HiPPO and Flipism are no longer the only way to take decisions. In the Big Data / Data Science era one can dream of data-driven organization. If the data were "oil", Big Data technologies extract, transport, and store it, while Data Science methods provide the a way to "refine the crude oil". This presentation elaborates on the Ws (What, Why, When, Who and How) of Big Data and Data Science.
Spring cleaning in the house of analytics - Superweek 2016Steen Rasmussen
Presentation held by Steen Rasmussen at the Superweek conference in Hungary 2016. Subejct is focused on the possibilities for "Big Answers" for management from the data in the digital marketing department
Big Data - Introduction and Research Topics - for Dutch KadasterJust van den Broecke
Presentation (in Dutch) on Big Data (BD) given on Oct 10, 2013 for Dutch Kadaster. To provide an introduction on BD, what could be BD in the geospatial domain, what could be opportunities and research topics for Dutch Kadaster. A personal view, i.e. by no means that this represents opinions or positions of Dutch Kadaster. Just a clarification beyond the buzzword...
Why CxOs care about Data Governance; the roadblock to digital masteryCoert Du Plessis (杜康)
This talk covered how data governance are scaled in large organisations, defining self-sustaining ownership models, a mechanism for managing risk and delegating decisions to those with the most knowhow.
odi peter wells - presentation - economics of dataPeter Wells
Presentation at #odrs2016 alongside #iodc16
Full research paper: https://drive.google.com/file/d/0B4TpC6ecmrM7Ny1LQlNpMEVIMVk/view
All papers linked here: http://odresearch.org/agenda.html
Socialytics: Accelerating IBM Connections Adoption with Watson Analyticspanagenda
Social adoption is a challenge for many companies. What is the most effective utilization of the environment? Who is using which resources, what in the environment is dormant or orphaned? Where should efforts focus in order to improve adoption? All of these questions can be difficult to answer and there is no “one size fits all” solution as each organization has their own unique needs. Join us to learn how to tackle this topic using IBM Connections and Watson. Starting out with IBM Bluemix Data Connect to collect and combine data from relevant sources, they use the cognitive power of IBM Watson Analytics to answer those tricky questions and provide solutions to real-world adoption challenges.
Ten years after the term ‘Big Data’ infiltrated the world of marketing, why is it still complex to embed it in the decision-making process? In this webinar, we delve into exploiting data and analytics in favor of your business.
Spring cleaning in the house of analytics - Superweek 2016Steen Rasmussen
Presentation held by Steen Rasmussen at the Superweek conference in Hungary 2016. Subejct is focused on the possibilities for "Big Answers" for management from the data in the digital marketing department
Big Data - Introduction and Research Topics - for Dutch KadasterJust van den Broecke
Presentation (in Dutch) on Big Data (BD) given on Oct 10, 2013 for Dutch Kadaster. To provide an introduction on BD, what could be BD in the geospatial domain, what could be opportunities and research topics for Dutch Kadaster. A personal view, i.e. by no means that this represents opinions or positions of Dutch Kadaster. Just a clarification beyond the buzzword...
Why CxOs care about Data Governance; the roadblock to digital masteryCoert Du Plessis (杜康)
This talk covered how data governance are scaled in large organisations, defining self-sustaining ownership models, a mechanism for managing risk and delegating decisions to those with the most knowhow.
odi peter wells - presentation - economics of dataPeter Wells
Presentation at #odrs2016 alongside #iodc16
Full research paper: https://drive.google.com/file/d/0B4TpC6ecmrM7Ny1LQlNpMEVIMVk/view
All papers linked here: http://odresearch.org/agenda.html
Socialytics: Accelerating IBM Connections Adoption with Watson Analyticspanagenda
Social adoption is a challenge for many companies. What is the most effective utilization of the environment? Who is using which resources, what in the environment is dormant or orphaned? Where should efforts focus in order to improve adoption? All of these questions can be difficult to answer and there is no “one size fits all” solution as each organization has their own unique needs. Join us to learn how to tackle this topic using IBM Connections and Watson. Starting out with IBM Bluemix Data Connect to collect and combine data from relevant sources, they use the cognitive power of IBM Watson Analytics to answer those tricky questions and provide solutions to real-world adoption challenges.
Ten years after the term ‘Big Data’ infiltrated the world of marketing, why is it still complex to embed it in the decision-making process? In this webinar, we delve into exploiting data and analytics in favor of your business.
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
By Phani Mitra VP Analytics & Strategy at Dr. Reddy’s
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com
Big data: why, what, paradigm shifts enabled , tools and market landscapeEmanuele Della Valle
This presentation brings together many contents you may have seen before (reports by McKinsey, Gatner and IBM, and info-graphics by Intel and Go-Globe) are agglomerated in one comprehensive and up-to-date view of Big Data.
Session delivered with Franz Walder at Engage Usergroup in antwerp on May 9th.
(content was the same as delivered at Connect 2017, see https://www.slideshare.net/femware/socialytics-accelerating-ibm-connections-adoption-with-watson-analytics)
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
How To Activate Employee Engagement Through Digital TransformationDynamic Signal
“Digital” continues to disrupt and create new opportunities for businesses and brands across multiple areas of their organizations. From real-time response to hyper-connected customers and employees, companies often struggle to fulfill the increasing demand for fast, personalized responses to their requests and concerns.
Highly trusted employees can be an organization’s greatest asset during this transformative time. But today’s employees have very different expectations and needs as well.
Join David Armano, Global Strategy Director of Edelman, and Cydney Roach, U.S. Practice Lead for Edelman’s Employee Engagement Practice, for an exclusive webinar with Dynamic Signal. The panel will discuss:
- What got us here and how an organization’s digital transformation journey impacts its future
- How to engage employees using new technology and techniques
- How trust factors into brand reputation and employee advocacy
Social Connections 14 - Watson Analytics: accelerate your Connections adoptionpanagenda
Recording: http://pan.news/X8DL30mvmnr
Abstract: Watson Analytics is a powerful tool to explore and analyze data. Join Franz Walder to learn about how to get started, by working through three social adoption use cases. IBM Connections adoption is a challenge for many companies. What is the most effective utilization of the environment? Who is using which resources, what in the environment is dormant or orphaned? Where should efforts focus in order to improve adoption? All of these questions can be difficult to answer and there is no “one size fits all” solution as each organization has their own unique needs. This session will give you a place to start by utilizing the cognitive power of Watson Analytics.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Using Data to Inform Information Architecture and User ExperienceElementive
Data about users is all around us and, more and more, we're being asked to utilize that data when architecting information, designing experiences and creating content. But, what is the right way to utilize all the data we have to understand our users?
Watch video of Matthew Edgar, web consultant at Elementive, delivering this presentation at IA Summit 2016. https://blueprintdigital.com/ia-summit-2016/matthew-edgar/
"Data Informed vs Data Driven" by Casper Sermsuksan (Kulina)Tech in Asia ID
Casper is currently the Head of Product & Growth at Kulina, an online food subscription service in Jakarta. Casper is responsible for driving product management and growth initiatives as well as leading marketing efforts. Previously, he led the product marketing teams at Product Madness in San Francisco. During his tenure at Product Madness, he helped the company's top app, Heart of Vegas achieve the record of $200M in annual revenue. Outside of his day-to-day work, he advises corporations and startups on product and growth, and writes frequently on Startup Grind, Mind the Product & Muzli. He graduated with a business degree from the University of Southern California in Los Angeles.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Data streams take many forms and their velocity is hard to tame. They can be myriads of tiny flows that you can collect to tame with Time-series Databases; continuous massive flows than you cannot stop to tame with Data Stream Management Systems; Continuous numerous flows that can turn into a torrent to tame with Event-based Systems; and myriads of continuous flows of any size and speed that form an immense delta to tame with Event-Driven Architectures. Enjoy this introductory talk!
This is the presentation that I did for PoliMI Data Scientists on Stream Reasoning, an approach to blend Artificial Intelligence and Stream Processing.
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
By Phani Mitra VP Analytics & Strategy at Dr. Reddy’s
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com
Big data: why, what, paradigm shifts enabled , tools and market landscapeEmanuele Della Valle
This presentation brings together many contents you may have seen before (reports by McKinsey, Gatner and IBM, and info-graphics by Intel and Go-Globe) are agglomerated in one comprehensive and up-to-date view of Big Data.
Session delivered with Franz Walder at Engage Usergroup in antwerp on May 9th.
(content was the same as delivered at Connect 2017, see https://www.slideshare.net/femware/socialytics-accelerating-ibm-connections-adoption-with-watson-analytics)
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
How To Activate Employee Engagement Through Digital TransformationDynamic Signal
“Digital” continues to disrupt and create new opportunities for businesses and brands across multiple areas of their organizations. From real-time response to hyper-connected customers and employees, companies often struggle to fulfill the increasing demand for fast, personalized responses to their requests and concerns.
Highly trusted employees can be an organization’s greatest asset during this transformative time. But today’s employees have very different expectations and needs as well.
Join David Armano, Global Strategy Director of Edelman, and Cydney Roach, U.S. Practice Lead for Edelman’s Employee Engagement Practice, for an exclusive webinar with Dynamic Signal. The panel will discuss:
- What got us here and how an organization’s digital transformation journey impacts its future
- How to engage employees using new technology and techniques
- How trust factors into brand reputation and employee advocacy
Social Connections 14 - Watson Analytics: accelerate your Connections adoptionpanagenda
Recording: http://pan.news/X8DL30mvmnr
Abstract: Watson Analytics is a powerful tool to explore and analyze data. Join Franz Walder to learn about how to get started, by working through three social adoption use cases. IBM Connections adoption is a challenge for many companies. What is the most effective utilization of the environment? Who is using which resources, what in the environment is dormant or orphaned? Where should efforts focus in order to improve adoption? All of these questions can be difficult to answer and there is no “one size fits all” solution as each organization has their own unique needs. This session will give you a place to start by utilizing the cognitive power of Watson Analytics.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Using Data to Inform Information Architecture and User ExperienceElementive
Data about users is all around us and, more and more, we're being asked to utilize that data when architecting information, designing experiences and creating content. But, what is the right way to utilize all the data we have to understand our users?
Watch video of Matthew Edgar, web consultant at Elementive, delivering this presentation at IA Summit 2016. https://blueprintdigital.com/ia-summit-2016/matthew-edgar/
"Data Informed vs Data Driven" by Casper Sermsuksan (Kulina)Tech in Asia ID
Casper is currently the Head of Product & Growth at Kulina, an online food subscription service in Jakarta. Casper is responsible for driving product management and growth initiatives as well as leading marketing efforts. Previously, he led the product marketing teams at Product Madness in San Francisco. During his tenure at Product Madness, he helped the company's top app, Heart of Vegas achieve the record of $200M in annual revenue. Outside of his day-to-day work, he advises corporations and startups on product and growth, and writes frequently on Startup Grind, Mind the Product & Muzli. He graduated with a business degree from the University of Southern California in Los Angeles.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Data streams take many forms and their velocity is hard to tame. They can be myriads of tiny flows that you can collect to tame with Time-series Databases; continuous massive flows than you cannot stop to tame with Data Stream Management Systems; Continuous numerous flows that can turn into a torrent to tame with Event-based Systems; and myriads of continuous flows of any size and speed that form an immense delta to tame with Event-Driven Architectures. Enjoy this introductory talk!
This is the presentation that I did for PoliMI Data Scientists on Stream Reasoning, an approach to blend Artificial Intelligence and Stream Processing.
While the state of the art in Machine Learning offers practitioners effective tecniques to deal with static data sets, there are only accademic results tailored to data streams. In this presentation for the 4th Stream Reasoning workshop, I report on an effort of Alessio Bernardo (a student of mines) to set up a benchmark enviroment to (i) repeat academic results, (ii) perform studies on real data for confirming the academic results, and (iii) study the research problem of "incremental rebalancing learning on evolving data streams".
From the semantic interoperability problem to Google's knowledge graph passing from the Semantic Web, Linked Data, Yahoo! search monkey, Facebook Open Graph, and schema.org.
La Città dei Balocchi, con le sue luci, è un evento chiave nel panorama dell'offerta turistica Natalizia Lombarda. La presentazione riporta i risultati di un'analisi di chi è venuto e quando.
Realizzato da Fluxedo srl e Olivetti spa per il Consorzio Como Turistica, con la collaborazione di Politecnico di Milano, TIM e Comune di Como, nel contesto del progetto CrowdInsights finanziato da EIT Digital.
Stream Reasoning: a summary of ten years of research and a vision for the nex...Emanuele Della Valle
Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and this talk analyses its growth. In the first part, it traces the main results obtained so far, by presenting the most prominent studies. It starts by an overview of the most relevant studies developed in the context of semantic web, and then it extends the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: the second part presents a set of open challenges and issues that stream reasoning will face in the next future.
Stream reasoning: an approach to tame the velocity and variety dimensions of ...Emanuele Della Valle
Big Data tech can tame volume and velocity. Taming Variety in presence of volume and velocity is the real challenge. I’ve been working on taming variety and velocity simultaneously (Stream Reasoning) for 10 years, now. In this talk, I give you some examples of application domains where this is necessary. I explain where the Stream Reasoning community went so far in theory, applications and products. In particular I focus on my applications and my startup Fluxedo, which is offering real-time social media analytics across social networks. I conclude the talk discussing what comes next: 1) the need to focus on languages and abstractions able to easily capture user needs; 2) the need to find the sweet-spot between scalability and expressive semantics; 3) the need to used semantics to model more than the data access; and 4) the need to get over imperfect data. If you are exited, I did my job for today!
Every body talks about Big Data, but why? Do it create value? Do it enable some paradigmatic shifts in the way we work with data? This talk I did at ComoNext research and technological park cast some light on those questions.
Listening to the pulse of our cities with Stream Reasoning (and few more tech...Emanuele Della Valle
The digital reflection of our cities is sharpening and it is tracking their evolution with a decreasing delay. However, we risk that data piles up without easing decision making. This key note, which I gave at the 12th Semantic Web Summer School, presents how stream reasoning (an approach to tame simultaneously the variety and velocity dimensions of Big Data) and advance visual analytics can support decision makers and discusses the lesson learnt.
The forth lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It presents an introduction to RDF. It starts presenting the data model. Then it presents the turtle serialization. It compares XML vs. RDF. Finally, it provides few informations about RDFa and Linked Data.
The third lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It presents an introduction to the Semantic Web taking a brief walk through in this 15 years of research, standardisation and industrial uptake.
The second lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It discusses interoperability using HL7 v2 and v3 as examples of syntactic and semantic interoperability, respectively.
Stream reasoning: mastering the velocity and the variety dimensions of Big Da...Emanuele Della Valle
More and more applications require real-time processing of heterogeneous data streams. In terms of the “Vs” of Big Data (volume, velocity, variety and veracity), they require addressing velocity and variety at the same time. Big Data solutions able to handle separately velocity and variety have been around for a while, but only Stream Reasoning approaches those two dimensions at once. Current results in the Stream Reasoning field are relevant for application areas that require to: handle massive datasets, process data streams on the fly, cope with heterogeneous incomplete and noisy data, provide reactive answers, support fine-grained information access, and integrate complex domain models. This talk starting from those requirements, frames the problem addressed by Stream Reasoning. It poses the research question and operationalise it with four simpler sub-questions. It describes how the database group of Politecnico di Milano positively answered those sub-questions in the last 7 years of research. It briefly surveys alternative approaches investigated by other research groups world wide and it elaborates on current limitations and open challenges.
The 10 minutes presentation I gave at my PhD defence on 21.9.2015 in Amsterdam. Prof. Frank van Harmelen was my promoter. Prof. Ian Horrocks, prof. Manfred Hauswirth, prof. Geert-Jan Houben, Peter Boncz and prof. Guus Schreiber were my opponents.
Listening to the pulse of our cities fusing Social Media Streams and Call Dat...Emanuele Della Valle
The digital reflection of our cities is sharpening and it is tracking their evolution with a decreasing delay. This happens thanks to the pervasive deployment of sensors, the wide adoption of smart phones, the usage of (location-based) social networks and the availability of datasets about urban environment. So while data becomes every day more abundant, decision makers face the challenge to increase their capability to create value out of the analysis of this data. This key note presents how advance visual analytics, ontology base data access and information flow processing methods can help in making sense of Social Media Streams and Call Data Records from Mobile Network Operators during city scale events. Real-world deployments demonstrate the ability of those methods to advance our ability to feel the pulse of our cities in order to deliver innovative services.
C’è un modo di raccontare un evento che passa attraverso la lettura dei flussi social che genera. Quella traccia digitale che ogni partecipante lascia sui social network quando condivide la sua partecipazione o la sua opinione. E’ possibile fondere e interpretare in tempo reale tali tracce utilizzando tecnologie d’analisi d’avanguardia e modelli avanzati di visualizzazione dei dati. Nel 2014 in collaborazione con StudioLabo e Telecom Italia, il Politecnico di Milano ha realizzato CitySensing, per mostrare l’impronta lasciata dal FuoriSalone sui social network. Focalizzando, in seguito, CitySensing sulle esigenze del gestore dell’evento, il Politecnico di Milano ha mostrato la potenzialità dell’approccio per il Festival della Comunicazione di Camogli e per il Festival delle Letterature di Pescara. La soluzione è ora offerta da Fluxedo.
C'è un modo di racocontare la città che passa attraverso la lettura dei flussi di dati che essa genera. Quelle tracce digitali che ciascuno di noi lascia ogni volta che compie un piccolo gesto quotidiano, come fare una telefonata o inviare un tweet.
In City Data Fusion, il Politecnico di Milano e Telecom Italia raccontano le città fondendo, interpretando e visualizzando i Big Data, ovvero quell'enorme e continuo flusso di tracce digitali che i loro abitanti e visitotori lasciano utilizzando il proprio smartphone o i servizi della città.
Questa presentazione vi introduce all'osservazione alcune città italiane in una prospettiva nuova.
Bi-later integration are a short term approach to business integration, but only standards provide a long term solution. Unfortunately, agreeing on standards is hard and takes time, thus translation between standards is unavoidable. Embracing change is the only way to benefit from short term translation while developing over time comprehensive standards. Semantic technologies are design with flexibility in mind and, therefore, they can help in developing more comprehensive standards and easier to maintain translations.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
3. Why?
• In many organizations decisions are made by
"questionable" methodologies such as
– Highest Paid Person Opinion (HiPPO)
– Flipism (all decisions are made by flipping a coin)
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5. Why?
Flipism (all decisions are made by flipping a coin)
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6. Why?
• In many organizations decisions are made by the
"questionable" methodologies such as
– Highest Paid Person Opinion (HiPPO)
– Flipism (all decisions are made by flipping a coin)
• This could have been the right approach in the '70s …
– See the "Theory of Bounded Rationality" by Herbert Simons
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8. Why?
• In many organizations decisions are made by the
"questionable" methodologies such as
– Highest Paid Person Opinion (HiPPO)
– Flipism (all decisions are made by flipping a coin)
• This could have been the right approach in the '70s …
– See the "Theory of Bounded Rationality" by Herbert Simons
• … but in the Big Data era one can dream of
data-driven organization
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10. Why?
Decisions no longer have to be made in the dark
or based on gut instinct; they can be based on
evidence, experiments and more accurate
forecasts.
-- McKinsey
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11. Why?
• Data-driven organizations
– perform better
• The data shows where they can streamline their processes
– are operationally more predictable
• Data insights fuel current and future decision making
– are more profitable
• Constant improvements and better predictions help to
outsmart the competition and improve innovation.
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12. Why?
• Moneyball: data + analysis to win games
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[source: https://www.imdb.com/title/tt1210166/ ]
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18. What's Big Data?
• Big Data is "crude oil" … that we have to
– Extract
– Transport in mega-tankers
– Ship through pipelines
– Store in massive silos
– …
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19. What's Data Science?
• Data Science is "refining crude oil"
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[source:http://allabtinstru.blogspot.com/2016/09/ProcessofRefiningCrudeOil.html]
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20. What's Data Science?
• The Science [and Art] of…
– Discovering what we don’t know from data
– Obtaining predictive, actionable insight from data
– Creating Data Products that have business impact
now
– Communicating relevant business stories from data
– Building confidence in decisions that drive business
value
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21. Who's a Data Scientist?
• Drew Conway, 2010
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22. How?
• Statistics starts with data
• Two goals of analyzing data
– Descriptions: how nature associates responses to inputs
– Predictions: response for future input variables
[source: Statistical Modeling: The Two Cultures. Leo Breiman, 2001]
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nature xy
independent
variable
response
variable
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23. How?
[source: Marc Andrews, 2014]
Leverage more of the data being captured
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24. How?
[source: Marc Andrews, 2014]
Leverage more of the data being captured
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25. How?
[source: Marc Andrews, 2014]
Leverage more of the data being captured
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26. How?
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Reduce effort required to leverage data
[source: Marc Andrews, 2014]
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27. How?
18/06/2018 @manudellavalle - http://emanueledellavalle.org
Reduce effort required to leverage data
[source: Marc Andrews, 2014]
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28. What?
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Reduce effort required to leverage data
[source: Marc Andrews, 2014]
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42. Credits
• Big Data [sorry] & Data Science: What Does a Data Scientist Do?
Carlos Somohano, 2013
– https://www.slideshare.net/datasciencelondon/big-data-sorry-data-
science-what-does-a-data-scientist-do-world
• Becoming a data-driven organization The what, why and how.
SAS, 2018
– https://www.sas.com/en_us/whitepapers/becoming-data-driven-
organization-109150.html
• Never trust summary statistics alone; always visualize your data.
Alberto Cairo, 2016
– http://www.thefunctionalart.com/2016/08/download-datasaurus-
never-trust-summary.html
• 2017 Planning Guide for Data and Analytics. John Hagerty
(Gartner), 2016
– https://www.gartner.com/binaries/content/assets/events/keywords/
catalyst/catus8/2017_planning_guide_for_data_analytics.pdf
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