Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
It is almost impossible to escape the topic of Data Science. While the core of Data Science has remained the same over the last decade, it’s emergence to the forefront is spurred by both the availability of new data types and a true realization of the value that it delivers. In this session, we will provide an overview of data science, the different classes of machine learning algorithm and deliver an end-to-end demonstration of performing Machine Learning Using Hadoop. Audience: Developers, Data Scientist Architects and System Engineers.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=4175a7421d00257f33df146f50c41af8
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
It is almost impossible to escape the topic of Data Science. While the core of Data Science has remained the same over the last decade, it’s emergence to the forefront is spurred by both the availability of new data types and a true realization of the value that it delivers. In this session, we will provide an overview of data science, the different classes of machine learning algorithm and deliver an end-to-end demonstration of performing Machine Learning Using Hadoop. Audience: Developers, Data Scientist Architects and System Engineers.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=4175a7421d00257f33df146f50c41af8
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Data Wrangling on Hadoop - Olivier De Garrigues, Trifactahuguk
As Hadoop became mainstream, the need to simplify and speed up analytics processes grew rapidly. Data wrangling emerged as a necessary step in any analytical pipeline, and is often considered to be its crux, taking as much as 80% of an analyst's time. In this presentation we will discuss how data wrangling solutions can be leveraged to streamline, strengthen and improve data analytics initiatives on Hadoop, including use cases from Trifacta customers.
Bio: Olivier is EMEA Solutions Lead at Trifacta. He has 7 years experience in analytics with prior roles as technical lead for business analytics at Splunk and quantitative analyst at Accenture and Aon.
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
Data Science Courses - BigData VS Data ScienceDataMites
Go through the slides to know what is Big Data and what is Data Science and Know the difference between Big Data and Data Science.
DataMites is a global institute, providing industry-aligned courses in Data Science, Machine Learning, and
Artificial Intelligence.
The Certified Data Scientist certification offered by DataMites covers all the important aspects of data science knowledge. The course is designed based on the accepted standards which demonstrates the quality of knowledge of a data science professional.
For more details please visit: https://datamites.com/data-science-course-training-chennai/
Data Wrangling and the Art of Big Data DiscoveryInside Analysis
The Briefing Room with Dr. Robin Bloor, Trifacta and Zoomdata
Live Webcast March 10, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=dd9fed3c7c476ae3a0f881ae6b53dcc5
Square pegs and round holes don't get along, which is one reason why traditional data management approaches simply won't work for Big Data. The variety and velocity of data types flying at us today require a new strategy for identifying, streamlining and utilizing information assets and processes. Decades-old technology won’t cut it – a combination of new tools and techniques must be used to enable effective discovery of insights in a timely fashion.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why today's data landscape calls for a much different data management approach. He'll be briefed by Trifacta and Zoomdata, who will show how their technologies use a range of functionality – including machine learning – to help companies "wrangle" their data. They'll also demonstrate the optimal step-by-step process of working with new data types.
Visit InsideAnalysis.com for more information.
What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
AI-SDV 2021 - Tony Trippe - The Current State of Machine Learning for Patent ...Dr. Haxel Consult
The use of machine learning in IP activities has increased exponentially over the past five years. At the same time new tools, methods and systems have begun to emerge that seek to make the analysis of patent data easier to accomplish using these techniques. Included in these new developments are a significant number of machine learning systems that have begun coming to market. As these changes continue to occur, it would be useful to review some of the tools, systems, or methods that a patent practitioner has at their disposal. Examples and perspectives on the latest advances in machine learning for IP will be provided. There will also be a tour of ML4Patents.com which is devoted to aggregating content associated with the development of this area.
AI-SDV 2020: Special Hypertext Information Treatment in is Special Hypertext ...Dr. Haxel Consult
With all new technologies and intelligence one may think that all information issues will be solved in the (near) future. However, one of the most fundamental issues at hand is that with the lack of reliable, quality information there is no useable output to work with in the first place. This presentation looks at the global challenges that we are still faced with today relating to content that will keep us from truly intelligent discovery in the future if nothing is done.
The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe.
Hosted by:
Sanjeev Sinha, President, CRISIL Global Research & Analytics
Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics
Creating a Data Science Ecosystem for Scientific, Societal and Educational Im...Ilkay Altintas, Ph.D.
The new era of data science is here. Our lives and society are continuously transformed by our ability to collect data in a systematic fashion and turn that into value. The opportunities created by this change also comes with challenges that push for new and innovative data management and analytical methods as well as translating these new methods to applications in many areas that impact science, society, and education. Collaboration and ability of multi-disciplinary teams to work together and communicate to bring together the best of their knowledge in business, data and computing is vital for impactful solutions. This talk will discusses a reference ecosystem and question-driven methodology, called PPODS, to make impactful data science applications in many fields with specific examples in hazards, smart cities and biomedical research.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Data Wrangling on Hadoop - Olivier De Garrigues, Trifactahuguk
As Hadoop became mainstream, the need to simplify and speed up analytics processes grew rapidly. Data wrangling emerged as a necessary step in any analytical pipeline, and is often considered to be its crux, taking as much as 80% of an analyst's time. In this presentation we will discuss how data wrangling solutions can be leveraged to streamline, strengthen and improve data analytics initiatives on Hadoop, including use cases from Trifacta customers.
Bio: Olivier is EMEA Solutions Lead at Trifacta. He has 7 years experience in analytics with prior roles as technical lead for business analytics at Splunk and quantitative analyst at Accenture and Aon.
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
Data Science Courses - BigData VS Data ScienceDataMites
Go through the slides to know what is Big Data and what is Data Science and Know the difference between Big Data and Data Science.
DataMites is a global institute, providing industry-aligned courses in Data Science, Machine Learning, and
Artificial Intelligence.
The Certified Data Scientist certification offered by DataMites covers all the important aspects of data science knowledge. The course is designed based on the accepted standards which demonstrates the quality of knowledge of a data science professional.
For more details please visit: https://datamites.com/data-science-course-training-chennai/
Data Wrangling and the Art of Big Data DiscoveryInside Analysis
The Briefing Room with Dr. Robin Bloor, Trifacta and Zoomdata
Live Webcast March 10, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=dd9fed3c7c476ae3a0f881ae6b53dcc5
Square pegs and round holes don't get along, which is one reason why traditional data management approaches simply won't work for Big Data. The variety and velocity of data types flying at us today require a new strategy for identifying, streamlining and utilizing information assets and processes. Decades-old technology won’t cut it – a combination of new tools and techniques must be used to enable effective discovery of insights in a timely fashion.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why today's data landscape calls for a much different data management approach. He'll be briefed by Trifacta and Zoomdata, who will show how their technologies use a range of functionality – including machine learning – to help companies "wrangle" their data. They'll also demonstrate the optimal step-by-step process of working with new data types.
Visit InsideAnalysis.com for more information.
What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
AI-SDV 2021 - Tony Trippe - The Current State of Machine Learning for Patent ...Dr. Haxel Consult
The use of machine learning in IP activities has increased exponentially over the past five years. At the same time new tools, methods and systems have begun to emerge that seek to make the analysis of patent data easier to accomplish using these techniques. Included in these new developments are a significant number of machine learning systems that have begun coming to market. As these changes continue to occur, it would be useful to review some of the tools, systems, or methods that a patent practitioner has at their disposal. Examples and perspectives on the latest advances in machine learning for IP will be provided. There will also be a tour of ML4Patents.com which is devoted to aggregating content associated with the development of this area.
AI-SDV 2020: Special Hypertext Information Treatment in is Special Hypertext ...Dr. Haxel Consult
With all new technologies and intelligence one may think that all information issues will be solved in the (near) future. However, one of the most fundamental issues at hand is that with the lack of reliable, quality information there is no useable output to work with in the first place. This presentation looks at the global challenges that we are still faced with today relating to content that will keep us from truly intelligent discovery in the future if nothing is done.
The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe.
Hosted by:
Sanjeev Sinha, President, CRISIL Global Research & Analytics
Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics
Creating a Data Science Ecosystem for Scientific, Societal and Educational Im...Ilkay Altintas, Ph.D.
The new era of data science is here. Our lives and society are continuously transformed by our ability to collect data in a systematic fashion and turn that into value. The opportunities created by this change also comes with challenges that push for new and innovative data management and analytical methods as well as translating these new methods to applications in many areas that impact science, society, and education. Collaboration and ability of multi-disciplinary teams to work together and communicate to bring together the best of their knowledge in business, data and computing is vital for impactful solutions. This talk will discusses a reference ecosystem and question-driven methodology, called PPODS, to make impactful data science applications in many fields with specific examples in hazards, smart cities and biomedical research.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Single Nucleotide Polymorphism Analysis
Predictive Analytics and Data Science Conference May 27-28
Asst. Prof. Vitara Pungpapong, Ph.D.
Department of Statistics
Faculty of Commerce and Accountancy
Chulalongkorn University
Big Data Analytics to Enhance Security
Predictive Analtycis and Data Science Conference May 27-28
Anapat Pipatkitibodee
Technical Manager
anapat.p@Stelligence.com
Data Science fuels Creativity
DAAT Day - Digital Advertisitng Association Thailand
Komes Chandavimol, Data Science Thailand
Data Scientists Data Science Lab, Thailand
Marketing analytics
PREDICTIVE ANALYTICS AND DATA SCIENCECONFERENCE (MAY 27-28)
Surat Teerakapibal, Ph.D.
Lecturer, Department of Marketing
Program Director, Doctor of Philosophy Program in Business Administration
Brett S. Lininger, Esq., Principal at Semmes, Bowen, Semmes presented - “Property & Casualty Legislative Up-Date” at the October 2013 67th Annual F. Addison Fowler Seminar held by The Insurance Roundtable of Baltimore in Hunt Valley, MD
Finding the best patents in your portfolio
Sumair Riyaz (Dolcera, India)
Ever found a pile of hundred patents that you'd never seen before staring at you on a Friday afternoon, of which you had to pick the 'gems' by Monday morning? Ever wondered which of the 500 patents ("Gems") in your portfolio is really worth paying the maintenance fees on?
Key take aways from this session:
■Understanding the importance of the IP assessment and development to build the "Gem studded" IP portfolio
■IP Assessment and Strategy
■Gem Mining - Identifying valuable patents in a portfolio - Hands-on exercise
■Gem Faceting - Maximizing the Gem Value
Miniaturized Gas Sensors Patent Landscape 2016 SampleKnowmade
KEY FEATURES OF THE REPORT
• IP trends including time evolutions and countries of patent filings
• Current legal status of patents
• Ranking of main patent applicants
• Joint developments and IP collaboration network of main patent applicants
• Key patents and granted patents near expiration
• Relative strength of main companies’ IP portfolios
• Matrix showing patent applicants and their patented technologies
• Segmentation of patents by gas sensor technology: Electrical detector (FET, CMOS, MOS/MIS), chemical sensor, optical detector, acoustic detector, gas chromatography, electro-chemical gas sensor, thermal gas sensor, gas sensor with CNTs/Graphene, electro-mechanical gas sensors
• IP position vs. market positions for each key players
• MEMS gas sensor IP profiles of 19 key companies, with key patents, technological issues, partnerships, IP strength, IP strategy and latest market news
• Excel database with all patents analyzed in the report (2000+ patents), including technology segmentation
KEY FEATURES OF THE REPORT
• IP trends including time evolutions and countries of patent filings
• Current legal status of patents
• Ranking of main patent applicants
• Joint developments and IP collaboration network of main patent applicants
• Key patents and granted patents near expiration
• Relative strength of main companies’ IP portfolios
• Matrix showing patent applicants and their patented technologies
• Segmentation of patents by
- Technology (primary and secondary batteries w/o lithium)
- Design (micro-batteries, solid thin film, flexible, 3D …)
- Components/materials (anode, cathode, electrolyte, barrier layer ...)
- Manufacturing method (CVD, ALD, PVD, sputtering, electrodeposition …)
- Claimed invention (method, product, apparatus)
• Microbattery IP profiles of 10 major companies, with key patents, technological issues, partnerships, IP strength, IP strategy and latest market news
• Excel database with all patents analyzed in the report (3000+ patents), including technology segmentation
CambridgeIP: Case Studies Of Recent Client EngagementsCambridgeIP Ltd
CambridgeIP’s experience has shed light on multiple client needs that can be met through the use of patent-based intelligence. The set of anonymised case studies below illustrate some recurring client needs and solutions we have developed to meet these.
PatAnalyse is in the business of deliveringIP intelligence to its clients.
We take responsibility for finding the patent information required by our clients and then structure and make sense of it
To deliver a project we use a proprietary, comprehensive search management system to capture expert judgements and combine these with artificial intelligence analysis to produce a pre-analysed universe of data tailored exactly to each client’s needs
Our experience in technology consultancy allows us to provide an interpretation of the ‘competitive intelligence landscape’; our analysis is closely aligned to the client’s business strategy
Our client, as the user, first influences how the universe of patent data is gathered and structured and then can exploit it using the on-line patent management system provided by PatAnalyse
PatAnalyse Ltd (www.patanalyse.com) delivers investigative consultancy projects to answer specific IP related questions which address the strategic business needs of our clients and are critically dependent on the completeness of the patent searching results. We have developed revolutionary techniques for self-learning iteration patent searching. Within our tools the power of artificial intelligence algorithms is closely integrated with the judgement of subject area experts.
Patent Landscape Analysis: Navigating Intellectual Property | InventionIPInvention ip
Learn about intellectual property with our PowerPoint presentation on Patent Landscape Analysis by InventionIP. Discover trends in innovation, competitors' strategies, and the technology landscape. We'll show you how to analyze patent data. You'll gain practical knowledge to make better decisions, improve your R&D strategies, and reduce risks. We'll teach you how to find valuable information in patent data, so your organization can stay ahead in today's fast-changing market.
Unlock the full potential of your innovation journey with InventionIP's Patent Landscape Analysis services. Visit Patent Landscape Analysis to learn more and get started today!
Patent Landscape Analysis: https://inventionip.com/patent-landscape-analysis
Ip on a coffe break... be inventive... be creative... be freeTanja Kalezic
Milana VItas, RT-RK Computer Based Systems, Zaštita autroskih prava i upravljanje intelektualnom svojinom
CRINSS 2013 Creative Industries Conference, Novi Sad, Serbia
Konferencija kreativnih industrija
PMG Oct 2011 Patents and intellectual property 101 for product managers finalDerek Pettingale
Recent sales of patent portfolios for billions of dollars are leading executives to revisit potential dormant value. With visible portfolio valuations such as Nortel and Motorola IP making headline news, the value of Intellectual Property has never been higher than in today's economy. As a result, Product Managers are now being challenged with understanding basic patent protection, strategy and valuation.
Patent landscape efforts can get hampered either by voluminous patent search results or the perceived need to manually tag every single feature. It can increase uncertainty, costs, and complexity. Like chemical structure, biosequence, or freedom-to-operate patent searches, patent landscape searches have unique challenges. Delivering custom patent landscape analysis for effective decisions is beyond the skills of most end users. The final patent landscape report can vary depending on the allocated time and the optimal usage of advanced patent analytics tools by professional patent analysts (or by end-users). Many “state of the art” reports only provide patent search results with minimal analysis. Similarly, many automated “instant reports” only provide canned analysis and visuals that are too broad for POV analysis or corporate decision-making. This presentation describes the use of an efficient accelerated, intentional, and multifaceted (AIM™) patent landscape to alleviate these issues. The goal of an AIM™ patent landscape is to intentionally align scope with pending decisions. The results are delivered with appropriate level of analysis, including supporting charts, within an accelerated timeline of 2-3 weeks. AIM™ patent landscape analysis uses experienced patent analysts, a well-defined workflow process, and multiple best-in class patent data search, processing, and visualization tools. AIM™ patent landscapes are ideal in patent portfolio benchmarking efforts and delineating white space opportunities for well-defined projects.
How big data tranform your business? Data Science Thailand Meet up #6Data Science Thailand
How Big Data Transform Your Business?
โกเมษ จันทวิมล
Komes Chandavimol
komes@datascienceth.com
Data Science Thailand Meet up #6 - Chiang Mai University
Getting Ready For 3rd Generation Platform
Data Science Thailand Meetup#4
Asst. Prof. Dr. Jirapun Daengdej
Vincent Mary School of Science and Technology
Assumption University
jirapun@scitech.au.edu