Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00121
G. Oord, Xavier Yepes, M. Acosta
Climate models are steadily evolving towards higher resolution and the upcoming model inter-comparison projects require an unprecedented number of produced variables. As a consequence, the post-processing of the output of these models start to form a bottleneck for CMIP experiments. We discuss how the ece2cmor3 tool processes the EC-Earth model output in parallel, and present a new coupling of the ECMWF weather model to the XIOS library to allow on-the-fly post-processing of its atmospheric fields.
{"title":"Post-Processing Strategies for the ECMWF Model","authors":"G. Oord, Xavier Yepes, M. Acosta","doi":"10.1109/eScience.2018.00121","DOIUrl":"https://doi.org/10.1109/eScience.2018.00121","url":null,"abstract":"Climate models are steadily evolving towards higher resolution and the upcoming model inter-comparison projects require an unprecedented number of produced variables. As a consequence, the post-processing of the output of these models start to form a bottleneck for CMIP experiments. We discuss how the ece2cmor3 tool processes the EC-Earth model output in parallel, and present a new coupling of the ECMWF weather model to the XIOS library to allow on-the-fly post-processing of its atmospheric fields.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"26 1","pages":"401-401"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88028053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00060
S. Abdulkareem, Ellen-Wien Augustijn-Beckers, Katarzyna Musial, Yaseen T. Mustafa, T. Filatova
Epidemics have always been a source of concern to people, both at the individual and government level. To fight outbreaks effectively, we need advanced tools that enable us to understand the factors that influence the spread of life-threatening diseases.
{"title":"The Impact of Social Versus Individual Learning for Agents' Risk Perception During Epidemics","authors":"S. Abdulkareem, Ellen-Wien Augustijn-Beckers, Katarzyna Musial, Yaseen T. Mustafa, T. Filatova","doi":"10.1109/eScience.2018.00060","DOIUrl":"https://doi.org/10.1109/eScience.2018.00060","url":null,"abstract":"Epidemics have always been a source of concern to people, both at the individual and government level. To fight outbreaks effectively, we need advanced tools that enable us to understand the factors that influence the spread of life-threatening diseases.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"30 1","pages":"297-298"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79199976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00038
Karima Rafes, S. Abiteboul, Sarah Cohen Boulakia, B. Rance
SPARQL is the standard query language used to access RDF linked data sets available on the Web. However, designing a SPARQL query can be a tedious task, even for experienced users. This is often due to imperfect knowledge by the user of the ontologies involved in the query. To overcome this problem, a growing number of query editors offer autocompletetion features. Such features are nevertheless limited and mostly focused on typo checking. In this context, our contribution is four-fold. First, we analyze several autocompletion features proposed by the main editors, highlighting the needs currently not taken into account while met by a user community we work with, scientists. Second, we introduce the first (to our knowledge) autocompletion approach able to consider snippets (fragments of SPARQL query) based on queries expressed by previous users, enriching the user experience. Third, we introduce a usable, open and concrete solution able to consider a large panel of SPARQL autocompletion features that we have implemented in an editor. Last but not least, we demonstrate the interest of our approach on real biomedical queries involving services offered by the Wikidata collaborative knowledge base.
{"title":"Designing Scientific SPARQL Queries Using Autocompletion by Snippets","authors":"Karima Rafes, S. Abiteboul, Sarah Cohen Boulakia, B. Rance","doi":"10.1109/eScience.2018.00038","DOIUrl":"https://doi.org/10.1109/eScience.2018.00038","url":null,"abstract":"SPARQL is the standard query language used to access RDF linked data sets available on the Web. However, designing a SPARQL query can be a tedious task, even for experienced users. This is often due to imperfect knowledge by the user of the ontologies involved in the query. To overcome this problem, a growing number of query editors offer autocompletetion features. Such features are nevertheless limited and mostly focused on typo checking. In this context, our contribution is four-fold. First, we analyze several autocompletion features proposed by the main editors, highlighting the needs currently not taken into account while met by a user community we work with, scientists. Second, we introduce the first (to our knowledge) autocompletion approach able to consider snippets (fragments of SPARQL query) based on queries expressed by previous users, enriching the user experience. Third, we introduce a usable, open and concrete solution able to consider a large panel of SPARQL autocompletion features that we have implemented in an editor. Last but not least, we demonstrate the interest of our approach on real biomedical queries involving services offered by the Wikidata collaborative knowledge base.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"26 1","pages":"234-244"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86647332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00114
D. P. Mahato, Jasminder Kaur Sandhu
The load scheduling and reliability modeling in on-demand computing based transaction processing are complex tasks. This paper presents the CPNs (Coloured Petri Nets) based modeling for load balanced scheduling and reliability analysis for on-demand computing based transaction processing system.
{"title":"Modeling of Load Balanced Scheduling and Reliability Evaluation for On-demand Computing Based Transaction Processing System","authors":"D. P. Mahato, Jasminder Kaur Sandhu","doi":"10.1109/eScience.2018.00114","DOIUrl":"https://doi.org/10.1109/eScience.2018.00114","url":null,"abstract":"The load scheduling and reliability modeling in on-demand computing based transaction processing are complex tasks. This paper presents the CPNs (Coloured Petri Nets) based modeling for load balanced scheduling and reliability analysis for on-demand computing based transaction processing system.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"23 1","pages":"390-391"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90964847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00058
K. Parn, M. Pirinen, M. Kals, R. Mägi, V. Salomaa, M. Boehnke, I. Hall, N. Stitziel, N. Freimer, M. Daly, A. Palotie, S. Ripatti, P. Palta
n/a
{"title":"Differences in the Commonly used Genotype Imputation Algorithms and Their Imputation Accuracy Estimates","authors":"K. Parn, M. Pirinen, M. Kals, R. Mägi, V. Salomaa, M. Boehnke, I. Hall, N. Stitziel, N. Freimer, M. Daly, A. Palotie, S. Ripatti, P. Palta","doi":"10.1109/eScience.2018.00058","DOIUrl":"https://doi.org/10.1109/eScience.2018.00058","url":null,"abstract":"n/a","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"10 1","pages":"293-294"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84192313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ESCIENCE.2018.00141
J. O'Neal, K. Weide, A. Dubey
FLASH is a highly-configurable multiphysics software designed for solving a large class of problems that involve fluid flows and need adaptive mesh refinement (AMR). FLASH has been in existence for two decades and has undergone four major revisions. It is now undergoing its fifth major revision to deal with increasingly heterogeneous platforms. The architecture of previous versions of the code and the AMR package at its core, Paramesh, are inadequate to meet the challenges posed by heterogeneity. In this paper we describe our experience with refactoring the mesh interface of the code to work with a more modern AMR library, AMReX. The focus of the paper is the refactoring methodology and the attendant software process that we have found useful to ensure that code quality is maintained during the transition.
{"title":"Experience report: refactoring the mesh interface in FLASH, a multiphysics software","authors":"J. O'Neal, K. Weide, A. Dubey","doi":"10.1109/ESCIENCE.2018.00141","DOIUrl":"https://doi.org/10.1109/ESCIENCE.2018.00141","url":null,"abstract":"FLASH is a highly-configurable multiphysics software designed for solving a large class of problems that involve fluid flows and need adaptive mesh refinement (AMR). FLASH has been in existence for two decades and has undergone four major revisions. It is now undergoing its fifth major revision to deal with increasingly heterogeneous platforms. The architecture of previous versions of the code and the AMR package at its core, Paramesh, are inadequate to meet the challenges posed by heterogeneity. In this paper we describe our experience with refactoring the mesh interface of the code to work with a more modern AMR library, AMReX. The focus of the paper is the refactoring methodology and the attendant software process that we have found useful to ensure that code quality is maintained during the transition.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"29 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85117637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00037
Kunal Lillaney, D. Kleissas, Alexander Eusman, E. Perlman, William R. Gray Roncal, J. Vogelstein, R. Burns
We describe NDStore, a scalable multi-hierarchical data storage deployment for spatial analysis of neuroscience data on the AWS cloud. The system design is inspired by the requirement to maintain high I/O throughput for workloads that build neural connectivity maps of the brain from peta-scale imaging data using computer vision algorithms. We store all our data on the AWS object store S3 to limit our deployment costs. S3 serves as our base-tier of storage. Redis, an in-memory key-value engine, is used as our caching tier. The data is dynamically moved between the different storage tiers based on user access. All programming interfaces to this system are RESTful web-services. We include a performance evaluation that shows that our production system provides good performance for a variety of workloads by combining the assets of multiple cloud services.
{"title":"Building NDStore Through Hierarchical Storage Management and Microservice Processing","authors":"Kunal Lillaney, D. Kleissas, Alexander Eusman, E. Perlman, William R. Gray Roncal, J. Vogelstein, R. Burns","doi":"10.1109/eScience.2018.00037","DOIUrl":"https://doi.org/10.1109/eScience.2018.00037","url":null,"abstract":"We describe NDStore, a scalable multi-hierarchical data storage deployment for spatial analysis of neuroscience data on the AWS cloud. The system design is inspired by the requirement to maintain high I/O throughput for workloads that build neural connectivity maps of the brain from peta-scale imaging data using computer vision algorithms. We store all our data on the AWS object store S3 to limit our deployment costs. S3 serves as our base-tier of storage. Redis, an in-memory key-value engine, is used as our caching tier. The data is dynamically moved between the different storage tiers based on user access. All programming interfaces to this system are RESTful web-services. We include a performance evaluation that shows that our production system provides good performance for a variety of workloads by combining the assets of multiple cloud services.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"70 1","pages":"223-233"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81010668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00042
M. Stringer, Colin G. Jones, R. Hill, M. Dalvi, Colin Johnson, J. Walton
We describe a hybrid-resolution version of the UKESM earth system model that reduces the model’s computational costs by using high resolution for simulated resolved atmospheric dynamics and physical parameterizations, and a lower resolution for chemistry and aerosol calculations. Initial evaluations of its scientific performance are encouraging. We are currently working on coupling the hybrid-resolution atmosphere-chemistry-aerosol model to the ocean component of the full coupled system.
{"title":"A Hybrid-Resolution Earth System Model","authors":"M. Stringer, Colin G. Jones, R. Hill, M. Dalvi, Colin Johnson, J. Walton","doi":"10.1109/eScience.2018.00042","DOIUrl":"https://doi.org/10.1109/eScience.2018.00042","url":null,"abstract":"We describe a hybrid-resolution version of the UKESM earth system model that reduces the model’s computational costs by using high resolution for simulated resolved atmospheric dynamics and physical parameterizations, and a lower resolution for chemistry and aerosol calculations. Initial evaluations of its scientific performance are encouraging. We are currently working on coupling the hybrid-resolution atmosphere-chemistry-aerosol model to the ocean component of the full coupled system.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"25 1","pages":"268-269"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87960397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/eScience.2018.00046
M. Ramamurthy
The atmospheric modeling community in the United States has relied mostly on high performance computing facilities (e.g., NCAR-Wyoming Supercomputing facility and XSEDE resources) and local computing clusters to perform weather prediction research. Cloud computing represents a fundamental change in the way IT services are developed, deployed, operated, and paid for, placing science communities in the middle of a major paradigm shift. The cloud appears to be a potential avenue for atmospheric science researchers to gain access to significant and seamless computing resources beyond the traditional supercomputing centers for end-to-end weather and climate modeling studies, democratizing access to high performance computing resources, vast amounts of storage, and unprecedented access to large volumes of data.
{"title":"Toward a Cloud Ecosystem for Modeling as a Service","authors":"M. Ramamurthy","doi":"10.1109/eScience.2018.00046","DOIUrl":"https://doi.org/10.1109/eScience.2018.00046","url":null,"abstract":"The atmospheric modeling community in the United States has relied mostly on high performance computing facilities (e.g., NCAR-Wyoming Supercomputing facility and XSEDE resources) and local computing clusters to perform weather prediction research. Cloud computing represents a fundamental change in the way IT services are developed, deployed, operated, and paid for, placing science communities in the middle of a major paradigm shift. The cloud appears to be a potential avenue for atmospheric science researchers to gain access to significant and seamless computing resources beyond the traditional supercomputing centers for end-to-end weather and climate modeling studies, democratizing access to high performance computing resources, vast amounts of storage, and unprecedented access to large volumes of data.","PeriodicalId":6476,"journal":{"name":"2018 IEEE 14th International Conference on e-Science (e-Science)","volume":"20 1","pages":"274-275"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86604289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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