On Tuesday 08:30 - 10:15, the two keynote addresses are:
1. Engineering Database Hardware and Software Together
Since its inception, Oracle’s database software primarily ran on customer configured off-the-shelf hardware. A decade ago, the architecture of conventional systems started to become a bottleneck and Oracle developed the Oracle Exadata Database Machine to optimize the full hardware and software stack for database workloads. Exadata is based on a scale-out architecture of database servers and storage servers that optimizes both OLTP and Analytic workloads while hosting hundreds of databases simultaneously on the same system. By using database specific protocols for storage and networking we bypass limitations imposed by conventional network and storage layers. Exadata is now deployed at thousands of Enterprises including 4 of the 5 largest banks, telecoms, and retailers for varied workloads such as inter-bank funds transfers, e-commerce, ERP, Cloud SaaS applications, and petabyte data warehouses.
Five years ago, Oracle initiated a project to extend our database stack beyond software and systems and into the architecture of the microprocessor itself. The goal of this effort is to dramatically improve the performance, reliability and cost effectiveness of a new generation of database machines. The new SPARC M7 processor is the first step. The M7 is an extraordinarily fast conventional processor with 32-cores per socket and an extremely high bandwidth memory system. Added to it’s conventional processing capabilities are 32 custom on-chip database co-processors that run database searches at full memory bandwidth rates, and decompress data in real-time to increase memory bandwidth and capacity. Further, the M7 implements innovative fine-grained memory protection to secure sensitive business data.
In the presentation we will describe how Oracle’s engineering teams integrate software and hardware at all levels to achieve breakthrough performance, reliability, and security for the database and rest of the modern data processing stack.
As Senior Vice President of Systems Technology at Oracle, Juan Loaiza is in charge of developing the mission-critical capabilities of Oracle Database, including data and transaction management, high availability, performance, in-memory processing, enterprise replication, and Oracle Exadata. Mr. Loaiza joined the Oracle Database development organization in 1988.
Mr. Loaiza holds BS and MS degrees in computer science from the Massachusetts Institute of Technology.
2. Databases and Hardware: The Beginning and Sequel of a Beautiful Friendship
Top-level performance has been the target of 40 years of VLDB research and the holy grail of many a database system. In data management, system performance is defined as acceptable response time and throughput on critical-path operations, ideally with scalability guarantees. Performance is improved with top-of-the line research on fast data management algorithms; their efficiency, however, is contingent on seamless collaboration between the database software and hardware and storage devices. In 1980, target was to minimize disk accesses; in 2000, memory replaced disks in terms of access costs. Nowadays performance is synonymous to scalability; and scalability, in turn, translates into sustainable and predictable use of hardware resources in the face of embarrassing parallelism and deep storage hierarchies while minimizing energy needs — a multidimensionally challenging goal.
I will discuss the work done in the past four decades to tighten the interaction between the database software and underlying hardware and explain why, as application and microarchitecture roadmaps evolve, the effort of maintaining smooth collaboration blossoms into a multitude of interesting research questions with direct technological impact.
Anastasia Ailamaki is a Professor of Computer and Communication Sciences at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. Her research interests are in data-intensive systems and applications, and in particular (a) in strengthening the interaction between the database software and emerging hardware and I/O devices, and (b) in automating data management to support computationally-demanding, data-intensive scientific applications. She has received an ERC Consolidator Award (2013), a Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), eight best-paper awards in database, storage, and computer architecture conferences (2001-2012), and an NSF CAREER award (2002). She holds a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She is the vice chair of the ACM SIGMOD community, a senior member of the IEEE, and has served as a CRA-W mentor. She is a member of the Global Agenda Council for Data, Society and Development of the World Economic Forum.
On Wednesday 08:30 - 10:15, the two keynote addresses are:
1. Big Plateaus of Big Data on the Big Island
In ancient texts, 40 was a magic number. It meant “a lot” or “a long time”. 40 years represented the time it took for a new generation to arise. A look back at 40 years of VLDB suggests this applies to database researchers as well – the young researchers of the early VLDBs are now the old folks of the database world and a new generation is creating afresh. Over this period many plateaus of “Big Data” have challenged the database community and been conquered. But there is still no free lunch – database research is really the science of trade-offs many of which are no different today than 40 years ago. And of course the evolution of hardware technology continues to swing the trade-off pendulum while enabling new plateaus to be reached. Todd will take a look back at customer big data plateaus of the past. He will look at where we are today, then use his crystal ball and the lessons of the past to extrapolate the next several plateaus – how they will be the same and how will they be different. Along the way we will have a little fun with some VLDB and Teradata history.
Todd Walter is the Chief Technologist for Teradata across the Americas region. With substantive expertise in big data, database engineering and systems architecture, he works closely with Teradata customers, colleagues, and alliance partners to evaluate and prioritize initiatives — and implement data strategy and analytics. As a pragmatic visionary, Walter helps customer business analysts as well as technologists better understand all of the astonishing possibilities of big data and analytics in view of emerging as well as existing capabilities of information infrastructures. Todd works with organizations of all sizes and levels of experience, from start-ups to Fortune 100 companies at the leading edge of adopting big data, data warehouse and analytics technologies.
Walter has been with Teradata for nearly 28 years, contributing significantly to Teradata's unique design features and functionality. He holds more than a dozen Teradata patents and is a Teradata Fellow, the highest technical award granted by the company. Todd served for more than ten years as Chief Technical Officer of Teradata Labs, responsible for vision, strategy and technical leadership of the Teradata product line before taking on his current strategic consulting role.
2. Big Data Research: Will Industry Solve all the Problems?
The need for effective tools for big data data management and analytics continues to grow. While the ecosystem of tools is expanding many research problems remain open: they include challenges around efficient processing, flexible analytics, ease of use, and operation as a service. Many new systems and much innovation, however, come from industry (or from academic projects that quickly became big players in industry). An important question for our community is whether industry will solve all the problems or whether there is a place for academic research in big data and what is that place. In this talk, we will first look back at the past 40 years of VLDB research and will then discuss some recent research results and open problems.
Magdalena Balazinska is an Associate Professor in the department of Computer Science and Engineering at the University of Washington and the Jean Loup Baer Professor of Computer Science and Engineering. She’s the director of the IGERT PhD Program in Big Data and Data Science. She’s also a Senior Data Science Fellow of the University of Washington eScience Institute. Magdalena’s research interests are in the field of database management systems. Her current research focuses on big data management, scientific data management, and cloud computing. Magdalena holds a Ph.D. from the Massachusetts Institute of Technology (2006). She is a Microsoft Research New Faculty Fellow (2007), received an NSF CAREER Award (2009), a 10-year most influential paper award (2010), an HP Labs Research Innovation Award (2009 and 2010), a Rogel Faculty Support Award (2006), a Microsoft Research Graduate Fellowship (2003-2005), and multiple best-paper awards.