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New Era in Distributed Computing with Blockchains and Databases

New Era in Distributed Computing with Blockchains and Databases

Dr. C. Mohan

Date: Tuesday, June 27th, 2017

Speaker: Dr. C Mohan, IBM Fellow and Former IBM India Chief Scientist 

Time: 6:30 PM (PT) Networking/Refreshments,
7:00 PM Presentation.

Location: Texas Instruments
Silicon Valley Auditorium
2900 Semiconductor Dr., Building E,
Santa Clara, CA 95051 (map)

Abstract

A new era is emerging in the world of distributed computing with the growing popularity of blockchains (shared, replicated and distributed ledgers) and the associated databases as a way of integrating inter-organizational work. Originally, the concept of a distributed ledger was invented as the underlying technology of the cryptocurrency Bitcoin. But the adoption and further adaptation of it for use in the commercial or permissioned environments is what is of utmost interest to me and hence will be the focus of this keynote. Computer companies like IBM and Microsoft, and many key players in different vertical industry segments have recognized the applicability of blockchains in environments other than cryptocurrencies. IBM did some pioneering work by architecting and implementing Fabric, and then open sourcing it. Now Fabric is being enhanced via the Hyperledger Consortium as part of The Linux Foundation. A few of the other efforts include Enterprise Ethereum, R3 Corda and BigchainDB.

While there is no standard in the blockchain space currently, all the ongoing efforts involve some combination of database, transaction, encryption, consensus and other distributed systems technologies. Some of the application areas in which blockchain pilots are being carried out are: smart contracts, supply chain management, know your customer, derivatives processing and provenance management. In this talk, I will survey some of the ongoing blockchain projects with respect to their architectures in general and their approaches to some specific technical areas. I will focus on how the functionality of traditional and modern data stores are being utilized or not utilized in the different blockchain projects. I will also distinguish how traditional distributed database management systems have handled replication and how blockchain systems do it. Since most of the blockchain efforts are still in a nascent state, the time is right for database and other distributed systems researchers and practitioners to get more deeply involved to focus on the numerous open problems.

Speaker Bio

Dr. C. Mohan has been an IBM researcher for 35 years in the database area, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the ARIES family of database locking and recovery algorithms, and the Presumed Abort commit protocol. This IBM (1997), and ACM/IEEE (2002) Fellow has also served as the IBM India Chief Scientist for 3 years (2006-2009). In addition to receiving the ACM SIGMOD Innovation Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the US and Indian National Academies of Engineering (2009), and was named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. He is currently focused on Blockchain, Big Data and HTAP technologies (http://bit.ly/CMbcDB, http://bit.ly/CMgMDS). Since 2016, he has been a Distinguished Visiting Professor of China’s prestigious Tsinghua University. He has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. Mohan is a frequent speaker in North America, Europe and India, and has given talks in 40 countries. He is very active on social media and has a huge network of followers. More information could be found in the Wikipedia page at http://bit.ly/CMwIkP

Eventbrite - New Era in Distributed Computing with Blockchains and Databases

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Predictive and Prescriptive Analytics Using Machine Learning

Predictive and Prescriptive Analytics Using
Machine Learning

Date: Tuesday, April 18th, 2017

Speaker: Paul Hofmann, Ph.D., CTO at Space-Time Insight

Time: 6:30 PM (PT) Networking/Refreshments,
7:00 PM Presentation.

Location: Cadence / Bldg 10,
2655 Seely Ave, San Jose, CA (map)

Abstract

The complexity, criticality, and real-time demands of the energy sector make it a prime candidate to benefit from applying machine learning. This session presents two case studies of machine learning automating decisions for energy companies.

For the largest windfarm operator in North America, machine learning applies predictive and prescriptive analytics to the complex task of scheduling crews for maintenance and repairs. Automating the scheduling process across multiple windfarm sites saves the operator millions in labor costs per year and frees managers and crews to do actual work. Machine learning also evaluates ever-changing conditions and automatically reschedules workers and tasks as necessary.

For a large European energy company, online machine learning provides a systematic and automated approach to commodities trading, including creating and executing trading strategy and predicting prices.

Speaker Bio

As Chief Technology Officer at Space-Time Insight, Dr. Paul Hofmann draws on more than twenty years of experience in enterprise software, analytics and machine learning. He has held executive roles at BASF and SAP, where he was vice president of R&D. He has conducted academic research at MIT, Technical University in Munich, and Northwestern University. Most recently, Paul served as CTO for Saffron Technology, which is now part of Intel. He is a Computer Science Advisory Board Member at Stony Brook University and on the Advisory Board of the Dean’s Advisory Council at UC Santa Cruz’s Baskin School of Engineering. His Ph.D., in Physics, is from Technical University Darmstadt.

Eventbrite - Predictive and Prescriptive Analytics Using Machine Learning

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Transforming Nanodevices into Nanosystems: The N3XT 1,000X

Transforming Nanodevices into Nanosystems: The N3XT 1,000X

Subhasish Mitra

Date: Tuesday, February 28th, 2017

Speaker: Professor Subhasish Mitra, Robust Systems Group, Stanford University

Time: 6:30 PM (PT) Networking/Refreshments,
7:00 PM Presentation.

Location: Cadence / Bldg 10,
2655 Seely Ave, San Jose, CA (map)

Abstract

The computing demands of future abundant-data applications far exceed the capabilities of today’s electronics, and cannot be met by isolated improvements in transistor technologies, memories, or integrated circuit (IC) architectures alone. Transformative nanosystems, which leverage the unique properties of emerging nanotechnologies to create new IC architectures, are required to deliver unprecedented performance and energy efficiency. However, emerging nanomaterials and nanodevices face major obstacles such as inherent imperfections and variations. Thus, realizing working circuits, let alone transformative nanosystems, has been infeasible.

 

The N3XT (Nano-Engineered Computing Systems Technology) approach overcomes these challenges through recent advances across the computing stack: (a) transistors using nanomaterials such as one-dimensional carbon nanotubes (and two-dimensional semiconductors) for high performance and energy efficiency, (b) high-density non-volatile resistive and magnetic memories, (c) Ultra-dense (e.g., monolithic) three-dimensional integration of logic and memory for fine-grained connectivity, (d) new architectures for computation immersed in memory, and (e) new materials technologies and their integration for efficient heat removal.

 

N3XT hardware prototypes represent leading examples of transforming scientifically-interesting nanomaterials and nanodevices into actual nanosystems. Compared to conventional approaches, N3XT architectures promise to improve the energy efficiency of abundant-data applications significantly, in the range of three orders of magnitude, thereby enabling new frontiers of applications for both mobile devices and the cloud.

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