Christ University, Bangalore, India Department of Computer Science , Jain University, Bangalore, India
BIG DATA ANALYTICS AND E LEARNING IN HIGHER EDUCATIONIJCI JOURNAL
With the advent of internet and communication technology the penetration of e-learning has increased. The
digital data being created by the higher educational institutions is also on ascent. The need for using “Big
Data” platforms to handle, analyse these large amount of data is prime. Many educational institutions are
using analytics to improve their process. Big Data analytics when applied onto teaching learning process
might help in improvising as well as developing new paradigms. Usage of Big Data supported databases
and parallel programming models like MapReduce may facilitate the analysis of the exploding educational
data. This paper focuses on the possible application of Big Data Techniques on educational data.
Symposium 2018 - Innovation systématique - Alejandro Gutierrez-Lopez et Ma-Lo...PMI-Montréal
Conférence du Symposium 2018 - Le grand événement de la gestion de projet
Certaines conférences sont disponibles pour visionnement en différé, voir ici : https://www.pmimontreal.org/webconferences
Les entreprises doivent être plus créatives et innovantes pour être compétitives sur les marchés actuels. Pour améliorer l'innovation et la créativité de l'équipe, les chefs de projet peuvent mettre en œuvre de petits changements ou des variations dans les outils, les techniques, les processus et les politiques à leur disposition. Dans cette session, nous présentons les améliorations et les changements potentiels à ces éléments du point de vue des processus systématiques d'innovation et de collaboration, dans le but d'accroître la créativité et la collaboration dans les équipes de projet.
MEXTESOL Regional Conference - March 5, 2022
Flow is what we feel when we are fully alive.
Flow involves what we do and is in harmony with the environment around us.
Flow is something that happens most easily when we sing, dance, do sports – but it can happen when we work, read a good book, or have a good conversation.
Data challenge accepted - an Overview of Data Science Practices and Competenc...Alina Stoicescu
In today’s competitive research environment, the need for librarians to be knowledgeable about all things digital is growing. Data-savvy librarians are able to better assist their patrons with the resources they need for their research, as well as extract useful insights from library data.
Data science as a discipline aims to provide solutions for managing the steeply growing amount of data in the world. Due to their educational background and inquisitive approach to information and knowledge, librarians are well-positioned to use data science in their work. Yet how prepared are they to work with data science? Areas discussed within this presentation are data science competencies, data librarianship as a profession and the three roles of data librarianship.
Putting Data to Work: Moving science forward together beyond where we thought...Erin Robinson
Citation: Robinson, Erin. (2020, December). Putting Data to Work: Moving science forward together beyond where we thought possible!. Presented at the 2020 Fall American Geophysical Union Meeting (AGU), Remote: Zenodo. http://doi.org/10.5281/zenodo.4323170
We live in a world rich with data, where use and reuse would benefit not just science but also serve national security and society-at-large. Air quality impacts from forest fires, which are increasing in frequency, is one example of large, data-intensive science with societal impacts. Understanding long-range transport of smoke where I started my career and worked with Dr. Greg Leptoukh, for whom this lecture is named, required a variety of datasets from satellite, surface observations and models. Together with Greg, we formed the ESIP Air Quality Cluster, a community of practice, to determine which and how to use data access standards and metadata standards agreed to best support the broader Air Quality research community. Forest fire smoke analysis was based on datasets not originally intended for our purpose, but because the data was findable, accessible, interoperable and reusable (FAIR) and we were willing to reuse it, we reduced the time to wrangle data and were able to ask and answer new questions about each smoke event.
Today, we are seeing more and more examples like mine of science that was not possible without open data, standards and tools. However, our scientific data enterprise is evolving and maturing in an unmanaged fashion and due to insufficient coordination across planning, management, and resources, the potential benefits of all these data and distributed infrastructure are not fully realized. Reliable, long term funding as well as cultural changes including financial incentives and rewards are needed to turn Science Data Infrastructure into a first class citizen equal to Science. This talk will explore what it means to put data to work and explore the relationship between data-intensive science, data management and collaborative community efforts like the Earth Science Information Partners (ESIP) and Openscapes to move science forward beyond where we thought possible!
Conducting a Cross Tabulation Analysis in the Qualtrics Research SuiteShalin Hai-Jew
This article is about how to conduct a cross tabulation analysis in the Qualtrics Research Suite. This is forthcoming in an issue of the C2C Digital Magazine.
Future of education project overview oct 2018 lrFuture Agenda
Future of Education
The broadening world of education is undergoing several major shifts. Driven in part by technology innovation and new business models, the learning process is being reinvented and there is a transformation of education economics and outcomes. Alongside this, there are government imperatives to improve access and address the growing requirement for flexible knowledge workers with transferable skills who can adapt to the changing job market. An ageing workforce also means that there is an increasing need for lifelong learning and re-skilling. In addition there is an increasing demand for a more personalised, immersive and mobile learning experience. All this is challenging the traditional expectations around higher education and the role that universities should play. While countries such as Finland and Singapore are consistently seen as leaders in the field, other nations are trying hard to catch up.
Ahead of a series of global expert events during 2019, this is an overview of the Future of Education project. It provides some background on Future Agenda and preceding multi-nation programmes, highlights some of the questions being raised and outlines options for organisations around the world to get involved. Different governments, technology companies, universities and education service providers are collaborating to support this programme that will develop a clear, shared and detailed view of how the future of education may unfold. If you would like to join in and host one of these events in your region, do let us know (tim.jones@futureagenda.org) and we can integrate that into the planning.
Long nonfiction chapters are not in-style and may never have been. Where average chapter lengths of nonfiction book chapters are about 4,000 – 7,000 words in length, some may be several times that max range number. The explanation is that there is some irreducible complexity that that chapter addresses that cannot be addressed in shorter form. This slideshow explores some methods for writing longer chapters while still maintaining coherence, focus, and reader interest…and while using some technological tools to write and edit more efficiently.
Overcoming Reluctance to Pursuing Grant Funds in AcademiaShalin Hai-Jew
Starting as an organization’s new grant writer can be a challenge, especially in a case where there has been a time lapse since the last one left. People get out of the habit of pursuing grant funds. This slideshow addresses some of the reasons for such reluctance and proposes some ways to mitigate these.
More Related Content
Similar to Google Correlate(TM): Exploring Big "Google Search" Data
Symposium 2018 - Innovation systématique - Alejandro Gutierrez-Lopez et Ma-Lo...PMI-Montréal
Conférence du Symposium 2018 - Le grand événement de la gestion de projet
Certaines conférences sont disponibles pour visionnement en différé, voir ici : https://www.pmimontreal.org/webconferences
Les entreprises doivent être plus créatives et innovantes pour être compétitives sur les marchés actuels. Pour améliorer l'innovation et la créativité de l'équipe, les chefs de projet peuvent mettre en œuvre de petits changements ou des variations dans les outils, les techniques, les processus et les politiques à leur disposition. Dans cette session, nous présentons les améliorations et les changements potentiels à ces éléments du point de vue des processus systématiques d'innovation et de collaboration, dans le but d'accroître la créativité et la collaboration dans les équipes de projet.
MEXTESOL Regional Conference - March 5, 2022
Flow is what we feel when we are fully alive.
Flow involves what we do and is in harmony with the environment around us.
Flow is something that happens most easily when we sing, dance, do sports – but it can happen when we work, read a good book, or have a good conversation.
Data challenge accepted - an Overview of Data Science Practices and Competenc...Alina Stoicescu
In today’s competitive research environment, the need for librarians to be knowledgeable about all things digital is growing. Data-savvy librarians are able to better assist their patrons with the resources they need for their research, as well as extract useful insights from library data.
Data science as a discipline aims to provide solutions for managing the steeply growing amount of data in the world. Due to their educational background and inquisitive approach to information and knowledge, librarians are well-positioned to use data science in their work. Yet how prepared are they to work with data science? Areas discussed within this presentation are data science competencies, data librarianship as a profession and the three roles of data librarianship.
Putting Data to Work: Moving science forward together beyond where we thought...Erin Robinson
Citation: Robinson, Erin. (2020, December). Putting Data to Work: Moving science forward together beyond where we thought possible!. Presented at the 2020 Fall American Geophysical Union Meeting (AGU), Remote: Zenodo. http://doi.org/10.5281/zenodo.4323170
We live in a world rich with data, where use and reuse would benefit not just science but also serve national security and society-at-large. Air quality impacts from forest fires, which are increasing in frequency, is one example of large, data-intensive science with societal impacts. Understanding long-range transport of smoke where I started my career and worked with Dr. Greg Leptoukh, for whom this lecture is named, required a variety of datasets from satellite, surface observations and models. Together with Greg, we formed the ESIP Air Quality Cluster, a community of practice, to determine which and how to use data access standards and metadata standards agreed to best support the broader Air Quality research community. Forest fire smoke analysis was based on datasets not originally intended for our purpose, but because the data was findable, accessible, interoperable and reusable (FAIR) and we were willing to reuse it, we reduced the time to wrangle data and were able to ask and answer new questions about each smoke event.
Today, we are seeing more and more examples like mine of science that was not possible without open data, standards and tools. However, our scientific data enterprise is evolving and maturing in an unmanaged fashion and due to insufficient coordination across planning, management, and resources, the potential benefits of all these data and distributed infrastructure are not fully realized. Reliable, long term funding as well as cultural changes including financial incentives and rewards are needed to turn Science Data Infrastructure into a first class citizen equal to Science. This talk will explore what it means to put data to work and explore the relationship between data-intensive science, data management and collaborative community efforts like the Earth Science Information Partners (ESIP) and Openscapes to move science forward beyond where we thought possible!
Conducting a Cross Tabulation Analysis in the Qualtrics Research SuiteShalin Hai-Jew
This article is about how to conduct a cross tabulation analysis in the Qualtrics Research Suite. This is forthcoming in an issue of the C2C Digital Magazine.
Future of education project overview oct 2018 lrFuture Agenda
Future of Education
The broadening world of education is undergoing several major shifts. Driven in part by technology innovation and new business models, the learning process is being reinvented and there is a transformation of education economics and outcomes. Alongside this, there are government imperatives to improve access and address the growing requirement for flexible knowledge workers with transferable skills who can adapt to the changing job market. An ageing workforce also means that there is an increasing need for lifelong learning and re-skilling. In addition there is an increasing demand for a more personalised, immersive and mobile learning experience. All this is challenging the traditional expectations around higher education and the role that universities should play. While countries such as Finland and Singapore are consistently seen as leaders in the field, other nations are trying hard to catch up.
Ahead of a series of global expert events during 2019, this is an overview of the Future of Education project. It provides some background on Future Agenda and preceding multi-nation programmes, highlights some of the questions being raised and outlines options for organisations around the world to get involved. Different governments, technology companies, universities and education service providers are collaborating to support this programme that will develop a clear, shared and detailed view of how the future of education may unfold. If you would like to join in and host one of these events in your region, do let us know (tim.jones@futureagenda.org) and we can integrate that into the planning.
Long nonfiction chapters are not in-style and may never have been. Where average chapter lengths of nonfiction book chapters are about 4,000 – 7,000 words in length, some may be several times that max range number. The explanation is that there is some irreducible complexity that that chapter addresses that cannot be addressed in shorter form. This slideshow explores some methods for writing longer chapters while still maintaining coherence, focus, and reader interest…and while using some technological tools to write and edit more efficiently.
Overcoming Reluctance to Pursuing Grant Funds in AcademiaShalin Hai-Jew
Starting as an organization’s new grant writer can be a challenge, especially in a case where there has been a time lapse since the last one left. People get out of the habit of pursuing grant funds. This slideshow addresses some of the reasons for such reluctance and proposes some ways to mitigate these.
Writing grants is one common way that those in institutions of higher education may acquire some funds—small and big, one-off and continuing—to conduct research, hire faculty and researchers and learners and others, update equipment, update or build up new buildings, and achieve other work. This slideshow explores some aspects of the work of grant writing in the present moment in higher education.
Contrasting My Beginner Folk Art vs. Machine Co-Created Folk Art with an Art-...Shalin Hai-Jew
The SARS-CoV-2 pandemic inspired several years of experimentation with common or folk art, involving mixed media, alcohol ink painting, and other explorations. Then, with the emergence of art-making generative AIs, there were further experiments, particularly with one that enables generation of visuals from scanned art and photos, text prompts, style overlays, and text-based visual modifiers. While both types of artmaking are emotionally satisfying and helpful for stress management, there are some contrasting differences. This exploratory slideshow explores some of these differences in order to partially shed light on the informal usage of an art-making generative AI (artificial intelligence).
Creating Seeding Visuals to Prompt Art-Making Generative AIsShalin Hai-Jew
Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
Common Neophyte Academic Book Manuscript Reviewer MistakesShalin Hai-Jew
The work of academic book reviewing, as a volunteer (most often), is a common academic practice. The presenter has served as a neophyte one for some years before settling into this invited volunteer work for several decades. There have been lessons learned over time about avoidable mistakes…from both experience and observation.
Fashioning Text (and Image) Prompts for the CrAIyon Art-Making Generative AIShalin Hai-Jew
CrAIyon (formerly DALL-E after Salvador “Dali”) is a web-facing art-making generative AI tool online (https://www.craiyon.com/) that enables the uses of text (and image) prompts for the creation of watermarked, lightweight visuals. Counterintuitively, the rough visuals are much more usable for recombinations and remixes and recreations into usable digital visuals for various digital learning objects. The textual prompts are not particularly intuitive because of how the generative AI program was trained on mass-scale visuals). There is an art and occasional indirection to working prompts after each try, with the resulting nine-image proof sheets that CrAIyon outputs. The tool can be used iteratively for different outputs.
The tool sometimes turns out serendipitous surprises, including an occasional work so refined that it can be used / shared almost unedited. One challenge in using CrAIyon comes from their request for credit (for all non-subscribers to their service). Another comes from the visual watermarking (orange crayon at the bottom right of the image). However, this tool is quite useful for practical applications if one is willing to engage deep digital image editing (Adobe Photoshop, Adobe Illustrator).
Augmented Reality in Multi-Dimensionality: Design for Space, Motion, Multiple...Shalin Hai-Jew
Augmented reality (AR)—the use of digital overlays over physical space—manifests in a wide range of spaces (indoor, outdoor; virtual) and ways (in real space (with unaided human vision); in head gear; in smart glasses; on mobile devices, and others). There are various authoring technologies that enable the making of AR experiences for various users. This work uses a particular tool (Adobe Aero®) to explore ways to build AR for multiple dimensions, including the fourth dimension (motion, changes over time).
Based on the respective purposes of the AR experience, some basic heuristics are captured for
space design (1),
motion design (2),
multiple perception design (sight, smell, taste, sound, touch) (3),
and virtual- and tangible- interactivity (4).
Some Ways to Conduct SoTL Research in Augmented Reality (AR) for Teaching and...Shalin Hai-Jew
One of the extant questions about augmented reality (AR) is how (in)effective it is for the teaching and learning in various formal, nonformal, and informal contexts. The research literature shows mixed findings, which are often highly context-based (and not generalizable). There are some non-trivial costs to the design/development/deployment of AR for teaching and learning. For the users, there is cognitive load on the working memory [(1) extraneous/poor design, (2) intrinsic/inherent difficulty in topic, and (3) germane/forming schemas]. For teachers, there are additional knowledge, skills, and abilities / attitudes (KSAs) that need to be brought to bear.
Exploring the Deep Dream Generator (an Art-Making Generative AI) Shalin Hai-Jew
The Deep Dream Generator was created by Google engineer Alexander Mordvintsev in 2014. It has a public facing instance at https://deepdreamgenerator.com/, which enables people to use text prompts and image prompts (individually or in combination) to inspire the art-generating generative AI to output images. This work highlights some process-based walk-throughs of the tool, some practical uses, some lightweight art learning, some aspects of the online social community on this platform, and other insights. Some works by the AI prompted by the presenter may be seen here: https://deepdreamgenerator.com/u/sjjalinn.
(This is the first draft of a slideshow that will be used in a conference later in the year.)
Augmented Reality for Learning and AccessibilityShalin Hai-Jew
Recently, the presenter conducted a systematic review of the academic literature and an environmental scan to learn how to set up an augmented reality (AR) shop at an institution of higher education. The ambition was to not only set up AR in an accessible and legal way but also be able to test for potential +/- effects of AR on teaching and learning. The research did not go past the review stage, because of a lack of funding, but some insights about accessibility in AR were acquired.
(The visuals are from Deep Dream Generator and CrAIyon.)
Engaging Pixabay as an open-source contributor to hone digital image editing,...Shalin Hai-Jew
This slideshow describes the author's early experiences with creating two accounts on Pixabay in order to advance digital editing skills in multimedia. The two accounts are located at https://pixabay.com/users/sjjalinn-28605710/ and https://pixabay.com/users/wavegenerics-29440244/ ...
This work explores four main spaces where researchers publish about educational technology: academic-commercial, open-access, open-source, and self-publishing.
Human-Machine Collaboration: Using art-making AI (CrAIyon) as cited work, o...Shalin Hai-Jew
It is early days for generative art AIs. What are some ways to use these to complement one's work while staying legal (legal-ish)?
Correction: .webp is a raster format
Getting Started with Augmented Reality (AR) in Online Teaching and Learning i...Shalin Hai-Jew
University creative shops are exploring whether they can get into the game of producing AR-enhanced experiences: campus tours, interactive gaming, virtual laboratories, exploratory art spaces, simulations, design labs, online / offline / blended teaching and learning modules, and other AR applications.
This work offers a basic environmental scan of the AR space for online teaching and learning, and it includes pedagogical design leads from the current research, technological knowhow, hands-on design / development / deployment of learning objects, and online teaching and learning methods.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Google Correlate(TM): Exploring Big "Google Search" Data
1. 6/17/2017 Google Correlate(TM): Exploring Big "Google Search" Data
http://scalar.usc.edu/works/c2cdigitalmagazinespringsummer2017/googlecorrelateexploringbiggooglesearchdata?t=1497707738311 1/12
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Dashboard | Index | Guide
C2C Digital Magazine (Spring / Summer 2017)
Colleague 2 Colleague, Author
C2C Digital Magazine
(Spring / Summer 2017)
1. Cover
2. Issue Navigation
3. Letter from the
Chair: Dr. Anna J.
Catterson
4. Discover, Connect
& Engage: SIDLIT
2017
5. C2C's Inaugural
LMS Preconference
2017
6. A Multimodal and
Multidisciplinary
Conversation about
Online Instruction
7. State History
Digital Resource
Packaging
8. Sentiment Analysis
of Real-Time Twitter
Data
9. Data Conversion
for Relational and
Object Oriented
Databases
10. Using iMovie to
Inspire Creative Top-
Notch Projects in the
Classroom
11. How Do You
Know What You
Don’t Know?
12. Affective
Computing
13. Using the Content
Collection in
Blackboard for
Course Development
14. "It’s Not Politics;
It's Technology,
Stupid"
15. Creating a
Streamgraph in
Microsoft Excel 2016
16. Wrangling Big
Data in a Small Tech
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18. Google
Correlate(TM):
Exploring Big
"Google Search" Data
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Google Correlate(TM): Exploring Big "Google Search" Data
By Shalin HaiJew, Kansas State University
Figure 1: “Kansas” and “Overland Park Kansas” Query Correlation and U.S. ByState Mapping in Google Correlate
Ever wonder which Google Search terms correlate with other query terms over time (based on billions of
records)?
Ever wonder if a timeseries dataset that one has correlates with particular Google Searches over time?
Ever consider how a particular search term time pattern instantiates in a particular country in the world? Or how
correlations between search terms (over time) may vary between states in the U.S.?
Ever want to draw a linegraph curve and dare Google to find search term frequencies over time that match that
curve?
Google Correlate (https://www.google.com/trends/correlate) is an online application that enables users to access
Google Search query data from 2003 to the present (with up to a week's lag). A core assumption of this tool is that
people’s search queries occur in realtime and realspace, and these queries may be observed en masse as reflections of
inworld events. Based on the time and space dimensions, the queries themselves may reveal different types of insights
about people and their lived concerns. How search terms vary in frequency over time shows times of heightened interest
for particular terms as well as times of lessened interest for particular terms. This data variability over time may
provide insights about inworld phenomena as well as population interests.
To unpack what the multifaceted Google Correlate can do (at least at a beginner’s level), it is important to focus on
different capabilities, one at a time.
A Simple Query Correlation
To start a simple query correlation, just write a query term in the “Search correlations” text box (anything representable
in UTF8, or any language representable on the Web and Internet). The search terms may be phrases, sentences,
Main menu
“Kansas” and “Overland Park Kansas” Query Correlation and U.S. By-State Annotations
Details
2. 6/17/2017 Google Correlate(TM): Exploring Big "Google Search" Data
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Educational
Technologies
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Search
names, formulas, and so on. There is not a limit to a onegram or unigram; in other words, there can be a number of
alphanumeric terms in a particular order that may be explored. Make sure that the Country in the left dropdown menu
is correct (or engage the dropdown if the default “United States” is not correct), and click the “Search correlations”
button at the top.
For this article, “educational technology” was used as the seeding term. In Figure 2, “Top 10 ‘educational technology’
Pairwise Correlations in the U.S. (Google Correlate),” the top 10 correlations may be seen. To the left are the
correlations between the selected seeding search term and the phrase. A 0.9706 is a very high correlation, and that may
be seen in the red and blue lines of the line graph below. In this case, the xaxis is time (2003 – present) in equal
increments. The yaxis represents standard deviations away from the mean (whether higher or lower or right on the
mean). The yaxis shows the normalized search activity so that the respective patterns over time may be legitimately
compared. (Raw counts would show similar changes over time patternwise, but they would result in large gaps between
the lines in the line graph depending on the volume of the two datasets being compared. Normalizing would show how
far off the mean an aggregated query count is for a particular time period—either weekly or monthly—and this allows
the yaxis to have a smaller range of possible values for clearer expressions of the correlations.) [Note: As a reminder,
correlations are reflected as a number from 1 to +1. If r=0, there is no observable correlation between the two
variables. If r = 1, there is a perfect positive correlation between the two variables. A correlation coefficient or rscore
shows the statistical relationship between two variables. Sometimes, this coefficient is referred to as the “Pearson
productmoment correlation coefficient” or “Pearson’s r.” Google Correlate only shows positive correlations, and they
show the search terms with the highest correlation coefficients with the target seeding terms and then others in
descending correlation order.] For more about “Standard_score,” please see the following article on Wikipedia.
In other words, when people search “educational technology” over time from 2003 to the present, “education research”
has the closest data pattern over time in Google Search. The next most highly correlated pattern is “technology,” then
“abstracts,” “educational research,” “information systems,” “c++,” “research journal,” “ecommerce,” “biotechnology,”
and “dissertations.”
Figure 2: Top 10 “educational technology” Pairwise Correlations in the U.S. (Google Correlate)
From the same data, it is possible to have Google Correlate draw a scatterplot. Note that the xaxis of the scatterplot
represents “educational technology,” and the yaxis represents “education research.” The bottom left quadrant
contains the lessthanaverage adjusted counts for the particular observed week. The mapping of the two sets of dots is
to enable the visual analysis of whether the dots cluster and if it is possible to draw a line of best fit through the dots to
see if there is an association between the variable represented on the xaxis and the one on the yaxis. The normalized
data has the mean (μ for full population means, and x̅ for means of samples of populations) at zero for both sets. The
placed dots show where the respective aggregate queries land (in terms of standard deviations from the mean) from the
two sets. The diagonal line of best fit is drawn through the data to see if there may be a linear correlation between
“educational technology” (on the xaxis) and “education research” on the yaxis. In Figure 3, the scatter plot shows a
very high correlation, both in the r and the fit to the diagonal line.
View Recent
Top 10 “educational technology” Pairwise Correlations in the U.S. Annotations
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Nearest Neighbor Search in Google Correlate
https://www.google.com/trends/correlate/nnsearch.pdf
Search by Drawing (in Google Correlate)
https://www.google.com/trends/correlate/draw
About the Author
Shalin HaiJew works as an instructional designer at Kansas State University. Her email is shalin@kstate.edu.
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web search forecasting Google Search web search activity human sensor network surveillance
Google Correlate time-series data