Pub Date : 2013-10-22DOI: 10.1109/eScience.2013.17
Kejun Dong, Ji Li, Kai Nan, Wilfred W. Li
Rapid advances in scientific research have led to unprecedented data deluge and significant challenges in data interoperability, certification and collaboration. The Collaboration LiBrary (CLB) is designed to manage and collate millions of data files by setting up a unified, robust, and scalable data repository, especially in support of experimental data collaboration and timeline-based data life cycle management. It has recently been released as a component of Duckling, an open-source collaboration environment toolkit developed by the Chinese Academy of Sciences (CAS) and widely adopted in many disciplines. In this paper, we present newly developed components for data synchronization and snapshots in an updated architecture for CLB. We have also extended CLB with new data cloud service modules (CLB+) that enables data mapping and synchronization from the cloud to user workspace. CLB+ is implemented as CLB plugins that provide interfaces with biomedical research cloud services from a computer aided drug discovery (CADD) workflow for ensemble-based virtual screening. The flexible plug in architecture of CLB makes it easy to develop a prototype biomedical research data cloud environment. Many other e-science applications may leverage or expand CLB functionalities in data life cycle management in a similar fashion.
{"title":"Biomedical Research Data Cloud Services with Duckling Collaboration LiBrary (CLB)","authors":"Kejun Dong, Ji Li, Kai Nan, Wilfred W. Li","doi":"10.1109/eScience.2013.17","DOIUrl":"https://doi.org/10.1109/eScience.2013.17","url":null,"abstract":"Rapid advances in scientific research have led to unprecedented data deluge and significant challenges in data interoperability, certification and collaboration. The Collaboration LiBrary (CLB) is designed to manage and collate millions of data files by setting up a unified, robust, and scalable data repository, especially in support of experimental data collaboration and timeline-based data life cycle management. It has recently been released as a component of Duckling, an open-source collaboration environment toolkit developed by the Chinese Academy of Sciences (CAS) and widely adopted in many disciplines. In this paper, we present newly developed components for data synchronization and snapshots in an updated architecture for CLB. We have also extended CLB with new data cloud service modules (CLB+) that enables data mapping and synchronization from the cloud to user workspace. CLB+ is implemented as CLB plugins that provide interfaces with biomedical research cloud services from a computer aided drug discovery (CADD) workflow for ensemble-based virtual screening. The flexible plug in architecture of CLB makes it easy to develop a prototype biomedical research data cloud environment. Many other e-science applications may leverage or expand CLB functionalities in data life cycle management in a similar fashion.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128222321","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 : 2013-10-22DOI: 10.1109/eScience.2013.27
Ferry Hendrikx, K. Bubendorfer
Social clouds are a relatively new paradigm that allow users of an underlying social network to share their resources with their "friends", using previously established relationships. However, this sharing has a number of issues, including granularity of friendships, resource costs and maintenance. In this paper we argue that sharing decisions should be based on relationship information augmented by supplementary metadata derived from multiple sources. Users should be able to leverage the information available on their non-uniform friend relationships when making decisions, allowing them to confidently share their resources with those that would normally be outside of their immediate social circle. We introduce Graft, our Generalised Recommendation Architecture, that provides us with a mechanism to support this new approach.
{"title":"Policy Derived Access Rights in the Social Cloud","authors":"Ferry Hendrikx, K. Bubendorfer","doi":"10.1109/eScience.2013.27","DOIUrl":"https://doi.org/10.1109/eScience.2013.27","url":null,"abstract":"Social clouds are a relatively new paradigm that allow users of an underlying social network to share their resources with their \"friends\", using previously established relationships. However, this sharing has a number of issues, including granularity of friendships, resource costs and maintenance. In this paper we argue that sharing decisions should be based on relationship information augmented by supplementary metadata derived from multiple sources. Users should be able to leverage the information available on their non-uniform friend relationships when making decisions, allowing them to confidently share their resources with those that would normally be outside of their immediate social circle. We introduce Graft, our Generalised Recommendation Architecture, that provides us with a mechanism to support this new approach.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115142134","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 : 2013-10-22DOI: 10.1109/eScience.2013.24
Roselyne B. Tchoua, J. Choi, S. Klasky, Qing Liu, Jeremy S. Logan, K. Moreland, Jingqing Mu, M. Parashar, N. Podhorszki, D. Pugmire, M. Wolf
Scientific communities have benefitted from a significant increase of available computing and storage resources in the last few decades. For science projects that have access to leadership scale computing resources, the capacity to produce data has been growing exponentially. Teams working on such projects must now include, in addition to the traditional application scientists, experts in various disciplines including applied mathematicians for development of algorithms, visualization specialists for large data, and I/O specialists. Sharing of knowledge and data is becoming a requirement for scientific discovery, providing useful mechanisms to facilitate this sharing is a key challenge for e-Science. Our hypothesis is that in order to decrease the time to solution for application scientists we need to lower the barrier of entry into related computing fields. We aim at improving users' experience when interacting with a vast software ecosystem and/or huge amount of data, while maintaining focus on their primary research field. In this context we present our approach to bridge the gap between the application scientists and the visualization experts through a visualization schema as a first step and proof of concept for a new way to look at interdisciplinary collaboration among scientists dealing with big data. The key to our approach is recognizing that our users are scientists who mostly work as islands. They tend to work in very specialized environment but occasionally have to collaborate with other researchers in order to take full advantage of computing innovations and get insight from big data. We present an example of identifying the connecting elements between one of such relationships and offer a liaison schema to facilitate their collaboration.
{"title":"ADIOS Visualization Schema: A First Step Towards Improving Interdisciplinary Collaboration in High Performance Computing","authors":"Roselyne B. Tchoua, J. Choi, S. Klasky, Qing Liu, Jeremy S. Logan, K. Moreland, Jingqing Mu, M. Parashar, N. Podhorszki, D. Pugmire, M. Wolf","doi":"10.1109/eScience.2013.24","DOIUrl":"https://doi.org/10.1109/eScience.2013.24","url":null,"abstract":"Scientific communities have benefitted from a significant increase of available computing and storage resources in the last few decades. For science projects that have access to leadership scale computing resources, the capacity to produce data has been growing exponentially. Teams working on such projects must now include, in addition to the traditional application scientists, experts in various disciplines including applied mathematicians for development of algorithms, visualization specialists for large data, and I/O specialists. Sharing of knowledge and data is becoming a requirement for scientific discovery, providing useful mechanisms to facilitate this sharing is a key challenge for e-Science. Our hypothesis is that in order to decrease the time to solution for application scientists we need to lower the barrier of entry into related computing fields. We aim at improving users' experience when interacting with a vast software ecosystem and/or huge amount of data, while maintaining focus on their primary research field. In this context we present our approach to bridge the gap between the application scientists and the visualization experts through a visualization schema as a first step and proof of concept for a new way to look at interdisciplinary collaboration among scientists dealing with big data. The key to our approach is recognizing that our users are scientists who mostly work as islands. They tend to work in very specialized environment but occasionally have to collaborate with other researchers in order to take full advantage of computing innovations and get insight from big data. We present an example of identifying the connecting elements between one of such relationships and offer a liaison schema to facilitate their collaboration.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129311341","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 : 2013-10-22DOI: 10.1109/eScience.2013.35
Charith Wickramaarachchi, Yogesh L. Simmhan
Continuous data flows complement scientific work-flows by allowing composition of real time data ingest and analytics pipelines to process data streams from pervasive sensors and "always-on" scientific instruments. Such data flows are mission-critical applications that cannot suffer downtime, need to operate consistently, and are long running, but may need to be updated to fix bugs or add features. This poses the problem: How do we update the continuous dataflow application with minimal disruption? In this paper, we formalize different types of dataflow update models for continuous dataflow applications, and identify the qualitative and quantitative metrics to be considered when choosing an update strategy. We propose five dataflow update strategies, and analytically characterize their performance trade-offs. We validate one of these consistent, low-latency update strategies using the Floe dataflow engine for an eEngineering application from the Smart Power Grid domain, and show its relative performance benefits against a naïve update strategy.
{"title":"Continuous Dataflow Update Strategies for Mission-Critical Applications","authors":"Charith Wickramaarachchi, Yogesh L. Simmhan","doi":"10.1109/eScience.2013.35","DOIUrl":"https://doi.org/10.1109/eScience.2013.35","url":null,"abstract":"Continuous data flows complement scientific work-flows by allowing composition of real time data ingest and analytics pipelines to process data streams from pervasive sensors and \"always-on\" scientific instruments. Such data flows are mission-critical applications that cannot suffer downtime, need to operate consistently, and are long running, but may need to be updated to fix bugs or add features. This poses the problem: How do we update the continuous dataflow application with minimal disruption? In this paper, we formalize different types of dataflow update models for continuous dataflow applications, and identify the qualitative and quantitative metrics to be considered when choosing an update strategy. We propose five dataflow update strategies, and analytically characterize their performance trade-offs. We validate one of these consistent, low-latency update strategies using the Floe dataflow engine for an eEngineering application from the Smart Power Grid domain, and show its relative performance benefits against a naïve update strategy.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129694553","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 : 2013-10-22DOI: 10.1109/ESCIENCE.2013.48
Andréa M. Matsunaga, A. Thompson, R. Figueiredo, Charlotte C. Germain-Aubrey, Matthew Collins, R. Beaman, B. MacFadden, G. Riccardi, P. Soltis, L. Page, J. Fortes
A core mission of the Integrated Digitized Biocollections (iDigBio) project is the building and deployment of a cloud computing environment customized to support the digitization workflow and integration of data from all U.S. non-federal biocollections. iDigBio chose to use cloud computing technologies to deliver a cyber infrastructure that is flexible, agile, resilient, and scalable to meet the needs of the biodiversity community. In this context, this paper describes the integration of open source cloud middleware, applications, and third party services using standard formats, protocols, and services. In addition, this paper demonstrates the value of the digitized information from collections in a broader scenario involving multiple disciplines.
{"title":"A Computational- and Storage-Cloud for Integration of Biodiversity Collections","authors":"Andréa M. Matsunaga, A. Thompson, R. Figueiredo, Charlotte C. Germain-Aubrey, Matthew Collins, R. Beaman, B. MacFadden, G. Riccardi, P. Soltis, L. Page, J. Fortes","doi":"10.1109/ESCIENCE.2013.48","DOIUrl":"https://doi.org/10.1109/ESCIENCE.2013.48","url":null,"abstract":"A core mission of the Integrated Digitized Biocollections (iDigBio) project is the building and deployment of a cloud computing environment customized to support the digitization workflow and integration of data from all U.S. non-federal biocollections. iDigBio chose to use cloud computing technologies to deliver a cyber infrastructure that is flexible, agile, resilient, and scalable to meet the needs of the biodiversity community. In this context, this paper describes the integration of open source cloud middleware, applications, and third party services using standard formats, protocols, and services. In addition, this paper demonstrates the value of the digitized information from collections in a broader scenario involving multiple disciplines.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122664977","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 : 2013-10-22DOI: 10.1109/eScience.2013.54
Richard Whaling, T. Malik, Ian T Foster
Research networking platforms, such as VIVO and Profiles Networking provide an information infrastructure for scholarship, representing information about research and researchers-their scholarly works, research interests, and organizational relationships. These platforms are open information infrastructures for scholarship, consisting of linked open data and open-source software tools for managing and visualizing scholarly information. Being RDF based, faceted browsing is a natural technique for navigating such data, partitioning the scholarly information space into orthogonal conceptual dimensions. However, this technique has so far been explored through limited queries in research networking platforms-not allowing for instance full graph based navigation on RDF data. In this paper we present Lens a client-side user interface for faceted navigation of scholarly RDF data. Lens is based on Exhibit, which is a lightweight structured data-publishing framework, but extends Exhibit for expressive SPARQL-like queries and scales it up for navigating amounts of RDF data. Lens consumes data in VIVO ontology, the de facto schema for researcher networking systems. We show how Lens provides better usability over current faceted browsers for research networking platforms.
{"title":"Lens: A Faceted Browser for Research Networking Platforms","authors":"Richard Whaling, T. Malik, Ian T Foster","doi":"10.1109/eScience.2013.54","DOIUrl":"https://doi.org/10.1109/eScience.2013.54","url":null,"abstract":"Research networking platforms, such as VIVO and Profiles Networking provide an information infrastructure for scholarship, representing information about research and researchers-their scholarly works, research interests, and organizational relationships. These platforms are open information infrastructures for scholarship, consisting of linked open data and open-source software tools for managing and visualizing scholarly information. Being RDF based, faceted browsing is a natural technique for navigating such data, partitioning the scholarly information space into orthogonal conceptual dimensions. However, this technique has so far been explored through limited queries in research networking platforms-not allowing for instance full graph based navigation on RDF data. In this paper we present Lens a client-side user interface for faceted navigation of scholarly RDF data. Lens is based on Exhibit, which is a lightweight structured data-publishing framework, but extends Exhibit for expressive SPARQL-like queries and scales it up for navigating amounts of RDF data. Lens consumes data in VIVO ontology, the de facto schema for researcher networking systems. We show how Lens provides better usability over current faceted browsers for research networking platforms.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"704 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116963480","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 : 2013-10-22DOI: 10.1109/eScience.2013.20
Juan Zhao, Kejun Dong, Jianjun Yu, Wei Hu
In this paper, we carry out a knowledge-based scholar network practice towards Research community, named Research Social Networking, shortly DSN, by setting up a large knowledge base of scientists. We discuss key technologies in the paper, including scholar disambiguation and relationship extraction with the better performance evaluation than traditional methods. The DSN system has been implemented and integrated with Duckling cloud service, known as Research Online, with more than 60 thousand scientists and 100 thousand papers.
在本文中，我们通过建立一个庞大的科学家知识库，对科研社区进行了基于知识的学者网络实践，称为科研社交网络(Research Social Networking，简称DSN)。本文讨论了学者消歧和关系提取等关键技术，并对其进行了性能评价。DSN系统已实施并与小鸭云服务集成，称为“研究在线”，拥有6万多名科学家，10万多篇论文。
{"title":"DSN: A Knowledge-Based Scholar Networking Practice Towards Research Community","authors":"Juan Zhao, Kejun Dong, Jianjun Yu, Wei Hu","doi":"10.1109/eScience.2013.20","DOIUrl":"https://doi.org/10.1109/eScience.2013.20","url":null,"abstract":"In this paper, we carry out a knowledge-based scholar network practice towards Research community, named Research Social Networking, shortly DSN, by setting up a large knowledge base of scientists. We discuss key technologies in the paper, including scholar disambiguation and relationship extraction with the better performance evaluation than traditional methods. The DSN system has been implemented and integrated with Duckling cloud service, known as Research Online, with more than 60 thousand scientists and 100 thousand papers.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124473020","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 : 2013-10-22DOI: 10.1109/ESCIENCE.2013.51
R. Cushing, M. Bubak, A. Belloum, C. D. Laat
The multitude of scientific services and processes being developed brings about challenges for future in silico distributed experiments. Choosing the correct service from an expanding body of processes means that the the task of manually building workflows is becoming untenable. In this paper we propose a framework to tackle the future of scientific collaborative distributed computing. We introduce the notion of Networked Open Processes whereby processes are exposed, published, and linked using semantics in the same way as is done with Linked Open Data. As part of the framework we introduce several novel concepts including Process Object Identifiers, Semantic Function Templates, and TReQL, a SQL-like language for querying networked open process graphs.
{"title":"Beyond Scientific Workflows: Networked Open Processes","authors":"R. Cushing, M. Bubak, A. Belloum, C. D. Laat","doi":"10.1109/ESCIENCE.2013.51","DOIUrl":"https://doi.org/10.1109/ESCIENCE.2013.51","url":null,"abstract":"The multitude of scientific services and processes being developed brings about challenges for future in silico distributed experiments. Choosing the correct service from an expanding body of processes means that the the task of manually building workflows is becoming untenable. In this paper we propose a framework to tackle the future of scientific collaborative distributed computing. We introduce the notion of Networked Open Processes whereby processes are exposed, published, and linked using semantics in the same way as is done with Linked Open Data. As part of the framework we introduce several novel concepts including Process Object Identifiers, Semantic Function Templates, and TReQL, a SQL-like language for querying networked open process graphs.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689605","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 : 2013-10-22DOI: 10.1109/eScience.2013.22
S. Schlachter, Stephen Herbein, M. Taufer, S. Ou, Sandeep Patel, Jeremy S. Logan
Efficiently studying Sodium Dodecyl Sulfate (SDS) molecules' formations in the presence of different molar concentrations on high-end GPU clusters whose nodes share accelerators exposes us to several challenges, including the need to dynamically adapt the job lengths. Neither virtualization nor lightweight OS solutions can easily support generality, portability, and maintainability in concert. Our solution complements rather than rewrites existing workflow and resource managers with a companion module that complements functions of the workflow manager and a wrapper module that extends functions of the resource managers. Results on the Keene land cluster show how, by using our modules, accelerated SDS simulations more efficiently use the cluster's GPUs while leading to relevant scientific observations.
{"title":"Efficient SDS Simulations on Multi-GPU Nodes of XSEDE High-End Clusters","authors":"S. Schlachter, Stephen Herbein, M. Taufer, S. Ou, Sandeep Patel, Jeremy S. Logan","doi":"10.1109/eScience.2013.22","DOIUrl":"https://doi.org/10.1109/eScience.2013.22","url":null,"abstract":"Efficiently studying Sodium Dodecyl Sulfate (SDS) molecules' formations in the presence of different molar concentrations on high-end GPU clusters whose nodes share accelerators exposes us to several challenges, including the need to dynamically adapt the job lengths. Neither virtualization nor lightweight OS solutions can easily support generality, portability, and maintainability in concert. Our solution complements rather than rewrites existing workflow and resource managers with a companion module that complements functions of the workflow manager and a wrapper module that extends functions of the resource managers. Results on the Keene land cluster show how, by using our modules, accelerated SDS simulations more efficiently use the cluster's GPUs while leading to relevant scientific observations.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122567097","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 : 2013-10-22DOI: 10.1109/eScience.2013.18
Damian J. Matuszewski, R. Lopes, R. M. C. Junior
Plankton microorganisms constitute the base of the marine food web and play a great role in global atmospheric carbon dioxide draw down. Moreover, being very sensitive to any environmental changes they allow noticing (and potentially counteracting) them faster than with any other means. As such they not only influence the fishery industry but are also frequently used to analyze changes in exploited coastal areas and the influence of these interferences on local environment and climate. As a consequence, there is a strong need for highly efficient systems allowing long time and large volume observation of plankton communities. The adopted sensors typically provide huge amounts of data that must be processed efficiently. This would provide us with better understanding of their role on global climate as well as help maintain the fragile environmental equilibrium. In this paper a new system for large volume plankton monitoring system is presented. It is based on visual analysis of small particles immersed in a water flux. The image sequences are analyzed with Visual Rhythm-based method which greatly accelerates the processing time and allows higher volume throughput. To assure maximal performance the algorithm was implemented using CUDA for GPGPU. The method was then tested on a large data set and compared with alternative frame-by-frame approach. The results prove that the method can be successfully applied for the large volume plankton monitoring problem, as well as in any other application where targets are to be detected and counted while moving in a unidirectional flux.
{"title":"Visual Rhythm-Based Method for Continuous Plankton Monitoring","authors":"Damian J. Matuszewski, R. Lopes, R. M. C. Junior","doi":"10.1109/eScience.2013.18","DOIUrl":"https://doi.org/10.1109/eScience.2013.18","url":null,"abstract":"Plankton microorganisms constitute the base of the marine food web and play a great role in global atmospheric carbon dioxide draw down. Moreover, being very sensitive to any environmental changes they allow noticing (and potentially counteracting) them faster than with any other means. As such they not only influence the fishery industry but are also frequently used to analyze changes in exploited coastal areas and the influence of these interferences on local environment and climate. As a consequence, there is a strong need for highly efficient systems allowing long time and large volume observation of plankton communities. The adopted sensors typically provide huge amounts of data that must be processed efficiently. This would provide us with better understanding of their role on global climate as well as help maintain the fragile environmental equilibrium. In this paper a new system for large volume plankton monitoring system is presented. It is based on visual analysis of small particles immersed in a water flux. The image sequences are analyzed with Visual Rhythm-based method which greatly accelerates the processing time and allows higher volume throughput. To assure maximal performance the algorithm was implemented using CUDA for GPGPU. The method was then tested on a large data set and compared with alternative frame-by-frame approach. The results prove that the method can be successfully applied for the large volume plankton monitoring problem, as well as in any other application where targets are to be detected and counted while moving in a unidirectional flux.","PeriodicalId":325272,"journal":{"name":"2013 IEEE 9th International Conference on e-Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126289682","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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