Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains

Designing and implementing an open infrastructure for location-based, tourism-related content delivery

The globally observed recession of mobile services market has pushed mobile network operators into looking for opportunities to provide value-added services on top of their high cost infrastructures. Latest advances in self and network-assisted positioning technologies, enable the provision of services that make use of the actual mobile user location. This paper presents the key points and considerations of a detailed approach for designing, developing and evaluating a very low-cost infrastructure, capable of providing tourism content related location-based services. The main effort is taken into allowing the potential integration of various market and technology stakeholders into such services, thus supporting open business models, while at the same time safeguarding end-user privacy

A distributed infrastructure for earth-science big data retrieval

Earth-Science data are composite, multi-dimensional and of significant size, and as such, continue to pose a number of ongoing problems regarding their management. With new and diverse information sources emerging as well as rates of generated data continuously increasing, a persistent challenge becomes more pressing: To make the information existing in multiple heterogeneous resources readily available. The widespread use of the XML data-exchange format has enabled the rapid accumulation of semi-structured metadata for Earth-Science data. In this paper, we exploit this popular use of XML and present the means for querying metadata emanating from multiple sources in a succinct and effective way. Thereby, we release the user from the very tedious and time consuming task of examining individual XML descriptions one by one. Our approach, termed Meta-Array Data Search (MAD Search), brings together diverse data sources while enhancing the user-friendliness of the underlying information sources. We gather metadata using different standards and construct an amalgamated service with the help of tools that discover and harvest such metadata; this service facilitates the end-user by offering easy and timely access to all metadata. The main contribution of our work is a novel query language termed xWCPS, that builds on top of two widely-adopted standards: XQuery and the Web Coverage Processing Service (WCPS). xWCPS furnishes a rich set of features regarding the way scientific data can be queried with. Our proposed unified language allows for requesting metadata while also giving processing directives. Consequently, the xWCPS-enabled MAD Search helps in both retrieval and processing of large data sets hosted in an heterogeneous infrastructure. We demonstrate the effectiveness of our approach through diverse use-cases that provide insights into the syntactic power and overall expressiveness of xWCPS. We evaluate MAD Search in a distributed environment that comprises five high-volume array-databases whose sizes range between 20 and 100 GB and so, we ascertain the applicability and potential of our proposal. © 2015 World Scientific Publishing Company

A functionality perspective on digital library interoperability

Digital Library (DL) interoperability requires addressing a variety of issues associated with functionality. We report on the analysis and solutions identified by the Functionality Working Group of the DL.org project during its deliberations on DL interoperability. Ultimately, we hope that work based on our perspective will lead to improved architectures and software, as well as to greater interoperability, for next-generation DL systems. © 2010 Springer-Verlag Berlin Heidelberg