Data Tactics Corporation gave a presentation to the U.S. Senate Select Subcommittee on Intelligence in January 2013. The presentation overview Data Tactics, advocated for open source software and government open source to lower costs for intelligence community IT. It described Data Tactics' contributions to open source, including code contributions and use of open source tools and frameworks. The presentation also outlined the benefits of government open source software for governance, security, and collaboration across agencies.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Big Social Data: The Spatial Turn in Big Data (Video available soon on YouTube)Rich Heimann
Big Social Data: The Spatial Turn in Big Data
By Richard Heimann & Abe Usher
University of Maryland Baltimore County Webinar Description:
The increased access to spatial data and overall improved application of spatial analytical methods present certain potential to social scientific research. This webinar is designed to focus on substantive social science research perspectives while exposing rewards involved in the application of geographic information systems (GIS), Big Data, and spatial analytics in their own research.
What is witnessed as the hype of Web 2.0 has worn off and the collaborative use of the Internet becomes a societal norm is an unprecedented explosion in the creation and analysis of geospatial data. Just as major governments are reducing their investments in location intelligence, individuals and non-government organizations are fueling a bonfire of innovation in the world of GIS data.
Traditional spatial analyses grew up in an era of sparse data and very weak computational power. Today, both of those circumstances are reversed and many of the old solutions are no longer suitable to answer todays questions.
"Big Social Data: The Spatial Turn in Big Data" reflects this change and combines two things which, until recently, engaged quite different groups of researchers and practitioners. Together, they require particular techniques and a sophisticated understanding of the special problems associated with spatial social data. Geographic Data Mining, or Geographic Knowledge Discovery, is not new, but is developing and changing rapidly as both more, and different, data becomes available, and people see new applications. The days of ‘Big Data’ require fresh thinking.
The webinar will highlight connections between spatial concepts and data availability. New emerging social media data will be promoted over traditional social science data, which better reflect some of the more recently developments in Big Data - most notably the socially critical exploration of such data.
Big Data Analytics: Discovering Latent Structure in Twitter; A Case Study in ...Rich Heimann
Big Social Data Analysis: Using location & Twitter to explore the tragic aftermath of the Sandy Hook Elementary School shooting.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.
In this webinar you will gain, by example insights to mining social media data and exposing underlying latent structures relating to ideology and sentiment as well as space and time.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Big Social Data: The Spatial Turn in Big Data (Video available soon on YouTube)Rich Heimann
Big Social Data: The Spatial Turn in Big Data
By Richard Heimann & Abe Usher
University of Maryland Baltimore County Webinar Description:
The increased access to spatial data and overall improved application of spatial analytical methods present certain potential to social scientific research. This webinar is designed to focus on substantive social science research perspectives while exposing rewards involved in the application of geographic information systems (GIS), Big Data, and spatial analytics in their own research.
What is witnessed as the hype of Web 2.0 has worn off and the collaborative use of the Internet becomes a societal norm is an unprecedented explosion in the creation and analysis of geospatial data. Just as major governments are reducing their investments in location intelligence, individuals and non-government organizations are fueling a bonfire of innovation in the world of GIS data.
Traditional spatial analyses grew up in an era of sparse data and very weak computational power. Today, both of those circumstances are reversed and many of the old solutions are no longer suitable to answer todays questions.
"Big Social Data: The Spatial Turn in Big Data" reflects this change and combines two things which, until recently, engaged quite different groups of researchers and practitioners. Together, they require particular techniques and a sophisticated understanding of the special problems associated with spatial social data. Geographic Data Mining, or Geographic Knowledge Discovery, is not new, but is developing and changing rapidly as both more, and different, data becomes available, and people see new applications. The days of ‘Big Data’ require fresh thinking.
The webinar will highlight connections between spatial concepts and data availability. New emerging social media data will be promoted over traditional social science data, which better reflect some of the more recently developments in Big Data - most notably the socially critical exploration of such data.
Big Data Analytics: Discovering Latent Structure in Twitter; A Case Study in ...Rich Heimann
Big Social Data Analysis: Using location & Twitter to explore the tragic aftermath of the Sandy Hook Elementary School shooting.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.
In this webinar you will gain, by example insights to mining social media data and exposing underlying latent structures relating to ideology and sentiment as well as space and time.
Data Scientist Salary, Skills, Jobs And Resume | Data Scientist Career | Data...Simplilearn
This presentation about "Data Science Engineer Career, Salary, and Resume" will help you understand who is a Data Science Engineer, the salary of a Data Science Engineer, Data Science Engineer Skillset and Data Science Engineer Resume. Data science is a systematic way to analyze a massive amount of data and extract information from them. Data Science can answer a lot of questions, as well. Data Science is mainly required for
better decision making, predictive analysis, and pattern recognition.
Below are topics that we will be discussing in this presentation:
1. Introduction to Data Science
2. Who is a Data Science Engineer
3. Data Science Engineer Skillset
4. Data Science Engineer job roles
5. Data Science Engineer salary trends
6. Data Science Engineer Resume
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
Learn more at https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Social media monitoring with ML-powered Knowledge GraphGraphAware
Ever wondered how can be ML used to build Knowledge Graph for allowing businesses to successfully differentiate and compete today? We will show how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.
About the speakers:
Vlasta Kus - Lead Data Scientist at GraphAWare - Machine Learning, Deep Learning and Natural Language Processing expert.
Background in particle physics research at CERN. 10+ years of experience in software development (C/C++, Java, Python) and statistical data analysis.
Neo4j certified professional.
Specialised in using Machine Learning for building Knowledge Graphs (Hume @ GraphAware).
Golven Leroy - Student - I am a engineering student who is interested in everything graph. I love travelling and good food, especially when it is cheese-related and accompanied by good wine. Wannabe Gyro Gearloose, early-age spiderman fan, and beatmaker in my free time.
NODES 2019 - Neo4j Online Developer Expo & Summit - 10th October 2019
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
Top 8 Data Science Tools | Open Source Tools for Data Scientists | EdurekaEdureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Closing keynote by Trey Grainger from Activate 2018 in Montreal, Canada. Covers trends in the intersection of Search (Information Retrieval) and Artificial Intelligence, and the underlying capabilities needed to deliver those trends at scale.
From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2Connected Data World
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Parts 1 & 2
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
The Biggest Picture: Situational Awareness on a Global LevelInside Analysis
The Briefing Room with Dr. Robin Bloor and Modus Operandi
Live Webcast July 28, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=efc4082d9b0b0adfcd753a7435d2d6a1b
The analytic bottlenecks of yesterday need not apply today. The boundaries are also falling thanks in large part to the abundance of third-party data. The most data-driven companies these days are finding creative ways to dynamically incorporate data from within and beyond the firewall, thus building highly accurate, multidimensional views of their business, customer, competition or other subject areas.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the magnitude of change that's occurring in the world of data, why it's happening now, and how you can take advantage. He'll be briefed by Mike Gilger and Boris Pelakh, who will showcase their company's enterprise analytics platform, which combines a range of battle-tested functionality to deliver dynamic situational awareness that can leverage a comprehensive array of data sets. They'll explain how the platform's reasoner benefits from a highly scalable rules engine, and a flexible modeling capability that can optimize data storage virtually on the fly.
Visit InsideAnalysis.com for more information.
Best Python Libraries For Data Science & Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/LepMvJdr2-w
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka session will focus on the top Python libraries that you should know to master Data Science and Machine Learning. Here’s a list of topics that are covered in this session:
Introduction To Data Science And Machine Learning
Why Use Python For Data Science And Machine Learning?
Python Libraries for Data Science And Machine Learning
Python libraries for Statistics
Python libraries for Visualization
Python libraries for Machine Learning
Python libraries for Deep Learning
Python libraries for Natural Language Processing
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
NETWORK CENTRALITY IN SUB-NATIONAL AREAS OF INTEREST USING GDELT DATADataTactics
USMA Cadet leverages GDELT Global Knowledge Graph (GKG) to quantify global human society beyond cataloging physical occurrences and network structure of the global news.
Data Scientist Salary, Skills, Jobs And Resume | Data Scientist Career | Data...Simplilearn
This presentation about "Data Science Engineer Career, Salary, and Resume" will help you understand who is a Data Science Engineer, the salary of a Data Science Engineer, Data Science Engineer Skillset and Data Science Engineer Resume. Data science is a systematic way to analyze a massive amount of data and extract information from them. Data Science can answer a lot of questions, as well. Data Science is mainly required for
better decision making, predictive analysis, and pattern recognition.
Below are topics that we will be discussing in this presentation:
1. Introduction to Data Science
2. Who is a Data Science Engineer
3. Data Science Engineer Skillset
4. Data Science Engineer job roles
5. Data Science Engineer salary trends
6. Data Science Engineer Resume
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
Learn more at https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Social media monitoring with ML-powered Knowledge GraphGraphAware
Ever wondered how can be ML used to build Knowledge Graph for allowing businesses to successfully differentiate and compete today? We will show how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.
About the speakers:
Vlasta Kus - Lead Data Scientist at GraphAWare - Machine Learning, Deep Learning and Natural Language Processing expert.
Background in particle physics research at CERN. 10+ years of experience in software development (C/C++, Java, Python) and statistical data analysis.
Neo4j certified professional.
Specialised in using Machine Learning for building Knowledge Graphs (Hume @ GraphAware).
Golven Leroy - Student - I am a engineering student who is interested in everything graph. I love travelling and good food, especially when it is cheese-related and accompanied by good wine. Wannabe Gyro Gearloose, early-age spiderman fan, and beatmaker in my free time.
NODES 2019 - Neo4j Online Developer Expo & Summit - 10th October 2019
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
Top 8 Data Science Tools | Open Source Tools for Data Scientists | EdurekaEdureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Closing keynote by Trey Grainger from Activate 2018 in Montreal, Canada. Covers trends in the intersection of Search (Information Retrieval) and Artificial Intelligence, and the underlying capabilities needed to deliver those trends at scale.
From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2Connected Data World
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Parts 1 & 2
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
The Biggest Picture: Situational Awareness on a Global LevelInside Analysis
The Briefing Room with Dr. Robin Bloor and Modus Operandi
Live Webcast July 28, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=efc4082d9b0b0adfcd753a7435d2d6a1b
The analytic bottlenecks of yesterday need not apply today. The boundaries are also falling thanks in large part to the abundance of third-party data. The most data-driven companies these days are finding creative ways to dynamically incorporate data from within and beyond the firewall, thus building highly accurate, multidimensional views of their business, customer, competition or other subject areas.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the magnitude of change that's occurring in the world of data, why it's happening now, and how you can take advantage. He'll be briefed by Mike Gilger and Boris Pelakh, who will showcase their company's enterprise analytics platform, which combines a range of battle-tested functionality to deliver dynamic situational awareness that can leverage a comprehensive array of data sets. They'll explain how the platform's reasoner benefits from a highly scalable rules engine, and a flexible modeling capability that can optimize data storage virtually on the fly.
Visit InsideAnalysis.com for more information.
Best Python Libraries For Data Science & Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/LepMvJdr2-w
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka session will focus on the top Python libraries that you should know to master Data Science and Machine Learning. Here’s a list of topics that are covered in this session:
Introduction To Data Science And Machine Learning
Why Use Python For Data Science And Machine Learning?
Python Libraries for Data Science And Machine Learning
Python libraries for Statistics
Python libraries for Visualization
Python libraries for Machine Learning
Python libraries for Deep Learning
Python libraries for Natural Language Processing
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
NETWORK CENTRALITY IN SUB-NATIONAL AREAS OF INTEREST USING GDELT DATADataTactics
USMA Cadet leverages GDELT Global Knowledge Graph (GKG) to quantify global human society beyond cataloging physical occurrences and network structure of the global news.
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Sphere 3D presentation for Credit Suisse technology conference 2014Peter Bookman
Peter Tassiopoulos presented this at the Credit Suisse technology conference in Arizona sharing what Sphere 3D is doing, where we are going, and some of the validations we have received to date.
How to add security in dataops and devopsUlf Mattsson
The emerging DataOps is not Just DevOps for Data. According to Gartner, DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization.
The goal of DataOps is to create predictable delivery and change management of data, data models and related artifacts. DataOps uses technology to automate data delivery with the appropriate levels of security, quality and metadata to improve the use and value of data in a dynamic environment.
This session will discuss how to add Security in DataOps and DevOps.
Denodo DataFest 2017: Conquering the Edge with Data VirtualizationDenodo
Watch the live session on-demand: https://goo.gl/qAL3Q7
No time like the present! That's one reason why edge analytics continues to grow in value and importance. With the right analytic architecture in place, companies can not only identify opportunities at the edge, they can take appropriate actions.
Watch this Denodo DataFest 2017 session to discover:
• The growing importance of edge computing in IoT
• How data virtualization plays a critical role in enabling edge analytics
• How Denodo’s innovative customers exploit edge for a winning business model
CIO priorities and Data Virtualization: Balancing the Yin and Yang of the ITDenodo
Watch here: https://bit.ly/3iGMsH6
Today’s CIOs carry a paradoxical responsibility of balancing the yin and yang of the Business – IT interface. That is, "Backroom IT’s quest for Stability" with the “Frontline Business’ need for Agility".
A paradox that is no longer optional, but is essential. A paradox that defines the business competitiveness, business survival, and business sustainability. Also enables the visibility to the fuzzy future.
“Trusted Data Foundation with Data Virtualization” provides a powerful ammunition in the hands of the CIO, to effectively balance these Yin and Yang at the speed of the business. In a trusted, compliant, auditable, flexible and regulated fashion.
Find out more on how you can enhance the competitive edge for your business in the CIO special webinar from COMPEGENCE and DENODO.
Cloud services are critical sources of speed and agility, and have evolved beyond the simple benefits of cost reduction. Cloud helps companies profit from disruption by allowing innovation in both the front and back office.
This Digital Realty webinar features Michael Bohlig (@bohlig), KC Mares (@kcmares) and Forrester Principal Analyst Dave Bartoletti (@davebartoletti).
For more information visit http://www.digitalrealty.com
Unlocking Engineering Observability with advanced IT analyticssource{d}
In this webinar, source{d} CEO Eiso Kant will introduce source{d} Enterprise Edition (EE), the data platform for the software development life cycle (SDLC), With built-in visualization, management capabilities and advanced analytic functions, source{d} EE provide IT executives with visibility into their software portfolio, engineering processes and workforce.
Learn how source{d} EE can help everyone in the IT organization to quickly get access to customizable analytic solutions for IT modernization and software compliance, cloud-native and DevOps transformation, engineering effectiveness, and talent management.
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Building Confidence in Big Data - IBM Smarter Business 2013 IBM Sverige
Success with big data comes down to confidence. Without confidence in the underlying data, decision makers may not trust and act on analytic insight. You need confidence in your data – that it’s correct, trusted, and protected through automated integration, visual context, and agile governance. You need confidence in your ability to accelerate time to value, with fast deployments of big data appliances. Learn how clients have succeeded with big data by building confidence in their data, ability to deploy, and skills. Presenter: David Corrigan, Big Data specialist, IBM. Mer från dagen på http://bit.ly/sb13se
Presentation to Local Government GIS Officers on the Potential for Open Source in GIS. Its a huge one.. grasp it with open arms.. think about standards... standards... standards..
Bridging the Last Mile: Getting Data to the People Who Need ItDenodo
Watch full webinar here: https://bit.ly/3cUA0Qi
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
This presentation compared the challenges RTI experienced as it adopted its new pricing model to the challenges FACE continually overcomes through its business model.
Die Big Data Fabric als Enabler für Machine Learning & AIDenodo
Ansehen: https://bit.ly/2Cet17K
Erstklassige Big Data Fabrics liefern verlässliche Insights, gewährleisten höchste End-to-End Sicherheitsstandards und ermöglichen eine konsistente Datenintegration in Echtzeit – während den Business-Anwendern agile Werkzeuge zum selbstgesteuerten Datenkonsum bereitgestellt werden.
Erfahren Sie in dem Vortrag, wie die Big Data Fabric als Enabler für ML & AI:
- den Business-Anwendern und Data Scientists einen schnellen und agilen Datenzugriff via Self-Services ermöglicht
- Data Governance und Security Richtlinien zentral und verlässlich managebar macht
- relevante Insights aus aktuellen und konsistenten Daten liefert
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
2. 2 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics Overview
Big Data and Open Source Software
Government Open Source
Data Tactics Contributions to Open Source
Questions
3. 3 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics Overview
4. 4 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Our Staff
200+ Employees
• 90% TS/SCI cleared, many with polygraphs
• 25% have Advanced Degrees and Doctorates
• High percentage of Military and Intelligence
• Community Veterans
• Over 10% of Staff are ―Data Scientists‖
• Three World Class Semantic Researchers
Certification Highlights
• Project Management: CMMI, Project+, and PMP
• Software Development
• VMware
• Cyber Security
• Cloudera Certified Engineers
• Over 40% of Technical Staff
• Hadoop
• Puppet
• MapR
• Greenplum
5. 5 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics: What We Do
• Data Architecture
• Innovation and Design
• Assessment and Benchmarking
• Collaboration and Uniformity
• DataEngineering
• Discovery, Ingestion, and Cleansing
• Scientific Analysis
• Large Scale Computation and Platforms
• DataManagement
• Security and Assurance
• Infrastructure and Administration
• Visualization and Dissemination
6. 6 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Our Methodologies
• Bridging the Academic and Operational Gap
• Translating our operational experience from tactical ground
operations and academia into actionable requirements
• Tactical Engineering
• Team diversity provides a greater understanding of customer
requirements which translates to focused and efficient solutions
• Experience comes from the DoD and Intelligence Community not
just analytical or technical experience
• Staff is trained across multiple technical disciplines
• Right to Left Approach
• Solving our customers’ problems starting at the ―right‖ with ―what do
they want from their data‖
• No ―cookie cutter‖ approach
7. 7 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics Core Customers
"...cultivating, strengthening, and advancing your data…"
Today's decisions makers are tasked to gather, correlate and analyze information from
ever increasing data sources in shorter amounts of time. Data Tactics is focused on
solving the problems of data management facing the DoD, intelligence community, law
enforcement and the private sector. From tactical to strategic efforts, our team has lead
the creation, integration and implementation of innovative and proven solutions in the
world of data alignment, modeling and analytics
8. 8 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics Key Open Source Big Data Efforts
DNI
INFORMATION TECHNOLOGY EFFICIENCY (ITE): FIRST IC/DoD Cloud
NSA
TEST, INTEGRATION, DEPLOYMENT AND SUSTAINMENT (TIDS)
T3 - Security Engineering / Information Assurance
ARMY
DCGS-A STANDARD CLOUD (DSC): FIRST DoD Production Cloud
DCGS-A EDGE NODE (DEN) / TACTICAL EDGE NODE (D-TEN)
INSCOM ENTERPRISE PLATFORM (IEP): FIRST DoD R&D Cloud
AIR FORCE
AIR FORCE TENCAP: FIRST DoD Implementation of NSA Ghost Machine Architecture
DARPA
NEXUS 7: FIRST DARPA Cloud
MORE EYES: FIRST Deployed DARPA Cloud
XDATA: Integration and CASE Project
9. 9 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Globally Deployed Open Source Clouds
5 Clouds on UNCLASS
17 Clouds on SIPRNET 7 Clouds on TS/SCI 4 Clouds on Coalition
Networks
• 4 at DT in Tyson’s • 1 at AF TENCAP, CO • 4 at DT in VA (DARPA)
• 1 at GISA, Ft. Bragg • 1 at NRL, DC • 1 at DT in VA (IRAD)
• 2 in Hawaii • 1 at DT in VA
• 2 in Germany • 1 at INSCOM • Coalition Networks:
• 7 at APG in MD • 3 for DSC • 1 on CX-I in Afghanistan
• 1 in Afghanistan • 1 on CX-T in Afghanistan
• 1 Cloud on BICES
• 1 Cloud in Germany
Cloud and Big Data Domains are where we
live.
Data is the hard problem.
10. 10 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Big Data and Open Source Software
11. 11 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Government World of Big Data
• The government is moving to BigData
through incorporation of IaaS, PaaS,
DaaS, and SaaS
• Data center consolidation
requirements
• The federal government current IT
budget of $76B of which $19B is
infrastructure
• Migration to Government Open
Source Software (GOSS) Cloud
solutions is expected to save $24B
• The White House BigData Initiative
2012
• $200M in R&D funding
• Six federal departments
participating
12. 12 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Definition: OSS
Open Source Software (OSS)
• Software that is licensed to users with the following
freedoms:
• To run the software for any purpose;
• To study and modify the software; and
• To freely redistribute copies of either the original
or modified software without royalty payments or
other restrictions on who can receive them.
"Free software is a matter of liberty, not price. To understand the concept, you
should think of free as in free speech, not as in free beer.“
—Richard Stallman[2]
13. 13 Data Tactics – Open Source Advocate for Lowering IC IT Costs
OSS as a Focus Area for Success
• Maximizing use of Open
Sourcesoftware lower costs
and provides ahigher ROI
• Lowers the cost for entry for
new systems
• Lowers the Operations and
Maintenance (O&M) costs
14. 14 Data Tactics – Open Source Advocate for Lowering IC IT Costs
OSS as a Focus Area for Success
Enterprise availability at neutral cost provides affordability
at scale
Linked Open Data Cloud Linked Classified Data Cloud
15. 15 Data Tactics – Open Source Advocate for Lowering IC IT Costs
OSS as a Focus Area for Success
• Supports Participation by many Agencies and Developers
• Encourages Transparency, Participation, Task Sharing, and
Collaboration between all agencies in the Intelligence Community
• Provides Enterprise Platforms, Standards, and Rules of the Road to
provide the fertile substrate for innovation
16. 16 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Development Process: OSS*
Any Agency can contribute by contributing,
fixing, or extending the codebase
Governance to validate
Developers
fixes and contributions
Trusted
Developer
Trusted
Trusted Repository governed by Repository
Trusted Developers manage the ―Official‖
version of the program. All developers can
contribute, not all code goes into ―trunk‖ Distributor
Governance for stable releases
User
To the user community
*Open Source Software (OSS) in U.S. Government Acquisitions Constant Beta
by David A. Wheeler User as a Developer
March 2008 (Revised Dec. 17, 2010)
17. 17 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Government Open Source
18. 18 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Government Open The Shelf (GOTS)
• Definition: • Challenges:
• Software and/or • Limited Governance
hardware products that • Participation and
are custom developed Collaboration Muted
by technical staff of the • Result:
government agency for
• Duplication of effort
One agency for a
Mission Need • LOE and TTM increase
Minimal Awareness or Mechanisms to Address Problem
19. 19 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Government Open Source Software
(GOSS)
• Definition: • Benefits:
• Computer software • The power of
available in source code distributed peer review
form, for which the and transparency of
source code and certain process
other rights normally • Improved quality,
reserved for the reliability and flexibility
development agency, • Result:
are provided broadly
• Lower Cost
(within government
community) • End to predatory
vendor lock-in
Knowledge and Insight, vs. Best Guess
20. 20 Data Tactics – Open Source Advocate for Lowering IC IT Costs
GOSS – Benefits to Governance
• Ability to submit patches, features and enhancements for
inclusion in the baseline and for your custom requirements
• Costs reduced if patch is accepted: source agency no longer needs
to reapply and retest patch against new releases.
• Capabilities enhanced, costs reduced if make use of patches
submitted by other agencies
• Interoperability across the government
• Influence through the GOSS community at large to request
additional core features
• Risk management through full visibility into ongoing
development
• Voting power through the GOSS Advisory Board to set
priorities for features (aka Project Governance)
The talent and technology throughout the extended community is
leveraged in a cohesive and productive fashion
21. 21 Data Tactics – Open Source Advocate for Lowering IC IT Costs
GOSS – Benefits to Security
Eliminate the Fear, Uncertainty, and Doubt
• Enterprise Class Security is comparable to commercial applications
• Open Source projects quickly fix security issues –100% faster
• More eyes on:
• Specialties of extended community are brought to bear
• External vulnerability analysis and testing
Visibility, Transparency, and Broad Expertise Delivers a More Secure
Product
Aligned with DFARS clauses and DoD Open Source Agreements
22. 22 Data Tactics – Open Source Advocate for Lowering IC IT Costs
What is Needed for GOSS Project
• A Product that has Broad Value
and Relevancy
• Community Space to Work
• Code Repository, Bug
Tracking, Feature
Requests, Roadmaps,
Documentation,
Collaboration
• Governance Processes
• Charters, Roles &
Responsibilities
• A Community with a Passion for
Success
23. 23 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics Contributions to Open
Source
24. 24 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Contributions to OSS Community
• GeoTools
• Part of the Open GeoServer project
• Contributed MongoDB database driver
• Katta
• Various Code fixes and contributions
• Xarm Motif C++ library
• eXtended Template Library
• MilDroid
• Many other individual contributions by our software
engineers
25. 25 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data Tactics Use of OSS
• Integration and Analytic Tools • Distributed Computation Frameworks
• Eclipse and Services
• Gephi • Hadoop MR
• Git • Spark
• Subversion • Storm
• Pig • Giraph
• Sqoop • Resource Scheduling, Management,
• Flume Coordination, Queue
• Mahout • Mesos
• R • Yarn
• Data Storage, Access, and • Zookeeper
Organization • Apache Active MQ
• Katta • Operating Systems, other
• Accumulo • RHEL
• Hbase • Centos
• Riak • Debian
• SOLR • Puppet
• CouchDb • Nagios
• MongoDb • Ganglia
• Terrastore • OpenLDAP
• Hive • Apache HTTPD
• Mysql • JBOSS
• HDFS
26. 26 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Champions of OSS and GOS
• DNI • DoD
• Alex S. Voultepsis • FalconView
• NSA • NASA
• SE-Linux • OpenStack
• SE-Android • Open Government
• Ozone Widget Framework Initiative
(OWF) • Whirlwind
• Accumulo
• DOE
• DISA • 28 projects
• Forge.mil
• White House
• Mil-OSS • Drupal
• DoD-CIO • Army Research Lab
• Dan Risacher
• Ping
• MITRE • Navy
• Dr. David Wheeler
• TOR
27. 27 Data Tactics – Open Source Advocate for Lowering IC IT Costs
28. Contact Info
Lee Shabe
Vice President
Data Tactics Corporation
LShabe@Data-Tactics.com 7901 Jones Branch Dr.
Cell: (703) 963-3523
Office: (571) 297-2136 Suite 700
Will Conroy McLean, VA 22102
Fed/Intel Division Manager
WConroy@Data-Tactics.com www.Data-Tactics.com
Cell: (703) 307-4359
Office: (571) 297-2125
Twitter: @DataTactics
Bruce Goldfeder Blog: http://datatactics.blogspot.com
BGoldfeder@Data-Tactics.com
LinkedIn:
Cell: (703) 304-7518 http://www.linkedin.com/company/data-
Office: (571) 297-2157 tactics-corporation
Eric Whyne
EWhyne@Data-Tactics.com
Cell: (570) 205-3283
29. 29 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Backup Slides
30. Follow Successful Open Source Methods,
Policies, and Governance Models
• Apache Project
• JBOSS
• GNU Licensing (Copyleft)
• Linux
• PostgreSQL
• Many, many more
31. Government Paradigm Shift
Commercial Reality
• Realization that purchasing software is fundamentally
different than battleships or airframes
• Enterprise cost savings for GOSS is tremendous
• Enterprise capability gains far exceed cost concerns
• Faster, cheaper, more secure products
• Unlimited application and extension
• Government Oversight Monitors the Entire GOSS
Ecosystem and Government IT Consumers
• Support those that leverage GOSS
• Financial Incentives
• Infrastructure, Standards, and Best Practices
• Follow the ABC’s of procurement, Adopt, Buy, Create
• Adopt, adapt, and extend existing GOSS
• If not available consider next buying commercial COTS capability
• If not available or not cost effective, create a new GOSS project
32. GOSS – OMB Guidance
From: VivekKundra
Daniel Gordon
Victoria Espinel
...agencies should analyze
alternatives that include
proprietary, open-source, and
mixed source technologies.
... considering factors such as
performance, cost, security,
interoperability, ability to share
or re-use, and availability of
quality support.
33. Updating the Model for Government Software
• Current Model of GOTS procurement is inefficient with
respect to:
• Cost
• Technical Risk
• Schedule Risk
• Mission Risk
• Lock in Risk for both product and service providers
• Currently 93 Federal Government Agencies with their own IT
development infrastructures, budgets, with little to no interaction or
collaboration
• Missions may differ, but system commonalities exist and can be
exploited for efficiencies
34. 34 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Linked IC Data Cloud
Linked IC Data Cloud
Linked Open Data Cloud Linked Classified Data Cloud
35. 35 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Linked IC Data Cloud
Linked Open Data Cloud
Linked Classified Data Cloud
Linked IC Data Cloud
36. 36 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Government Cloud Nodes
#1: ―DI/ODNI‖
#6: ―DI/FBI‖ #2: ―DI/DHS‖
#5: ―DI/CIA‖ #3: ―DI/DOS‖
#7: ―Q2J‖ #4: ―DI/NSA‖
Q2J program
DEEPINSIGHT program
CATALYST program -- Phase A
37. 37 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Data and Utility Clouds
LAYER 4
Cloud Analytics
LAYER 3
Cloud Services
LAYER 2
Cloud Software
Hardware
UtilityCLOUD
LAYER 1 GHOSTMACHINE
Cloud Hardware
38. 38 Data Tactics – Open Source Advocate for Lowering IC IT Costs
IC Network Domains
SCION (FBI) JWICS (ODNI) NSA Net (NSA)
ADN (CIA)
= Compute Cluster, a collection of
computing and storage resources
39. 39 Data Tactics – Open Source Advocate for Lowering IC IT Costs
IC Compute Clusters
SCION (FBI) JWICS (ODNI) NSA Net (NSA)
ADN (CIA)
QL program Q2J program NSA’s SECURE HUB
FBI’s SECURE HUB I2P program WOLFDEN program
CIA’s SECURE HUB DEEPINSIGHT program
= Compute Cluster, a collection of
computing and storage resources
40. 40 Data Tactics – Open Source Advocate for Lowering IC IT Costs
Virtual Network Overlay
Editor's Notes
Maximizing use of Free and Open Source Software Lower Costs and Higher ROILowers the cost for entry for new systemsLowers the Operations and Maintenance (O&M) costsEnterprise availability at neutral costProvides affordability at scaleSupports Participation by many Agencies and DevelopersEncourages Transparency, Participation, Task Sharing, and Collaboration between all agencies in the Intelligence Community Provides Enterprise Platforms, Standards, and Rules of the Road to provide the fertile substrate for innovation
Maximizing use of Free and Open Source Software Lower Costs and Higher ROILowers the cost for entry for new systemsLowers the Operations and Maintenance (O&M) costsEnterprise availability at neutral costProvides affordability at scaleSupports Participation by many Agencies and DevelopersEncourages Transparency, Participation, Task Sharing, and Collaboration between all agencies in the Intelligence Community Provides Enterprise Platforms, Standards, and Rules of the Road to provide the fertile substrate for innovation
Maximizing use of Free and Open Source Software Lower Costs and Higher ROILowers the cost for entry for new systemsLowers the Operations and Maintenance (O&M) costsEnterprise availability at neutral costProvides affordability at scaleSupports Participation by many Agencies and DevelopersEncourages Transparency, Participation, Task Sharing, and Collaboration between all agencies in the Intelligence Community Provides Enterprise Platforms, Standards, and Rules of the Road to provide the fertile substrate for innovation
Maximizing use of Free and Open Source Software Lower Costs and Higher ROILowers the cost for entry for new systemsLowers the Operations and Maintenance (O&M) costsEnterprise availability at neutral costProvides affordability at scaleSupports Participation by many Agencies and DevelopersEncourages Transparency, Participation, Task Sharing, and Collaboration between all agencies in the Intelligence Community Provides Enterprise Platforms, Standards, and Rules of the Road to provide the fertile substrate for innovation
Infrequently shared within or beyond agencyAgency provides funding and sets priorities“Department Level” applicationTypically not built with the Enterprise in mindLimited extensibilityFinished product may be shared with other agencies Limited Governance of Product One agency controls root codebase Many agencies desire additional features As the codebase changes interoperability, accreditation, and merging are lessened Specific mission codebase not built for extensibility Participation and Collaboration are Muted Many agencies have the talent and technology on hand to contribute to a better product Programmatic Fundamentals limit collaboration How does one program for one agency contribute resources to another program at another agency? Agencies “extend” the product with local patches Sharing produces codebase forks Duplication of development effort You need it, I need it –we both do it Duplication of effort across the full lifecycle C&A, Operations, Maintenance Time to market increases LOE increases over time Product interoperability is perturbed or eliminated over timeMinimal Awareness or Mechanisms to Address Problem
Government Open Source Software (GOSS)• Computer software available in source code form, for which the source code and certain other rights normally reserved for the development agency, are provided broadly (within government community) • Permits other agencies to study, change, improve, and contribute to the software in a cohesive and synchronized fashion using Program Governance• An evolutionary way of developing, distributing, licensing and consuming software taking advantage of cost and common task sharingThe power of distributed peer review and transparency of processThe power of code visibility and transparencyResults:Better qualityHigh reliabilityMore flexibilityLower costEnd to predatory vendor lock-in
Infrequently shared within or beyond agencyAgency provides funding and sets priorities“Department Level” applicationTypically not built with the Enterprise in mindLimited extensibilityFinished product may be shared with other agencies Limited Governance of Product One agency controls root codebase Many agencies desire additional features As the codebase changes interoperability, accreditation, and merging are lessened Specific mission codebase not built for extensibility Participation and Collaboration are Muted Many agencies have the talent and technology on hand to contribute to a better product Programmatic Fundamentals limit collaboration How does one program for one agency contribute resources to another program at another agency? Agencies “extend” the product with local patches Sharing produces codebase forks Duplication of development effort You need it, I need it –we both do it Duplication of effort across the full lifecycle C&A, Operations, Maintenance Time to market increases LOE increases over time Product interoperability is perturbed or eliminated over timeMinimal Awareness or Mechanisms to Address Problem