IoT is no longer the “next big thing” – it’s here, real, and transforming the world. McKinsey estimates that 127 devices are connected to the Internet every second; Statista expects 75B devices to be connected by 2025; and Gartner predicts that by 2023, more than 50% of enterprise-generated data will be created and processed outside of the data center or cloud. Yet the adoption of IoT technologies by the industry is moving slower than expected. While this can be attributed to multiple factors, the capability (or lack of) to cope with “data deluge” – how to store and manage data efficiently as well as to make sense of them – stands as a key one.
To support efficient storage and management of the vast data collected from IoT devices, the market has created solutions on two fronts: cloud infrastructure and embedded storage in edge devices. Even though, challenges still linger as for how to best distribute and process the data in the spectrum of varying computing power, storage cost, power consumption, and communication latency in the IoT networks. This has to be optimized to meet the application requirements and system constraints. The emerging Distributed Cloud concept, which seeks to extend cloud infrastructure to the network edge to provide a uniform environment in which to manage data and deploy analytics, may help to address some of the challenges.
Data provides no value unless we transform it into actionable insights. To that end, IoT practitioners have been utilizing data analytics tools to unlock the value hidden inside the seemingly chaotic data vaults. And let’s not lose sight of the ultimate goal of IoT – to help make better decisions, whether it’s improving user experiences, optimizing manufacturing, or personalizing marketing campaigns. The advance in data analytics, including AI, has produced early encouraging results.
In this track, we will focus on the challenges and opportunities surrounding, but not limited to, the following themes: IoT data storage and management, distributed cloud, predictive analytics including AI and ML, data quality and trustworthiness, and data governance, data gravity and data privacy.
Chung-Min Chen, Lutron Electronics
Chung-Min Chen is currently with Lutron where he provides data science consulting in various business areas. Prior to that he was VP of Data Science at Sidecar Interactive, a startup leveraging machine learning in digital marketing, and Sr. Director of Data Science and Analytics at iconectiv (an Ericsson company) where he helped build Ericsson’s analytics products that enable telecom carriers to better understand and manage mobile user experiences. He had also held research and management positions with Bellcore, Telcordia, and subsequently Applied Communication Sciences.
His interests span across database systems, distributed and parallel computing, mobile computing, and machine learning. He has published over 50 papers in leading ACM and IEEE journals and conferences including JACM, TOSN, TKDE, TMC, TON, JSAC, SIGKDD, PODS, SIGMOD. He received a PhD in CS from University of Maryland, College Park and BS in CSIE from National Taiwan University.
Mark Cusack, Yellowbrick Data
Mark Cusack is the CTO at Yellowbrick Data, which offers a high-performance MPP data warehouse for the hybrid cloud. Prior to joining Yellowbrick, Mark was vice president for data and analytics at Teradata, with lead product management responsibility for the data warehouse and machine learning portfolio. During his six-year tenure at Teradata, Mark also led the product management function responsible for the data warehouse ecosystem of products, supporting data loading, data virtualization, near real-time streaming, monitoring and management, and application development. He was chief architect of Teradata’s IoT analytics effort, applying novel machine learning approaches to automate the management of workloads and the detection of anomalies based on real-time analysis of telemetry data. Mark holds a PhD in computational physics from the University of Newcastle, UK.
Alok Srivastava, Lead IoT Architect, Microsoft
Alok Srivastava has been with IoT devices and AI technologies for a long time. Over years, he has learned nuances of IoT solutions at scale. He has worked on very large scale IoT and AI powered business process optimization, critical decision support system, fully automated alerting systems and many other IoT architectures. His background in electrical engineering with biomedical sensors, robotics, AI and distributed computing had finally converged to enable him in designing cloud powered intelligent solutions. Alok has been a lead IoT architect at Microsoft for many years. He has electrical engineering degree from IIT Kanpur and MS degree in distributed computing from University of Louisville.
Title: Distributed intelligence from Cloud to Edge as a key enabler for Intelligent Edge roadmap for IoT
Abstract: In past couple of years, intelligent edge has become a key discussion and focus of many product offerings in the industry. While the concepts are not new; at scale implementations are now taking shape and true distributed intelligence is driving a lot of technical advancements. IoT sensors were always at the edge and were being connected to cloud to power intelligence that learned from data piping in from various environments across the globe. It did not take long before the true challenges of scale and distances finally took root. Sub seconds decisions with long distance networks connecting to cloud posed serious problems giving rise to intelligent edge. In this talk, we will talk about intelligent edge, what this means, what design challenges it presents and where intelligent edge powered by cloud is potentially headed.
David Lu, Vice President, Network Systems & Automation, AT&T Labs
David Lu currently leads a global team of more than 2,500 people responsible for the architecture, development and engineering of AT&T’s next generation SDN (Software Defined Network) automation platform and open source (Linux Foundation ONAP) enabling AT&T’s network virtualization and target network systems transformation including PaaS & SaaS, policy control & orchestration, hyper-automation and AI/ML driven data analytics.
David is a well-respected leader in large scale, real time software architecture and development, network performance and traffic management, work flow and policy-controlled automation, large databases and big data implementation/mining/analytics, machine learning, artificial intelligence, software reliability and quality, and network operations process engineering. He has led major software platform transformation initiatives from sales to network, service delivery/assurance and billing platforms. Examples of his achievements include large scale platforms he has led and engineered that process annually: 984 Trillion network performance events and 348 Billion alarms with 99.99%+ automation; 60 Million dispatches with 14.4 Billion automated manual steps; and over 90 Billion API transactions.
Since joining AT&T Bell Labs in 1987, David has served in various leadership positions at AT&T. He has led numerous automation initiatives in AT&T that resulted in multibillion-dollar savings over the past 20 years and won AT&T’s CIO 100 Award in 2010. David holds 54 patents and frequently appears as a guest speaker at technical and leadership seminars and conferences throughout the world. He has received numerous industry awards including the 2015 Chairman’s Award from IEEE Communication Society for Network and Systems Quality and Reliability and 2017 CIE AAEOY (Asian America Engineer of Year) Award. David is very active in community organizations and activities including AT&T InspirAsian, AT&T OASIS, AT&T Women of Technology, DFW-CIE and DFW Asian American Chamber of Commerce. He was recognized by AT&T InspirAsian with the 2015 Corporate Leadership Award. David is an acclaimed cellist, passionate about technology and mentoring and a very proud husband and father.
Talk Title: Converged Network for Next Generation IoT
Abstract: The rapid advancement in mobile and broadband technology has opened previously unimaginable new use cases. Technology advancements including the convergence of hardware and software, physical and virtual devices, open architecture, industry based APIs, ultrafast speeds with extremely low latency, high capacity, improved reliability and security, and AI enabled sensors are rapidly changing the business landscape. All of these bring forward unprecedented opportunities for entrepreneurship and new applications.
This talk will focus on the evolutionary path of converged network technologies in the past decade and the journey into the next decade that will unleash the power of innovation with extraordinary speed and scale.
Jacobus Geluk, CEO, Founder, Enterprise Knowledge Graph Foundation & Agnos.ai
Jacobus Geluk has worked on the definition of what an Enterprise Knowledge Graph (EKG) is for more than a decade and delivered multiple EKGs in production at large institutions such as the first EKG at scale in the financial industry (BNY Mellon, 2016). In 2019 he founded the Enterprise Knowledge Graph Foundation (ekgf.org) which is currently supported by all major vendors in the semantic technology industry.
Title: Knowledge Graphs and a foundational approach to Data – The Data Point Protocol