Presentation given at the 2014 CODE4LIB Conference, Raleigh, NC. Mar. 25, 2014.
Conference Info: http://code4lib.org/conference/2014/schedule
Example code: http://github.com/bohyunkim/examples
Take Better Care of Library Data and Spreadsheets with Google Visualization A...Bohyun Kim
Presentation given at 2013 LITA Forum on Nov 8, 2013. http://www.ala.org/lita/conferences/forum/2013 ; Example files are at http://github.com/bohyunkim/examples
Hadoop Summit 2011 - Using a Hadoop Data Pipeline to Build a Graph of Users a...Bill Graham
At CBSi we’re developing a scalable, flexible platform to provide the ability to aggregate large volumes of data, to mine it for meaningful relationships and to produce a graph of connected users and content. This will enable us to better understand the connections between our users, our assets, and our authors.
Crunching Data with Google BigQuery. JORDAN TIGANI at Big Data Spain 2012Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/crunching-data-with-google-bigquery/jordan-tigani
Take Better Care of Library Data and Spreadsheets with Google Visualization A...Bohyun Kim
Presentation given at 2013 LITA Forum on Nov 8, 2013. http://www.ala.org/lita/conferences/forum/2013 ; Example files are at http://github.com/bohyunkim/examples
Hadoop Summit 2011 - Using a Hadoop Data Pipeline to Build a Graph of Users a...Bill Graham
At CBSi we’re developing a scalable, flexible platform to provide the ability to aggregate large volumes of data, to mine it for meaningful relationships and to produce a graph of connected users and content. This will enable us to better understand the connections between our users, our assets, and our authors.
Crunching Data with Google BigQuery. JORDAN TIGANI at Big Data Spain 2012Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/crunching-data-with-google-bigquery/jordan-tigani
This is a talk I gave at Web Analytics Wednesday in June 2017. It discusses automating the delivery of marketing channel data to Google BigQuery / Data Studio
Google cloud Professional Data Engineer practice exam test 2020SkillCertProExams
Google Cloud Certified Professional Data Engineer Exam questions pdf
https://skillcertpro.com/yourls/gcpdataeng
Want to practice more questions?We have 390+ Practice set questions for Google Cloud Certified -Professional Data Engineer certification (Taken from previous exams)
[Webinar] Interacting with BigQuery and Working with Advanced QueriesTatvic Analytics
In this webinar, we will cover advanced concepts and some complex queries. We will give a demo of how to fetch data from BigQuery into tools like Excel, R and python so we can continue further analysis. Along With Hands-on exercise, we will demonstrate how to automate queries using Apps Script and Command Line tool.
Webinar replay: » https://www.owox.com/c/1vp
One of the steps to making better business decisions is to convey data in a compelling, easy-to-understand manner.
This is when visualization tools come to the aid.
Google Data Studio 360 in Google Analytics 360 Suite is a whole new visualization tool that takes data-driven decision making to a much higher level. It features everything you need to bring your data together, turn it into informative, beautiful reports and dashboards and share them with your colleagues and clients.
Join us for the webinar to find out:
✔How to start using the free version of Google Data Studio;
✔How to connect data sources to Google Data Studio: opportunities and pitfalls;
✔How to create interactive dashboards and charts, how to use filters and manage date ranges;
✔How to add and manage access to the data;
✔How to answer the key questions on a single screen: examples of ready-made dashboards for marketing, IT and UX specialists.
Supercharge your data analytics with BigQueryMárton Kodok
Powering interactive data analysis require massive architecture, and Know-How to build a fast real-time computing system. BigQuery solves this problem by enabling super-fast, SQL-like queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, creating tables, columns, views, working with partitions, clustering for cost optimizations, streaming inserts, User Defined Functions, and several use cases for everydaay developer: funnel analytics, behavioral analytics, exploring unstructured data.
The other part will be about BigQuery ML, which enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
This is our contributions to the Data Science projects, as developed in our startup. These are part of partner trainings and in-house design and development and testing of the course material and concepts in Data Science and Engineering. It covers Data ingestion, data wrangling, feature engineering, data analysis, data storage, data extraction, querying data, formatting and visualizing data for various dashboards.Data is prepared for accurate ML model predictions and Generative AI apps
This is our project work at our startup for Data Science. This is part of our internal training and focused on data management for AI, ML and Generative AI apps
Databricks Data Analyst Associate Exam Dumps 2024.pdfSkillCertProExams
• For a full set of 270+ questions. Go to
https://skillcertpro.com/product/databricks-data-analyst-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
Exploring Machine Learning for Libraries and Archives: Present and FutureBohyun Kim
A conference presentation given by Bohyun Kim, Chief Technology Officer & Professor, University of Rhode Island Libraries, USA for the Bite-sized Internet Librarian International 2021 on September 22, 2021.
This is a talk I gave at Web Analytics Wednesday in June 2017. It discusses automating the delivery of marketing channel data to Google BigQuery / Data Studio
Google cloud Professional Data Engineer practice exam test 2020SkillCertProExams
Google Cloud Certified Professional Data Engineer Exam questions pdf
https://skillcertpro.com/yourls/gcpdataeng
Want to practice more questions?We have 390+ Practice set questions for Google Cloud Certified -Professional Data Engineer certification (Taken from previous exams)
[Webinar] Interacting with BigQuery and Working with Advanced QueriesTatvic Analytics
In this webinar, we will cover advanced concepts and some complex queries. We will give a demo of how to fetch data from BigQuery into tools like Excel, R and python so we can continue further analysis. Along With Hands-on exercise, we will demonstrate how to automate queries using Apps Script and Command Line tool.
Webinar replay: » https://www.owox.com/c/1vp
One of the steps to making better business decisions is to convey data in a compelling, easy-to-understand manner.
This is when visualization tools come to the aid.
Google Data Studio 360 in Google Analytics 360 Suite is a whole new visualization tool that takes data-driven decision making to a much higher level. It features everything you need to bring your data together, turn it into informative, beautiful reports and dashboards and share them with your colleagues and clients.
Join us for the webinar to find out:
✔How to start using the free version of Google Data Studio;
✔How to connect data sources to Google Data Studio: opportunities and pitfalls;
✔How to create interactive dashboards and charts, how to use filters and manage date ranges;
✔How to add and manage access to the data;
✔How to answer the key questions on a single screen: examples of ready-made dashboards for marketing, IT and UX specialists.
Supercharge your data analytics with BigQueryMárton Kodok
Powering interactive data analysis require massive architecture, and Know-How to build a fast real-time computing system. BigQuery solves this problem by enabling super-fast, SQL-like queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, creating tables, columns, views, working with partitions, clustering for cost optimizations, streaming inserts, User Defined Functions, and several use cases for everydaay developer: funnel analytics, behavioral analytics, exploring unstructured data.
The other part will be about BigQuery ML, which enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
This is our contributions to the Data Science projects, as developed in our startup. These are part of partner trainings and in-house design and development and testing of the course material and concepts in Data Science and Engineering. It covers Data ingestion, data wrangling, feature engineering, data analysis, data storage, data extraction, querying data, formatting and visualizing data for various dashboards.Data is prepared for accurate ML model predictions and Generative AI apps
This is our project work at our startup for Data Science. This is part of our internal training and focused on data management for AI, ML and Generative AI apps
Databricks Data Analyst Associate Exam Dumps 2024.pdfSkillCertProExams
• For a full set of 270+ questions. Go to
https://skillcertpro.com/product/databricks-data-analyst-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
Exploring Machine Learning for Libraries and Archives: Present and FutureBohyun Kim
A conference presentation given by Bohyun Kim, Chief Technology Officer & Professor, University of Rhode Island Libraries, USA for the Bite-sized Internet Librarian International 2021 on September 22, 2021.
Practical Considerations for Open InfrastructureBohyun Kim
A conference presentation "Practical Considerations for Open Infrastructure" given by Bohyun Kim, CTO and Associate Professor, University of Rhode Island Libraries, at the NISO virtual conference: Community Owned Infrastructure: Partnerships & Collaboration, on March 24 2021. https://www.niso.org/events/2021/03/community-owned-infrastructure-partnerships-collaboration
DELNET (Developing Library Network) Annual Lecture, given by Bohyun Kim, CTO and Associate Professor, University of Rhode Island Libraries, at the 23rd National Convention on Knowledge, Library and Information Networking (NACLIN), Online(/India), Sep. 23, 2020. http://www.naclin.org/
The Potential and Challenges of Today's AIBohyun Kim
A preconference presentation given by Bohyun Kim, CTO and Associate Professor, University of Rhode Island Libraries, at the NISO Plus Conference at Baltimore, MD on February 23, 2020.
Robots: What Could Go Wrong? What Could Go Right? Bohyun Kim
A presentation given at the ALA Midwinter Conference, Philadelphia, PA. Jan. 26, 2020 by Bohyun Kim, CTO/Associate Professor at the University of Rhode Island Libraries.
The NFAIS Foresight Webinar - Artificial Intelligence: Weighing the Value for the Information Community, given by Bohyun Kim.
https://www.niso.org/events/2019/09/nfais-foresight-artificial-intelligence-weighing-value-information-community
Machine Intelligence and Moral Decision-MakingBohyun Kim
A presentation given at the IMLS project of "Libraries Facilitating Cross-disciplinary Research," DC Workshop, Washington D.C., May 31, 2019 by Bohyun Kim, CTO, University of Rhode Island Libraries.
Taking on a New Leadership Challenge: Student-Focused Learning in Artificial ...Bohyun Kim
A conference talk given at the Internet Librarian International, London, UK. October 16, 2018 by Bohyun Kim, Chief Technology Officer & Associate Professor at the University of Rhode Island Libraries, USA.
Moving Forward with Digital Disruption: A Right MindsetBohyun Kim
A keynote presented at the MentorNJ In-Person Networking Event 2018 organized by LibraryLinkNJ -The New Jersey Library Cooperative, held at Monroe Township, NJ. on October 5, 2018.
http://librarylinknj.org/MentorNJ/programs/networking-event-2018
Blockchain Overview: Possibilities and IssuesBohyun Kim
Slides for the opening panel discussion given at the Blockchain National Forum, San Jose, CA., August 6, 2018, by Bohyun Kim - https://ischoolblogs.sjsu.edu/blockchains/national-forum/
AI Lab at a Library? Why Artificial Intelligence Matters & What Libraries Can DoBohyun Kim
A talk given at the American Libraries Association Annual Conference, June 25, 2018 by Bohyun Kim, Chief Technology Officer, University of Rhode Island Libraries.
From Virtual Reality to Blockchain: Current and Emerging Tech TrendsBohyun Kim
Webinar given for the LibraryLinkNJ, The New Jersey Library Cooperative on May 8, 2018. http://librarylinknj.org/
CC-BY-NC 4.0
[https://creativecommons.org/licenses/by-nc/4.0/]
Innovating Together: the UX of DiscoveryBohyun Kim
A keynote given at the IFLA 83rd World Library and Information Congress Joint Satellite Conference by RIS & IT Section, Warsaw, Poland. August 16-17, 2017. http://libapps.libraries.uc.edu/sites/ifla-riss-its-satelite/
Cleaning Up the Mess: Modernizing Your Dev Team’s Outdated WorkflowBohyun Kim
A talk given at the 2017 ALA (American Library Association) Annual Conference, Chicago, June 25, 2017. Presenters: Bohyun Kim, Associate Director for Library Applications and Knowledge Systems, Brad Gerhart, Web Developer, Zak Burke, Senior Web Developer from
University of Maryland, Baltimore - Health Sciences and Human Services Library.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Quick & Easy Data Visualization with Google Visualization API + Google Chart Libraries
1. Quick & Easy Data Visualization
with Google Visualization API
+ Google Chart Libraries
Bohyun Kim ( Twitter: @bohyunkim)
Associate Director for Library Applications & Knowledge Systems
University of Maryland, Baltimore
Health Sciences & Human Services Library
CODE4LIB Conference, Raleigh, NC. Mar. 25, 2014.
3. Creating a web application with a database is not always a
solution available, convenient, or efficient.
Image from Flickr CreativeCommons http://www.flickr.com/photos/88575173@N00/2445955296/
4. 1. But What If They Were … Almost?
• Data Source
<-- Google Spreadsheet
• Query data
<-- Google Visualization API Query Language
• Display on the web
<-- Google Chart Libraries
5. 2. Google Visualization API
Query Language
• Allows you to perform various data manipulations with the
query to the data source, such as Google spreadsheet, fusion
table, or your own database.
• Enables you to query the data source and visualize data with
Google Chart Libraries
• Similar to SQL
https://developers.google.com/chart/interactive/docs/query
language
• Google Visualization API Reference:
https://developers.google.com/chart/interactive/docs/refere
nce
7. URLs for a G Spreadsheet
• In Google Spreadsheet – Sheet3
https://docs.google.com/spreadsheet/ccc?key=0AqAPbBT_k2VUdDc3aC1xS2o
0c2ZmaVpOQWkyY0l1eVE&usp=drive_web#gid=2
• HTML Table
https://spreadsheets.google.com/tq?tqx=out:html&tq=&key=0AqAPbBT_k2VUd
Dc3aC1xS2o0c2ZmaVpOQWkyY0l1eVE&gid=2
• Raw Data Response
: includes a DataTable that you can retrieve by calling getDataTable()
https://spreadsheets.google.com/tq?&tq=&key=0AqAPbBT_k2VUdDc3aC1xS2o0
c2ZmaVpOQWkyY0l1eVE&gid=2
• google.visualization.DataTable
Classhttps://developers.google.com/chart/interactive/docs/reference#DataTabl
e
• Query Language Reference (Version
0.7)https://developers.google.com/chart/interactive/docs/querylanguage#Setti
ng_the_Query_in_the_Data_Source_URL
8. Raw Data response from G spreadsheet
https://spreadsheets.google.com/tq?&tq=&key=0AqAPbBT_k2VUdDc3aC1xS2o0c2ZmaVpOQWkyY0l1eVE&gid=2
11. 3-1. Query the Spreadsheet
& Display the Result as a Web page
• Step 1. Form a query
: Show column B, C, D, F where C contains ‘Florida’
• Step 2. Output the query result
As a stand-alone web page with a table
(Needed: URL)
OR
Into your web page as a table / chart
(Needed: JavaScript, G Chart Libraries)
13. What Goes into the URL
https://spreadsheets.google.com/
tq?
tqx=out:html
&tq=select B,C,D,F where (C contains 'Florida’)
&key=0AqAPbBT_k2VUdEtXYXdLdjM0TXY1YUV
hMk9jeUQ0NkE
&gid=0 (optional for Sheet1)
16. SELECT D,F COUNT(C) WHERE
(B CONTAINS ‘Agoulnik’) GROUP BY D, F
https://spreadsheets.google.com/tq?tqx=out:html&tq=select+D,F,count(C)+where+%28B+
contains+%27Agoulnik%27%29+group+by+D,F&key=0AqAPbBT_k2VUdEtXYXdLdjM0TXY1Y
UVhMk9jeUQ0NkE
19. select sum(B) pivot C
https://spreadsheets.google.com/tq?tqx=out:html&tq=select%20sum%28B%29%20pivo
t%28C%29&key=0AqAPbBT_k2VUdHg4X08zMXFMeXRmdURJNUx5blpYUmc
20. select c, sum(B) group by (C)
https://spreadsheets.google.com/tq?tqx=out:html&tq=select%20C,sum%28B%29%20gr
oup%20by%28C%29&key=0AqAPbBT_k2VUdHg4X08zMXFMeXRmdURJNUx5blpYUmc&g
id=0
21. select A, sum(B) group by A pivot C
https://spreadsheets.google.com/tq?tqx=out:html&tq=select%20A,%20sum%28B%29%2
0group%20by%20A%20pivot%20C&key=0AqAPbBT_k2VUdHg4X08zMXFMeXRmdURJNUx
5blpYUmc&gid=0
22. select C, sum(B) group by C pivot A
https://spreadsheets.google.com/tq?tqx=out:html&tq=select%20C,%20sum%28B%29%
20group%20by%20C%20pivot%20A&key=0AqAPbBT_k2VUdHg4X08zMXFMeXRmdURJN
Ux5blpYUmc&gid=0
23. 3-2. Use JS instead of a URL
• More flexible and you can add control options
for users.
• To set the query string from within JavaScript
code, call the setQuery method of the
google.visualization.Query class.
24. Drop It into Your Own Web Page
Query: SELECT B, C, D, F WHERE C contains ‘Florida’
25. Parameters
• headers=N - Specifies how many rows are header rows, where N is
an integer zero or greater. These will be excluded from the data and
assigned as column labels in the data table. If not specified, the
spreadsheet will guess how many rows are header rows.
• Range=NN:NN
Example: range=A1:C6
• gid=N - Specifies which sheet in a multi-sheet document to link to, if
you are not linking to the first sheet. N is the sheet's ID number.
Example: gid=0 (for Sheet1)
• sheet=sheet_name - Specifies which sheet in a multi-sheet
document you are linking to, if you are not linking to the first sheet.
sheet_name is the display name of the sheet. You can use the gid
parameter instead of this parameter.
Example: sheet=Sheet5
26. 4. Visualize Your G Spreadsheet Data
Using Google Chart Libraries
The most common way to use Google Charts is with
simple JavaScript that you embed in your web page.
1. You load some Google Chart libraries.
2. List the data to be charted.
3. Select options to customize your chart.
4. Create a chart object with an id that you choose.
5. Then, later in the web page, you create a <div> with
that id to display the Google Chart.
https://developers.google.com/chart/interactive/docs/
27. Visualize as a table or a chart
• To set the query string from within JavaScript code, call the
setQuery method of the google.visualization.Query class.
• 3 chart drawing techniques:
• chart.draw()
• ChartWrapper
• DrawChart()
• See also:
https://developers.google.com/chart/interactive/docs/dra
wing_charts
28. (a) chart.draw()
Process
• Load the JSAPI, Google Visualization, and chart libraries.
• Prepare your data.
• Prepare any chart options.
• Instantiate the chart class, passing in a handle to the page container
element.
• Optionally register to receive any chart events. If you intend to call methods
on the chart, you should listen for the "ready" event.
• Call chart.draw(), passing in the data and options.
Advantages
• You have complete control over every step of the process.
• You can register to listen for all events thrown by the chart.
Disadvantages
• Verbose
• You must explicitly load all required chart libraries
• If you send queries, you must manually implement for the callback, check
for success, extract the returned DataTable, and pass it to the chart.
34. (b) chartwrapper
ChartWrapper is a convenience class that handles loading all the appropriate
chart libraries for you and also simplifies sending queries to Chart Tools
Datasources.
Advantages:
• Much less code
• Loads all the required chart libraries for you
• Makes querying data sources much easier by creating the Query object and
handling the callback for you
• Pass in the container element ID, and it will call getElementByID for you.
• Data can be submitted in a variety of formats: as an array of values, as a
JSON literal string, or as a DataTable handle
Disadvantages:
• ChartWrapper currently propagates only the select, ready, and error events.
To get other events, you must get a handle to the wrapped chart and
subscribe to events there. See the ChartWrapper documentation for
examples.
36. Line chart - query: 'SELECT A,D
WHERE D > 100 ORDER BY D',
https://developers.google.com/chart/interactive/docs/drawing_charts#chart
wrapper
37. (c) Drawchart()
DrawChart() is a global static method that wraps a
ChartWrapper.
Advantages:
• Same as ChartWrapper, but slightly shorter to
use.
Disadvantages:
• Does not return a handle to the wrapper, so you
cannot handle any events.
44. JSON Feed from G Spreadsheet
https://spreadsheets.google.com/feeds/list/0AqAPbBT_k2VUdFgwUm5oeHNEajlhODJ
oR2pVVWlTeFE/1/public/values?alt=json-in-script&callback=cb
45. An Example MySQL db table
Source for the PHP script on the next slide:
http://stackoverflow.com/questions/12994282/php-mysql-google-chart-json-
complete-example
46. Construct G DataTable from MySQL db
{
"cols":[{"label":"Weekly
Task","type":"string"},{"label":"Percentage","type":"
number"}],
"rows":[{"c":[{"v":"Sleep"},{"v":30}]},{"c":[{"v":"Watc
hing
Movie"},{"v":10}]},{"c":[{"v":"Job"},{"v":40}]},{"c":[{"
v":"Exercise"},{"v":20}]}]
}
50. 6. Controls and Dashboards
https://developers.google.com/chart/interactive/docs/gallery/controls
51. Draw a Dashboard
1. Create An HTML Skeleton For Your Dashboard
2. Load Your Libraries
3. Prepare Your Data
4. Create a Dashboard Instance
5. Create Control and Chart Instances
6. Establish Dependencies (=bind)
7. Draw Your Dashboard
8. Programmatic Changes after Draw
google.load('visualization', '1.0',{'packages':['controls']});
….
dashboard.bind(donutRangeSlider, pieChart);
dashboard.draw(data);
https://developers.google.com/chart/interactive/docs/gallery/controls
54. Dashboard with multiple controls
and a remote data source
new google.visualization.Dashboard(document.getElementById('dashboard'))
.bind([slider, categoryPicker], [pie, table]).draw(data);
60. Questions?
• Slides: http://slideshare.net/bohyunkim
• Code: https://github.com/bohyunkim/examples
• Twitter: @bohyunkim
• Web: http://bohyunkim.net/blog
Image credit for the title slide:
https://www.flickr.com/photos/44718928@N00/7219422352/
by the Flickr user @justgrimes