Converging Seismic and Geodetic Data Services

<p>One of the fundamental tenets of the Incorporated Research Institutions for Seismology’s (IRIS’s) mission is to “Promote exchange of seismic and other geophysical data … through pursuing policies of free and unrestricted data access.”  UNAVCO also adheres to a data policy that promotes free and unrestricted use of data.  A major outcome of these policies has been to reduce the time that researchers spend finding, obtaining, and reformatting data.  While rapid, easy access to large archives of data has been successfully achieved in seismology, geodesy and many other distinct disciplines, integrating different data types in a converged data center that promotes interdisciplinary research remains a challenge.  This challenge will be addressed in an integrated seismological and geodetic data services facility that is being mandated by the National Science Foundation (NSF).  NSF’s Seismological Facility for the Advancement of Geoscience (SAGE), which is managed by IRIS, will be integrated with NSF’s Geodetic Facility for the Advancement of Geoscience (GAGE), which is managed by UNAVCO.  The combined data services portion of the facility, for which a prototype will be developed over the next two to three years, will host a number of different data types including seismic, GNSS, magnetotelluric, SAR, infrasonic, hydroacoustic, and many others.  Although IRIS and UNAVCO have worked closely for many years on mutually beneficial projects and have shared their experience with each other, combining the seismic and geodetic data services presents challenges to the well-functioning SAGE and GAGE data facilities that have served their respective scientific communities for more than 30 years. This presentation describes some preliminary thoughts and guiding principles to ensure that we build upon the demonstrated success of both facilities and how an integrated GAGE and SAGE data services facility might address the challenges of fostering interdisciplinary research. </p>

Discovering Knowledge from Local Patterns in SAGE Data

The discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on global approaches such as clustering. An alternative way is to utilize local pattern mining techniques for global modeling and knowledge discovery. Nevertheless, moving from local patterns to models and knowledge is still a challenge due to the overwhelming number of local patterns and their summarization remains an open issue. This chapter is an attempt to fulfill this need: thanks to recent progress in constraint-based paradigm, it proposes three data mining methods to deal with the use of local patterns by highlighting the most promising ones or summarizing them. Ideas at the core of these processes are removing redundancy, integrating background knowledge, and recursive mining. This approach is effective and useful in large and real-world data: from the case study of the SAGE gene expression data, we demonstrate that it allows generating new biological hypotheses with clinical application.