The dawn of digital businesses is upon us, with reimagined business models that make the best use of digital technologies such as automation, analytics, integration and cloud. Digital businesses are efficient, continuously optimizing, proactive, flexible and are able to fully understand their customers. Analytics is a key technology that helps in doing so. It acts as the eyes and ears of the system and provides a holistic view on the past and present so that decision-makers can predict what will happen in the future. This webinar will explore
Why becoming a digital business is not a choice
The role of analytics in digital transformation with examples
How best to leverage state of the art analytics technology
We are at the dawn of digital businesses, that are reimagined to make the best use of digital technologies such as automation, analytics, cloud, and integration. These businesses are efficient, continuously optimizing, proactive, flexible and able to understand customers in detail. A key part of a digital business is analytics: the eyes and ears of the system that tracks and provides a detailed view on what was and what is and lets decision makers predict what will be.
This session will explore how the WSO2 analytics platform
Plays a role in your digital transformation journey
Collects and analyzes data through batch, real-time, interactive and predictive processing technologies
Lets you communicate the results through dashboards
Brings together all analytics technologies into a single platform and user experience
SoC Keynote:The State of the Art in Integration TechnologySrinath Perera
This talk discusses Outline of the state of the art of Enterprise Software and how we get there, as I see it. Also second part describes Ballerina, a new programming language WSO2 has built for Enterprise Computing.
It is presented as a Keynote at 11th Symposium and Summer School On Service-Oriented Computing.
Predictive Analytics - Big Data & Artificial IntelligenceManish Jain
Quick overview of the latest in big data and artificial intelligence. A lot of buzzwords being thrown around, hopefully this presentation will demystify many of the terms.
The Rise of Streaming SQL and Evolution of Streaming ApplicationsSrinath Perera
Srinath Perera discusses the rise of streaming SQL and evolution of streaming applications. He covers what streaming is, how almost all new data is streaming, the streaming processing market, building streaming apps, the history of stream processing, why streaming SQL is useful, common solutions with stream processing, how stream processors are stateful and need high availability, how most are resource-intensive, the need for machine learning and advanced query authoring with stream processing. He then introduces WSO2 Stream Processor as a lightweight option for streaming applications.
This document discusses leveraging social big data and the evolution from existing rigid operations to predictive analytics using social media. It begins with an overview of handouts and reference materials on big data, Hadoop, Spark, and data science projects. It then discusses areas for conversation around social content, structure and analytics, data science primers and resources, and data science innovation. It presents a roadmap showing the evolution from rigid and siloed operations to being more flexible, connected, adaptive and predictive using social media. Finally, it discusses types of intentionality and how social CRM can integrate social data.
The document discusses tools for analyzing dark data and dark matter, including DeepDive and Apache Spark. DeepDive is highlighted as a system that helps extract value from dark data by creating structured data from unstructured sources and integrating it into existing databases. It allows for sophisticated relationships and inferences about entities. Apache Spark is also summarized as providing high-level abstractions for stream processing, graph analytics, and machine learning on big data.
This document discusses several topics related to data and data-driven businesses. It begins by outlining trends in big data and machine learning. It then discusses how to build data-centric businesses by identifying data opportunities and sources, understanding the data lifecycle, and extracting value from data. Examples are provided of Netflix as a data-driven company. The future of professions in a data-driven world is also examined, as well as talent scarcity issues and the need for data-savvy managers. The document provides an overview of many relevant topics at the intersection of data and business.
The document discusses the changing landscape for accountants, from traditional on-premises software with high upfront costs to cloud/SaaS models with lower ongoing costs. It notes the rise of diverse and unstructured data sources and the importance of analytics. Key drivers include new ways of analyzing accounting data, innovation from new data sources, predictive capabilities from big data, connecting insights to processes, and improved client experiences through mobile and messaging. R is highlighted as a widely used open-source statistical programming language.
This document discusses real-time big data analytics from deployment to production. It covers:
1) Distilling raw data like log files and sensor streams into structured data using Hadoop for analytics.
2) Developing predictive models using techniques like decision trees, clustering, and ensembles on structured data.
3) Deploying models for real-time scoring via SQL, code, or PMML on either batch lookup tables or streaming data factors.
4) Scoring billions of predictions daily for applications like determining why customers buy products and attributing marketing channels.
5) Regularly refreshing models to incorporate new data and outcomes using techniques like exploratory analysis and time-to-event modeling
The document discusses data science innovation and the future of professions in light of new technologies. It describes how accounting work may be automated or replaced by computer-assisted techniques and predictive analytics software. This would allow accountants to shift from reactive to proactive work by leveraging accounting data and insights to predict client scenarios and advise clients. Key areas discussed include systems of insight using big data, machine engineering to create applications from insights, and the role of data science.
Big data and predictive analytics will transform accounting work and require accountants to develop new skills. By 2018, there will be a shortage of 30,000 data-savvy managers in Australia who can make effective decisions based on big data analysis. Accountants will need to shift from reactive to proactive roles by leveraging accounting data and predictive tools to find patterns, gain insights, and predict client scenarios in order to maximize opportunities and minimize risks for their clients. The "predictive accountant" who adopts these new data-focused skills will be well-positioned for the future of the profession.
This document discusses real-time analytics and H2O's integration with Storm for streaming data analytics. It provides an overview of the evolution of real-time analytics, factors that influence the speed of information like bandwidth and infrastructure, and describes H2O's role in the analytics workflow for data preparation, modeling, validation, and tracking models over time using streaming data. The document concludes with a link to a demo of H2O and Storm's capabilities for real-time predictive analytics on streaming data.
What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation?
Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at [email protected]
Big data is very large data that is difficult to process using traditional methods. It is characterized by high volume, velocity, and variety. Examples of real-life big data implementations include using social media to understand customer behavior, tracking social media for marketing campaigns, and analyzing medical data to predict readmissions. Challenges include integrating diverse data sources and ensuring ethical access. Common techniques for processing big data are parallel database management systems and MapReduce frameworks like Hadoop.
This document discusses big data and machine learning. It defines big data as large amounts of data that are analyzed by machines. It describes how data is increasingly coming from sources like smartphones, sensors, and the Internet. It also discusses how machine learning allows computers to learn from large amounts of data without being explicitly programmed, and how this is enabling automation and new applications of artificial intelligence.
This document discusses several topics related to big data, data science, and their impact on jobs and skills. It notes that big data comes from a wide variety of sources and in large volume, variety and velocity. Analyzing this data requires new tools and techniques from data science. The growth of big data and data science is changing many professions as new types of data and analytics allow work to be automated or done differently. By 2018, there will be significant shortages of workers with deep data skills and the ability to leverage data in decision-making. Countries like Australia will need tens of thousands of "data savvy managers" to address this talent gap.
A look back at how the practice of data science has evolved over the years, modern trends, and where it might be headed in the future. Starting from before anyone had the title "data scientist" on their resume, to the dawn of the cloud and big data, and the new tools and companies trying to push the state of the art forward. Finally, some wild speculation on where data science might be headed.
Presentation given to Seattle Data Science Meetup on Friday July 24th 2015.
This document discusses the rise of big data and data science. It notes that while data volumes are growing exponentially, data alone is just an asset - it is data scientists that create value by building data products that provide insights. The document outlines the data science workflow and highlights both the tools used and challenges faced by data scientists in extracting value from big data.
Predictive Analysis for Airbnb Listing Rating using Scalable Big Data PlatformSavita Yadav
KMIS International Conference 2021.
This talk aims to provide insights and performance of predictive models for Airbnb Rating using Big Data and distributed parallel computing systems. We have predicted and classified using Two-Class Classification models if a property has a high or a low rating based on the features of the listing. It helps the hosts to know if their property is suitable and how their listing compares to other similar listings. We compare the results and the performance of rating prediction models with accuracy and computing time metrics.
The document discusses tools for analyzing unstructured data. It describes unstructured data as data that does not have a predefined format or structure. The document then discusses sources of unstructured data like machine-generated and human-generated sources. It also discusses the differences between data analysis and analytics. Finally, it describes several tools that can be used to analyze unstructured data including RapidMiner, Weka, KNIME, and R Language. It provides characteristics and descriptions of each tool.
Traffic Data Analysis and Prediction using Big DataJongwook Woo
- Denser traffic on Freeways 101, 405, 10
- Rush hours from 7 am to 9 am produce a lot of traffic, the heaviest traffic time start from 3pm and gets better after 6pm.
- Major areas of traffic in DTLA, Santa Monica, Hollywood
- More insights can be found with bigger dataset using this framework for analysis of traffic
- Using such data and platform can also give an opportunity to predict traffic congestions. Prediction can be performed using machine learning algorithm – Decision Forest with the accuracy of 83% for predicting the heaviest traffic jam.
Introduction to Big Data: Smart FactoryJongwook Woo
Jongwook Woo presents an introduction to big data and smart factories. He discusses his background working with big data technologies and partnerships. The document then covers what big data is, common tools like Hadoop and Spark, and how big data is used in smart factories to collect, analyze and visualize machine data to improve operations. It concludes with a high-level summary of using big data for smart factory applications.
The document summarizes the causes and consequences of the conflict between Sinhalese and Tamil ethnic groups in Sri Lanka. The causes included citizenship rights denied to Indian Tamils, the adoption of Sinhala as the sole official language, higher university admission standards for Tamils, and the resettlement of Sinhalese peoples into Tamil areas. The consequences were political armed conflict, economic issues like unemployment and reduced investment and tourism, and social issues such as Tamils being driven from their homeland.
The document provides an overview of the Sinhalese-Tamil conflict in Sri Lanka, covering citizenship rights, jobs in government service, university admission policies, resettlement of populations, and the consequences of the conflict including violence, unemployment, loss of investment, and foreign intervention. It also provides sample exam questions on assessing blame for the conflict between the Sri Lankan government, Tamil Tigers, and India's role.
This document discusses several topics related to data and data-driven businesses. It begins by outlining trends in big data and machine learning. It then discusses how to build data-centric businesses by identifying data opportunities and sources, understanding the data lifecycle, and extracting value from data. Examples are provided of Netflix as a data-driven company. The future of professions in a data-driven world is also examined, as well as talent scarcity issues and the need for data-savvy managers. The document provides an overview of many relevant topics at the intersection of data and business.
The document discusses the changing landscape for accountants, from traditional on-premises software with high upfront costs to cloud/SaaS models with lower ongoing costs. It notes the rise of diverse and unstructured data sources and the importance of analytics. Key drivers include new ways of analyzing accounting data, innovation from new data sources, predictive capabilities from big data, connecting insights to processes, and improved client experiences through mobile and messaging. R is highlighted as a widely used open-source statistical programming language.
This document discusses real-time big data analytics from deployment to production. It covers:
1) Distilling raw data like log files and sensor streams into structured data using Hadoop for analytics.
2) Developing predictive models using techniques like decision trees, clustering, and ensembles on structured data.
3) Deploying models for real-time scoring via SQL, code, or PMML on either batch lookup tables or streaming data factors.
4) Scoring billions of predictions daily for applications like determining why customers buy products and attributing marketing channels.
5) Regularly refreshing models to incorporate new data and outcomes using techniques like exploratory analysis and time-to-event modeling
The document discusses data science innovation and the future of professions in light of new technologies. It describes how accounting work may be automated or replaced by computer-assisted techniques and predictive analytics software. This would allow accountants to shift from reactive to proactive work by leveraging accounting data and insights to predict client scenarios and advise clients. Key areas discussed include systems of insight using big data, machine engineering to create applications from insights, and the role of data science.
Big data and predictive analytics will transform accounting work and require accountants to develop new skills. By 2018, there will be a shortage of 30,000 data-savvy managers in Australia who can make effective decisions based on big data analysis. Accountants will need to shift from reactive to proactive roles by leveraging accounting data and predictive tools to find patterns, gain insights, and predict client scenarios in order to maximize opportunities and minimize risks for their clients. The "predictive accountant" who adopts these new data-focused skills will be well-positioned for the future of the profession.
This document discusses real-time analytics and H2O's integration with Storm for streaming data analytics. It provides an overview of the evolution of real-time analytics, factors that influence the speed of information like bandwidth and infrastructure, and describes H2O's role in the analytics workflow for data preparation, modeling, validation, and tracking models over time using streaming data. The document concludes with a link to a demo of H2O and Storm's capabilities for real-time predictive analytics on streaming data.
What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation?
Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at [email protected]
Big data is very large data that is difficult to process using traditional methods. It is characterized by high volume, velocity, and variety. Examples of real-life big data implementations include using social media to understand customer behavior, tracking social media for marketing campaigns, and analyzing medical data to predict readmissions. Challenges include integrating diverse data sources and ensuring ethical access. Common techniques for processing big data are parallel database management systems and MapReduce frameworks like Hadoop.
This document discusses big data and machine learning. It defines big data as large amounts of data that are analyzed by machines. It describes how data is increasingly coming from sources like smartphones, sensors, and the Internet. It also discusses how machine learning allows computers to learn from large amounts of data without being explicitly programmed, and how this is enabling automation and new applications of artificial intelligence.
This document discusses several topics related to big data, data science, and their impact on jobs and skills. It notes that big data comes from a wide variety of sources and in large volume, variety and velocity. Analyzing this data requires new tools and techniques from data science. The growth of big data and data science is changing many professions as new types of data and analytics allow work to be automated or done differently. By 2018, there will be significant shortages of workers with deep data skills and the ability to leverage data in decision-making. Countries like Australia will need tens of thousands of "data savvy managers" to address this talent gap.
A look back at how the practice of data science has evolved over the years, modern trends, and where it might be headed in the future. Starting from before anyone had the title "data scientist" on their resume, to the dawn of the cloud and big data, and the new tools and companies trying to push the state of the art forward. Finally, some wild speculation on where data science might be headed.
Presentation given to Seattle Data Science Meetup on Friday July 24th 2015.
This document discusses the rise of big data and data science. It notes that while data volumes are growing exponentially, data alone is just an asset - it is data scientists that create value by building data products that provide insights. The document outlines the data science workflow and highlights both the tools used and challenges faced by data scientists in extracting value from big data.
Predictive Analysis for Airbnb Listing Rating using Scalable Big Data PlatformSavita Yadav
KMIS International Conference 2021.
This talk aims to provide insights and performance of predictive models for Airbnb Rating using Big Data and distributed parallel computing systems. We have predicted and classified using Two-Class Classification models if a property has a high or a low rating based on the features of the listing. It helps the hosts to know if their property is suitable and how their listing compares to other similar listings. We compare the results and the performance of rating prediction models with accuracy and computing time metrics.
The document discusses tools for analyzing unstructured data. It describes unstructured data as data that does not have a predefined format or structure. The document then discusses sources of unstructured data like machine-generated and human-generated sources. It also discusses the differences between data analysis and analytics. Finally, it describes several tools that can be used to analyze unstructured data including RapidMiner, Weka, KNIME, and R Language. It provides characteristics and descriptions of each tool.
Traffic Data Analysis and Prediction using Big DataJongwook Woo
- Denser traffic on Freeways 101, 405, 10
- Rush hours from 7 am to 9 am produce a lot of traffic, the heaviest traffic time start from 3pm and gets better after 6pm.
- Major areas of traffic in DTLA, Santa Monica, Hollywood
- More insights can be found with bigger dataset using this framework for analysis of traffic
- Using such data and platform can also give an opportunity to predict traffic congestions. Prediction can be performed using machine learning algorithm – Decision Forest with the accuracy of 83% for predicting the heaviest traffic jam.
Introduction to Big Data: Smart FactoryJongwook Woo
Jongwook Woo presents an introduction to big data and smart factories. He discusses his background working with big data technologies and partnerships. The document then covers what big data is, common tools like Hadoop and Spark, and how big data is used in smart factories to collect, analyze and visualize machine data to improve operations. It concludes with a high-level summary of using big data for smart factory applications.
The document summarizes the causes and consequences of the conflict between Sinhalese and Tamil ethnic groups in Sri Lanka. The causes included citizenship rights denied to Indian Tamils, the adoption of Sinhala as the sole official language, higher university admission standards for Tamils, and the resettlement of Sinhalese peoples into Tamil areas. The consequences were political armed conflict, economic issues like unemployment and reduced investment and tourism, and social issues such as Tamils being driven from their homeland.
The document provides an overview of the Sinhalese-Tamil conflict in Sri Lanka, covering citizenship rights, jobs in government service, university admission policies, resettlement of populations, and the consequences of the conflict including violence, unemployment, loss of investment, and foreign intervention. It also provides sample exam questions on assessing blame for the conflict between the Sri Lankan government, Tamil Tigers, and India's role.
The document summarizes Sri Lanka's cybercrime legislation and policies from the perspective of a developing country. It provides an overview of Sri Lanka's key cybercrime laws, including the Computer Crimes Act of 2007, and discusses some of the challenges in enforcing cybercrime laws and addressing them through awareness programs, improved digital forensics capabilities, and the creation of computer emergency response teams.
An Individual project given in order to complete the module named Macro Economics which expresses analysis of the trends of inflation rates of Sri Lanka during recent years.
Sri Lanka is an island nation located off the southeast coast of India with a population of over 20 million people. It has a long history and was formerly known as Ceylon. Sri Lanka has a diverse landscape that includes beaches, hills, rivers and lakes. The majority of the population is Sinhalese and Buddhist, while there are also significant Tamil, Muslim and Burgher populations. Sri Lanka has a high literacy rate and a developing economy focused on industries like tourism, tea, rubber and textiles. The country is working to develop its infrastructure after decades of civil war and has promising prospects for continued economic growth.
The document lists several popular places to visit in Sri Lanka and provides brief descriptions of each location. Kandy is described as the cultural capital of Sri Lanka and the last kingdom of the country. Sigiriya is noted for its citadel atop Lion Rock which provides stunning views. Kithulgala offers adventure activities like white water rafting and hiking. Anuradhapura is an ancient capital with archaeological sites.
Sri Lanka is known for its beautiful Buddha statues at sites like Dambulla Cave Temple and Polonnaruwa's Gal Vihara. The country has a rich cultural heritage like the Sinhala New Year festival and Kandyan dancing. Sri Lankan tea is renowned as some of the best in the world while scenic areas feature hills, waterfalls, and coastlines ideal for scuba diving.
Sri Lanka gained independence from British rule in 1948. It has a population of over 20 million and its capital and largest city is Sri Jayawardenapura Kotte. Sri Lanka has a diverse landscape that includes rainforests, coastal areas, and inland plains. It has a long history and was known as Ceylon under British rule. Sri Lanka has a predominantly Buddhist population and Buddhism has had a significant influence on the country's culture and heritage. The economy has shifted from agriculture to services and industries like tourism and tea production remain important.
Sri Lanka is a republic and a unitary state which is governed by a semi-presidential system with its official seat of government in Sri Jayawardenapura - Kotte, the capital.
The country is famous for the production and export of tea, coffee, coconuts, rubber and cinnamon, the last of which is native to the country.
The natural beauty of Sri Lanka has led to the title The Pearl of the Indian Ocean. The island is laden with lush tropical forests, white beaches and diverse landscapes with rich biodiversity.
Sri Lanka's rich culture can be attributed to the many different communities on the island
Sri Lanka is a founding member state of SAARC and a member United Nations, Commonwealth of Nations, G77 and Non-Aligned Movement. As of 2010, Sri Lanka was one of the fastest growing economies of the world. Its stock exchange was Asia's best performing stock market during 2009 and 2010
Sri Lanka is called the pearl of the Indian Ocean due to its beautiful shape. It has a high per capita GDP for Southern Asia and beautiful tropical beaches that are worth visiting. Sri Lanka also has important religious sites like the Grand Mosque, predominantly Buddhist culture, English speakers, and a top cricket team - making it a great tourist destination.
Sri Lanka is an island nation off the southern tip of India in the Indian Ocean. It has a population density that is over 9 times greater than the United States. The official language is Sinhala, and the capital and largest city is Colombo. Leslie went to Sri Lanka to assist an elephant veterinarian and visit elephant sanctuaries because she loves elephants. While there, she taught English, visited cultural sites, and saw effects of the long-running civil war and 2004 tsunami.
The document discusses the benefits of open source software. It notes that open source software is freely available to use, transparent in how it works, and trustworthy as it respects privacy and is developed by communities with shared visions. Large companies widely use high-quality open source software. The document outlines additional advantages like learning opportunities, potential career benefits, and the ability to contribute code and make an impact.
The document discusses the open source movement and its origins among software developers and local groups in Turkey that support open source. It describes how open source is growing beyond these groups to include open data, hardware, science and more. Major software companies are adapting by releasing open source code and moving to pricing models based on cloud services and subscriptions rather than licensing fees.
SIM RTP Meeting - So Who's Using Open Source Anyway?Alex Meadows
Open Source has been around for several decades now, but there is still a bit of mystery around what makes open source work and concern about using it in the enterprise. Open Source technologies are being widely used in many industries, including analytics, software development, social media, data center management, and more.
The discussion will be moderated by Julie Batchelor and panelists include:
* Todd Lewis, Open Source evangelist
* Jason Hibbets, Open Source Community Manager
* Jim Salter, Co-Owner and Chief Technology Officer at Openoid, LLC
* Alex Meadows, data scientist
The document provides an overview of open source projects, discussing what open source is, how open source communities work, and tips for contributing to open source projects, including identifying relevant skills, finding a project to contribute to, and understanding how to engage with an open source community. It uses examples like Wikipedia, Linux, and OpenStack to illustrate open source trends and best practices for participation. The presentation aims to educate people on open source and lower barriers to contributing for the first time.
Government ICT 2.0 London 2014 – Open Source Drupal Empowering GovernmentJeffrey McGuire
1) How open source software, and the Drupal CMS specifically, empower government agencies and bodies to practice "good digital government," which I define as enabling innovation, collaboration, transparency, and participation.
2) Examples of how Drupal is supporting this today, with live websites and other examples.
3) How Acquia, its products and services, and its network of implementation partners fit into the picture and ensure success for clients.
4) Case studies and examples include: alert.mta.info, Saïd Business School at the Oxford University, GOTO.sbs.ox.ac.uk, it.dashboard.gov, the dkan Drupal-based open data platform, Peer to Patent, Help4OK, We the People, westminster.gov.uk, Surrey University.
Global Open Source Development 2011-2014 Review and 2015 ForecastSammy Fung
1) The document reviews global open source development from 2011-2014 and provides a forecast for 2015. Key technologies discussed include JavaScript, Node.js, Python, R, and open data.
2) Major open source organizations discussed include Mozilla, the Python Software Foundation, and the Open Knowledge Foundation. Events like PyCon and the Open Data Day hackathon are also mentioned.
3) The document concludes by discussing opportunities to get involved in and contribute to the global open source community, such as upcoming events in Hong Kong like HKOSCon 2015 and PyCon HK 2015.
How do volunteer open-source projects create and maintain so many
compelling, competitive products? What is the Open Source Secret
Sauce? Join open-source insider, Ted Husted, as he takes us deep
inside the Apache Software Foundation, to show how the sausages are
made.
In this session, you will learn
* Why open source matters;
* How open source development works at the ASF;
* What makes open source projects successful.
This document provides an introduction to open source software, including its history and definition. It discusses some important open source projects like Linux, Apache web server, and Samba. It also describes some risks associated with open source like licensing complexity and security issues. Finally, it summarizes Squid, an open source proxy caching server, and how it can be configured to implement access control policies and network monitoring.
The document discusses open source software and its impact on education. It provides definitions of open source from organizations like OSI and notes that open source promotes collaboration, peer review and rapid evolution. It outlines how open source has benefited education through open courseware from universities, online encyclopedias, open access journals and libraries, and open source software for operating systems, browsers, and more. Individuals and organizations around the world contribute to open education resources.
Open Source Software, due to its accessibility, is a wonderful tool for folks who want to get into technology & the field of STEM. In this talk, I go over what Open Source is, who's using it, how it ignited the MOOC & OpenCourseWare movement & how you can get started & jump right in for FREE! I also provide Open Source alternatives to popular software. Enjoy.
The document discusses the history and evolution of open source software. It began in the 1970s-1980s as the free software movement. Over time it evolved into both the free and open source software movements. Open source refers to software with source code that is openly available and can be modified. It offers benefits like lower costs, availability of source code, and vendor independence. Examples of widely used open source software include Linux, Apache, MySQL, PHP, and Mozilla. The document also discusses open source languages, databases, and operating systems. It provides case studies on Sahana and an IT project in Kerala, India. Challenges of open source include steep learning curves and lack of standardization. However, it provides flexibility and custom
This presentation is delivered as part of the Faculty training program at Kristu Jayanthi College, Bangalore. The intent was to help students build competency and contribute to open source projects. Also which will eventually help them to build professional career in open source connected domains.
This event was organized by the SODA Foundation and lots of fabulous speakers delivered the series. Thank you SODA!!!!
This document provides an introduction to open source technology. It defines open source software as software with available source code that allows users to modify and improve it, in contrast to proprietary software where the source code is not available. Examples of open source software include Linux, Firefox, and OpenOffice, while proprietary software examples include Windows and Microsoft Office. The document then discusses the history of open source software and lists some benefits like free availability and customizability as well as drawbacks like a steeper learning curve. It concludes by discussing Nepal's current status with open source and possibilities for its future use.
This presentations covers meaning of open source, history of open source, open source software available in market, why developers and company create open source software.
Open Source for Enterprise: Architecting Digital Change. Reading Room
Digital is a strategic competency, not just another channel for your company marketing message.
How can your company use the nature of Open Source as a strategy to cope with change.
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
An API is an application programming interface that allows machines like apps and systems to access and use data and functionality over a network. APIs are intended for machine use rather than human use directly. Organizations use APIs to integrate their systems, reuse functionality, and increase automation. Individuals can use APIs to enhance their experiences and automate tasks. The API economy refers to the growing use of APIs, and API marketplaces help connect API providers and consumers.
An Introduction to Blockchain for Finance ProfessionalsSrinath Perera
This document provides an introduction to blockchain technology for finance professionals. It discusses how blockchain can be used to establish trust in record-keeping in a similar way to double-entry bookkeeping. Blockchain acts as an indestructible append-only ledger that allows entries to be recorded but not edited, deleted, or repudiated. This enables trust to be established for processes like land registry and financial transactions. Blockchains can be public, with anyone able to participate, or private, restricted to certain participants. While not risk-free, blockchain ledgers can replace or reduce the need for intermediaries in areas like auditing, supply chain management, and financial transactions.
AI in the Real World: Challenges, and Risks and how to handle them?Srinath Perera
This document discusses challenges, risks, and how to handle them with AI in the real world. It covers:
- AI can perform tasks like driving a car faster and cheaper than humans, but can't fully explain how.
- Deploying and managing AI models at scale is complex, as is integrating models with user experiences. Bias and lack of transparency are also risks.
- When applying AI, such as in high-risk domains like medicine, it is important to audit models, gradually introduce them with trials, monitor outcomes, and find ways to identify and address errors or unfair impacts. With care and oversight, AI can be developed to help more people than it harms.
This document discusses the potential of artificial intelligence (AI) and emerging technologies in healthcare. It begins with brief introductions and then outlines several key AI use cases in healthcare, including data collection and management, personal health data management, diagnosis, patient management, and macro health analysis. It also discusses challenges like skilled labor shortages and lack of large data sets. Risks of AI like bias, privacy issues, and dangerous mistakes are presented. The conclusion is that AI has great potential to transform healthcare if applications are handled carefully and data is managed appropriately.
This document discusses how AI could shape future integrations. It begins by explaining different types of tasks that AI can perform, such as those that can be precisely explained versus those requiring examples and feedback to learn. The document then covers benefits of AI like speed, lower costs, and ability to learn and extrapolate. It discusses using AI for cost savings, competitive advantages, and new revenue streams through insights. Challenges of AI like lack of data and skilled professionals are presented along with risks such as bias, privacy issues, and how mistakes can be more harmful than for humans. Various use cases of AI in integration are explored such as enhancing inputs, security, and automatic integration. The document concludes that AI will create many new integration opportunities
The Role of Blockchain in Future IntegrationsSrinath Perera
We have critically evaluated blockchain-based integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study. Based on our analysis, we observe that blockchain can significantly impact integration use cases.
In our paper, we identify 30-plus blockchain-based use cases for integration and four architecture patterns. Notably, each use case we identified can be implemented using one of the architecture patterns. Furthermore, we also discuss challenges and risks posed by blockchains that would affect these architecture patterns.
Our webinar presents a critical analysis of serverless technology and our thoughts about its future. We use Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, as the methodology of our study. Based on our analysis, we believe that serverless can significantly impact applications and software development workflows.
We’ve also made two further observations:
Limitations, such as tail latencies and cold starts, are not deal breakers for adoption. There are significant use cases that can work with existing serverless technologies despite these limitations.
We see a significant gap in required tooling and IDE support, best practices, and architecture blueprints. With proper tooling, it is possible to train existing enterprise developers to program with serverless. If proper tools are forthcoming, we believe serverless can cross the chasm in 3-5 years.
A detailed analysis can be found here: A Survey of Serverless: Status Quo and Future Directions. Join our webinar as we discuss this study, our conclusions, and evidence in detail.
1. Blockchain potential impact is real. If successful, Blockchain technologies can transform the way we live our day to day lives.
2. We believe technology is ready for limited applications in Digital Currency, Lightweight financial systems, Ledgers (of identity, ownership, status, and authority), Provenance (e.g. supply chains and other B2B scenarios) and Disintermediation, which we believe will happen in next three years.
3. However, with other use cases, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. These are hard problems and
4. It is not clear whether blockchain can sustain the current level of effort for extended period of 5+ years. There are many startups and they run the risk of running out of money before markets are ready. Failure of startups can inhibit further funding and investments.
5. Value and need of decentralization compared to centralized and semi-centralized alternatives is not clear.
A Visual Canvas for Judging New TechnologiesSrinath Perera
The document proposes an Emerging Technology Canvas (ETAC) framework to analyze emerging technologies. It is inspired by the Business Model Canvas and aims to provide a compact visual representation to capture the narrative around an emerging technology. The ETAC seeks to find the right questions to analyze technologies and understand their environment, impact, drivers, future potential and risks through a set of factors. It is presented as a tool to critically evaluate technologies and communicate insights about their adoption and development. Guidelines are provided on how to build an ETAC analysis and contribute insights to an online repository.
The talk discusses how analytics can attack privacy and what we can do about it. It discusses the legal responses (e.g. GDPR) as well technical responses ( differential privacy and homomorphic encryption).
The video is in https://www.facebook.com/eduscopelive/videos/314847475765297/ from 1.18.
Blockchain is often cited as one of the most impactful technology along with AI. It has attracted many startups, venture investments, and academic research. If successful, Blockchain technologies can transform the way, we live our day to day lives.
However, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. They are hard problems, and likely it will take at least 5-10 years to find answers to those problems.
Given the risk involved as well as the significant potential returns, we recommend a cautiously optimistic approach for blockchain with the focus on concrete use cases.
Today's Technology and Emerging Technology LandscapeSrinath Perera
We have seen the rise and fall of many technologies, some disappearing without a trace while others redefining the world. Collectively they have shaped our world beyond recognition. In this talk, Srinath will start with past technologies exploring their behavior. Then he will explore current middleware landscape, its composition, and relationships between different segments. He will discuss significant developments and discuss their future. Further, he will discuss emerging technologies, forces that shape them, and the promise of each technology, and finally, speculate about their evolution. You will walk away with knowledge on the evolution of middleware, the status quo, and discussion about how, at WSO2, we think those technologies will evolve.
Some died, some get by, but some have woven themselves to today's middleware so much that we do not notice them. The point I want to make is that not all emerging technologies are fads. Some are, and some are too early, like AI. But some are lasting.
Analytics and AI: The Good, the Bad and the UglySrinath Perera
Analytics let us question the data, which in effect questions the world around us. This let us understand, monitor, and shape the world. AI let us discover connections, predict the possible futures and automate tasks.
These twin technologies can change the world around us. On one hand, make us efficient, connected, and fulfilled. At the same time, the change of status quo can replace jobs, affect lives and build biases into our systems that can marginalize millions.
In this talk, we will discuss core ideas behind analytics and AI, their possible impact, both good and bad outcomes, and challenges.
This document provides guidance on conducting online research in 4 parts: 1) Get a feel for the topic by searching broadly and reading a variety of sources to understand different perspectives, 2) Select and refine relevant facts, 3) Make an argument by logically connecting facts, and 4) Write down the argument, distinguishing conclusions from opinions and citing sources. It emphasizes finding trustworthy sources, questioning biases and incentives, and thinking critically about one's own opinions through discussion with others. The goal of online research is finding truth by deriving conclusions from verified facts through logical reasoning and common sense.
1) Machine learning and predictive analytics can be used to analyze large datasets and build models to find useful insights, predict outcomes, and provide competitive advantages.
2) WSO2 Machine Learner is a product that allows users to upload data, train machine learning models using various algorithms, compare results, and iterate on models.
3) Example use cases demonstrated by WSO2 Machine Learner include predicting airport wait times, tracking people via Bluetooth, predicting the Super Bowl winner, detecting defective manufacturing equipment, and identifying promising customers.
Introduction to WSO2 Data Analytics PlatformSrinath Perera
This document provides an introduction to the WSO2 Analytics Platform. It discusses how the platform allows users to collect data from various sources using a sensor API, then perform analysis on the data through both batch and real-time means. Batch analysis uses technologies like Apache Spark and Hadoop to perform tasks like finding averages, max/min, and building KPIs. Real-time analysis uses complex event processing to run queries over streaming data and detect patterns. The platform also enables predictive analytics using machine learning algorithms and anomaly detection. Results are then communicated through dashboards and alerts.
Ethical Frameworks for Trustworthy AI – Opportunities for Researchers in Huma...Karim Baïna
Artificial Intelligence (AI) is reshaping societies and raising complex ethical, legal, and geopolitical questions. This talk explores the foundations and limits of Trustworthy AI through the lens of global frameworks such as the EU’s HLEG guidelines, UNESCO’s human rights-based approach, OECD recommendations, and NIST’s taxonomy of AI security risks.
We analyze key principles like fairness, transparency, privacy, robustness, and accountability — not only as ideals, but in terms of their practical implementation and tensions. Special attention is given to real-world contexts such as Morocco’s deployment of 4,000 intelligent cameras and the country’s positioning in AI readiness indexes. These examples raise critical issues about surveillance, accountability, and ethical governance in the Global South.
Rather than relying on standardized terms or ethical "checklists", this presentation advocates for a grounded, interdisciplinary, and context-aware approach to responsible AI — one that balances innovation with human rights, and technological ambition with social responsibility.
This rich Trustworthy and Responsible AI frameworks context is a serious opportunity for Human and Social Sciences Researchers : either operate as gatekeepers, reinforcing existing ethical constraints, or become revolutionaries, pioneering new paradigms that redefine how AI interacts with society, knowledge production, and policymaking ?
apidays New York 2025 - API Platform Survival Guide by James Higginbotham (La...apidays
API Platform Survival Guide
James Higginbotham, API Strategist at LaunchAny
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
The final presentation of our time series forecasting project for the "Data Science for Society and Business" Master's program at Constructor University Bremen
15 Benefits of Data Analytics in Business Growth.pdfAffinityCore
Explore how data analytics boosts business growth with insights that improve decision-making, customer targeting, operations, and long-term profitability.
apidays New York 2025 - Turn API Chaos Into AI-Powered Growth by Jeremy Water...apidays
Turn API Chaos Into AI-Powered Growth
Jeremy Waterkotte, Solutions Consultant, Alliances at Boomi
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
The final presentation of our time series forecasting project in the "Data Science for Society and Business" Master's program at Constructor University Bremen
Embracing AI in Project Management: Final Insights & Future VisionKavehMomeni1
🚀 Unlock the Future of Project Management: Embracing AI – Final Session!
This presentation is the culminating session (Session 13) of the "AI Applications in Project Management Workshop," hosted by OnAcademy and instructed by Kaveh Momeni, PMP®, COB & AI Lead at Chaharsotoon.
Dive deep into "Embracing AI: Empowering Project Managers for an AI-Driven Future." We consolidate critical learnings from the entire workshop and provide a forward-looking perspective on how AI is revolutionizing project management.
Inside, you'll discover:
A Comprehensive Course Recap: Key takeaways from across the workshop, covering everything from knowledge management and predictive analytics to AI agents.
Cutting-Edge AI Trends: The latest developments in AI impacting PM, including market growth, task automation, and the rise of autonomous project assistants.
AI vs. Human Capabilities: Understanding the unique strengths of AI and the irreplaceable value of human intuition, strategic thinking, and leadership in PM.
Optimizing Human-AI Collaboration: Practical models and frameworks for seamlessly integrating AI tools into PM workflows, emphasizing prompt engineering and growth mindsets.
Cultivating AI-Ready Mindsets: Strategies to foster organizational cultures that embrace AI as an opportunity for innovation and competitive advantage.
Essential Skills for Future-Proof PMs: Identifying the core competencies, including AI literacy, data-driven decision-making, and ethical AI governance, crucial for thriving in an AI-augmented world.
Implementation Roadmap & Best Practices: A strategic guide for integrating AI into your projects and organizations, from pilot projects to establishing Centers of Excellence.
Ethical & Practical Considerations: Navigating data quality, bias, transparency, regulatory compliance (like the EU AI Act), and human-centric values in AI-driven PM.
A Vision for AI-Enabled PM: Envisioning AI as a strategic partner, leading to enhanced outcomes, sustainable competitive advantage, and the rise of the "AI-Augmented PM."
Actionable Next Steps: Concrete steps you can take today to advance your AI journey in project management.
Presented by Kaveh Momeni, a seasoned Project Manager with 15+ years of experience and extensive AI/ML certifications from leading institutions. This session is designed to empower project managers, team leaders, and decision-makers to confidently navigate and leverage AI for transformative project success.
Perfect for anyone looking to understand the strategic implications of AI in project delivery and how to prepare for an AI-driven future.
apidays New York 2025 - From UX to AX by Karin Hendrikse (Netlify)apidays
From UX to AX: Designing for an AI Agent World
Karin Hendrikse, Senior Software Engineer at Netlify
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Understanding Large Language Model Hallucinations: Exploring Causes, Detectio...Tamanna36
This presentation delves into Large Language Model (LLM) hallucinations—incorrect or fabricated outputs that undermine reliability. It covers their causes (e.g., data limitations, transformer architecture), detection methods (like semantic entropy), prevention strategies (fine-tuning, RAG), and ethical concerns (misinformation, bias). The role of tokens and MLOps in managing hallucinations is explored, alongside the feasibility of hallucination-free LLMs. Designed for researchers, developers, and AI enthusiasts, it offers insights and practical approaches to enhance LLM accuracy and trustworthiness in critical applications like healthcare and legal systems.
Mastering Data Science: Unlocking Insights and Opportunities at Yale IT Skill...smrithimuralidas
The Data Science Course at Yale IT Skill Hub in Coimbatore provides in-depth training in data analysis, machine learning, and AI using Python, R, SQL, and tools like Tableau. Ideal for beginners and professionals, it covers data wrangling, visualization, and predictive modeling through hands-on projects and real-world case studies. With expert-led sessions, flexible schedules, and 100% placement support, this course equips learners with skills for Coimbatore’s booming tech industry. Earn a globally recognized certification to excel in data-driven roles. The Data Analytics Course at Yale IT Skill Hub in Coimbatore offers comprehensive training in data visualization, statistical analysis, and predictive modeling using tools like Power BI, Tableau, Python, and R. Designed for beginners and professionals, it features hands-on projects, expert-led sessions, and real-world case studies tailored to industries like IT and manufacturing. With flexible schedules, 100% placement support, and globally recognized certification, this course equips learners to excel in Coimbatore’s growing data-driven job market.
3. Success Stories
• Money Ball ( Baseball drafting)
• Nate Silver predicted outcomes in 49 of
the 50 states in the 2008 U.S. Presidential
election
• Cancer detection from Biopsy cells ( Big
Data find 12 patterns while we only knew
9), http://go.ted.com/CseS
• Bristol-Myers Squibb reduced the time it
takes to run clinical trial simulations by
98%
• Xerox used big data to reduce the attrition
rate in its call centers by 20%.
• Kroger Loyalty programs ( growth in 45
consecutive quarters)
4. If you collect data about your business, and feed it to a Big Data
system, you will find useful insights that will provide competitive
advantage
– (e.g. Analysis of data sets can find new correlations to "spot business
trends, prevent diseases, combat crime and so on”. [Wikipedia])
5. Putting Analytics to Work
What happened? And
Why? ( Hindsight)
What is Happening
right now? (
oversight)
What will happen?
(Foresight)
6. Open Source Market Share
• Apache (60%)
• Linux (Servers 16%)
• Firefox (25%)
• Tomcat and most of
middleware
• Android (43%)
• Even Microsoft looking
favorably at Opensource
projects
• There are lot of open
source projects bundled
inside the proprietary
products
Copyright kafka4prez and licensed for reuse under CC License ,
http://www.flickr.com/photos/kafka4prez/198465913
7. What is Open Source?
• Most commercial software does
not distribute the source code, and
developed and managed in a
closed world.
• Idea of open source is to have the
code in the open, and to improve
it though volunteer contributions
using “open development”
• Idea is that the project becomes a
eco-system
– More ideas
– More developers
– More Testers
– More Bug fixers
“There is no delight in
owning anything
unshared.”
Seneca (Roman philosopher,
mid-1st century AD)
8. How does a Open Source Work?
• Open code repository (SVN or Git
etc.)
• Two parts of the community
– Developer Community
– User Community
• Communication through Mailing
lists / IRC Channel
– Develop mailing list
– User mailing list
• Bug tracking database to track errors
(Jira, Bugzilla)
• People submit improvements as
patches through Jira etc.
Committers have write access to repository
Committers review and apply patches, and when you
submit lot of them, they will make you a committer.
9. History of Opensource
• 1970s – UNIX, Emacs
• 1984-85 - GNU project and
Free Software Foundation
• 1990 - GNU project almost
complete .. well not OS
• 1991 - Linus Torvalds announce
Linux, Phython
• 1993 - Net BSD and Free BSD
• 1994-95 - Linux 1.0 released
• 1995 - Apache, KDE, PHP
• 1997 - Genome
• 1999 Linux 2.2, OpenOffice
• 2003 - Firefox, Android
http://www.geograph.org.uk/photo/916456
http://www.fotopedia.com/items/flickr-3320704544
10. Why People Contribute?
• As a way to improve your profile
(looking for a Job)
• To gain experience
• To work with “like minded” People
• To be part of something bigger
• To be a “Geek”
• As a Job – if you a well known
open source developer, chances are
that you will get payed for
contribution
• As a competitive strategy
Copyright U. S. Fish and Wildlife Service and licensed for reuse under CC License ,
http://www.flickr.com/photos/usfwsnortheast/4754624921 and Copyright WxMom and licensed
for reuse under CC License , http://www.flickr.com/photos/wxmom/1359996991.
11. • Sahahna
• Apache Axis2 and
other projects
http://www.geograph.org.uk/photo/1842872
LKA Success Stories
12. Why People use Open Source
Software?
• It is cheaper
• It is better
• Because it is open source (Religiously)
• More visibility into the code, better security,
auditing
• If there is a problem, I can fix it
• More control over releases, roadmap
• Patches become available faster
• Easy to understand how it works
• Can fork the code if needed
• Not own by one person, less risk to depend
on it.
• Do not have to maintain the code
13. Big Data and Opensource
Most Big data tools are free
Even the state of the art is
being released as opensource
Give countries like a unique
opportunity with a level
playing field
14. Open Data
Make the data public
Advanced form of the RTI act
Opensource idea applied to data science
E.g. programs like “Code for America”
15. Code Red: US healthcare.gov
Rescue
$300M project, that is failing
and small group of volunteers
go to hackathon mode to fix
it, and fix it.
See
http://radar.oreilly.com/2014/03/cod
e-red_-they-have-no-use-for-someone-
who-looks-and-dresses-like-me.html
http://content.time.com/time/magazi
ne/article/0,9171,2166770-1,00.html
16. Filtering Information with Big
Data Big Data can filter
information (e.g. SPAM)
Rank Information ( show
most relevant articles)
Find Anomalies ( detect
Fraud)
Make recommendations (
product
recommendations)
Handle reputations (e.g.
Ebay, Amazon)
George Caleb Bingham, 1846
17. Example: Reddit, Hacker
News( Ranking)
Keep Your
Customers
Get New Customers
Improve Operations
Monetize your data
19. Urban Planning and Policy
Decisions
• Urban Planning
– People distribution
– Mobility
– Waste Management
– Parking
• Policy Decision
– What if we change
minimum wage?
– What are economic impact
of a new law?
By Aqwis - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=6810430
20. Example: Big Data for
Development
• Done using CDR data
• People density noon vs. midnight
(red => increased, blue =>
decreased)
From: http://lirneasia.net/2014/08/what-does-big-data-say-about-sri-lanka/
21. Traffic
Lot of us waste time on
traffic
Know where is traffic (
Google traffic does that)
Emergency Response
Know the traffic patterns
Long term planning
22. Manage Donors and
Charities
Sri Lanka donates a lot (even the poorest)
Does the money goes to intended place
Can we track how money is spent?
https://iwringer.files.wordpress.com/2015/09/
traffic2.jpg?w=656
23. Day to day Maintenance
Does the news papers are the best way to get day to
day things done?
Can crowd sourcing help?
How to stop false tickets?
24. Disease spread
Earlier Malaria and now dengue
Know current situation
Know overall trends ( focus on problematic
areas)
Emergency Response
25. Summary
• There are lot Opensource, Open
data, and Big Data can do for Sri
Lanka
• Some cases needs money!! And
might be beyond us
• But not for many cases
– e.g. Sahana
– Hackathon to build an app to decide
what topics to take up in the
parliament
• What we really need is
collaborations between domain
experts and computer scientists