15,814 research outputs found
Development of a New Framework for Distributed Processing of Geospatial Big Data
Geospatial technology is still facing a lack of “out of the box” distributed processing solutions which are suitable for the amount and heterogeneity of geodata, and particularly for use cases requiring a rapid response. Moreover, most of the current distributed computing frameworks have important limitations hindering the transparent and flexible control of processing (and/or storage) nodes and control of distribution of data chunks. We investigated the design of distributed processing systems and existing solutions related to Geospatial Big Data. This research area is highly dynamic in terms of new developments and the re-use of existing solutions (that is, the re-use of certain modules to implement further specific developments), with new implementations continuously emerging in areas such as disaster management, environmental monitoring and earth observation. The distributed processing of raster data sets is the focus of this paper, as we believe that the problem of raster data partitioning is far from trivial: a number of tiling and stitching requirements need to be addressed to be able to fulfil the needs of efficient image processing beyond pixel level. We attempt to compare the terms Big Data, Geospatial Big Data and the traditional Geospatial Data in order to clarify the typical differences, to compare them in terms of storage and processing backgrounds for different data representations and to categorize the common processing systems from the aspect of distributed raster processing. This clarification is necessary due to the fact that they behave differently on the processing side, and particular processing solutions need to be developed according to their characteristics. Furthermore, we compare parallel and distributed computing, taking into account the fact that these are used improperly in several cases. We also briefly assess the widely-known MapReduce paradigm in the context of geospatial applications. The second half of the article reports on a new processing framework initiative, currently at the concept and early development stages, which aims to be capable of processing raster, vector and point cloud data in a distributed IT ecosystem. The developed system is modular, has no limitations on programming language environment, and can execute scripts written in any development language (e.g. Python, R or C#)
Development of a New Framework for Distributed Processing of Geospatial Big Data
Geospatial technology is still facing a lack of “out of the box” distributed processing solutions which are suitable for the amount and heterogeneity of geodata, and particularly for use cases requiring a rapid response. Moreover, most of the current distributed computing frameworks have important limitations hindering the transparent and flexible control of processing (and/or storage) nodes and control of distribution of data chunks. We investigated the design of distributed processing systems and existing solutions related to Geospatial Big Data. This research area is highly dynamic in terms of new developments and the re-use of existing solutions (that is, the re-use of certain modules to implement further specific developments), with new implementations continuously emerging in areas such as disaster management, environmental monitoring and earth observation. The distributed processing of raster data sets is the focus of this paper, as we believe that the problem of raster data partitioning is far from trivial: a number of tiling and stitching requirements need to be addressed to be able to fulfil the needs of efficient image processing beyond pixel level. We attempt to compare the terms Big Data, Geospatial Big Data and the traditional Geospatial Data in order to clarify the typical differences, to compare them in terms of storage and processing backgrounds for different data representations and to categorize the common processing systems from the aspect of distributed raster processing. This clarification is necessary due to the fact that they behave differently on the processing side, and particular processing solutions need to be developed according to their characteristics. Furthermore, we compare parallel and distributed computing, taking into account the fact that these are used improperly in several cases. We also briefly assess the widely-known MapReduce paradigm in the context of geospatial applications. The second half of the article reports on a new processing framework initiative, currently at the concept and early development stages, which aims to be capable of processing raster, vector and point cloud data in a distributed IT ecosystem. The developed system is modular, has no limitations on programming language environment, and can execute scripts written in any development language (e.g. Python, R or C#)
Development of Distributed Research Center for analysis of regional climatic and environmental changes
We present an approach and first results of a collaborative project being carried out by a joint team of researchers from the Institute of Monitoring of Climatic and Ecological Systems, Russia and Earth Systems Research Center UNH, USA. Its main objective is development of a hardware and software platform prototype of a Distributed Research Center (DRC) for monitoring and projecting of regional climatic and environmental changes in the Northern extratropical areas. The DRC should provide the specialists working in climate related sciences and decision-makers with accurate and detailed climatic characteristics for the selected area and reliable and affordable tools for their in-depth statistical analysis and studies of the effects of climate change. Within the framework of the project, new approaches to cloud processing and analysis of large geospatial datasets (big geospatial data) inherent to climate change studies are developed and deployed on technical platforms of both institutions. We discuss here the state of the art in this domain, describe web based information-computational systems developed by the partners, justify the methods chosen to reach the project goal, and briefly list the results obtained so far
Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services
One of the most widely-implemented service standards provided by the Open
Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS).
WMS is widely employed globally, but there is limited knowledge of the global
distribution, adoption status or the service quality of these online WMS
resources. To fill this void, we investigated global WMSs resources and
performed distributed performance monitoring of these services. This paper
explicates a distributed monitoring framework that was used to monitor 46,296
WMSs continuously for over one year and a crawling method to discover these
WMSs. We analyzed server locations, provider types, themes, the spatiotemporal
coverage of map layers and the service versions for 41,703 valid WMSs.
Furthermore, we appraised the stability and performance of basic operations for
1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major
reasons for request errors and performance issues, as well as the relationship
between service response times and the spatiotemporal distribution of client
monitoring sites. This paper will help service providers, end users and
developers of standards to grasp the status of global WMS resources, as well as
to understand the adoption status of OGC standards. The conclusions drawn in
this paper can benefit geospatial resource discovery, service performance
evaluation and guide service performance improvements.Comment: 24 pages; 15 figure
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Apache Calcite is a foundational software framework that provides query
processing, optimization, and query language support to many popular
open-source data processing systems such as Apache Hive, Apache Storm, Apache
Flink, Druid, and MapD. Calcite's architecture consists of a modular and
extensible query optimizer with hundreds of built-in optimization rules, a
query processor capable of processing a variety of query languages, an adapter
architecture designed for extensibility, and support for heterogeneous data
models and stores (relational, semi-structured, streaming, and geospatial).
This flexible, embeddable, and extensible architecture is what makes Calcite an
attractive choice for adoption in big-data frameworks. It is an active project
that continues to introduce support for the new types of data sources, query
languages, and approaches to query processing and optimization.Comment: SIGMOD'1
Improving Big Data Visual Analytics with Interactive Virtual Reality
For decades, the growth and volume of digital data collection has made it
challenging to digest large volumes of information and extract underlying
structure. Coined 'Big Data', massive amounts of information has quite often
been gathered inconsistently (e.g from many sources, of various forms, at
different rates, etc.). These factors impede the practices of not only
processing data, but also analyzing and displaying it in an efficient manner to
the user. Many efforts have been completed in the data mining and visual
analytics community to create effective ways to further improve analysis and
achieve the knowledge desired for better understanding. Our approach for
improved big data visual analytics is two-fold, focusing on both visualization
and interaction. Given geo-tagged information, we are exploring the benefits of
visualizing datasets in the original geospatial domain by utilizing a virtual
reality platform. After running proven analytics on the data, we intend to
represent the information in a more realistic 3D setting, where analysts can
achieve an enhanced situational awareness and rely on familiar perceptions to
draw in-depth conclusions on the dataset. In addition, developing a
human-computer interface that responds to natural user actions and inputs
creates a more intuitive environment. Tasks can be performed to manipulate the
dataset and allow users to dive deeper upon request, adhering to desired
demands and intentions. Due to the volume and popularity of social media, we
developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing
emerging technologies of today to create a fully immersive tool that promotes
visualization and interaction can help ease the process of understanding and
representing big data.Comment: 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing
Conference (HPEC '15); corrected typo
Developing an open data portal for the ESA climate change initiative
We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges.
An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence
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