Keynote Speakers
Wes Bethel, Lawrence Berkeley National Laboratory
Why High Performance Visual Data Analytics is both Relevant and Difficult
Monday, February 4 from 2pm to 3pm – Data visualization, as well as data analysis and data analytics, are all an integral part of the scientific process. Collectively, these technologies provide the means to gain insight into data of ever-increasing size and complexity. Over the past two decades, a substantial amount of visualization, analysis, and analytics R&D has focused on the challenges posed by increasing data size and complexity, as well as on the increasing complexity of a rapidly changing computational platform landscape. While some of this research focuses on solely on technologies, such as indexing and searching or novel analysis or visualization algorithms, other R&D projects focus on applying technological advances to specific application problems. Some of the most interesting and productive results occur when these two activities -- R&D and application -- are conducted in a collaborative fashion, where application needs drive R&D, and R&D results are immediately applicable to real-world problems.
“Wes is a Senior Computer Scientist at Lawrence Berkeley National Laboratory, where he is a researcher and technical manager of research and production programs in visualization and analytics. His research interests include architectures for high performance visual data analysis, computer graphics, and remote/distributed visualization.
He was the Coordinating Principal Investigator for DOE's Scientific Discovery through Advanced Computing (SciDAC)'s Visualization and Analytics Center for Enabling Technologies, which produced production-quality, petascale-capable visual data analysis software infrastructure, and collaborated with several different science application domains to apply it to some of the world's most challenging scientific data understanding problems.” [LBL]
Meichun Hsu, HP Labs
Social Media Analysis and Platform
Tuesday, February 5 from 2pm to 3pm – Traditional business intelligence (BI) consists of moving enterprise-internal transactional data into a structured data warehouse and producing multi-dimensional analysis reports and data visualization on top of the data warehouse; such BI framework is often focused on curated, structured data with batch ingestion. The emergence of pervasive sensors, mobile devices and the web promise to transform the way we manage many aspects of our businesses (and our lives too). In particular, social media has presented both an opportunity and a challenge in managing relationships with customers and other stakeholder of an enterprise. Social media data has helped create a surge in interest in big and unstructured data, whose analysis is often associated with predominantly open source, schema-free, extreme scale-out technologies such as Hadoop and MapReduce; these latter technologies appear to represent a departure from traditional BI. In this talk, we propose a dataflow framework for social media analysis, motivate an analytics platform for dataflows which unifies traditional BI technologies and their newer counterparts, and discuss some use cases and implications.
“Mei is Director of Intelligent Information Management at HP Labs. Her current work focuses on data-intensive analytics, near real-time business intelligence, and fusion of structured and unstructured information. Her prior industry R&D experience includes head of engineering at Commerce Once Inc., department head of data mining research at HP Labs, and chief architect of workflow systems at Digital Equipment Corporation. Before joining the industry, she was a member of the computer science faculty at Harvard University. She received her PhD from Massachusetts Institute of Technology.” [HP]