scholarly journals Multidimensional enrichment of spatial RDF data for SOLAP

Semantic Web ◽  
2021 ◽  
pp. 1-35
Author(s):  
Nurefşan Gür ◽  
Torben Bach Pedersen ◽  
Katja Hose ◽  
Mikael Midtgaard
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Spatial Information ◽  
Complex Geometry ◽  
Open Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Wide Range ◽  
Advanced Analysis ◽  
Rdf Data

Large volumes of spatial data and multidimensional data are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) and QB4OLAP vocabularies have been widely used for annotating and publishing statistical and multidimensional RDF data. Although such statistical data sets might have spatial information, such as coordinates, the lack of spatial semantics and spatial multidimensional concepts in QB4OLAP and QB prevents users from employing SOLAP queries over spatial data using SPARQL. The QB4SOLAP vocabulary, on the other hand, fully supports annotating spatial and multidimensional data on the Semantic Web and enables users to query endpoints with SOLAP operators in SPARQL. To bridge the gap between QB/QB4OLAP and QB4SOLAP, we propose an RDF2SOLAP enrichment model that automatically annotates spatial multidimensional concepts with QB4SOLAP and in doing so enables SOLAP on existing QB and QB4OLAP data on the Semantic Web. Furthermore, we present and evaluate a wide range of enrichment algorithms and apply them on a non-trivial real-world use case involving governmental open data with complex geometry types.

scholarly journals QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 265
Author(s):  
Irya Wisnubhadra ◽  
Safiza Kamal Baharin ◽  
Nurul A. Emran ◽  
Djoko Budiyanto Setyohadi
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Moving Objects ◽  
Open Data ◽  
Moving Object ◽  
Online Analytical Processing ◽  
Multidimensional Data ◽  
Trajectory Data ◽  
Advanced Analysis ◽  
Analytical Processing

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

Semantic Web Standards for Publishing and Integrating Open Data

2014 ◽  
pp. 28-47
Author(s):  
Axel Polleres ◽  
Simon Steyskal
Keyword(s):  
Semantic Web ◽  
World Wide ◽  
Open Data ◽  
Standard Data ◽  
Web Standards ◽  
Web Of Data ◽  
Structured Information ◽  
Potential Risks ◽  
Rdf Data ◽  
The Web

The World Wide Web Consortium (W3C) as the main standardization body for Web standards has set a particular focus on publishing and integrating Open Data. In this chapter, the authors explain various standards from the W3C's Semantic Web activity and the—potential—role they play in the context of Open Data: RDF, as a standard data format for publishing and consuming structured information on the Web; the Linked Data principles for interlinking RDF data published across the Web and leveraging a Web of Data; RDFS and OWL to describe vocabularies used in RDF and for describing mappings between such vocabularies. The authors conclude with a review of current deployments of these standards on the Web, particularly within public Open Data initiatives, and discuss potential risks and challenges.

Database for monitoring the spatial management plans of the Polish marine areas. Updating and practical use of data

2018 ◽  
Vol 33 (1) ◽  
pp. 130-138
Author(s):  
Łukasz Szydłowski
Keyword(s):  
Spatial Data ◽  
Database Management ◽  
Planning Process ◽  
Spatial Information ◽  
Local Approach ◽  
Effective Work ◽  
Spatial Management ◽  
Wide Range ◽  
Management Plans ◽  
Marine Areas

Planning and marine spatial development are based on an exceptionally wide range of knowledge and information which are used in the process of creating plans and their later evaluation. The first type is spatial data describing the present state of the natural and anthropogenic environment within a widely understood spectrum. Second type data are statistical information describing spatial occurrence in the environment. Geoinformation within a planning process is hugely important, as the quality of spatial data influences decisions made and final results of planning work. Undoubtedly, efficient spatial database management and creating a compatible system to operate it are the key elements of effective work in a planning process. In Europe, the monitoring process together with creation of tools supporting database management is highly developed. There is a range of examples for the use of the spatial information systems in work linked to preparation and evaluation of spatial management plans at sea. Due to the specifics of works related to spatial planning in Poland, this paper presents a new solution for the future monitoring of the generated plans. The nature of this paper is determined by the local approach to taking advantage of the ArcGis software related with the use of a range of tools in the monitoring approach to plans of spatial management of the Polish marine areas. The purpose is to demonstrate a selected tool which is supposed to improve the planners’ work, from the point of view of the use of statistical data linked to the dynamics of changes in the coastal area. This is an exemplary use of the tool which might be modified at will, according to the needs of a user. Due to such a solution, the tool can be adjusted to the most of required data based on statistical tables.

scholarly journals Integrating Open Data Cube and Brazil Data Cube Platforms for Land Use and Cover Classifications

2021 ◽  
Vol 73 (4) ◽  
pp. 1036-1047
Author(s):  
Felipe Menino Carlos ◽  
Vitor Conrado Faria Gomes ◽  
Gilberto Ribeiro de Queiroz ◽  
Felipe Carvalho de Souza ◽  
Karine Reis Ferreira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Open Data ◽  
Data Cube ◽  
Machine Learning Techniques ◽  
Multidimensional Data ◽  
Temporal Dimension ◽  
Data Cubes

The potential to perform spatiotemporal analysis of the Earth's surface, fostered by a large amount of Earth Observation (EO) open data provided by space agencies, brings new perspectives to create innovative applications. Nevertheless, these big datasets pose some challenges regarding storage and analytical processing capabilities. The organization of these datasets as multidimensional data cubes represents the state-of-the-art in analysis-ready data regarding information extraction. EO data cubes can be defined as a set of time-series images associated with spatially aligned pixels along the temporal dimension. Some key technologies have been developed to take advantage of the data cube power. The Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform provide capabilities to access and analyze EO data cubes. This paper introduces two new tools to facilitate the creation of land use and land over (LULC) maps using EO data cubes and Machine Learning techniques, and both built on top of ODC and BDC technologies. The first tool is a module that extends the ODC framework capabilities to lower the barriers to use Machine Learning (ML) algorithms with EO data. The second tool relies on integrating the R package named Satellite Image Time Series (sits) with ODC to enable the use of the data managed by the framework. Finally, water mask classification and LULC mapping applications are presented to demonstrate the processing capabilities of the tools.

scholarly journals Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary

2020 ◽  
Vol 68 ◽  
pp. 103378 ◽  
Author(s):  
Pilar Escobar ◽  
Gustavo Candela ◽  
Juan Trujillo ◽  
Manuel Marco-Such ◽  
Jesús Peral
Keyword(s):  
Open Data ◽  
Data Cube ◽  
Linked Open Data ◽  
Multidimensional Model ◽  
Rdf Data ◽  
Model Approach

Applications, Methodologies, and Technologies for Linked Open Data

2020 ◽  
Vol 16 (3) ◽  
pp. 53-69
Author(s):  
Cecilia Avila-Garzon
Keyword(s):  
Semantic Web ◽  
Open Data ◽  
Linked Open Data ◽  
Business Culture ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Wide Range ◽  
Research And Education ◽  
User Friendly ◽  
Culture Education

Advances in semantic web technologies have rocketed the volume of linked data published on the web. In this regard, linked open data (LOD) has long been a topic of great interest in a wide range of fields (e.g. open government, business, culture, education, etc.). This article reports the results of a systematic literature review on LOD. 250 articles were reviewed for providing a general overview of the current applications, technologies, and methodologies for LOD. The main findings include: i) most of the studies conducted so far focus on the use of semantic web technologies and tools applied to contexts such as biology, social sciences, libraries, research, and education; ii) there is a lack of research with regard to a standardized methodology for managing LOD; and iii) a plenty of tools can be used for managing LOD, but most of them lack of user-friendly interfaces for querying datasets.

Semantic Web Standards for Publishing and Integrating Open Data

2015 ◽  
pp. 1-20 ◽  
Author(s):  
Axel Polleres ◽  
Simon Steyskal
Keyword(s):  
Semantic Web ◽  
World Wide ◽  
Open Data ◽  
Standard Data ◽  
Web Standards ◽  
Web Of Data ◽  
Structured Information ◽  
Potential Risks ◽  
Rdf Data ◽  
The Web

The World Wide Web Consortium (W3C) as the main standardization body for Web standards has set a particular focus on publishing and integrating Open Data. In this chapter, the authors explain various standards from the W3C's Semantic Web activity and the—potential—role they play in the context of Open Data: RDF, as a standard data format for publishing and consuming structured information on the Web; the Linked Data principles for interlinking RDF data published across the Web and leveraging a Web of Data; RDFS and OWL to describe vocabularies used in RDF and for describing mappings between such vocabularies. The authors conclude with a review of current deployments of these standards on the Web, particularly within public Open Data initiatives, and discuss potential risks and challenges.

scholarly journals A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

2021 ◽  
Vol 10 (2) ◽  
pp. 87
Author(s):  
Jean-Paul Kasprzyk ◽  
Guénaël Devillet
Keyword(s):  
Spatial Data ◽  
Unified Modeling Language ◽  
Social Economy ◽  
Original Data ◽  
Heterogeneous Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Data Cubes ◽  
Geographic Analysis

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration.

scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

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