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Big Biomedical data as the key resource for discovery science

Arthur W Toga, Ian Foster, Carl Kesselman, Ravi Madduri, Kyle Chard, Eric W Deutsch, Nathan D Price, Gustavo Glusman, Benjamin D Heavner, Ivo D Dinov, Joseph Ames, John Van Horn (+2 others)
2015 JAMIA Journal of the American Medical Informatics Association  
This pipeline highlights the ability to process distributed data via an optimized cloud-based analysis platform.  ...  Thus, new methods are required to develop and evaluate modeling and analytical pipelines when applied to big data.  ... 
doi:10.1093/jamia/ocv077 pmid:26198305 pmcid:PMC5009918 fatcat:mkyc2zhcsbahllwbj6yfo6jf2e

OUP accepted manuscript

2017 Briefings in Bioinformatics  
Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators.  ...  As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary.  ...  and João Bosco Jares for helping them in drawing the Figure 2 .  ... 
doi:10.1093/bib/bbx039 pmid:28419324 pmcid:PMC6169675 fatcat:xsdfepwqmnb7pgkmnep2ifn2wi

Interactive and automated debugging for big data analytics

Muhammad Ali Gulzar
2018 Proceedings of the 40th International Conference on Software Engineering Companion Proceeedings - ICSE '18  
We seek to address these challenges with the development of BIGDEBUG, a framework providing interactive debugging primitives and tool-assisted fault localization services for big data analytics.  ...  We showcase the data provenance and optimized incremental computation features to effectively and efficiently support interactive debugging, and investigate new research directions on how to automatically  ...  Data Provenance for Error Tracing A common scenario when debugging big data analytics is to first identify the subset of intermediate data that causes a failure, and then deduce which input data is the  ... 
doi:10.1145/3183440.3190334 dblp:conf/icse/Gulzar18 fatcat:o36lxubmjzfqxmv6p2kfkufkia

Data provenance tracking as the basis for a biomedical virtual research environment [article]

Richard McClatchey
2018 arXiv   pre-print
Scientific workflow based studies are beginning to realize the benefit of capturing this provenance of data and the activities used to process, transform and carry out studies on those data.  ...  test results, patient records, epidemiological analyses etc.) and the workflows (pipelines) used to process those data, together with their provenance data and results sets are captured in the CRISTAL  ...  highlight the support of their home institutes and acknowledge funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 211714 ("neuGRID") and n. 283562 ("neuGRID for  ... 
arXiv:1803.07433v1 fatcat:5pe5trly6fesjb4rmx3npnwbdu

Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model: The Art of "Reinventing Yourself" to Analysis the World in Which We Live

Kareem Nagy Areed, Mahmoud Badawy, Amira Haikal, Mostafa Elhosseini
2020 European Journal of Electrical Engineering and Computer Science  
Big data analysis is therefore a topical area of research and development. The main objective of this survey is to propose Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model.  ...  In recent years, the convergence trend of Big Data, Cloud and IoT has received considerable attention in industry and academia.  ...  Trust and Provenance: For proper Big Data management and based on a trustworthy Big Data Analytics, being able to verify data provenance is essential.  ... 
doi:10.24018/ejece.2020.4.1.183 fatcat:y4tqbtf245aehgjbwpnso6mg5q

mProv: Provenance-Based Data Analytics Cyberinfrastructure for High-Frequency Mobile Sensor Data

Zachary Ives, Santosh Kumar, Mani Srivastava, Ida Sim, Timothy Hnat, Nasir Ali, Ananda Tirtha, Sandeep Singh Sanda, John Frommeyer, Nan Zheng, Soonbo Han, Simona Carini
2022 Zenodo  
mProv develops standards, libraries, and compressed storage/query services to capture fine-grained data provenance over streaming data -- with applications in mobile health sensors and more.  ...  Among other capabilities, our infrastructure facilitates reasoning about data quality, enforcing privacy filter conditions, and high-fidelity sensor data stream replay.  ...  queries Marker Data Streams for Mobile Health Big Data Platform Based on Apache Spark Standards Capture/Storage Privacy Query/Replay Open metadata Services for Access control standards for automatically  ... 
doi:10.5281/zenodo.6851493 fatcat:5bvvd7xau5erzg2bdeac5lf2xm

Big Data Technologies in DataBio [chapter]

Caj Södergård, Tomas Mildorf, Arne J. Berre, Aphrodite Tsalgatidou, Karel Charvát
2021 Big Data in Bioeconomy  
Thereafter, we discuss data pipelines and the Big Data Value (BDV) Reference Model that is referred to repeatedly in the book.  ...  We start with basic concepts of Big Data including the main characteristics volume, velocity and variety.  ...  This chapter also discusses the emerging role of cloud-based platforms for managing Earth observation data in bioeconomy.  ... 
doi:10.1007/978-3-030-71069-9_1 fatcat:wyluyt34bfa2xabz5mjgcsiice

Cloud Based Big Data Infrastructure: Architectural Components And Automated Provisioning

Yuri Demchenko, Fatih Turkmen, Christophe Blanchet, Charles Loomis, Caees de Laat
2016 Zenodo  
the following components of the Big Data technologies: Big Data definition, Data Management including data lifecycle and data structures, Big Data Infrastructure (generically cloud based), Data Analytics  ...  This paper describes the general architecture and functional components of the cloud based Big Data Infrastructure (BDI).  ...  Figure 2 provides a general view of the major components of the Big Data infrastructure that includes the infrastructure for general data management, typically cloud based, and Big Data Analytics part  ... 
doi:10.5281/zenodo.59159 fatcat:yxvau4m65vgkffgwwmy7nwrdmy

The beckman report on database research

Daniel Abadi, Michael J. Franklin, Johannes Gehrke, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, H. V. Jagadish, Donald Kossmann, Samuel Madden, Sharad Mehrotra, Rakesh Agrawal (+18 others)
2016 Communications of the ACM  
and understanding of data; cloud services; and managing the diverse roles of people in the data life cycle.  ...  To do so, the report recommends significantly more attention to five research areas: scalable big/fast data infrastructures; coping with diversity in the data management landscape; end-to-end processing  ...  This area would benefit from attention, as it is a must for coping with Big Data volumes. Analytical data management is knowledge-intensive.  ... 
doi:10.1145/2845915 fatcat:szxbo6tgprcy7jzscvl7wnyfry

The Beckman Report on Database Research

Daniel Abadi, Michael J. Franklin, Johannes Gehrke, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, H. V. Jagadish, Donald Kossmann, Samuel Madden, Sharad Mehrotra, Rakesh Agrawal (+18 others)
2014 SIGMOD record  
and understanding of data; cloud services; and managing the diverse roles of people in the data life cycle.  ...  To do so, the report recommends significantly more attention to five research areas: scalable big/fast data infrastructures; coping with diversity in the data management landscape; end-to-end processing  ...  This area would benefit from attention, as it is a must for coping with Big Data volumes. Analytical data management is knowledge-intensive.  ... 
doi:10.1145/2694428.2694441 fatcat:xwpojiwqyjeqhj6whrgkwalrga

Merging Agents and Cloud Services in Industrial Applications

Francisco P. Maturana, Juan L. Asenjo, Neethu S. Philip, Shweta Chatrola
2014 Applied Computational Intelligence and Soft Computing  
This reporting capability has been architected using networked agents, worker roles, and scripts for building a scalable data pipeline and analytics system.  ...  Cloud infrastructure has been leveraged as the main mechanism for hosting the data and processing needs of a modern industrial information system.  ...  Data Pipeline Architecture The cloud is all about "unlimited" storage and compute resources to process "big data" (volume, velocity, and variety).  ... 
doi:10.1155/2014/124872 fatcat:npqgn5wqqne6bkmqcqj7dju6ay

Big Data Analytics As-A-Service: Issues And Challenges

Ernesto Damiani, Claudio Agostino Ardagna, Paolo Ceravolo
2017 Zenodo  
Under these premises, we propose Big Data Analytics-as-a-Service (BDAaaS) as the next-generation Big Data Analytics paradigm and we discuss issues and challenges from the BDAaaS design and development  ...  The full potential of Big Data Analytics (BDA) can be unleashed only through the definition of approaches that accomplish Big Data users' expectations and requirements, also when the latter are fuzzy and  ...  Being able to verify data provenance is fundamental for a proper Big Data management and at the basis of a trustworthy Big Data analytics.  ... 
doi:10.5281/zenodo.887903 fatcat:etawfqpl4jgr3ll6w52cr2kh5m

Big data analytics as-a-service: Issues and challenges

Claudio A. Ardagna, Paolo Ceravolo, Ernesto Damiani
2016 2016 IEEE International Conference on Big Data (Big Data)  
Under these premises, we propose Big Data Analytics-as-a-Service (BDAaaS) as the next-generation Big Data Analytics paradigm and we discuss issues and challenges from the BDAaaS design and development  ...  The full potential of Big Data Analytics (BDA) can be unleashed only through the definition of approaches that accomplish Big Data users' expectations and requirements, also when the latter are fuzzy and  ...  Being able to verify data provenance is fundamental for a proper Big Data management and at the basis of a trustworthy Big Data analytics.  ... 
doi:10.1109/bigdata.2016.7841029 dblp:conf/bigdataconf/ArdagnaCD16 fatcat:dimjvyqfpvcdxbztn4vjfx73qy

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Venkata Satagopam, Wei Gu, Serge Eifes, Piotr Gawron, Marek Ostaszewski, Stephan Gebel, Adriano Barbosa-Silva, Rudi Balling, Reinhard Schneider
2016 Big Data  
Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline.  ...  We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization  ...  Acknowledgments We would like to thank the reviewers of this article for their constructive remarks that helped in improving the article.  ... 
doi:10.1089/big.2015.0057 pmid:27441714 pmcid:PMC4932659 fatcat:n7fcnszchrhk5of3kvkmvvjbty

D3.1 - Initial setup of policy aware Linked Data architecture and engine [article]

Bert Van Nuffelen (TF)
2017 Zenodo  
This document describes the deployment of a development environment based on the BDE platform with the semantification extensions  ...  The Big Data Europe project's effort in this area is twofold and includes: 1. a Semantic Data Lake, and 2. a Semantic Analytics Stack.  ...  It is cloud vendor neutral and most cloud software systems support it.  ... 
doi:10.5281/zenodo.2543647 fatcat:nwvipaky2rdfpbutdstu2h25py
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