This document outlines a model called stRDF for representing geospatial and temporal data in RDF, along with a query language called stSPARQL. It also describes Strabon, a scalable geospatial RDF store for storing and querying stRDF data. Strabon extends the Semantic Web toolkit Sesame and uses PostGIS for geospatial indexing and functions. The document evaluates Strabon's performance against Sesame on geospatial linked data and synthetic datasets. Finally, it discusses other extensions like the RDFi framework for representing data with incomplete information.
Presentation of the paper:
Szymon Klarman and Thomas Meyer. Querying Temporal Databases via OWL 2 QL (with appendix). In Proceedings of the 8th International Conference on Web Reasoning and Rule Systems (RR-14), 2014.
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)Hansol Kang
Original GAN 논문 리뷰 및 PyTorch 기반의 구현.
딥러닝 개발환경 및 언어 비교.
[참고]
Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
Wang, Su. "Generative Adversarial Networks (GAN) A Gentle Introduction."
초짜 대학원생의 입장에서 이해하는 Generative Adversarial Networks (https://jaejunyoo.blogspot.com/)
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기 (https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network)
프레임워크 비교(https://deeplearning4j.org/kr/compare-dl4j-torch7-pylearn)
AI 개발에AI 개발에 가장 적합한 5가지 프로그래밍 언어 (http://www.itworld.co.kr/news/109189#csidxf9226c7578dd101b41d03bfedfec05e)
Git는 머꼬? GitHub는 또 머지?(https://www.slideshare.net/ianychoi/git-github-46020592)
svn 능력자를 위한 git 개념 가이드(https://www.slideshare.net/einsub/svn-git-17386752)
Stockage, manipulation et analyse de données matricielles avec PostGIS RasterACSG Section Montréal
La plus importantes nouveautés de la base de données spatiale open source PostgreSQL/PostGIS 2.0 est le support pour les données raster. PostGIS Raster comprend un outil d’importation similaire à shp2pgsql basé sur GDAL et une série d’opérateurs SQL pour la manipulation et l'analyse des données matricielles. Le nouveau type RASTER est géoréférencé, multi-résolutions et multi-bandes et il supporte une valeur nulle (nodata) et un type de valeur de pixel par bande. PostGIS raster s’inspire de la simplicité de l’expérience vecteur offerte par PostGIS pour rendre toutes les opérations raster aussi simples que possible. Comme pour une couverture vecteur, une couverture raster est divisée en un ensemble d’enregistrements (une ligne = une tuile) stockés dans une seule table (contrairement à Oracle Spatial qui utilise deux types et donc deux tables ou plus). Il est possible d’importer une couverture complète et de la retuiler en une seule commande avec l’outil d’importation et de multiples résolutions de la même couverture peuvent être importées dans des tables adjacentes. Les propriétés des objets raster et de chacune des bandes peuvent être consultées et modifiées ainsi que les valeurs des pixels. Des fonctions existent pour obtenir le minimum, le maximum, la somme, la moyenne, la déviation standard, l’histogramme d’une tuile ou d’une couverture complète. Les fonctions ST_Intersection() et ST_Intersects() fonctionnent pratiquement de manière transparente entre des données raster et vecteur et une série de fonctions pour l’algèbre matricielle (ST_MapAlgebra()) permet de faire de l’analyse de type raster. Il est possible de reclasser les bandes et de les convertir en n’importe quel format d’écriture GDAL. Des fonctions pour générer des rasters et des bandes existent également pour du développement PL/pgSQL. Un driver GDAL pour convertir les couvertures raster en fichiers images est en développement et des plugins pour QGIS et svSIG existent déjà pour les visualiser.
Presentation of the paper:
Szymon Klarman and Thomas Meyer. Querying Temporal Databases via OWL 2 QL (with appendix). In Proceedings of the 8th International Conference on Web Reasoning and Rule Systems (RR-14), 2014.
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)Hansol Kang
Original GAN 논문 리뷰 및 PyTorch 기반의 구현.
딥러닝 개발환경 및 언어 비교.
[참고]
Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
Wang, Su. "Generative Adversarial Networks (GAN) A Gentle Introduction."
초짜 대학원생의 입장에서 이해하는 Generative Adversarial Networks (https://jaejunyoo.blogspot.com/)
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기 (https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network)
프레임워크 비교(https://deeplearning4j.org/kr/compare-dl4j-torch7-pylearn)
AI 개발에AI 개발에 가장 적합한 5가지 프로그래밍 언어 (http://www.itworld.co.kr/news/109189#csidxf9226c7578dd101b41d03bfedfec05e)
Git는 머꼬? GitHub는 또 머지?(https://www.slideshare.net/ianychoi/git-github-46020592)
svn 능력자를 위한 git 개념 가이드(https://www.slideshare.net/einsub/svn-git-17386752)
Stockage, manipulation et analyse de données matricielles avec PostGIS RasterACSG Section Montréal
La plus importantes nouveautés de la base de données spatiale open source PostgreSQL/PostGIS 2.0 est le support pour les données raster. PostGIS Raster comprend un outil d’importation similaire à shp2pgsql basé sur GDAL et une série d’opérateurs SQL pour la manipulation et l'analyse des données matricielles. Le nouveau type RASTER est géoréférencé, multi-résolutions et multi-bandes et il supporte une valeur nulle (nodata) et un type de valeur de pixel par bande. PostGIS raster s’inspire de la simplicité de l’expérience vecteur offerte par PostGIS pour rendre toutes les opérations raster aussi simples que possible. Comme pour une couverture vecteur, une couverture raster est divisée en un ensemble d’enregistrements (une ligne = une tuile) stockés dans une seule table (contrairement à Oracle Spatial qui utilise deux types et donc deux tables ou plus). Il est possible d’importer une couverture complète et de la retuiler en une seule commande avec l’outil d’importation et de multiples résolutions de la même couverture peuvent être importées dans des tables adjacentes. Les propriétés des objets raster et de chacune des bandes peuvent être consultées et modifiées ainsi que les valeurs des pixels. Des fonctions existent pour obtenir le minimum, le maximum, la somme, la moyenne, la déviation standard, l’histogramme d’une tuile ou d’une couverture complète. Les fonctions ST_Intersection() et ST_Intersects() fonctionnent pratiquement de manière transparente entre des données raster et vecteur et une série de fonctions pour l’algèbre matricielle (ST_MapAlgebra()) permet de faire de l’analyse de type raster. Il est possible de reclasser les bandes et de les convertir en n’importe quel format d’écriture GDAL. Des fonctions pour générer des rasters et des bandes existent également pour du développement PL/pgSQL. Un driver GDAL pour convertir les couvertures raster en fichiers images est en développement et des plugins pour QGIS et svSIG existent déjà pour les visualiser.
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
"Scalable Link Discovery for Modern Data-Driven Applications" as presented in the 15th International Semantic Web Conference ISWC, Doctoral Consortium, October 18th, 2016, held in Kobe, Japan
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
PG Day'14 Russia, GIN — Stronger than ever in 9.4 and further, Александр Коро...pgdayrussia
Доклад был представлен на официальной российской конференции PG Day'14 Russia, посвященной вопросам разработки и эксплуатации PostgreSQL.
Доклад посвящен улучшениям в GIN-индексах в PostgreSQL 9.4 и далее, которые выводят GIN на новый уровень производительности и расширяемости. Наиболее важные улучшения:
Сжатие постинг-листов. Индексы становятся в среднем в 2 раза компактнее. При это не требуется никаких изменений со стороны opclass'ов. pg_upgrade поддерживается, индексы сжимаются "на лету".
Алгоритм быстрого сканирования GIN-индексов позволяет пропускать части больших постинг-деревьев при сканировании. Этот алгоритм кардинально улучшает скорость поиска для hstore и jsonb операторов, а также случай "частое_слово & редкое_слово" для полнотекстового поиска.
Хранение дополнительной информации в постинг-листах. Содержимое этой дополнительной информации зависит от конкретной разновидности GIN-индекса (определяется opclass'ом). Дополнительная информация может быть полезна при самых разных видах поиска: поиск по фразам, поиск по похожести массивов, обратный полнотекстовый поиск (поиск тех tsquery, которые подходят под tsvector), обратный поиск по регулярным выражением (поиск регулярных выражений, подходящих под строку), поиск по строковой "похожести" с использованием позиционных n-грам.
Ранжирование по индексу. Это улучшение позволяет возвращать результаты из индекса таким образом, как это определяет opclass. Наиболее важное применение — возвращение результатов полнотекстового поиска в порядке релевантности, кардинально снижающее загрузку IO. Но есть также и другие применения, такие как возврат массивов или строк в порядке их "похожести".
В докладе представлены результаты "бенчмарков" полнотектового поиска, использующие реальные наборы данных (6М и 15М документов) и реальные поисковые запросы, которые демонстрируют, что улучшенный полнотекстовый поиск PostgreSQL (со всеми накладными расходами ACID) может превосходить по скорости Sphinx.
Processing Reachability Queries with Realistic Constraints on Massive Network...BigMine
Massive graphs are ubiquitous in various application domains, such as social networks, road networks, communication networks, biological networks, RDF graphs, and so on. Such graphs are massive (for example, with hundreds of millions of nodes and edges or even more) and contain rich information (for example, node/edge weights, labels and textual contents). In such massive graphs, an important class of problems is to process various graph structure related queries. Graph reachability, as an example, asks whether a node can reach another in a graph. However, the large graph scale presents new challenges for efficient query processing.
In this talk, I will introduce two new yet important types of graph reachability queries: weight constraint reachability that imposes edge weight constraint on the answer path, and k-hop reachability that imposes a length constraint on the answer path. With such realistic constraints, we can find more meaningful and practically feasible answers. These two reachablity queries have wide applications in many real-world problems, such as QoS routing and trip planning.
QA Fest 2018. Никита Кричко. Методология использования машинного обучения в н...QAFest
Сегодня никого не удивишь высоконагруженными системами. И мало кого в нашей индустрии удивишь отдельно выделенным человеком который занимается нагрузочным тестирование. Большинство людей думают, что они все могут автоматизировать и тесты будут запускаться автоматически. Вот только мало кто знает, что львиная доля времени уходит на анализ результатов (логов и графиков). В докладе будут рассказаны подходы, как возможно сократить время для поиска узких мест при анализе различных логов. Как можно применить простейшие модели машинного обучения для поиска узких мест. И как получить требования с помощью исторических данных.
Regular Expressions (RE) are widely used to find patterns among data, like in genomic markers research for DNA analysis, deep packet inspection or signature-based detection for network intrusion detection system. This paper proposes a novel and efficient RE matching architecture for FPGAs, based on the concept of matching core. RE can be software-compiled into sequences of basic matching instructions that a matching core runs on input data, and can be replaced to change the RE to be matched. This architecture can easily scale up with the available resources and is customizable to multiple usage scenarios. We ran several experiments and compared the obtained results with a software solution, reaching speedups over 100x, while running at 130MHz, over a Flex-based matching application running on an Intel i7 CPU at 2.8GHz.
Accumulo Collections is a lightweight library that dramatically simplifies development of fast NoSQL applications by encapsulating many powerful, distributed features of Accumulo in the familiar Java Collections interface. Accumulo is a giant sorted map with rich server-side functionality, and our AccumuloSortedMap is a robust java SortedMap implementation that is backed by an Accumulo table. It handles serialization and foreign keys, and provides extensive server-side features like entry timeout, aggregates, filtering, efficient one-to-many mapping, partitioning and sampling. Users can define custom server-side transformations and aggregates with Accumulo iterators.
More information on this project can be found on github at: https://github.com/isentropy/accumulo-collections/wiki
– Speaker –
Jonathan Wolff
Founder, Director of Engineering, Isentropy LLC
Jonathan is an ex-physicist who operates a consultancy specializing in big data and data science project work. He worked for Bloomberg last year and built their Accumulo File System, which was presented as 2015 Accumulo Summit's keynote speech. He's also done distributed computing project work for Yahoo! in Pig.
Jonathan holds a BA in Physics (Harvard, magna cum laude 2001) and an MS in Mechanical Engineering (Columbia, 2003), and has been avidly programming since the 1980's.
— More Information —
For more information see http://www.accumulosummit.com/
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
Geographica: A Benchmark for Geospatial RDF StoresKostis Kyzirakos
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores.
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
"Scalable Link Discovery for Modern Data-Driven Applications" as presented in the 15th International Semantic Web Conference ISWC, Doctoral Consortium, October 18th, 2016, held in Kobe, Japan
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
PG Day'14 Russia, GIN — Stronger than ever in 9.4 and further, Александр Коро...pgdayrussia
Доклад был представлен на официальной российской конференции PG Day'14 Russia, посвященной вопросам разработки и эксплуатации PostgreSQL.
Доклад посвящен улучшениям в GIN-индексах в PostgreSQL 9.4 и далее, которые выводят GIN на новый уровень производительности и расширяемости. Наиболее важные улучшения:
Сжатие постинг-листов. Индексы становятся в среднем в 2 раза компактнее. При это не требуется никаких изменений со стороны opclass'ов. pg_upgrade поддерживается, индексы сжимаются "на лету".
Алгоритм быстрого сканирования GIN-индексов позволяет пропускать части больших постинг-деревьев при сканировании. Этот алгоритм кардинально улучшает скорость поиска для hstore и jsonb операторов, а также случай "частое_слово & редкое_слово" для полнотекстового поиска.
Хранение дополнительной информации в постинг-листах. Содержимое этой дополнительной информации зависит от конкретной разновидности GIN-индекса (определяется opclass'ом). Дополнительная информация может быть полезна при самых разных видах поиска: поиск по фразам, поиск по похожести массивов, обратный полнотекстовый поиск (поиск тех tsquery, которые подходят под tsvector), обратный поиск по регулярным выражением (поиск регулярных выражений, подходящих под строку), поиск по строковой "похожести" с использованием позиционных n-грам.
Ранжирование по индексу. Это улучшение позволяет возвращать результаты из индекса таким образом, как это определяет opclass. Наиболее важное применение — возвращение результатов полнотекстового поиска в порядке релевантности, кардинально снижающее загрузку IO. Но есть также и другие применения, такие как возврат массивов или строк в порядке их "похожести".
В докладе представлены результаты "бенчмарков" полнотектового поиска, использующие реальные наборы данных (6М и 15М документов) и реальные поисковые запросы, которые демонстрируют, что улучшенный полнотекстовый поиск PostgreSQL (со всеми накладными расходами ACID) может превосходить по скорости Sphinx.
Processing Reachability Queries with Realistic Constraints on Massive Network...BigMine
Massive graphs are ubiquitous in various application domains, such as social networks, road networks, communication networks, biological networks, RDF graphs, and so on. Such graphs are massive (for example, with hundreds of millions of nodes and edges or even more) and contain rich information (for example, node/edge weights, labels and textual contents). In such massive graphs, an important class of problems is to process various graph structure related queries. Graph reachability, as an example, asks whether a node can reach another in a graph. However, the large graph scale presents new challenges for efficient query processing.
In this talk, I will introduce two new yet important types of graph reachability queries: weight constraint reachability that imposes edge weight constraint on the answer path, and k-hop reachability that imposes a length constraint on the answer path. With such realistic constraints, we can find more meaningful and practically feasible answers. These two reachablity queries have wide applications in many real-world problems, such as QoS routing and trip planning.
QA Fest 2018. Никита Кричко. Методология использования машинного обучения в н...QAFest
Сегодня никого не удивишь высоконагруженными системами. И мало кого в нашей индустрии удивишь отдельно выделенным человеком который занимается нагрузочным тестирование. Большинство людей думают, что они все могут автоматизировать и тесты будут запускаться автоматически. Вот только мало кто знает, что львиная доля времени уходит на анализ результатов (логов и графиков). В докладе будут рассказаны подходы, как возможно сократить время для поиска узких мест при анализе различных логов. Как можно применить простейшие модели машинного обучения для поиска узких мест. И как получить требования с помощью исторических данных.
Regular Expressions (RE) are widely used to find patterns among data, like in genomic markers research for DNA analysis, deep packet inspection or signature-based detection for network intrusion detection system. This paper proposes a novel and efficient RE matching architecture for FPGAs, based on the concept of matching core. RE can be software-compiled into sequences of basic matching instructions that a matching core runs on input data, and can be replaced to change the RE to be matched. This architecture can easily scale up with the available resources and is customizable to multiple usage scenarios. We ran several experiments and compared the obtained results with a software solution, reaching speedups over 100x, while running at 130MHz, over a Flex-based matching application running on an Intel i7 CPU at 2.8GHz.
Accumulo Collections is a lightweight library that dramatically simplifies development of fast NoSQL applications by encapsulating many powerful, distributed features of Accumulo in the familiar Java Collections interface. Accumulo is a giant sorted map with rich server-side functionality, and our AccumuloSortedMap is a robust java SortedMap implementation that is backed by an Accumulo table. It handles serialization and foreign keys, and provides extensive server-side features like entry timeout, aggregates, filtering, efficient one-to-many mapping, partitioning and sampling. Users can define custom server-side transformations and aggregates with Accumulo iterators.
More information on this project can be found on github at: https://github.com/isentropy/accumulo-collections/wiki
– Speaker –
Jonathan Wolff
Founder, Director of Engineering, Isentropy LLC
Jonathan is an ex-physicist who operates a consultancy specializing in big data and data science project work. He worked for Bloomberg last year and built their Accumulo File System, which was presented as 2015 Accumulo Summit's keynote speech. He's also done distributed computing project work for Yahoo! in Pig.
Jonathan holds a BA in Physics (Harvard, magna cum laude 2001) and an MS in Mechanical Engineering (Columbia, 2003), and has been avidly programming since the 1980's.
— More Information —
For more information see http://www.accumulosummit.com/
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
Geographica: A Benchmark for Geospatial RDF StoresKostis Kyzirakos
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores.
We present a new version of the data model stRDF and the query language stSPARQL for the representation and querying of geospatial data. The new versions of stRDF and stSPARQL use OGC standards to represent geometries where the original version of stSPARQL used linear constraints. In this sense stSPARQL is a subset of the recent standard GeoSPARQL proposed by OGC. We discuss the implementation of the system Strabon which is a storage and query evaluation module for stRDF/stSPARQL and the corresponding subset of GeoSPARQL. We study the performance of Strabon experimentally and show that it scales to very large data volumes.
SSN-TC workshop talk at ISWC 2015 on EmroozMarkus Stocker
Slides for the talk describing the paper on Emrooz, a scalable database for sensor observations with semantics according to the Semantic Sensor Network ontology.
Geoprocessing(Building Your Own Tool) and Geostatistical Analysis(An Introdu...Nepal Flying Labs
Its a presentation slide prepared by me and my team for a workshop at my college.Don't hesitate to mail me at utmpudasaini@hotmail.com or utmpudasaini@gmail.com if you want to know more or details regarding the demos.
2017 02-07 - elastic & spark. building a search geo locatorAlberto Paro
Presentazione dell'evento EsInRome del 7 Febbraio 2017 - Integrazione Elasticsearch in architettura BigData e facilità di integrazione con Apache Spark.
2017 02-07 - elastic & spark. building a search geo locatorAlberto Paro
Using Elasticsearch in a BigData environment is very simple. In this talk, we analyse what's Big Data and we show how it is easy integrating ElasticSearch with Apache Spark
Recent developments in Hadoop version 2 are pushing the system from the traditional, batch oriented, computational model based on MapRecuce towards becoming a multi paradigm, general purpose, platform. In the first part of this talk we will review and contrast three popular processing frameworks. In the second part we will look at how the ecosystem (eg. Hive, Mahout, Spark) is making use of these new advancements. Finally, we will illustrate "use cases" of batch, interactive and streaming architectures to power traditional and "advanced" analytics applications.
WMS Benchmarking presentation and results, from the FOSS4G 2011 event in Denver. 6 different development teams participated in this exercise, to display common data through the WMS standard the fastest. http://2011.foss4g.org/sessions/web-mapping-performance-shootout
Getting Started with Geospatial Data in MongoDBMongoDB
MongoDB supports geospatial data and specialized indexes that make building location-aware applications easy and scalable.
In this session, you will learn the fundamentals of working with geospatial data in MongoDB. We will explore how to store and index geospatial data and best practices for using geospatial query operators and methods. By the end of this session, you should be able to implement basic geolocation functionality in an application.
In this webinar, you will learn:
- Getting geospatial data into MongoDB and how to build geospatial indexes.
- The fundamentals of MongoDB's geospatial query operators and how to design queries that meet the needs of your application.
- Advanced geospatial capabilities with Java geospatial libraries and MongoDB.
Postgres Vision 2018: PostGIS and Spatial ExtensionsEDB
The extensibility of PostgreSQL has enabled the combination of PostgreSQL and the geospatial extension PostGIS, creating what many say is the most powerful SQL/MM compliant database for location-based applications. This presentation, delivered at Postgres Vision 2018 by Regina Obe, Co-founder of Paragon Corporation and PostGIS Project Steering Committee member, and Leo Hsu, Co-founder of Paragon Corporation, examines PostgreSQL spatial extensions that work with PostGIS.
ESWC2015 - Tutorial on Publishing and Interlinking Linked Geospatial DataKostis Kyzirakos
In this tutorial we present the life cycle of linked geospatial data and we focus on two important steps: the publication of geospatial data as RDF graphs and interlinking them with each other. Given the proliferation of geospatial information on the Web many kinds of geospatial data are now becoming available as linked datasets (e.g., Google and Bing maps, user-generated geospatial content, public sector information published as open data etc.). The topic of the tutorial is related to all core research areas of the Semantic Web (e.g., semantic information extraction, transformation of data into RDF graphs, interlinking linked data etc.) since there is often a need to re-consider existing core techniques when we deal with geospatial information. Thus, it is timely to train Semantic Web researchers, especially the ones that are in the early stages of their careers, on the state of the art of this area and invite them to contribute to it.
In this tutorial we give a comprehensive background on data models, query languages, implemented systems for linked geospatial data, and we discuss recent approaches on publishing and interlinking geospatial data. The tutorial is complemented with a hands-on session that will familiarize the audience with the state-of-the-art tools in publishing and interlinking geospatial information.
http://event.cwi.nl/eswc2015-geo/
Presentation of the spatiotemporal RDF store Strabon at the Linked Data Europe Workshop, co-located with the European Data Forum in Athens, Greece (21 March 2014)
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
2. Outline
™ Motivation
™ The Model stRDF
™ The Query Language stSPARQL
™ The Scalable Geospatial RDF Store Strabon
™ Experimental Evaluation
™ Other Extensions to RDF
™ Conclusions
3. Motivation
™ Need for modeling of
™ Geospatial and temporal information
™ Product metadata
™ Product content
™ Need to link to other data sources
™ GIS data
™ Other information on the Web
4. TELEIOS Project
™ TELEIOS EU project
™ Development of a Virtual Earth Observatory
™ Accessible to expert and non-expert users
™ Scalable (Storing/Querying PBs)
™ Use Cases
™ DLR: A Virtual Observatory for TerraSAR-X data
™ NOA: Real-time fire monitoring based on continuous
acquisitions of EO images and geospatial data
™ Technologies
™ NKUA: Geospatial extensions for RDF and SPARQL
™ CWI: Array extensions to SQL
5. The Data Model stRDF
™ stRDF extends RDF with:
™ Spatial literals encoded by Boolean combinations of
linear constraints
™ New Datatype for spatial literals (strdf:geometry)
™ Valid time of triples encoded by Boolean combinations
of temporal constraints
™ stRDF (most recent version, TELEIOS)
™ Spatial literals encoded in Well-Known Text/GML
(OGC standards)
™ Valid time of triples ignored for the time being
7. stRDF: An example
ex:BurntArea1 rdf:type noa:BurntArea.
ex:BurntArea1 noa:hasID "1"^^xsd:decimal.
ex:BurntArea1 noa:hasArea "23.7636"^^xsd:double.
ex:BurntArea1 strdf:geometry "POLYGON(( 38.16 23.7, 38.18
23.7, 38.18 23.8, 38.16 23.8, 38.16 23.7));<http://
spatialreference.org/ref/epsg/4121/>"^^strdf:WKT .
Spatial Data Type
Well-Known Text
Spatial Literal
8. stRDF: An example (GML)
ex:BurntArea1 rdf:type noa:BurntArea.
ex:BurntArea1 noa:hasID "1"^^xsd:decimal.
ex:BurntArea1 noa:hasArea "23.7636"^^xsd:double.
ex:BurntArea1 strdf:geometry "<gml:Polygon
srsName='http://www.opengis.net/def/crs/EPSG/0/4121'>
<gml:outerBoundaryIs><gml:LinearRing><gml:coordinates>38.16
,23.70 38.18,23.70 38.18,23.80 38.16,23.80
38.16,23.70</gml:coordinates></gml:LinearRing></
gml:outerBoundaryIs></gml:Polygon>"^^strdf:GML .
Spatial Data Type
GML
Spatial Literal
9. stSPARQL: An example
™ Find all burnt forests within 10km of a city
SELECT ?BA ?BAGEO
WHERE {
?R rdf:type noa:Region .
?R strdf:geometry ?RGEO .
?R noa:hasCorineLandCoverUse ?F .
?F rdfs:subClassOf clc:Forests .
?CITY rdf:type dbpedia:City .
?CITY strdf:geometry ?CGEO .
?BA rdf:type noa:BurntArea .
?BA strdf:geometry ?BAGEO .
FILTER(strdf:Intersect(?RGEO,?BAGEO) &&
strdf:Distance(?BAGEO,?CGEO) < 0.02) }
Spatial
Functions
10. stSPARQL: Geospatial SPARQL 1.1
™ We define a SPARQL extension function for each function
defined in the OpenGIS Simple Features Access standard
™ Basic functions
™ Get a property of a geometry (e.g., strdf:srid)
™ Get the desired representation of a geometry (e.g., strdf:AsText)
™ Test whether a certain condition holds (e.g., strdf:IsEmpty,
strdf:IsSimple)
™ Functions for testing topological spatial relationships
(e.g., strdf:equals, strdf:intersects)
™ Spatial analysis functions
™ Construct new geometric objects from existing geometric objects
(e.g., strdf:buffer, strdf:intersection, strdf:convexHull)
™ Spatial metric functions (e.g., strdf:distance, strdf:area)
™ We define spatial aggregate functions (e.g., strdf:union,
strdf:extent)
11. stSPARQL: Geospatial SPARQL 1.1
™ Spatial terms
™ Constants (e.g., "POLYGON((38.16 23.7, ...)) " ^^strdf:WKT)
™ Variables (e.g., ?GEO)
™ Results of set operations (e.g., strdf:intersection, strdf:union,)
™ Results of geometric operations (e.g., strdf:boundary, strdf:buffer)
™ Select clause
™ Construction of new geometries (e.g., strdf:buffer(?geo, 0.1))
™ Spatial aggregate functions (e.g., strdf:extent(?geo))
™ Metric functions (e.g., strdf:area(?geo))
™ Filter clause
™ Functions for testing topological spatial relationships between spatial terms (e.g., strdf:contains(?
G1, strdf:union(?G2, ?G3)))
™ Numeric expressions involving spatial metric functions (e.g., strdf:area(?G1) ≤ 2*strdf:area(?G2)+1)
™ Boolean combinations
™ Having clause
™ Boolean expressions involving spatial aggregate functions and spatial metric functions or functions
testing for topological relationships between spatial terms (e.g., strdf:area(strdf:union(?geo) ) > 1 )
™ Updates
™ Similar to the OGC standard GeoSPARQL
12. Strabon: A Scalable Geospatial
RDF Store
™ Storing: stRDF
™ Querying: stSPARQL, GeoSPARQL
™ Storage Backend: PostGIS, MonetDB
™ Extends: Sesame 2.6.3
™ Open Source: http://strabon.di.uoa.gr/
™ EU Projects: Semsorgrid4Env and TELEIOS
14. Query Evaluation
Sesame’s Native Store (Baseline)
™ Index (Geometries): None
(geometries treated as ordinary literals)
™ Query Evaluation
1. Triple pattern evaluation
2. Evaluation of spatial functions in Sesame using JTS library
Strabon + PostGIS
™ Index (Geometries): R-Tree
™ Query Evaluation
PostGIS optimizer
Evaluation of spatial functions in PostGIS
Triple pattern evaluation might come before/after evaluation of spatial
functions
15. Experimental Evaluation
™ Workload based on geospatial linked datasets
q 150 million triples
q Test queries based on logs from Dbpedia,
LinkedGeoData
1. Detailed evaluation is coming up
[ISWC paper submission under preparation]
17. LinkedGeoData
™ Q1: Find the names of the points of interest located in the
city of Rotterdam.
SELECT ?poiName
WHERE {
?poi rdf:type lgd:Amenity .
?poi rdfs:label ?poiName .
?poi geo:geometry ?poiGeo .
FILTER (strdf:inside(?poiGeo, "POLYGON((
3.9111328125 51.7401123046875, 3.9111328125 52.05322265625,
4.6087646484375 52.05322265625, 4.6087646484375 51.7401123046875,
3.9111328125 51.7401123046875))"^^strdf:WKT)) }
18. LinkedGeoData/GADM
™ Q2: Find the points of interest located in Luxembourg.
SELECT ?label
WHERE {
?poi rdf:type lgd:Amenity .
?poi rdfs:label ?label .
?poi geo:geometry ?poiGeo .
gadm:LUXEMBOURG gadm:hasGeometry ?luxGeo .
FILTER (strdf:inside(?luxGeo, ?poiGeo)) }
19. Response Time for Queries Q1, Q2
0
50
100
150
200
250
cold caches warm caches cold caches warm caches
Q1 Q2
ResponseTime(seconds)
Workload based on Geospatial Linked Datasets
Strabon
Sesame
20. Experimental Evaluation
™ Workload based on a synthetic dataset
q Dataset based on OpenStreetMaps
q 10 million triples (2 GB) up to 1 billion triples (110 GB)
q Triples with spatial literals: 1.4 and 140 million triples
™ Response time of queries with various thematic and
spatial selectivities
26. The Framework RDFi
™ Extension of RDF with incomplete information
™ New kind of literals (e-literals) for each datatype
™ Property values that exist but are unknown or partially known
™ Partial knowledge: captured by constraints
(appropriate constraint language L)
™ RDF graphs extended to RDFi databases: pair (G, φ)
G: RDF graph with e-literals
φ: quantifier-free formula of L
™ Formal semantics for RDFi and SPARQL query evaluation
™ Representation System: CONSTRUCT with AUF graph patterns
™ Certain Answer: semantics, algorithms, computational complexity when L is
a language of spatial topological constraints
™ Implementation in the context of Strabon has started with L being PCL
(topological constraints between variables and polygon constants)
[conference submission]
27. Challenges
™ Query processing in face of linked geospatial
data
™ Query optimization for spatial data
™ Federation
™ Efficient query processing with incomplete
information
28. Conclusions
™ stRDF/stSPARQL
™ Model and query language for the representation and
querying of geospatial data
™ Strabon
™ Scalable Geospatial RDF Store
™ RDFi
™ Framework for representation and querying of RDF
data with incomplete information