Integrating Remotely Sensed and Ground Observations for Modeling, Analysis, and Decision Support

Andrea Donnellan, Margaret Glasscoe, Jay Dennis McLeod W. Parker, Robert Granat University of Southern California Jet Propulsion Laboratory Mail Code 0781, 3651 Trousdale Parkway California Institute of Technology Los Angeles, CA 90089 4800 Oak Grove Dr. [email protected] Pasadena, CA 91109 818-354-4737 John Rundle Andrea.Donnellan@jpl.nasa.gov Department of Physics, University of [email protected] California, Davis [email protected] One Shields Avenue [email protected] Davis, CA, 95616 [email protected] Marlon Pierce, Jun Wang, Geoffrey Fox Indiana University Lisa Grant Ludwig 2719 East 10th Street University of California, Irvine Bloomington IN 47408 Irvine, CA 92697 [email protected] [email protected] [email protected] [email protected]

Abstract—Earthquake science and emergency response require 5. CLOUD COMPUTING ...... 6 integration of many data types and models that cover a broad range of scales in time and space. Timely and efficient 6. SUMMARY ...... 7 earthquake analysis and response require automated processes ACKNOWLEDGEMENTS ...... 7 and a system in which the interfaces between models and REFERENCES ...... 7 applications are established and well defined. Geodetic imaging data provide observations of crustal deformation from BIOGRAPHIES ...... 8 which strain accumulation and release associated with earthquakes can be inferred. Data products are growing and tend to be either large in size, on the order of 1 GB per image, 1. INTRODUCTION or high data rate, such as from 1 Hz GPS solution. As a result, the products can be computationally intensive to manipulate, QuakeSim is a computational environment integrating data analyze, or model. Required computing resources can be large, and models for understanding earthquake processes. even for a few users, and can spike when new data are made QuakeSim focuses on using remotely sensed geodetic available or when an earthquake occurs. Moving to a cloud imaging data to study the interseismic part of the earthquake computing environment is the natural next extension for some cycle (Figure 1). Geodetic imaging data provide components of QuakeSim as an increasing number of data observations of crustal deformation from which strain products and model applications become available to users. An accumulation and release associated with earthquakes can additional consideration is that moving large images consumes be inferred. The goal is to understand earthquakes for a tremendous amount of bandwidth. Storing the data near the model applications improves performance for the user. mitigation and response. Remotely sensed geodetic imaging observations provide information on motions of the surface Table of Contents of the Earth’s crust, expressing the cycle of strain accumulation and release. These data products are fused with geologic and seismicity information in the QuakeSim 1. INTRODUCTION ...... 1 environment with visualization, modeling, and pattern 2. DATA PRODUCTS ...... 3 analysis tools. E-DECIDER is an earthquake response 3. MODELING AND ANALYSIS ...... 4 decision support system that also uses remotely sensed 4. DECISION SUPPORT ...... 5 observations and some QuakeSim components.

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Figure 1. Schematic of earthquake cycle of strain accumulation and release with the long-term tectonic rate removed. QuakeSim and E-DECIDER are downstream data product milliseconds to millions of years, and from microns to consumers that perform forward modeling, inverse global scales. Crustal deformation data products derived modeling, forecasting, event detection, and response on the from GPS and interferometric synthetic aperture radar data products. Both E-DECIDER and QuakeSim rely on (InSAR) observations enable an understanding of the cycle processed data products, which are typically velocities and of strain accumulation and release. Geological and seismic time series from GPS, and ground range changes from data complement the spaceborne observations. Various Interferometric Synthetic Aperture Radar (InSAR). These domain experts study aspects of these processes using and other data products such as seismicity, geologic fault experimental, theoretical, modeling, or statistical tools. Over data, or civil infrastructure, are integrated and assimilated the last decade, in part due to the advent of new into models for understanding, forecasting, and response. As computational methods, there has been an increasing focus a result of this reliance on data product providers, it is on mining and integrating the heterogeneous data products imperative that data specifications, standards, and locations into complex models of solid Earth processes. are well understood. QuakeSim focuses on the cycle of strain accumulation and QuakeSim is distributed and heterogeneous because of the release that is well addressed by geodetic imaging data and many different data products and numerous different related model applications. Coseismic offset is the slip that applications. Decomposing QuakeSim into its different occurs during an earthquake. Postseismic deformation is parts, strategically using cloud resources where necessary, continued motion that occurs following an earthquake and and using other resources as practical result in greater deviates from the long-term slip rate. Postseismic efficiency. In cases where data volumes are large, such as deformation is usually some combination of afterslip, InSAR, it is important to ensure that model applications relaxation, or poroelastic rebound. Interseismic strain reside close to the data products to reduce latency from accumulation occurs as a result of plate tectonic motion. bandwidth limitations. Alternatively, data products can be QuakeSim addresses the size and location of events, connected to applications through high bandwidth coseismic offsets associated with the earthquakes, and the pathways. time varying displacements that occur throughout the earthquake cycle. Seismicity and geology yield information Earthquakes are the result of complex solid Earth processes. on size and location of events. GPS provides position time Plate tectonics drive motion of the Earth’s crust, series. Interferometric synthetic aperture radar from airborne accumulating deformation primarily along plate boundaries. and spaceborne platforms provide images of displacement. Strain stored as elastic energy is released in earthquakes All data sources provide information on geometry of faults along faults. These processes occur on scales of and time varying slip.

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Figure 2. QuakeSim inputs, outputs, and feedback to data providers are shown. Multiple data products are used from numerous providers. These are used in applications for understanding faults and forecasting earthquake likelihood. At times output from the applications identifies issues with the data products, which can be in the form of previously unrecognized faults, unmodeled error sources, or processing issues. While we use seismicity as a data source for size, location, data products that have typically been generated elsewhere; and mechanism of events, we do not focus on seismic raw data is not ingested or processed but higher level data waveforms. Understanding crustal deformation and fault products from other data providers are made available behavior leads to improved forecasting, emergency through Open Geospatial Consortium (OGC)-compliant planning, and disaster response. Analysis of crustal web services (Donnellan, et al., 2012, Wang, et al., 2012). deformation data can be used to indicate the existence of otherwise unknown faults (e.g., Donnellan et al., 1993), 2. DATA PRODUCTS particularly when they are not exposed at the surface. Accurate fault models are required for understanding QuakeSim applications use multiple data products from earthquake processes and require the integration of multiple multiple providers (Figure 2). Data products include faults, types of data. Identifying, characterizing, modeling and GPS position time series and velocities, Radar repeat pass considering the consequences of unknown faults, and interferometry (RPI) or Interferometric Synthetic Aperture improving the models of known faults contributes to seismic Radar (InSAR) ground range change images, and seismicity. risk mitigation. It is important that the data products come from trusted The goal of QuakeSim is to improve understanding of sources and are reliable in order to construct valid models of earthquake processes by integrating remotely sensed earthquake processes and produce accurate earthquake geodetic imaging and other data with modeling, simulation, probabilities. Understanding the history of the data products and pattern analysis applications. Model and analysis and how they were produced allows a user to make applications are delivered as standalone offline software informed decisions and interpretations. It is also important packages or online through web services and Science to ensuring reproducibility of results. Tracking data Gateway user interfaces [1]. Data products are available provenance is necessary, particularly when data are online for browsing, download, and online or offline reprocessed or originate from a variety of solutions, such as analysis through a database and services. QuakeSim utilizes with GPS data.

978-1-4673-1813-6/13/$31.00 ©2013 IEEE 3 Figure 3. Simplified E-DECIDER workflow where inputs, outputs, and delivery mechanisms are shown. Input data in the form of remote sensing imagery or simulation results generate derived decision support products that are then delivered as OGC-compliant products through the DHS UICDS software and the E-DECIDER interfaces. The requirements for QuakeSim as a data product consumer displacement information, and geologic and geodetic data and upstream users of QuakeSim results are the same. quickly and easily. Available and trusted data products in Standards and services are necessary for efficient times of crisis are essential in the decision making process. consumption and analysis of products. Data product Quick-look products that may not be as accurate are of more customers specifically need: 1) Standard data formats like value than accurate products with a long latency. GeoTIFF (UAVSAR), GeoJSON, etc.; 2) Better metadata, such as how the data product was explicitly created 3. MODELING AND ANALYSIS (particularly for GPS, in our experience); 3) No undocumented, post-publication changes; 4) Delivery of Data products are used in for main ways by QuakeSim data via standard mechanisms, e.g., OGC services; 5) modeling and analysis tools. The goal is to identify active Cloud-based delivery of data sets, such as with Amazon S3 faults or regions, understand earthquake fault behavior, and Elastic Block Store services; and 6) Notification search for anomalies, and estimate earthquake likelihood. services for new products and changes. Web Services are QuakeSim provides integrated map-based interfaces and key to effectively supporting downstream users; these applications for 1) accessing remotely-sensed and ground- should not be entangled with Web and desktop clients. based data products; 2) exploring and analyzing Effective utilization of data products and analysis results by observations; 3) modeling earthquake processes; and 4) emergency responders require standard interfaces and conducting pattern analysis to focus attention and identify products that are easily and readily interpretable. significant and/or subtle features in the data. Data products A key issue for earthquake response efforts is data and model tools can be accessed by network service (Web availability and reliability, especially in the first hours to Services and REST) interfaces, directly through web-based days following the event. Responders and decision makers user interfaces, or run offline on local or high-performance require access to rapid fault solutions, deformation and computers.

978-1-4673-1813-6/13/$31.00 ©2013 IEEE 4 Figure 4. Example E-DECIDER output with deformation simulation result (tilt map – red/blue dots) generated from QuakeSim RSSDisloc tool with epicenter and 40 mile radius denoted with E-DECIDER’s On-Demand HAZUS KML Generation service. Critical infrastructure can be overlain and attention can be focused on potential facilities that may have been exposed to damage resulting earthquakes. The call out bubble identifies a fire station within the 40 mile radius of the epicenter of the 21 October M 5.3 Central California earthquake.

Cloud computing is applicable in cases where multiple runs users in first response and disaster management agencies, can be made in an embarrassingly parallel fashion. This such as the US Geological Survey (USGS), California includes analysis of high rate GPS time series analysis, Geological Survey (CGS), and the Department of Homeland during post event response, when numerous users might be Security (DHS). A recent addition to the suite of tools using the QuakeSim environment, and for simulating includes delivery of E-DECIDER information through the earthquakes or inverting crustal deformation observations in DHS Unified Incident Command and Decision Support which numerous runs are carried out with different initial (UICDS) software, which allows data providers a means to conditions. communicate and send their products in a standard manner in the event of a disaster. 4. DECISION SUPPORT An example workflow includes an end-to-end process that QuakeSim tools and Web services are used as a back end for starts with the triggering of a deformation simulation from a E-DECIDER. E-DECIDER provides decision support for >M5 earthquake from the USGS feed and ends with those earthquake disaster management and response utilizing results posted to an RSS feed. We have developed NASA and other available remote sensing data and algorithms and web services to automatically process output QuakeSim modeling software (Figure 3). E-DECIDER from the Disloc model to produce tilt and deformation delivers a web-based infrastructure and mobile interface gradient information using a Sobel operator. The tilt is a designed for ease-of-use by decision makers, including measurement of the change of slope that can affect water rapid and readily accessible satellite imagery and synthetic distribution, drainage, and sewage services. The interferograms following earthquakes; standards-compliant deformation gradient is a measure of the rate of change of map data products; and deformation modeling and deformation and can detect locations of possible fault earthquake aftershock forecasting results [2]. The decision rupture on the surface as well as locations where support tools are developed working in partnership with end 978-1-4673-1813-6/13/$31.00 ©2013 IEEE 5 infrastructure may be impacted by tension, compression, or performed on very large data sets, but little communication shear at the surface. between individual processes or movement of data is needed. Further, even in problems needing communication, The tilt and gradient processing can be performed as an on- these are not small messages familiar from parallel demand process or as a web service. The web service simulations but rather large “reductions” (such as global version will be chained with RSSDisloc to provide sums or broadcasts) supported by MapReduce and its automatic mapping of tilt and gradient for earthquakes as iterative extensions in the cloud [4-7]. they appear on the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) feed. The tilt and A growing body of research indicates that Cloud computing gradient information can be mapped either with KML for approaches are a good match for many large-scale data- display in Google Earth or with ArcGIS or other desktop centric scientific computing problems [8-13]. Cloud GIS tools. Mapping products generated in ArcGIS are computing infrastructure in many fields is overtaking the shared either as image/PDF files or in native mapping traditional data center. The size of data in many fields (such formats. Additionally, these products will be disseminated as the life sciences) is growing so rapidly that frequent data automatically via UICDS to responders (in locations where movement is impractical, and so computing must be brought UICDS is in use). The JPL UICDS core has been installed to the data. This is likely the case for InSAR geodetic and is operational and has been tested in a response exercise imaging data, as volumes grow over time. High rate GPS to push E-DECIDER products to end users in an operational data products also are a good candidate for analysis on the environment during the ShakeOut in mid-October 2012. cloud. Other applications may be impractical for cloud Future plans to send epicenter and potentially exposed computing, and analysis should be done to gain efficiency infrastructure from Hazards-United States (HAZUS) with cloud computing while not incurring expense where databases from the E-DECIDER GeoServer directly to unnecessary. UICDS upon the USGS feed trigger are also underway. It is important to prototype these approaches for NASA data 5. CLOUD COMPUTING collections, particularly the anticipated large collections of InSAR imagery from the DESDynI-R mission. QuakeSim’s Cloud computing in the general sense represents the QuakeTables database houses some processed InSAR data centralization of general computing capacity into highly products and also the complete set of processed repeat pass scalable data centers [3]. The National Institute of Standards interferometry products from the airborne UAVSAR InSAR is standardizing cloud computing concepts and terminology, project. These data provide essential information for which we briefly summarize here. modeling earthquake processes and particularly for developing accurate fault models. We are collaborating with In brief summary, we can think of clouds as consisting of data product providers to ensure standard interfaces formats the following service layers. Infrastructure as a Service: as well as jointly used cloud infrastructure where through Virtual Machine technologies, clouds provide appropriate. The infrastructure must be flexible enough to access to hosted servers and resources that appear to the support other data sets and use cases. user as “just another server.” The advantage from the user’s point of view is that the resources are elastic and can be Under the present cloud models storage at existing data scaled up or down as needed. Amazon, Azure and Google center appears more cost effective than storage on the cloud Compute Engine are examples. Open source examples where recurring costs are at present cost prohibitive. include OpenStack, OpenNebula, CloudStack, Nimbus, and However, this may change in the future. Microsoft Azure’s Eucalyptus. Platform as a Service: This allows the user to Blob storage service, Amazon’s S3, and the Lustre file deploy user-created applications within a specific cloud system-based Whamcloud are examples of unstructured framework into the cloud for use by others. Google App storage, and BigTable, HBase, and the Azure Table Service Engine and Amazon’s Elastic MapReduce [4] are prominent are examples of structured data storage. We will evaluate examples. Here the cloud provider creates a framework that these for the storage and access of large collections of can be used to build applications while hiding underlying individually large data sets. A key observation from our complexities (such as those exposed in Infrastructure as a research on cloud systems for science is that data storage Service). Software as a Service: The cloud provider and computing must be coupled. We must therefore couple provides a specific capability or software to the user. our IaaS prototyping with SaaS prototyping. Specifically, Examples are seen in the individual services corresponding the SaaS models that we will consider are MapReduce and to different QuakeSim tools or to individual services its derivatives. MapReduce-style approaches are corresponding to different forecasting models. particularly useful for data-parallel computing problems such as DESDynI-R image processing and GPS signal Compared to traditional high performance computing, analysis. Simulations and inversions where similar runs are Cloud Computing provides much potential greater done with different initial conditions are also candidates for computing capacity but with larger latencies. This makes cloud computing. Cloud Computing an excellent candidate for “big data” problems in which the same operations need to be

978-1-4673-1813-6/13/$31.00 ©2013 IEEE 6 We are exploring use of Amazon Cloud and Indiana [3] Kai Hwang, Geoffrey Fox, and Jack Dongarra, University’s FutureGrid system. FutreGrid is an NSF- Distributed and Cloud Computing: from Parallel funded testbed that is designed to foster research on Cloud Processing to The Internet of Things. 2011: Morgan Computing for science and other advanced topics. We will Kaufmann Publishers use FutureGrid to help build and evaluate prototypes. Ultimately, the goal is to package these tools with a [4] Jeffrey Dean and Sanjay Ghemawat, MapReduce: workflow engine and gateway. simplified data processing on large clusters. Commun. ACM, 2008. 51(1): p. 107-113. Generally QuakeSim can be run on cloud services, as from DOI:http://doi.acm.org/10.1145/1327452.1327492 QuakeSim's point of view there is little difference between the VM IaaS and a regular host. A major difference is that [5] Yingyi Bu, Bill Howe, Magdalena Balazinska, and hardware can be dynamically acquired, so we can test many Michael D. Ernst, HaLoop: Efficient Iterative Data different VM sizes to determine what we need to run and Processing on Large Clusters, in The 36th International under what circumstances (e.g. in the event of an Conference on Very Large Data Bases. September 13-17, earthquake). We do not have to spend weeks configuring 2010, VLDB Endowment: Vol. 3. Singapore. and ordering hardware only to find out it is too small or too http://www.ics.uci.edu/~yingyib/papers/HaLoop_camera_ big, which can waste time and money. Further, commercial ready.pdf. clouds dynamically reallocate resources on the fly to satisfy drastic changes in demand. [6] J.Ekanayake, H.Li, B.Zhang, T.Gunarathne, S.Bae, J.Qiu, and G.Fox, Twister: A Runtime for iterative MapReduce, 6. SUMMARY in Proceedings of the First International Workshop on MapReduce and its Applications of ACM HPDC 2010 Clouds are a promising new method for analysis and conference June 20-25, 2010. 2010, ACM. Chicago, modeling of large data product sets and for multiple parallel http://grids.ucs.indiana.edu/ptliupages/publications/hpdc- runs. In some instances it may be better to use standard camera-ready-submission.pdf. resources instead of clouds. Careful consideration should be given to what resources to use for various tasks. QuakeSim [7] Matei Zaharia, Mosharaf Chowdhury, Michael J. consists of several components interfacing with many Franklin, Scott Shenker, and Ion Stoica, Spark: Cluster different organizations, data products, and with several Computing with Working Sets, in 2nd USENIX different types of applications for modeling and analysis. Workshop on Hot Topics in Cloud Computing (HotCloud Collaboration between QuakeSim developers and data '10). June 22, 2010. Boston. product providers and downstream consumers early on will http://www.cs.berkeley.edu/~franklin/Papers/hotcloud.pdf result in improved efficiency across the board for earthquake studies and analysis of geodetic imaging [8] Geoffrey C. Fox, Data intensive applications on clouds, in observations. Proceedings of the second international workshop on Data intensive computing in the clouds. 2011, ACM. Seattle, ACKNOWLEDGEMENTS Washington, USA. pages. 1-2. DOI: 10.1145/2087522.2087524. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, Indiana University, [9] Jackson, R. Lavanya Ramakrishnan, Karl J. Runge, and University of Southern California, and University of Rollin C. Thomas, “Seeking supernovae in the clouds: a California’s Davis and Irvine campuses under contract with performance study,” Proceedings of the 19th ACM NASA. The work was sponsored by NASA’s Advanced International Symposium on High Performance Information Technologies, Earth Surface and Interior, and Distributed Computing. ACM. Chicago, Illinois. 421-429. Applied Sciences Programs. DOI: 10.1145/1851476.1851538. (2010).

REFERENCES [10] Pireddu, L, Simone Leo, and Gianluigi Zanetti, [1] Pierce, M., Gao, X., Pallickara, S. L.,Guo, Z., and Fox, “MapReducing a Genomic Sequencing Workflow,” G., The Quakesim portal and services: new approaches to MapReduce 2011 (2011). science gateway development techniques. Concurrency and Computation: Practice and Experience 22(12): 1732- [11] Stein, L., “The case for cloud computing in genome 1749 (2010). informatics,” Genome Biology. 11(5): 207, 2010.

[2] Wang, J., M. Pierce, Y. Ma, G. Fox, A. Donnellan, J. [12] Thilina Gunarathne, Bingjing Zhang, Tak-Lon Wu, and Parker, and M. Glasscoe, Using Service-Based GIS to Judy Qiu, Scalable Parallel Computing on Clouds Using Support Earthquake Research and Disaster Twister4Azure Iterative MapReduce Future Generation Response, Computing in Science and Engineering, 14, 21- Computer Systems 2012. To be published. 30, September 2012. [13] Geoffrey Fox and Dennis Gannon. Cloud Programming Paradigms for Technical Computing Applications. 2012. 978-1-4673-1813-6/13/$31.00 ©2013 IEEE 7 BIOGRAPHIES California, Los Angeles, and his B.S. from the California Institute of Technology. Since 1999, he has been working Andrea Donnellan is a principal on QuakeSim and related projects to perform statistical research scientist at NASA's Jet machine learning based health monitoring, signal Propulsion Laboratory, and Adjunct classification, and anomaly detection on seismic and GPS Assistant Professor of Research of sensor networks. His other research interests include Earth Sciences at the University of autonomous robotic navigation, scalable scientific Southern California. She is computing, and radiation fault tolerant algorithms for Principal Investigator of NASA's spaceborne computation. QuakeSim project, which was co- Maggi Glasscoe is a Geophysicist in the winner of NASA’s Software of the Solid Earth Group at the Jet Propulsion Year Award in 2012. Donnellan was Deputy Manager of Laboratory, California Institute of the JPL's Science Division, Pre-Project Scientist of an L- Technology. Glasscoe received a B.S. in band radar mission, and NASA's Applied Sciences Geological Sciences and a B.A. in Print Program Area Co-Lead for Natural Disasters. Donnellan Journalism from the University of received a B.S. in geology from the Ohio State University Southern California in 1997 and a M.S. in 1986, a master's and Ph.D. in geophysics from Caltech in Geology from the University of California, Davis in in 1988 and 1991 respectively, and an M.S. in Computer 2003. She has experience working with a number of Science from the University of Southern California in modeling codes, including viscoelastic finite element 2003. She held a National Research Council postdoctoral models (the JPL developed Geophysical Finite Element fellowship at NASA's Goddard Space Flight Center. Simulation Tool, GeoFEST, in particular). Her interests Donnellan has conducted field studies globally in include disaster response, modeling deformation of the tectonically active areas, and on ice sheets and glaciers, Earth’s crust to study postseismic response to large and has received numerous awards. earthquakes, numerical models of the rheological Jay Parker joined the Satellite behavior of the lower crust, and simulations of Geodesy and Geodynamics Systems interacting fault systems. She is is a researcher on the Group at JPL in 1996, and has been QuakeSim project and the Principal Investigator of E- part of the JPL technical staff since DECIDER (Earthquake Data Enhanced Cyber- 1989. He completed both a master's Infrastructure for Disaster Evaluation and Response). and PhD in Electrical Engineering Marlon Pierce is the Assistant Director from the University of Illinois for the Science Gateways Group in (Urbana-Champaign), and graduated Research Technologies Applications at with a Bachelors of Science from the Indiana University. Pierce received his California Institute of Technology in 1981. His Ph.D. Florida State University (Physics) professional interests lie in applications of fast and in 1998 in computational condensed accurate numerical models to geophysical remote matter physics. His current research sensing. Past modeling projects include vortex formation and development work focuses on in the ionospheric D region, parallel supercomputer computational sciences with an emphasis on Grid modeling of radar scattering and antenna power patterns, computing and computational Web portals. Prior to and high-fidelity modeling of clear-air infrared spectra forming the Science Gateway Group, Pierce served as for determining climate change and pollution sources. He assistant director for the Community Grids Laboratory at is currently working on methods to invert SCIGN GPS Indiana University's Pervasive Technologies Institute. data to determine earthquake and after-slip fault Pierce supervises the research activities of software movements, finite element models of earthquake cycles, engineering staff and Ph.D. students, and serves as and new methods for GPS data processing on principal investigator on multiple federally-funded supercomputers. Jay has been inducted into Tau Beta Pi, research projects. Pierce leads research efforts in the and received a JPL Technology and Applications following areas: the application of service-oriented Programs Group Achievement Award. He is a member of architectures and real-time streaming techniques to the American Geophysical Union, and the IEEE Antennas geographical information systems and sensor networks; and Propagation Society. the development of open source science Web portal Robert Granat is currently Group software for accessing Grid computing and data Supervisor of the Machine Learning and resources; and Grid-based distributed computing Instrument Autonomy group at JPL, and applications in computational chemistry and material has been a member of technical staff science, chemical informatics, and geophysics. there since 1996. He received his M.S.

and Ph.D. degrees in Electrical Engineering from the University of

978-1-4673-1813-6/13/$31.00 ©2013 IEEE 8 Jun Wang is a GIS specialist in the John Rundle is an Interdisciplinary Community Grids Lab, Indiana Professor of Physics, Civil University. His current research Engineering and Geology at interests are in the areas of large scale University of California, Davis. His spatial data processing and research is focused on understanding visualization with Cloud computing the dynamics of earthquakes through technology. He joined QuakeSim team in July 2010, and numerical simulations; pattern develops the software components for QuakeSim analysis of complex systems; dynamics visualization products. of driven nonlinear Earth systems; and adaptation in general complex systems. Computational science and Geoffrey Fox received a Ph.D. in engineering is an emerging method of discovery in Theoretical Physics from Cambridge science and engineering that is distinct from, and University and is now professor of complementary to, the two more traditional methods of Informatics and Computing, and Physics experiment/observation and theory. The emphasis in this at Indiana University where he is method is upon using the computer as a numerical director of the Digital Science Center laboratory to perform computational simulations to gain and Associate Dean for Research and insight into the behavior of complex dynamical systems, Graduate Studies at the School of Informatics and to visualize complex and voluminous data sets, to perform Computing. He previously held positions at Caltech, data mining to discover hidden information within large Syracuse University and Florida State University. He has data sets, and to assimilate data into computational supervised the PhD of 62 students and published over 600 simulations. Professor Rundle is a Fellow of the papers in physics and computer science. He currently American Geophysical Union. works in applying computer science to Bioinformatics, Defense, Earthquake and Ice-sheet Science, Particle Lisa Grant Ludwig is an Associate Physics and Chemical Informatics. He is principal Professor in Public Health at the investigator of FutureGrid – a facility to enable University of California, Irvine. She development of new approaches to computing. He is was Associate Director of the involved in several projects to enhance the capabilities of California Institute for Hazards Minority Serving Institutions. Research. Lisa earned a Ph.D. from Caltech in 1993 in Geology with Dennis McLeod is currently Professor Geophysics, M.S. degrees from Caltech in 1990 in of Computer Science at the University Geology and in 1989 in Environmental Engineering and of Southern California, and Director of Science, and a B.S. from Stanford in 1985 in Applied the Semantic Information Research Environmental Earth Sciences. Lisa's research interests Laboratory. He received his Ph.D., include natural hazards, paleoseismology, active faults, M.S., and B.S. degrees in Computer San Andreas fault, southern California faults, San Science and Electrical Engineering Joaquin Hills, seismic hazard, environmental health and from MIT. Dr. McLeod has published geology. Her research group addresses natural hazards widely in the areas of data and knowledge base systems, and disasters from a geologic perspective, with emphasis federated databases, database models and design, on earthquakes. Earthquakes are a major threat to public ontologies, knowledge discovery, scientific data health globally and locally in California. The group management, information trust and privacy, and focuses on defining the potential for large earthquakes, multimedia information management. His current and working collaboratively on developing forecasts, research focuses on: structured domain ontologies; hazard models and effective responses. Results of the semantic web; database semantic heterogeneity work are applied for disaster preparedness planning, resolution and inter-database correlation; personalized structural design, land-use planning, seismic risk information management and customization; information assessment and public education about earthquake management environments for Earth, marine, and climate hazard. science; the architecture of data centers providing massive storage via virtualization and data clouds; social networking information management and information trust; and service-based information access and delivery frameworks.