@conference {1414,
	title = {A Computational- and Storage-Cloud for Integration of Biodiversity Collections},
	booktitle = {Proceedings of the 2013 IEEE 9th International Conference on e-Science},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Beijing, China},
	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. nonfederal biocollections. iDigBio chose to use cloud computing technologies to deliver a cyberinfrastructure 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.},
	keywords = {appliances, biocollections infrastructure, biodiversity, cloud computing, data access, data store, digitization, EF-1115210},
	doi = {10.1109/eScience.2013.48},
	url = {http://dx.doi.org/10.1109/eScience.2013.48},
	author = {Matsunaga, Andr{\'e}a and Thompson, Alex and Figueiredo, Renato J. and Germain-Aubrey, Charlotte C. and Collins, Matthew and Beaman, Reed S. and MacFadden, Bruce J. and Riccardi, Greg and Soltis, Pamela S. and Page, Lawrence M. and Fortes, Jos{\'e} A.B.}
}
@conference {2439,
	title = {Lifemapper{\textquoteright}s species distribution modeling based on iDigBio Biocollections},
	booktitle = {SPNHC 2015},
	year = {2015},
	keywords = {EF-1115210, Presentation, SPNHC2015},
	author = {Stewart, Aimee and Matsunaga, Andr{\'e}a and Beach, James H. and Grady, C.J. and Cavner, Jeffrey and Fortes, Jos{\'e} A.B.}
}
@conference {2110,
	title = {Mapping Specifications for Ranked Hierarchical Trees in Data Integration Systems},
	booktitle = {2014 IEEE International Conference on Information Reuse and Integration (IRI 2014)},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {San Francisco, California, USA},
	abstract = {A popular approach to deal with data integration of heterogeneous data sources is to Extract, Transform and Load (ETL) data from disparate sources into a consolidated data store while addressing integration challenges including, but not limited to: structural differences in the source and target schemas, semantic differences in their vocabularies, and data encoding. This work focuses on the integration of tree-like hierarchical data or information that when modeled as a relational schema can take the shape of a flat schema, a self-referential schema or a hybrid schema. Examples include evolutionary taxonomies, geological time scales, and organizational charts. Given the observed complexity in developing ETL processes for this particular but common type of data, our work focuses on reducing the time and effort required to map and transform this data. Our research automates and simplifies the transformation from ranked self-referential to flat representations (and vice-versa), by: (a) proposing MSL+, an extension to IBM{\textquoteright}s Mapping Specification Language (MSL), to succinctly express the mapping between schemas while hiding the actual transformation implementation complexity from the user, and (b) implementing a transformation component for the Talend open-source ETL platform, called Tree Transformer (TT). We evaluated MSL+ and TT, in the context of biodiversity data integration, where this class of transformations is a recurring pattern. We demonstrate the effectiveness of MSL+ with respect to development time savings as well as a 2 to 25-fold performance improvement in transformation time achieved by TT when compared to existing implementations and to Talend built-in components.},
	keywords = {Data Integration, data transformation, EF-1115210, ETL, mapping language, self-referential schema},
	doi = {10.1109/IRI.2014.7051899},
	url = {http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=7051899},
	author = {Soomro, Sarfaraz and Matsunaga, Andr{\'e}a and Fortes, Jos{\'e} A.B.}
}
@article {1780,
	title = {Reaching Consensus in Crowdsourced Transcription of Biocollections Information},
	year = {2014},
	abstract = {Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical biocollections data, and a number of crowdsourcing platforms have been developed to facilitate interaction with the public and to design simple {\textquotedblleft}Human Intelligence Tasks{\textquotedblright}. However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data and a consensus-based controller to assign a dynamic number of workers per task, and (c) proposes further enhancements of future crowdsourcing tasks in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show up to a 45-fold increase in ability to reach consensus when compared to majority voting using exact string matching. In addition, the controller is able to decrease the crowdsourcing cost by 55\% when compared to a strategy that uses a fixed number of workers.},
	keywords = {Citizen Science, crowdsourcing},
	url = {https://www.idigbio.org/sites/default/files/internal-docs/presentations/matsunagacrowdsourcingescience2014-141106090530-conversion-gate01.pdf},
	author = {Matsunaga, Andr{\'e}a and Austin Mast and Fortes, Jos{\'e} A.B.}
}
@conference {1824,
	title = {Reaching Consensus in Crowdsourced Transcription of Biocollections Information},
	booktitle = {2014 IEEE 10th International Conference on e-Science},
	year = {2014},
	month = {Oct/2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Guaruj{\'a}, Brazil},
	abstract = {Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical bio collections data, and a number of crowd sourcing platforms have been developed to facilitate interaction with the public and to design simple "Human Intelligence Tasks". However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data and a consensus-based controller to assign a dynamic number of workers per task, and (c) proposes further enhancements of future crowd sourcing tasks in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show up to a 45-fold increase in ability to reach consensus when compared to majority voting using exact string matching. In addition, the controller is able to decrease the crowd sourcing cost by 55\% when compared to a strategy that uses a fixed number of workers.},
	keywords = {Accuracy, Approximation algorithms, biocollections infrastructure, biodiversity, consensus algorithms, crowdsourcing, digitization, EF-1115210, Heuristic algorithms, Materials, Optical character recognition software, Reliability},
	doi = {10.1109/eScience.2014.30},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6972249},
	author = {Matsunaga, Andr{\'e}a and Mast, Austin and Fortes, Jos{\'e} A.B.}
}
@inbook {2111,
	title = {Simplifying Extract-Transform-Load for Ranked Hierarchical Trees via Mapping Specifications},
	booktitle = {Formalisms for Reuse and Systems Integration},
	series = {Advances in Intelligent Systems and Computing},
	volume = {346},
	year = {2015},
	pages = {203-225},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {A popular approach to deal with data integration of heterogeneous data sources is to Extract, Transform and Load (ETL) data from disparate sources into a consolidated data store while addressing integration challenges including, but not limited to: structural differences in the source and target schemas, semantic differences in their vocabularies, and data encoding. This work focuses on the integration of tree-like hierarchical data or information that when modeled as a relational schema can take the shape of a flat schema, a self-referential schema or a hybrid schema. Examples include evolutionary taxonomies, geological time scales, and organizational charts. Given the observed complexity in developing ETL processes for this particular but common type of data, our work focuses on reducing the time and effort required to map and transform this data. Our research automates and simplifies all possible transformations involving ranked self-referential and flat representations, by: (a) proposing MSL+, an extension to IBM{\textquoteright}s Mapping Specification Language (MSL), to succinctly express the mapping between schemas while hiding the actual transformation implementation complexity from the user, and (b) implementing a transformation component for the Talend open-source ETL platform, called Tree Transformer (TT). We evaluated MSL+ and TT, in the context of biodiversity data integration, where this class of transformations is a recurring pattern. We demonstrate the effectiveness of MSL+ with respect to development time savings as well as a 2 to 25-fold performance improvement in transformation time achieved by TT when compared to existing implementations and to Talend built-in components.},
	keywords = {Data Integration, data transformation, EF-1115210, ETL, flat schema, hierarchical tree, mapping language, MSL, schema mapping, self-referential schema},
	issn = {978-3-319-16576-9},
	doi = {10.1007/978-3-319-16577-6_9},
	url = {http://dx.doi.org/10.1007/978-3-319-16577-6_9},
	author = {Soomro, Sarfaraz and Matsunaga, Andr{\'e}a and Fortes, Jos{\'e} A.B.},
	editor = {Bouabana-Tebibel, Thouraya and Rubin, Stuart H.}
}
