Spatio-Temporal Gridded Data Processing on the Semantic Web

Andrejev, Andrej; Mišev, Dimitar; Baumann, Peter; Risch, Tore

doi:10.1109/dsdis.2015.109

Cited by 18 publications

(12 citation statements)

References 12 publications

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“…The first option relies on a semantic representation of the raster pixels so that each pixel attribute (geometry, values) is maintained. [17] designed the RDF Grid coverage ontology to support a native integration of coverage in RDF triplestores. The gridded structure of the data is preserved and can be queried using SciSPARQL.…”

Section: Processing Of Raster Data In a Semantic Frameworkmentioning

confidence: 99%

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Tran

Aussenac-Gilles

Comparot

et al. 2020

IJGI

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Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison.

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Section: Processing Of Raster Data In a Semantic Frameworkmentioning

confidence: 99%

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Tran

Aussenac-Gilles

Comparot

et al. 2020

IJGI

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show abstract

“…Much of the work into searching semantically has been done in the context of searching the web. [17][18][19] Some of these works, such as RQL by Karvounarakis et al, 20 require users to formulate queries using some formal language or form, which leads to very precise searching that is inappropriate for naïve or everyday users. Glover et al 21 and Lei et al 22 aimed for a completely user-transparent solution where the user needs only to write a simple query with possible tags, while others still 23,24 aimed for a hybrid approach in which the system may ask a user for clarification on the meaning of their query.…”

Section: Semantic Searchmentioning

confidence: 99%

S3BD: Secure semantic search over encrypted big data in the cloud

Woodworth

Salehi²

2018

Concurrency and Computation

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Summary Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage service due to valid concerns about data privacy. An established solution to the cloud data privacy problem is to perform encryption on the client‐end. This approach, however, restricts data processing capabilities (eg, searching over the data). Accordingly, the research problem we investigate is how to enable real‐time searching over the encrypted big data in the cloud. In particular, semantic search is of interest to clients dealing with big data. To address this problem, in this research, we develop a system (termed S3BD) for searching big data using cloud services without exposing any data to cloud providers. To keep real‐time response on big data, S3BD proactively prunes the search space to a subset of the whole dataset. For that purpose, we propose a method to cluster the encrypted data. An abstract of each cluster is maintained on the client‐end to navigate the search operation to appropriate clusters at the search time. Results of experiments, carried out on real‐world big datasets, demonstrate that the search operation can be achieved in real‐time and is significantly more efficient than other counterparts. In addition, a fully functional prototype of S3BD is made publicly available.

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“…Within the Scientific SPARQL project [4][5][6][7][8], we define syntax and semantics for queries combining both RDF-structured metadata and multidimensional numeric arrays which are linked as values in the RDF graph. Different storage options are explored: our prior publications cover storing arrays in binary files [6], and a specialized array database [7]. In this work we focus on storing the arrays in a relational DBMS back-end.…”

Section: Introductionmentioning

confidence: 99%

Strategies for array data retrieval from a relational back-end based on access patterns

2020

Self Cite

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Multidimensional numeric arrays are often serialized to binary formats for efficient storage and processing. These representations can be stored as binary objects in existing relational database management systems. To minimize data transfer overhead when arrays are large and only parts of arrays are accessed, it is favorable to split these arrays into separately stored chunks. We process queries expressed in an extended graph query language SPARQL, treating arrays as node values and having syntax for specifying array projection, element and range selection operations as part of a query. When a query selects parts of one or more arrays, only the relevant chunks of each array should be retrieved from the relational database. The retrieval is made by automatically generated SQL queries. We evaluate different strategies for partitioning the array content, and for generating the SQL queries that retrieve it on demand. For this purpose, we present a mini-benchmark, featuring a number of typical array access patterns. We draw some actionable conclusions from the performance numbers.

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Spatio-Temporal Gridded Data Processing on the Semantic Web

Cited by 18 publications

References 12 publications

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

S3BD: Secure semantic search over encrypted big data in the cloud

Strategies for array data retrieval from a relational back-end based on access patterns

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