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The SPADES Framework for Scalable Management of Spatio-textual Data
2020
Panhellenic Conference on Informatics
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This paper presents the research activities in the context of the SPADES project for scalable indexing and processing of big spatial and spatio-textual data. ...
In this paper, we provide an overview of our contributions in this field, both for centralized and parallel processing. ...
ACKNOWLEDGMENTS This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No ...
doi:10.1145/3437120.3437293
dblp:conf/pci/VlachouDKPN20
fatcat:hngdbsr2zfgu3mjrg35lko63ju
A Survey on Spatio-temporal Data Analytics Systems
[article]
2021
arXiv
pre-print
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, and (3) programming languages and software tools for processing spatio-temporal data. ...
The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatio-temporal data processing infrastructures ...
DiStRDF [164] is a distributed Spatio-temporal RDF [249] data processing system based on Spark. ...
arXiv:2103.09883v1
fatcat:ndpqz67bvnghpng36o3liqlr2q
A Survey on Big Data for Trajectory Analytics
2020
ISPRS International Journal of Geo-Information
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Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. ...
A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. ...
[49]
2018
DiStRDF: Distributed Spatio-temporal RDF
Queries on Spark
I
Processing SPARQL spatio-temporal queries
in parallel Spark framework
Alsah et al. [14]
2019
A Survey on Trajectory Data ...
doi:10.3390/ijgi9020088
fatcat:bgpfxcx5jngd7cjcj6u4fl4jpi
Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events
[chapter]
2018
Lecture Notes in Computer Science
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Acknowledgments This work was supported by project datACRON, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 687591. ...
For batch processing and analysis, we have selected Apache Spark which is the most popular batch processing framework to-date, achieving scalability, high performance, and exploiting in-memory processing ...
Due to the immense data volume, parallel data processing is performed over RDF data stored in a distributed way. ...
doi:10.1007/978-3-319-90053-7_13
fatcat:mwpj6jpozfdmxmtbde3t3qqknq
Array databases: concepts, standards, implementations
2021
Journal of Big Data
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Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. ...
AbstractMulti-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image ...
using modern database architectures for massive spatio-temporal data sets. ...
doi:10.1186/s40537-020-00399-2
fatcat:mu4pujkjdndytmbgb67rgs733e
Efficient snapshot retrieval over historical graph data
2013
2013 IEEE 29th International Conference on Data Engineering (ICDE)
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temporal analytical tasks and for executing them in an efficient and scalable manner using Apache Spark. ...
Our experiments demonstrate our system's efficient storage, retrieval and analytics across a wide variety of queries on large volumes of historical graph data. ...
Multiple QPs query the datastore in parallel and process the raw deltas into the required result. ...
doi:10.1109/icde.2013.6544892
dblp:conf/icde/KhuranaD13
fatcat:7zgoe2txena55lxfgm56ro2w3y
Large scale distributed spatio-temporal reasoning using real-world knowledge graphs
2018
Knowledge-Based Systems
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We have implemented ParQR using the Apache Spark framework, and evaluated our approach using both large scale synthetic datasets and real-world knowledge graphs. ...
In this article we describe ParQR, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets. ...
ParQR isn't the first attempt to develop a parallel, distributed qualitative spatio-temporal reasoner. Mantle et al. ...
doi:10.1016/j.knosys.2018.08.035
fatcat:oemwftbv3ff4pbznn5zoa7dq5i
Stream reasoning: A survey and outlook
2017
Data Science
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Acknowledgements We would like to thank Fredrik Heintz, Ruben Verborgh and Paul Fodor for their valuable reviews. Their comments contributed in improving the quality of this article. ...
We would also like to thank the Swiss National Science Foundation (SNF) for partial support of this work unter contract number #407550_167177. ...
Where shall I spend my evening given the presence of people and what their doing (predicted analysing the spatio-temporal correlation between privacy-preserving aggregates of Mobile Telecom Data and of ...
doi:10.3233/ds-170006
dblp:journals/datasci/DellAglioVHB17
fatcat:fgnskaiz4fd2nlzeltpo356um4
Modeling Analytical Streams for Social Business Intelligence
2018
Informatics
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Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. ...
The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/informatics5030033
fatcat:g6p5evh4vvfpveje5rkxa7wwvy
A Survey of RDF Stores SPARQL Engines for Querying Knowledge Graphs
[article]
2021
arXiv
pre-print
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RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. ...
This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. ...
PRoST [60] (2018) is a distributed RDF store using HDFS storage and Spark query processing. ...
arXiv:2102.13027v4
fatcat:phontczhbfcvdjt5y75n3hfcge
Storing and Analyzing Historical Graph Data at Scale
[article]
2015
arXiv
pre-print
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complex temporal analytical tasks and for executing them in an efficient and scalable manner. ...
retrieving snapshots of the graph as of any timepoint in the past or evolution histories of individual nodes or neighborhoods; and a Spark-based Temporal Graph Analysis Framework (TAF), for expressing ...
[17] , describe a block-oriented and cache-enabled system to exploit spatio-temporal locality for solving temporal neighborhood queries. ...
arXiv:1509.08960v1
fatcat:nagvtvgi3fdq7oelhxu3xu6uha
D4.3 Bigdataocean Platform Architecture, Components Design And Apis – V2.00
2018
Zenodo
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This deliverable includes all necessary updates on the platform and components' architecture, as well as the APIs interfaces, based on the feedback received by the project's pilots. ...
High
Data Storage DS-TR-8 The system should be able to optimise the storage of spatio-temporal data facilitating their more efficient query execution. ...
Medium Big Data Framework & Storage
DS-TR-8 The system should be able to optimise the storage of spatio-temporal data facilitating their more efficient query execution. ...
doi:10.5281/zenodo.1249388
fatcat:ha2e7odfdvc3rjh34hqieiutc4
Managing Lifecycle of Big Data Applications
[chapter]
2017
Communications in Computer and Information Science
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The growing digitization and networking process within our society has a large influence on all aspects of everyday life. ...
The integration of the components inside the BDI Platform requires components homogenization, which leads to the standardization of the development process. ...
Acknowledgments This work was supported by grant from the European Union's Horizon 2020 research Europe flag and innovation program for the project Big Data Europe (GA no. 644564). ...
doi:10.1007/978-3-319-69548-8_18
fatcat:3dzrz55uc5dxnl662cm2tudary
Review on Integrating Geospatial Big Datasets and Open Research Issues
2021
IEEE Access
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The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. ...
A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. ...
In addition, scalability and parallelism are also issues, as with big data in general. ...
doi:10.1109/access.2021.3051084
fatcat:5rm6koonszaafkom24tufnkamq
Deploying a Strategy to Unlock Big Data Research and Teaching Activities in the West Balkan Region
2021
Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1
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Big Data Analytics is a crucial component of the Big Data paradigm and deals with the extraction of knowledge from the enormous amount of data. ...
In this article, we report on the deployment of a strategy we design, fostering the knowledge and awareness around the challenges of Big Data analytics in the West Balkan region. ...
This work was supported by the EU H2020 grant 809965 LAMBDA, the Vienna Science and Technology Fund (WWTF) grant VRG18-013, and the EPSRC grant EP/M025268/1. ...
doi:10.1145/3430665.3456325
fatcat:2r4j5t2ywreqhnglzgkx5jlfzy
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