Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
How telecommunication companies can leverage power Hadoop and Big Data to derive use cases.
Based on Cloudera Whitepaper - Big Data Use Cases for Telcos
Banalytics - Monetizing corporate big data | InstareaMatej Misik
How to use corporate big data for external applications, remain legally and ethically compliant and create a solution with clear public good? At the marketing edition of Banalytics in Bratislava, Matej Misik shared our approach to big data monetization for telcos, banks and other data rich industries.
Instarea is a "laboratory" for innovative big data monetization ideas within the international Adastra group. A young committed team, fresh thinking and a lust for adventure define us as a company. We yearn to change the world for the better through data.
Strategizing Big Data in Telco
Big data feels to be a very hot topic nowadays. Some industries depend on it completely, some have opportunities to roll out their strategies and execute, some just considering when it is a right time to hop in.
To my mind, Big Data is not about technology. Big data is about people generating data and data used for the benefit of people.
Big data is a pool of activities intended at processing the data a company owns (internal and external) so that to open new revenue opportunities, minimize costs and enhance UX.
I had some ideas and thoughts on what telecommunication companies may start from in formulating the Big Data Strategy and so packed some of the most important pieces of thoughts into a small presentation.
What is the difference between Small Data and Big Data?
What kind of data is used currently and which is to be relied on a new paradigm?
What kind of products are expected from telcos?
My personal ranking of operators in terms of their Big Data execution
What are the stages telcos should pass through to become a Big Data operator?
Prerequisites for Big Data transformation
Please take a look at the presentation to find answers to these questions and feel free to share your opinion.
Thanks!
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
MySQL Cluster 7.4 has been benchmarked executing over 200 million queries per second on commodity hardware. This presentation from Oracle OpenWorld 2015 describes MySQL Cluster's architecture and gives some detail on how this benchmark was achieved, as well as some tips on getting started with MySQL Cluster 7.4.
Norbert Kraft, Referent Research & Technology, Nokia Siemens Networks
Durch die weltweite Verfügbarkeit, Abdeckung und Nutzung sind Mobile Telekommunikationsnetze heute ein typisches Anwendungsgebiet für 'Big Data' und insbesondere für komplexe Datenanalyseverfahren. Norbert Kraft beschreibt in dieser Session Einsatzszenarien dieser Technologien in der Telekommunikationsindustrie anhand konkreter Beispiele, die im Rahmen eines Forschungsprojektes des Zentralbereiches 'Technologie und Innovation' von Nokia entstanden sind. In einen Entwicklungsprototypen wurden hier Möglichkeiten der Netzausfallvorhersage sowie der Ursachenanalyse für solche Ereignisse untersucht und entwickelt. Hierbei kommen unterschiedliche Data Mining und Machine Learning Verfahren zum Einsatz, z.B. (Un-)supervised Learning, Clustering Verfahren für die Klassifizierung von Kundenprofilen oder Radiozellen sowie eine Zeitreihenanalyse zur Vorhersage von Netzausfällen. Eine wichtige Rolle neben der Erkennung von Fehlerszenarien ist hierbei immer die Ermittlung einer möglichen Fehlerursache, wobei der erkannte Netzfehler mit einer Vielzahl möglicher Einflussgrößen (z.B. SW Konfiguration, Lastprofil) korreliert wird.
How telecommunication companies can leverage power Hadoop and Big Data to derive use cases.
Based on Cloudera Whitepaper - Big Data Use Cases for Telcos
Banalytics - Monetizing corporate big data | InstareaMatej Misik
How to use corporate big data for external applications, remain legally and ethically compliant and create a solution with clear public good? At the marketing edition of Banalytics in Bratislava, Matej Misik shared our approach to big data monetization for telcos, banks and other data rich industries.
Instarea is a "laboratory" for innovative big data monetization ideas within the international Adastra group. A young committed team, fresh thinking and a lust for adventure define us as a company. We yearn to change the world for the better through data.
Strategizing Big Data in Telco
Big data feels to be a very hot topic nowadays. Some industries depend on it completely, some have opportunities to roll out their strategies and execute, some just considering when it is a right time to hop in.
To my mind, Big Data is not about technology. Big data is about people generating data and data used for the benefit of people.
Big data is a pool of activities intended at processing the data a company owns (internal and external) so that to open new revenue opportunities, minimize costs and enhance UX.
I had some ideas and thoughts on what telecommunication companies may start from in formulating the Big Data Strategy and so packed some of the most important pieces of thoughts into a small presentation.
What is the difference between Small Data and Big Data?
What kind of data is used currently and which is to be relied on a new paradigm?
What kind of products are expected from telcos?
My personal ranking of operators in terms of their Big Data execution
What are the stages telcos should pass through to become a Big Data operator?
Prerequisites for Big Data transformation
Please take a look at the presentation to find answers to these questions and feel free to share your opinion.
Thanks!
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
MySQL Cluster 7.4 has been benchmarked executing over 200 million queries per second on commodity hardware. This presentation from Oracle OpenWorld 2015 describes MySQL Cluster's architecture and gives some detail on how this benchmark was achieved, as well as some tips on getting started with MySQL Cluster 7.4.
Norbert Kraft, Referent Research & Technology, Nokia Siemens Networks
Durch die weltweite Verfügbarkeit, Abdeckung und Nutzung sind Mobile Telekommunikationsnetze heute ein typisches Anwendungsgebiet für 'Big Data' und insbesondere für komplexe Datenanalyseverfahren. Norbert Kraft beschreibt in dieser Session Einsatzszenarien dieser Technologien in der Telekommunikationsindustrie anhand konkreter Beispiele, die im Rahmen eines Forschungsprojektes des Zentralbereiches 'Technologie und Innovation' von Nokia entstanden sind. In einen Entwicklungsprototypen wurden hier Möglichkeiten der Netzausfallvorhersage sowie der Ursachenanalyse für solche Ereignisse untersucht und entwickelt. Hierbei kommen unterschiedliche Data Mining und Machine Learning Verfahren zum Einsatz, z.B. (Un-)supervised Learning, Clustering Verfahren für die Klassifizierung von Kundenprofilen oder Radiozellen sowie eine Zeitreihenanalyse zur Vorhersage von Netzausfällen. Eine wichtige Rolle neben der Erkennung von Fehlerszenarien ist hierbei immer die Ermittlung einer möglichen Fehlerursache, wobei der erkannte Netzfehler mit einer Vielzahl möglicher Einflussgrößen (z.B. SW Konfiguration, Lastprofil) korreliert wird.
Telcos are challenged in their business. Telephony becomes a commodity. How to leverage new business? Data use is key for the future business and analytics is the way to do it. This presentation shows a high-level picture on analytics.
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
San Antonio’s electric utility making big data analytics the business of the ...DataWorks Summit
Being part of a municipality-owned electric utility offers a unique opportunity to lead in the area of big data analytics. What moves the electric utility of the 7th largest city in the U.S.? The answer is, people. For years, CPS Energy has invested in development of local talent, local technology development, city growth, its employees, and an asset infrastructure that is setting the stage for continued success. At CPS Energy, when such investments are topped by a data infrastructure and applications conducive to creation of business insights, we can justify and prioritize investments. For us, the biggest people opportunities in big data analytics are around operations, customer and employee engagement, and safety. The presenter will provide examples and share how his views have evolved from those of a researcher to global renewable energy consultant to technology innovator and more recently a “harvester of value” from within people, process, and technology assets. Lastly, current and anticipated future states with regards to San Antonio’s electric utility big data enablement platform will be presented...
Speaker
Rolando Vega, Manager of Analytics and Business Insight, CPS Engery
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
Monetizing Big Data with Streaming Analytics for Telecoms Service ProvidersCubic Corporation
The SQLstream Blaze (http://www.sqlstream.com) real-time data hub enables telecommunication service providers to leverage their streaming Big Data, and to integrate and analyze streams of CDR, device, network and service data in real-time. Streaming analytics and automated actions can be used to optimize service and network performance in real-time, optimize Customer Care workflows for efficient troubleshooting and reduced costs,and real-time fraud detection and prevention from CDR analytics. The result is improved operational efficiency, better delivered services and an customer satisfaction.
Nokia On Analyzing, With Wisdom, The Cognition Of The CrowdRomana Hai
Crowds draw crowds, and for retailers, drawing a crowd means drawing dollars. In an interview with PYMNTS’ Karen Webster, Shelley Schlueter, who heads Nokia’s analytics marketing ops, delves into how Nokia’s Cognitive Analytics for Crowd Insight can offer companies real-time knowledge about who wants what and when — and maybe even why.
Who changed my data? Need for data governance and provenance in a streaming w...DataWorks Summit
Enterprises have dealt with data governance over the years, but it has been mostly around master data. With the advent of IoT/web/app streams everywhere in the ecosystem surrounding an enterprise, data-in-motion has become a strong force to reckon. Data-in-motion passes through several levels of transformations and augmentation before it becomes data-at-rest. Through this, it is pertinent to preserve the sanctity of such data or at least track the provenance through the various changes. This is very important for a lot of verticals where there are strong regulatory and compliance laws that exist around "who changed what."
This session will go into detail around some specific use cases of how data gets changed, how it can be tracked seamlessly and why this is important for certain verticals. This will be presented in two parts. The first part will cover the industry angle to this and its importance weighed in by several regulatory bodies. The second part will address the technology aspect of it and discuss how companies can leverage Apache Atlas and Ranger in conjunction with NiFi and Kafka to embrace data governance and provenance of their data streams.
Speakers
Dinesh Chandrasekhar, Director, Hortonworks
Paige Bartley, Senior Analyst - Data and Enterprise Intelligence, Ovum
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
AWS re:Invent 2016: Predicting Customer Churn with Amazon Machine Learning (M...Amazon Web Services
In this session, we take a specific business problem—predicting Telco customer churn—and explore the practical aspects of building and evaluating an Amazon Machine Learning model. We explore considerations ranging from assigning a dollar value to applying the model using the relative cost of false positive and false negative errors. We discuss all aspects of putting Amazon ML to practical use, including how to build multiple models to choose from, put models into production, and update them. We also discuss using Amazon Redshift and Amazon S3 with Amazon ML.
Telcos are challenged in their business. Telephony becomes a commodity. How to leverage new business? Data use is key for the future business and analytics is the way to do it. This presentation shows a high-level picture on analytics.
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
San Antonio’s electric utility making big data analytics the business of the ...DataWorks Summit
Being part of a municipality-owned electric utility offers a unique opportunity to lead in the area of big data analytics. What moves the electric utility of the 7th largest city in the U.S.? The answer is, people. For years, CPS Energy has invested in development of local talent, local technology development, city growth, its employees, and an asset infrastructure that is setting the stage for continued success. At CPS Energy, when such investments are topped by a data infrastructure and applications conducive to creation of business insights, we can justify and prioritize investments. For us, the biggest people opportunities in big data analytics are around operations, customer and employee engagement, and safety. The presenter will provide examples and share how his views have evolved from those of a researcher to global renewable energy consultant to technology innovator and more recently a “harvester of value” from within people, process, and technology assets. Lastly, current and anticipated future states with regards to San Antonio’s electric utility big data enablement platform will be presented...
Speaker
Rolando Vega, Manager of Analytics and Business Insight, CPS Engery
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
Monetizing Big Data with Streaming Analytics for Telecoms Service ProvidersCubic Corporation
The SQLstream Blaze (http://www.sqlstream.com) real-time data hub enables telecommunication service providers to leverage their streaming Big Data, and to integrate and analyze streams of CDR, device, network and service data in real-time. Streaming analytics and automated actions can be used to optimize service and network performance in real-time, optimize Customer Care workflows for efficient troubleshooting and reduced costs,and real-time fraud detection and prevention from CDR analytics. The result is improved operational efficiency, better delivered services and an customer satisfaction.
Nokia On Analyzing, With Wisdom, The Cognition Of The CrowdRomana Hai
Crowds draw crowds, and for retailers, drawing a crowd means drawing dollars. In an interview with PYMNTS’ Karen Webster, Shelley Schlueter, who heads Nokia’s analytics marketing ops, delves into how Nokia’s Cognitive Analytics for Crowd Insight can offer companies real-time knowledge about who wants what and when — and maybe even why.
Who changed my data? Need for data governance and provenance in a streaming w...DataWorks Summit
Enterprises have dealt with data governance over the years, but it has been mostly around master data. With the advent of IoT/web/app streams everywhere in the ecosystem surrounding an enterprise, data-in-motion has become a strong force to reckon. Data-in-motion passes through several levels of transformations and augmentation before it becomes data-at-rest. Through this, it is pertinent to preserve the sanctity of such data or at least track the provenance through the various changes. This is very important for a lot of verticals where there are strong regulatory and compliance laws that exist around "who changed what."
This session will go into detail around some specific use cases of how data gets changed, how it can be tracked seamlessly and why this is important for certain verticals. This will be presented in two parts. The first part will cover the industry angle to this and its importance weighed in by several regulatory bodies. The second part will address the technology aspect of it and discuss how companies can leverage Apache Atlas and Ranger in conjunction with NiFi and Kafka to embrace data governance and provenance of their data streams.
Speakers
Dinesh Chandrasekhar, Director, Hortonworks
Paige Bartley, Senior Analyst - Data and Enterprise Intelligence, Ovum
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
AWS re:Invent 2016: Predicting Customer Churn with Amazon Machine Learning (M...Amazon Web Services
In this session, we take a specific business problem—predicting Telco customer churn—and explore the practical aspects of building and evaluating an Amazon Machine Learning model. We explore considerations ranging from assigning a dollar value to applying the model using the relative cost of false positive and false negative errors. We discuss all aspects of putting Amazon ML to practical use, including how to build multiple models to choose from, put models into production, and update them. We also discuss using Amazon Redshift and Amazon S3 with Amazon ML.
Roadmap to realizing the value of telco data – opportunities, challenges, use...Flytxt
Roadmap to Realizing the Value of Telco Data – Opportunities, Challenges, Use Cases- By Srinivasa Ravi, COO, Flytxt at Big Data Monetization Event Singapore 2015
Mobile Communication and Big Data by Prof. Richard Lingwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Mr. Mayank Sahai presented at SAS Forum 2011 - one of the largest Analytics conference in India. He enlightened the audience on the role Analytics plays in Customer Management and organizations can maximize the value
Layering Common Sense on Top of all that Rocket Science by Prof. Sharon Dunwoodywkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Telcos are facing mounting pressure to dramatically increase speed to market and cut costs. But how?
What if you could go to market in half the time using prebuilt libraries of telco offerings—and leveraging the cloud to lower costs?
In this presentation, find out how Capgemini’s end-to-end solution for telcos uses a hybrid cloud and the Oracle Communications Rapid Offer Design and Order Delivery (Oracle Communications RODOD) stack to provide a competitive edge in today’s tough, dynamic environment.
Learn how to accelerate digital transformation, increase agility, and simplify business to better respond to customer expectations and address growth opportunities. See a concrete demonstration of the solution and its best-in-class CX capabilities, processes, and deployment and run services.
First presented at Oracle OpenWorld 2015.
Telco Paper by Blueocean Market IntelligenceCourse5i
At Blueocean, we are committed to work with large telecom providers who want to go for omnichannel experience for their end consumers.
To learn more about our Digital Customer Experience solution and how it can integrate with your existing technology infrastructure, go through this Short Paper on Telco Industry Solutioning.
Patient Powered Research with Big Data and Connected Communities by Assoc. P...wkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Words and More Words: Challenges of Big Data by Prof. Edie Rasmussenwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Brand Building in the Age of Big Data by Mr. Gavin Coombeswkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
La telefonía móvil como fuente de información para el estudio de la movilidad...Esri España
Existe una multitud de sectores donde es necesario disponer de datos que permitan entender los patrones de comportamiento de la población: la planificación y la operación de los sistemas de transporte requiere información precisa, fiable y actualizada sobre la demanda de viajes; los patrones de actividad y movilidad de los turistas tienen profundas implicaciones para la planificación de infraestructuras, el desarrollo de la oferta turística y las estrategias de marketing turístico; entender el comportamiento espacial de los clientes es clave para optimizar las estrategias de distribución, comercialización y publicidad, determinar la localización de un nuevo comercio o punto de venta, o maximizar el retorno de la inversión en acciones de marketing. Las fuentes de datos tradicionales, basadas fundamentalmente en encuestas y registros administrativos, proporcionan información muy valiosa, pero no están exentas de inconvenientes. En general, las encuestas resultan caras y lentas de realizar, lo que limita el tamaño de la muestra y la frecuencia de actualización de la información, a lo que hay que añadir otras limitaciones intrínsecas, como las respuestas incorrectas e imprecisas, o la dependencia de la disposición a responder de los entrevistados. En los últimos años, la generalización del uso de dispositivos móviles ha abierto nuevas oportunidades para superar muchas de estas limitaciones. La posibilidad de recoger datos geolocalizados sobre la actividad de las personas, de manera dinámica y a un coste sensiblemente inferior al de los métodos tradicionales, abre la puerta a infinidad de aplicaciones. Las más evidentes son quizá las relacionadas con el transporte y la movilidad, pero el abanico es mucho más amplio, abarcando casi cualquier área que requiera información sobre los patrones de actividad y movilidad de la población. Las nuevas fuentes de datos plantean asimismo importantes retos, desde la necesidad de desarrollar nuevas metodologías de análisis, hasta la protección de la privacidad.
Vídeo de la ponencia: https://youtu.be/5PKC5Qm0eHM
Delivered by Venkata Rangarajan, Orzota India Development Center
This was delivered as a open talk at an academic institution as a walk through for the students of Communication Engineering on Big Data Applications in Wireless Communication Industry. The Talk has derived its contents from a variety of research articles and technical papers to put forth the potential areas for deployment.
Author duly thanks the authors and publishers of the articles and papers from which some of the artifacts have been derived or republished.
The contents of this document are solely that of the author and does not represent the views or opinions of the organization he is representing.
Sensing-as-a-Service - An IoT Service Provider's PerspectivesDr. Mazlan Abbas
UM-MCMC Connected Communities and Internet of Things (IoT): Building Value through Visibility
at Universiti Malaya (UM)
Wednesday, December 10, 2014 from 8:00 AM to 4:00 PM (MYT)
Kuala Lumpur, Malaysia
Mr. Paul Chang's presentation at QITCOM 2011QITCOM
QITCOM 2011
Presentation:
City Operations Centre for Managing City
Presenter:
Mr. Paul Chang - Business Development Executive for Emerging Markets, IBM
In this presentation how cloud is useful in big data analytics.It givers brief introduction to cloud service models and Big data 4V's.Here I'm describing how cloud is used in telecom and finance domain. How it is better than traditional methods.
CITE Start Thinking Big Data 2019 01-30 FINALJon Kostyniuk
Whatever the size or type of organization, Big Data has permeated our transportation industry. It is no longer a question of IF Big Data will be useful, but instead WHY is it useful and HOW can we best apply it. This presentation aims to address how we can leverage existing services and available partnerships in transportation, consider new and emerging technologies, and determine strategy for what’s to come in transportation, including connected and autonomous vehicles. While it may be a huge challenge to solve transportation problems with Big Data, it can help us make better travel decisions today and plan for better infrastructure tomorrow.
With collaborations with various City divisions and private service providers (in this case Streetlight data providers), our North York mobility innovation team uncovered several surprising suburban travel behaviour, patterns and distributions of trips that lead to meaningful and quantitative multimodal mobility planning. This presentation is a summary of project experiences and describes the key findings.
In these slides, we explore the unique challenges that mobile data present. The high cardinality, low signal to noise ratio and realtime needs have significant system implications. We outline how InMobi tackles these challenges. A specific Data Science use case is also presented. We outline our approach to user segmentation. A brief description of the challenges faced and our attempts to address them is also included.
Improve site selection and network planning with Dynamic DemographicsPrecisely
COVID-19 has changed consumer behaviour. Where and how people work and shop has changed considerably since the pandemic began. Methods used previously to determine where to expand (or rationalize) your business may not be effective in today’s environment. Insight into human movement patterns, demographics and customer spend behaviour is essential when making key business decisions around site selection and network planning.
Watch this webinar recording to hear about a new solution for Australian businesses that combines human movement data sourced from mobile phones, with demographic data (such as age, gender, income, occupation, and spending behaviour) to help businesses understand:
· The feasibility of a new store, branch location or product offering mix
· The demographics of people in a location by time of day
· The movement patterns of your customers (where they come from, where they go, how long they spend there and where else they go)
· Opportunities to improve customer targeting and go-to-market planning (deliver the right message, in the right place, at the right time)
Similar to A Big Data Telco Solution by Dr. Laura Wynter (20)
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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How to Make a Field invisible in Odoo 17Celine George
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2. The Global Reach of IBM Research
IBM Research Labs
IBM Research – Openings in 2011
IBM Research – Openings in 2012
China
WatsonAlmaden
Austin
Tokyo
Zurich
India
Dublin
Australia
Brazil
Africa
Next Gen Public Sector
Water & transportation
Human Capacity Development
Natural Resources
Disaster management
Healthcare/Life Sciences
Natural Resources
Smarter Devices
Human Systems/Events
Analytics & Intelligence
Systems &Software
Industry research
Internet of Things
Big Data / Analytics
Enterprise Cloud Services
Energy, Commerce, Traffic
Big Data Analytics
HW & SW Quality
Cloud
Mobile
Haifa
Smarter Cities
Analytics
Services
Big Data Analytics
Front Office Digitization
Semiconductors
Systems
Software
Services
Analytics
Semiconductors
Processors
Analytics
Storage
Nanotech
Healthcare
Nanotech
Security
Business
Analytics
Systems
Industry Solution Lab
Singapore
•Analytics
3. IBM Research Singapore Collaboratory: who we are
3
Statistics
Transportation science
Data mining & management
Computer science
Optimization
Computational
science
A team of research scientists and research software engineers with expertise in mathematical &
computer sciences, a branch of our global IBM Research presence
IBM Confidential
4. Telco Data Monetization
• Telcos have lot of data about their customers from daily operations –
especially location and movement data.
• Our objective is to build an asset for Telcos to leverage these data about
their customers to enable emerging new market opportunities.
• Key to such data monetization is the ability to connect different data pieces
to better understand customers, their preferences, life style, intent etc.
5. Two Scenarios: Aggregate and Individual
• Aggregate Anonymized Analytics: Sensing
City-scale people movement from Telco data
and leveraging for Transit Optimization
• Enriched Consumer Profile: Customer
Analytics with Mobility Profiles from Telco
Data
6. Let’s review the potential areas of Business Benefit of Big Data for Vodafone
GPS
External Data
Customer Service
Representatives
... could offer
personalized price
promotions to different
customer segments in
real-time
Business Development
... could find new mechanisms
to monetize network traffic and
partner with upstream content
providers
Network Operations
... could identify network bottlenecks in
real-time for faster resolution
Executive Leaders
... could get real-time reports and
analysis based on data inside as well as
outside the enterprise (web, social
media etc.)
Business Analysts
... Could analyze social
media buzz for the new
services/offerings to gauge
initial success and any
course correction needed
Finance
... could analyze all Call Detail
Records (CDRs) to identify and
reduce revenue leakage due to
unbilled / underbilled CDRs
Marketing
... could analyze subscriber usage pattern
in real-time and combine that with the
profile for delivering promotional or
retention offers
What if …
7. A data sharing platform should capture and structure location, time and content
about the consumer from multiple industries to drive profitable consumer
actions
Structured
Repeatable
Linear
Monthly sales reports
Profitability analysis
Customer surveys
Other
Industries
Other
Data
Industry Reports
Retail
Social
Media Data
Customer
• Segment
• Social Network
• Demographics
• Sex, Age Group, etc
• Tenure
• Rate plan
• Credit Rating, ARPU
Group
Device
•Class
•Manufacturer
•Model
•OS
•Media Capability
•Keyboard Type
Transactions
• Voice, SMS, MMS
• Data & Web Sessions
• Click Streams
• Purchases
• Downloads
• Signaling,
Authentication
• Probe/DPI
Network
• Availability
• Throughput/Speed
• Latency
• Location
• Facilities Interface
• Discovery
• Navigation
• Recommendations
Product/Service
• Subscriptions
• Rate Plans
• Media Type
• Category/Classification
• Price
Starts, Stops
Success Rates
Errors
Throughput
Setup Time
Connection Time
Usage
Recency
Frequency
Monetary
Latency
Telco Data Cross Industry Data
8. Enriched Consumer Profiles for
Enabling Telco Data Monetization
• We develop enriched consumer profiles by
deriving insights about consumer preferences,
life style, and intent from location, mobility and
call data joined with use case appropriate data
sources.
• Enriched consumer profiles are utilized to
enable new services and effective campaign
through targeted segmentation.
9. Two Scenarios: Aggregate and Individual
• Aggregate Anonymized Analytics: Sensing
City-scale people movement from Telco data
and leveraging for Transit Optimization
• Enriched Consumer Profile: Customer
Analytics with Mobility Profiles from Telco
Data
10. Sensing City Scale People Movement from Telco Data
Cities Demonstrated: Istanbul (Turkey), Dubuque (USA) for Transit Optimization
and a series of subsequent client pipeline
Challenge Cities have very little real understanding of where citizens, goods and
transportation move during the day. Without this information it is difficult to
accurately plan and manage the usage of roads and infrastructure.
Solution Using a variety of real time data from “smart phones”, GPS devices, terminals,
traffic cameras, public transportation schedules and transit data, develop models
of zonal density, flow of goods and origin / destination pairs. From these models,
drive processes to manage this flow against a specific objective.
Benefits Evaluates the efficacy of existing transit system and transportation infrastructure;
provides the structure for design incentive strategies to win new riders –
information, incentives, services; optimize fleet operations in situations where
demand outpaces supply; manage revenue through better zoning and permits.
comprehensive solution that will address the management of congestion, fleet
management, people attending events, and multimodal transit
10
12. Example Challenges
Objective: Derive people movement model from tower level information
(communication between cell phone and tower)
Key Challenges
• CDR data is typically sparse
– Uncertainty both in space and time domain
– Location/movement from sparse and often incorrect (tower information) information
• Tower oscillation is very common in cellular network
• Typically only short term (e.g. one week) data is available due to various privacy regulations
Figure: Example for CDR and GPS. Left: CDR with tower oscillation; Right: GPS points
13. 13
Meaningful Location Detection and O/D Estimation
• Meaningful locations are the locations
where people spend a significant
amount of time, e.g. home, work, mall.
• Duration of stay (dos) is used to
measure how meaningful each cluster is.
– i.e. Given a threshold (e.g. 30 min), if the
duration of stay (dos) in a cluster is more than
the threshold, then the location of the cluster
locates is a meaningful location.
• Home and work can be identified by
selecting the locations with the largest
accumulated dos in the night time and
day time of week days.
• After meaningful locations detection,
users’ traces are described in a set of
meaningful locations.
• Trips and O/D pair can be segmented
from users’ trace on these meaningful
locations.
• For example:
14. Identifying Meaningful Locations
Where People Live Where People Work
Istanbul Movement Analysis
- 4.7 million phones w. 3B+ events/week
- Accurate detection of home, work & meaningful
locations
15. Traffic Monitoring
Uses basic analytics building blocks already seen to display time based traffic flow levels
mapped to city road system. A snapshot at 8:30am:
15
19. Feeder Bus Route Optimization for M4 Metro Line on
Anatolian side of Istanbul
Feeder bus routes based on demand to 4 metro stations on Kadikoy-Kartal metro line
20. Optimal Bus Stop Location Design
• Stops are added by considering
the greatest potential demand
for transit and accessibility at
origin and destination
• Some stops are added to far
places in which demand to the
area already served by existing
stops is potentially large
20
21. 21
Clean sheet Optimization of Bus
Routes based on Demand Models
Clean sheet optimization to
minimize opex, unmet demand
and travel time
Constraints include fleet size,
max transfers, duration, etc.
Optimal routes can
• reduce OPEX cost up to
40%
• reduce unmet demand by
37%
• reduce avg. travel time from
37 minute average to 10-22
minute average
22. Two Scenarios: Aggregate and Individual
• Aggregate Anonymized Analytics: Sensing
City-scale people movement from Telco data
and leveraging for Transit Optimization
• Enriched Consumer Profile: Customer
Analytics with Mobility Profiles from Telco
Data
23. Consumer Analytics with Enhanced Consumer Profiles
• Derive advanced location/mobility attributes and patterns
from Telco data to enrich consumer profiles with mobility
context
• Derive predictive model about consumers location and
mobility patterns
• Leverage enriched consumer profiles for data monetization
opportunities by correlating and joining other data sources
• Build an operational asset on IBM Big Data platform to enable
Telco to extract mobility attributes and patterns efficiently
24. Set of example mobility attributes
• Base set of example mobility attributes
–Home and work location
–Weekday top locations
–Weekend top locations
–Meaningful location detection
–Classification of where and when time spent
–Detecting tourism pattern
–Detecting specified habits related to mobility
– Trip purpose
–Anomaly in mobility from baseline patterns
–Detecting who’s who in the household based on mobility pattern
• Advanced predictive models (Next Best Location)
–Likely place a person would be at a future time
–Likelihood of a person going to a Mall during this weekend
–When this person is likely to be a tourist
25. Determining Buddies, Hangouts, Life Style
Example Lifestyle Attributes for marketing demonstration
Subscriber Lifestyles
Popular Locations
Subscriber Pairings
Who Are You?
Homebody
Daily Grinder
Delivering the Goods
Globetrotter
Nomad
10 Top Hangouts
Best Buddies
Next Steps
• Given the lifestyles, popular locations, and best buddy data => predict where individuals or
groups of similar individuals will be and when.
• Use time series modeling and clustering we can create time/location based marketing
campaigns targeted at homogenous groups in specific locales.
26. 26
Enhanced Micro-segmentation with Mobility Model
Mobility
Patterns
Mobility Model
•Location and movement pattern
(space, time)
•Meaningful location detection
•Meaningful location
classification
•Trip purpose
•Estimated Duration of stay
•Estimated Duration of travel
•Mode of travel
•Calling patterns
•Detecting tourist patterns
•Detecting student patterns
•Estimated demographic profile
of user of phone
•Anomalies in regular patterns
Example Data Monetization Use Cases
Telcos cannot assume that
person who buys phone
is the user. Discovering profile
of actual user is helpful in
retail & marketing
Smarter LBS would take movement
patterns (i.e, likely to be in a shopping complex on
Saturday afternoon etc.) into account instead of merely
using momentary location
Telcos can find out inter-city travel
patterns which are helpful to T&T
Banks can correlate ATM usage with
Movement patterns for better mgmt
Life style and brand preference
determination from mobility data
for targeted segmentation
28. 28
Enhanced Micro-segmentation with Mobility Model
Mobility
Patterns
Buying
Patterns
Social Patterns
Demographics
•Gender
•Age group
•Address
•Income
Historical buying patterns
Social network
influencers
Mobility Model
•Location and movement pattern
(space, time)
•Meaningful location detection
•Meaningful location
classification
•Trip purpose
•Estimated Duration of stay
•Estimated Duration of travel
•Mode of travel
•Calling patterns
•Detecting tourist patterns
•Detecting student patterns
•Estimated demographic profile
of user of phone
•Anomalies in regular patterns
Enhanced Attributes for Customer Segmentation
29. Building Context and Intent from
Location data• Deriving location: location information may be derived using multi-modal
information
– CDR data, tower data, device data, Wi-fi etc.
– Accuracy of location information depends on data fidelity etc.
• Building context: making sense of the location information
– Correlate location information with business data
– Various other correlation rules may be used to build a rich context
• Inferring intent: infer consumer level intents by leveraging location and
mobility patterns
Deriving Location Inferring IntentBuilding Context
30. Enriched Consumer Profile Hub
Customer Profile Hub
IPTV
- Subscription Billing
-VOD Billing & viewed
- channel viewing history
-- contents purchased
-Logs & Tuning Events
- package subscription
Mobile
- Location
- URL+App Transactions
- xDRs and inb. roaming
- RAN (incl. HLR/VLR)
- Top Up
- Pkgs
- Billing
- SMS, browing URLs
Other:
- Devices
- Dealer Network
- Contact Center
- Call Recordings
- Trouble Tickeing
- Campaign Results (Imagine)
- Loyalty
- Competition Website
- Retail Transactions
Fixed
- CDR
- URL (IP)
-Radius (IP-Cust)
- Pkgs
- Billing
Historical
Transactions/
Events
Partners/Retailers
Advertisers
Other/Internal
GIS
- Business map and numbers
- Point of Interest maps
ConsumersofnewInsights
Feedback
Social
Media Data
31. 1
2
3
Advanced Analytics Platform
End-use
Applications
Analytics
Visualization
Big Data Analytics
Warehouse
Predictive Analytics
Sens
e
Analyze Act
Search / Explore
KPIs
Dashboards
Drill-Downs
Reports
Marketing
Campaigns
Rules Engine
Behavioral
Analysis
Outcome
Optimization
Propensity
Scoring
Model
Creation
Structured /
Unstructured
Data
Data Governance
Data Integration
ETL/ELT
ChangeCapture
DataQuality/Validity/Security-Privacy
Format/UnitConversion
Consolidation/De-duplication
DataRepositories
Network
Data
Customer
Behavior Data
Customer
Data
ProductDataNetworkTopology
Data
ContinuousFeed
Sources
Usage Data
Reference
Data
Historical
Analysis Data
Demographics
Segmentation
Location
Past Actions
Propensity
Scores
Behaviors
Predictive Model
Deployment
Actionable
Insight
Stream Processing
Streaming Data
Operational
Systems
4
5
AAP Capabilities
High Performance Historical analysis (Big Data Platform)
Model Based Analytics - behavioral scoring, micro segmentation,
correlation detection analysis
Real-time scoring, classification, detection and action
Visualize, explore, investigate, search and report
Take action on analytics
IBM’s Advanced Analytics
Platform (AAP) Supports Use
Cases across the business with
New Era Capabilities
Create new Services and
Business Models Transform Operations
Build Smarter
Networks
Personalize Customer
Engagements
1
1
2
3
4
5
5
32. Retailer Customer Profile
Real Time Targeted Advertisement for IPTV
AAP
(Advanced
Analytics
Platform)
3- AAP catches the
new football interest
flag, his frequent
sports shopping, and
realtime matches
Tom’s profile with an
offer for 20% off
coupon to an Nike
store.
4- Tom is also an
existing SMS Opt-
In mobile cust.
5– Tom receives
targeted IPTV
advertisements based
on his IPTV, mobility
and social profiles
2- Tom is channel surfing,
mostly sports channels,
primarily football games where
Nike advertises a lot (AAP enhances
his customer profile, after 10 football
games viewed in 1st month,
with an interest flag as a “football fan”)
Enhanced Cust. Profile
Interest / Mobile # / Email
1- Tom activates IPTV service
with the America 50 package and
adds the ESPN sports ala carte
option (we have an initial
customer profile with his fixed #
and a mobile#)
A la carte option
Sports Packages
tom@gmail.com
212-201-1234
Language
Package
33. Location Based Real Time Offering on Mobile Phone
Lisa
4- AAP catches that
Lisa is entering a mall,
and matches her
“Fashion” interest flag
and “Perfume”
preference, realtime
with an offer for 20%
off coupon for Byonce
fragrance at Sephora
in that mall.
5 - Lisa receives
an SMS/email/App
notification that
her mobile app
account contains a
new offer for
Beyonce perfume.
Beyonce Fan Page
2- She follows a
friend’s post on FB and
clicks the Like button on
the Beyonce Fan Page.
3- Lisa’s IPTV viewing
& mobile clickstream
behaviors set her Interest
flag to “Fashion” and one
preference to “Perfume”.
6- Lisa uses
the mWallet
app on her
smartphone to
purchase some
perfume at POS
via NFC.
1- Lisa is a mobile subscriber
with Telco and downloads the
mobile app and agrees to receive
offers related to her interests.
AAP
(Advanced
Analytics
Platform)
Retailer Customer Profile
Enhanced Cust. Profile
Interest & Preference
IPTV a la carte option &
Mobile Features/Apps
IPTV Lang
Pkg &
Mobile Pkg