Data management is becoming increasingly important in large-scale scientific applications such as computational astrophysics, severe weather monitoring, and genomics. In this talk, I present our recent work to address two major challenges raised by those scientific applications. The first challenge regards “data uncertainty”, due to the fact that scientific measurements are inherently noisy and uncertain. In particular, we address uncertain data management under the array model, which has gained popularity for large-scale scientific data processing due to performance benefits. We propose a suite of storage and evaluation strategies to support array operations under data uncertainty. Results from Sloan Digital Sky Survey (SDSS) datasets show that our techniques outperform state-of-the-art methods by 1.7x to 4.3x for the Subarray operation and 1 to 2 orders of magnitude for Structure-Join.
As scientific data continues to grow in size and diversity, it is becoming harder for the user to express her data interests precisely in a formal language like SQL. We refer to this second problem as “query uncertainty”. This leads to a strong need for “interactive data exploration,” a service that efficiently navigates the user through a large data space to identify the objects of interest. We present our initial work on interactive data exploration, with results suggesting that it is possible to predict user interests modeled by conjunctive queries with a small number of samples, while providing interactive performance.
Yanlei Diao is Associate Professor of Computer Science at the University of Massachusetts Amherst. Her research interests are in information architectures and data management systems, with a focus on big data analytics, scientific analytics, data streams, uncertain data management, and RFID and sensor data management. She received her PhD in Computer Science from the University of California, Berkeley in 2005, her M.S. in Computer Science from the Hong Kong University of Science and Technology in 2000, and her B.S. in Computer Science from Fudan University in 1998.
Yanlei Diao was a recipient of the 2013 CRA-W Borg Early Career Award (one female computer scientist selected each year), IBM Scalable Innovation Faculty Award, and NSF Career Award, and she was a finalist of the Microsoft Research New Faculty Award. She spoke at the Distinguished Faculty Lecture Series at the University of Texas at Austin. Her PhD dissertation “Query Processing for Large-Scale XML Message Brokering” won the 2006 ACM-SIGMOD Dissertation Award Honorable Mention. She is currently Editor-in-Chief of the ACM SIGMOD Record, Associate Editor of ACM TODS, Area Chair of SIGMOD 2015, and member of the SIGMOD Executive Committee and SIGMOD Software Systems Award Committee. In the past, she has served as Associate Editor of PVLDB, organizing committee member of SIGMOD, CIDR, DMSN, and the New England Database Summit, as well as on the program committees of many international conferences and workshops. Her research has been strongly supported by industry with awards from Google, IBM, Cisco, NEC labs, and the Advanced Cybersecurity Center.