Repository of NSF Funded Publications and Data Sets: "Back of Envelope" 15 year Cost Estimate

In this back of envelope study we calculate the 15 year fixed and variable costs of setting up and running a data repository (or database) to store and serve the publications and datasets derived from research funded by the National Science Foundation (NSF). Costs are computed on a yearly basis using a fixed estimate of the number of papers that are published each year that list NSF as their funding agency. We assume each paper has one dataset and estimate the size of that dataset based on experience. By our estimates, the number of papers generated each year is 64,340. The average dataset size over all seven directorates of NSF is 32 gigabytes (GB). A total amount of data added to the repository is two petabytes (PB) per year, or 30 PB over 15 years. The architecture of the data/paper repository is based on a hierarchical storage model that uses a combination of fast disk for rapid access and tape for high reliability and cost efficient long-term storage. Data are ingested through workflows that are used in university institutional repositories, which add metadata and ensure data integrity. Average fixed costs is approximately $.0.90/GB over 15-year span. Variable costs are estimated at a sliding scale of$150 - $100 per new dataset for up-front curation, or$4.87 – $3.22 per GB. Variable costs reflect a 3 The total projected cost of the data and paper repository is estimated at$167,000,000 over 15 years of operation, curating close to one million of datasets and one million papers. After 15 years and 30 PB of data accumulated and curated, we estimate the cost per gigabyte at $5.56. This$167 million cost is a direct cost in that it does not include federally allowable indirect costs return (ICR). After 15 years, it is reasonable to assume that some datasets will be compressed and rarely accessed. Others may be deemed no longer valuable, e.g., because they are replaced by more accurate results. Therefore, at some point the data growth in the repository will need to be adjusted by use of strategic preservation

CONGAS: a collaborative ontology development framework based on Named GrAphS

The process of ontology development involves a range of skills and know-how often requiring team work of different people, each of them with his own way of contributing to the definition and formalization of the domain representation. For this reason, collaborative development is an important feature for ontology editing tools, and should take into account the different characteristics of team participants, provide them with a dedicated working environment allowing to express their ideas and creativity, still protecting integrity of the shared work. In this paper we present CONGAS, a collaborative version of the Knowledge Management and Acquisition platform Semantic Turkey which, exploiting the potentialities brought by recent introduction of context management into RDF triple graphs, offers a collaborative environment where proposals for ontology evolution can emerge and coexist, be evaluated by team users, trusted across different perspectives and eventually converged into the main development stream

Repository of NSF-funded Publications and Related Datasets: “Back of Envelope” Cost Estimate for 15 years

In this back of envelope study we calculate the 15-year fixed and variable costs of setting up and running a data repository (or database) to store and serve the publications and datasets derived from research funded by the National Science Foundation (NSF). Costs are computed on a yearly basis using a fixed estimate of the number of papers that are published each year that list NSF as their funding agency. We assume each paper has one dataset and estimate the size of that dataset based on experience. By our estimates, the number of papers generated each year is 64,340. The average dataset size over all seven directorates of NSF is 32 gigabytes (GB). A total amount of data added to the repository is two petabytes (PB) per year, or 30 PB over 15 years. The architecture of the data/paper repository is based on a hierarchical storage model that uses a combination of fast disk for rapid access and tape for high reliability and cost efficient long-term storage. Data are ingested through workflows that are used in university institutional repositories, which add metadata and ensure data integrity. Average fixed costs is approximately 0.90 cents per GB over a 15-year span. Variable costs are estimated at a sliding scale of 150-100 dollars per new dataset for up-front curation, or 4.87-3.22 dollars per GB. Variable costs reflect a 3% annual decrease in curation costs as efficiency and automated metadata and provenance capture are anticipated to help reduce what are now largely manual curation efforts. The total projected cost of the data and paper repository is estimated at 167,000,000 dollars over 15 years of operation, curating close to one million of datasets and one million papers. After 15 years and 30 PB of data accumulated and curated, we estimate the cost per gigabyte at 5.56 dollars. This $167 million cost is a direct cost in that it does not include federally allowable indirect costs return (ICR). After 15 years, it is reasonable to assume that some datasets will be compressed and rarely accessed. Others may be deemed no longer valuable, e.g., because they are replaced by more accurate results. Therefore, at some point the data growth in the repository will need to be adjusted by use of strategic preservation

A Semantic Grid Oriented to E-Tourism

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.Comment: 12 PAGES, 7 Figure

The architecture of discovery net : towards grid-based discovery services

Imperial Users onl

Measuring Semantic and Structural Information for Data Oriented Workflow Retrieval with Cost Constraints

The reuse of data oriented workflows (DOWs) can reduce the cost of workflow system development and control the risk of project failure and therefore is crucial for accelerating the automation of business processes. Reusing workflows can be achieved by measuring the similarity among candidate workflows and selecting the workflow satisfying requirements of users from them. However, due to DOWs being often developed based on an open, distributed, and heterogeneous environment, different users often can impose diverse cost constraints on data oriented workflows. This makes the reuse of DOWs challenging. There is no clear solution for retrieving DOWs with cost constraints. In this paper, we present a novel graph based model of DOWs with cost constraints, called constrained data oriented workflow (CDW), which can express cost constraints that users are often concerned about. An approach is proposed for retrieving CDWs, which seamlessly combines semantic and structural information of CDWs. A distance measure based on matrix theory is adopted to seamlessly combine semantic and structural similarities of CDWs for selecting and reusing them. Finally, the related experiments are made to show the effectiveness and efficiency of our approach

Mapping heterogeneous research infrastructure metadata into a unified catalogue for use in a generic virtual research environment

Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers in data science by integrating tools for data discovery, data retrieval, workflow management and researcher collaboration, often coupled with a specific computing infrastructure. Recently, the push for better open data science has led to the creation of a variety of dedicated research infrastructures (RIs) that gather data and provide services to different research communities, all of which can be used independently of any specific VRE. There is therefore a need for generic VREs that can be coupled with the resources of many different RIs simultaneously, easily customised to the needs of specific communities. The resource metadata produced by these RIs rarely all adhere to any one standard or vocabulary however, making it difficult to search and discover resources independently of their providers without some translation into a common framework. Cross-RI search can be expedited by using mapping services that harvest RI-published metadata to build unified resource catalogues, but the development and operation of such services pose a number of challenges. In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata Portal, which uses X3ML mappings to build a single catalogue for describing data products and other resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard to structure its catalogued metadata. We consider the extent to which it addresses the challenges of cross-RI search, particularly in the environmental and earth science domain, and how it can be further augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic search across multiple domains of discourse