Talk about how Big Data and geospatial processing worlds are merging to get the best insights.
(The presenetation with effects here: https://docs.google.com/presentation/d/1EniUHMrRR3vQaJp6q0qBdOyZxv62DcSv3-iZXpcfwOM/edit?usp=sharing)
Dear Fiscal Board - Chief Data Officer shares thoughts and experiences with P...Giancarlo Gonzalez
Maksim Percherskiy from the City of San Diego presents a civic app for shelters developed during hurricane Irma for Puerto Rico, and also talks about the role of the Chief Data Officer and the benefits for Puerto Rico to have such a team.
Open source Geospatial Business Intelligence in action with GeoMondrian and S...Thierry Badard
This document introduces GeoMondrian and SOLAPLayers, open source geospatial business intelligence tools. GeoMondrian is a spatially-enabled version of the Pentaho Mondrian OLAP server that integrates spatial data and analysis capabilities into OLAP data cubes. SOLAPLayers is a lightweight cartographic component that enables interactive map-based exploration of geospatial data cubes from servers like GeoMondrian. The document discusses the architecture and capabilities of both tools, demonstrates them, and outlines the roadmap for future development including more advanced SOLAPLayers components for creating geospatial dashboards.
Open Source Geospatial Business Intelligence (GeoBI): Definition, architectur...Thierry Badard
The document discusses geospatial business intelligence (GeoBI), including its definition, architectures, open source solutions, and outlook. Specifically, it defines GeoBI as bringing maps and geospatial analysis tools into BI systems to fully analyze spatial dimensions in corporate data. It presents open source GeoBI solutions like GeoKettle and GeoMondrian, describing their roles in extracting, transforming, loading, and analyzing geospatial data in BI systems.
Introduction to JOSA's data science bootcamp, which includes introduction to data science itself and information for people interested in this domain.
More material publicly available here: http://bit.ly/josa-dsbc
Jan 2, 2016 - JOSA Data Science Bootcamp
This document provides an overview and introduction to analyzing spatial data using Python. It discusses what spatial data is, popular Python libraries for working with spatial data like Fiona, Shapely, GeoPy, and Mapnik, and how to perform spatial analysis tasks in Python such as geocoding, data conversion and visualization. Jupyter notebooks are presented as an interactive environment for exploring spatial data and libraries like Geopandas and PySAL are introduced for performing spatial analysis. Examples analyze Colombian location and point of interest data.
Corinne Hutchinson's 7/8/2015 PuPPy Presentation on GeoDjangoDon Sheu
This document discusses using location data in web applications. It provides an overview of GeoDjango for building geospatial web apps in Django. GeoDjango allows importing geospatial vector and raster data and provides ORM for geospatial queries. The document discusses setting up models and databases, importing geospatial data, making queries, and scaling applications using techniques like geohashes to cache location lookups. Geohashes represent locations as encoded strings to quickly determine containing polygons and scale point-in-polygon lookups.
Dear Fiscal Board - Chief Data Officer shares thoughts and experiences with P...Giancarlo Gonzalez
Maksim Percherskiy from the City of San Diego presents a civic app for shelters developed during hurricane Irma for Puerto Rico, and also talks about the role of the Chief Data Officer and the benefits for Puerto Rico to have such a team.
Open source Geospatial Business Intelligence in action with GeoMondrian and S...Thierry Badard
This document introduces GeoMondrian and SOLAPLayers, open source geospatial business intelligence tools. GeoMondrian is a spatially-enabled version of the Pentaho Mondrian OLAP server that integrates spatial data and analysis capabilities into OLAP data cubes. SOLAPLayers is a lightweight cartographic component that enables interactive map-based exploration of geospatial data cubes from servers like GeoMondrian. The document discusses the architecture and capabilities of both tools, demonstrates them, and outlines the roadmap for future development including more advanced SOLAPLayers components for creating geospatial dashboards.
Open Source Geospatial Business Intelligence (GeoBI): Definition, architectur...Thierry Badard
The document discusses geospatial business intelligence (GeoBI), including its definition, architectures, open source solutions, and outlook. Specifically, it defines GeoBI as bringing maps and geospatial analysis tools into BI systems to fully analyze spatial dimensions in corporate data. It presents open source GeoBI solutions like GeoKettle and GeoMondrian, describing their roles in extracting, transforming, loading, and analyzing geospatial data in BI systems.
Introduction to JOSA's data science bootcamp, which includes introduction to data science itself and information for people interested in this domain.
More material publicly available here: http://bit.ly/josa-dsbc
Jan 2, 2016 - JOSA Data Science Bootcamp
This document provides an overview and introduction to analyzing spatial data using Python. It discusses what spatial data is, popular Python libraries for working with spatial data like Fiona, Shapely, GeoPy, and Mapnik, and how to perform spatial analysis tasks in Python such as geocoding, data conversion and visualization. Jupyter notebooks are presented as an interactive environment for exploring spatial data and libraries like Geopandas and PySAL are introduced for performing spatial analysis. Examples analyze Colombian location and point of interest data.
Corinne Hutchinson's 7/8/2015 PuPPy Presentation on GeoDjangoDon Sheu
This document discusses using location data in web applications. It provides an overview of GeoDjango for building geospatial web apps in Django. GeoDjango allows importing geospatial vector and raster data and provides ORM for geospatial queries. The document discusses setting up models and databases, importing geospatial data, making queries, and scaling applications using techniques like geohashes to cache location lookups. Geohashes represent locations as encoded strings to quickly determine containing polygons and scale point-in-polygon lookups.
Spatial SQL is a variant of SQL that is designed to handle spatial data stored in a database. It provides functions and capabilities for working with spatial data types like points, lines, and polygons. These functions allow for spatial queries, transformations between coordinate systems, and other operations that are useful for GIS applications but difficult to perform with standard SQL. The key advantages of Spatial SQL are that it allows large volumes of spatial data to be stored and queried efficiently using specialized indexes and functions optimized for spatial data.
"What we learned from 5 years of building a data science software that actual...Dataconomy Media
"What we learned from 5 years of building a data science software that actually works for everybody." Dr. Dennis Proppe, CTO and Chief Data Scientist at GPredictive GmbH
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Dennis Proppe is the CTO and Chief Data Scientist at Gpredictive, where he helps building software that enables data scientists to build and deploy predictive models in a few minutes instead of weeks. He has 10 years+ of expertise in extracting business value from data. Before co-founding Gpredictive, he worked as a marketing science consultant. Dennis holds a Ph.D. in statistical marketing.
GeoKettle: A powerful open source spatial ETL toolThierry Badard
GeoKettle is an open source spatial ETL tool that is part of a geospatial business intelligence software stack. It is based on Pentaho Data Integration and provides consistent integration of spatial data types and capabilities. GeoKettle allows automated extraction, transformation, and loading of data across various sources into data warehouses. It supports spatial operations and can be deployed in cloud environments for scalable geospatial data processing.
Kris Buytaert discusses the past, present, and future of DevOps. He notes that while tools and technologies will continue to evolve, collaboration between development and operations remains a key requirement. DevOps adoption also faces challenges like broken certification processes, resistance to change from large organizations, and burnout. Ultimately, DevOps is still a work in progress with many organizations just starting their journeys, and its future will depend on continued education and bridging cultural divides between teams.
Plenary Talk from GeCoWest ~ Best of Breed for GeospatialMichael Terner
This document summarizes Michael Terner's presentation at the Geospatial Conference of the West in September 2013. The presentation discusses changes happening in the geospatial industry due to new technologies like mobile, cloud computing, and open source software. It argues that organizations should embrace a "best of breed" approach, using the best tools for each job rather than relying on a single vendor. Examples are given of projects that use different technology combinations including Esri, OpenGeo, Microsoft, and Google tools. The presentation encourages embracing ongoing changes and mixing technologies rather than taking an "all or nothing" approach.
Tracking Task Context To Support ResumptionDamien Clauzel
The document discusses tracking user activity and context on computer systems to enable task resumption. It proposes wanting to follow what users are doing and why, and manipulate systems by storing and restoring context. To do so, activity and action tracking is needed, as well as the ability to remotely control platforms. Possible approaches include screen mapping, spyware tools, and modifying applications to send event data. The conclusion notes the difficulty of collecting data and that solutions depend on the environment. Customizing applications is posed as the most promising solution.
Introducing GeoPySpark, a Big Data GeoSpatial LibraryJacob Bouffard
This document introduces GeoPySpark, an open source geospatial library for big data. It discusses how GeoPySpark can be used to process geospatial raster and vector data at scale, provides examples of local operations, focal operations, and distance calculations that can be performed. The document concludes with a demo of GeoPySpark capabilities and outlines future work including improving performance by integrating with Apache Arrow.
From the Introduction to GeoTools workshop!
Are you new to GeoSpatial? This GeoTools session is back by popular demand with Java 8 examples. Offering a visual introduction for Java developers we will exploring how you can integrate GIS services into your next project.
For those new to the GeoSpatial scene we provide an introduction to spatial concepts and how to avoid common pitfalls.
The workshop offers a steady series of workbooks introducing:
Feature creation
Geometry, Coordinate Reference Systems and Re-projection
Spatial Queries
Handling large format rasters
Working with Style
Raster Operations
Covering both the concepts and the science of map making the workbooks serve as an excellent reference, but the focus is always on you and the code you need to get the job done.
Digital Graph tour Rome: "Connect the Dots, Lorenzo SperanzoniNeo4j
This document provides information about a GraphTour Rome 2020 event hosted by LARUS, including:
- LARUS is an Italian company founded in 2004 that is a leader in graph database development and Neo4j partner.
- The event will discuss how viewing business problems as networks can provide benefits, as well as examples of how customers like banks and telecom companies are using Neo4j for applications like fraud detection and recommendations.
- LARUS' typical customer success roadmap for graph database projects is presented, outlining the assessment, use case identification, prototype, and production phases of a project.
This document provides an overview of geospatial analytics in Spark. It discusses the challenges of geospatial analysis including projections, indexing, data curation, and system libraries. It then presents case studies on large-scale geospatial joins, spatial disaggregation, and pattern of life analysis. Live demos are shown for each case study. Key lessons learned are to standardize data formats, leverage datalakes, use domain-driven design, test scaling, and leverage existing work from others.
Dirty data? Clean it up! - Datapalooza Denver 2016Dan Lynn
Dan Lynn (AgilData) & Patrick Russell (Craftsy) present on how to do data science in the real world. We discuss data cleansing, ETL, pipelines, hosting, and share several tools used in the industry.
OpenGeoData Italia 2014 - Marco Fago "Infrastrutture di dati territoriali, IN...giovannibiallo
(1) The document discusses GetLOD, an open source solution for publishing geographic data as Linked Open Data. It allows publishing geospatial data and metadata from traditional cartographic web services as open, linkable RDF data.
(2) GetLOD is integrated with Spatial Data Infrastructures through OGC standards and allows publishing geographic open data in RDF and other formats like Shapefile and GML.
(3) The Region Emilia-Romagna uses GetLOD and Moka to organize their SDI and build applications, while also publishing open data through GetLOD's services. SDI and open data infrastructures will interoperate through Moka.
This presentation is about best practices when using hibernate and Spring Data JPA. I'm sharing commons problems and possible solutions to them, and also the demo project is in Github, I hope you like.
This document provides tips and tricks for using Google Maps, definition modifier files, and asynchronous processes in Logi Info. It discusses overlaying state/province polygons on Google Maps, using definition modifier files to dynamically modify element definitions at runtime for purposes like translations or adding elements, and how asynchronous processes can be triggered through HTTP requests to perform tasks without blocking the user interface. The document demonstrates these techniques with examples and encourages attendees to try them out.
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
This document provides a summary of a project report on big data Twitter data retrieval and text mining. The project involved creating a Twitter application, installing and loading R packages for Twitter API access and text analysis, authenticating with Twitter via OAuth, extracting text from Twitter timelines, transforming and analyzing the text through techniques like stemming words and finding frequent terms and word associations, and showcasing results with a word cloud. The project was completed as part of a summer training program at the GEOPIC center of ONGC in India under the guidance of a chief manager.
El documento presenta una charla sobre cómo hacer que los datos sean atractivos mediante el uso de técnicas de machine learning como K-means. Explica conceptos como el entrenamiento de algoritmos, su ejecución a gran escala y la representación de datos. También describe las tecnologías Docker, Apache Spark, Jupyter Notebook y Apache Toree que se pueden utilizar para analizar y visualizar datos de forma interactiva.
Spatial SQL is a variant of SQL that is designed to handle spatial data stored in a database. It provides functions and capabilities for working with spatial data types like points, lines, and polygons. These functions allow for spatial queries, transformations between coordinate systems, and other operations that are useful for GIS applications but difficult to perform with standard SQL. The key advantages of Spatial SQL are that it allows large volumes of spatial data to be stored and queried efficiently using specialized indexes and functions optimized for spatial data.
"What we learned from 5 years of building a data science software that actual...Dataconomy Media
"What we learned from 5 years of building a data science software that actually works for everybody." Dr. Dennis Proppe, CTO and Chief Data Scientist at GPredictive GmbH
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Dennis Proppe is the CTO and Chief Data Scientist at Gpredictive, where he helps building software that enables data scientists to build and deploy predictive models in a few minutes instead of weeks. He has 10 years+ of expertise in extracting business value from data. Before co-founding Gpredictive, he worked as a marketing science consultant. Dennis holds a Ph.D. in statistical marketing.
GeoKettle: A powerful open source spatial ETL toolThierry Badard
GeoKettle is an open source spatial ETL tool that is part of a geospatial business intelligence software stack. It is based on Pentaho Data Integration and provides consistent integration of spatial data types and capabilities. GeoKettle allows automated extraction, transformation, and loading of data across various sources into data warehouses. It supports spatial operations and can be deployed in cloud environments for scalable geospatial data processing.
Kris Buytaert discusses the past, present, and future of DevOps. He notes that while tools and technologies will continue to evolve, collaboration between development and operations remains a key requirement. DevOps adoption also faces challenges like broken certification processes, resistance to change from large organizations, and burnout. Ultimately, DevOps is still a work in progress with many organizations just starting their journeys, and its future will depend on continued education and bridging cultural divides between teams.
Plenary Talk from GeCoWest ~ Best of Breed for GeospatialMichael Terner
This document summarizes Michael Terner's presentation at the Geospatial Conference of the West in September 2013. The presentation discusses changes happening in the geospatial industry due to new technologies like mobile, cloud computing, and open source software. It argues that organizations should embrace a "best of breed" approach, using the best tools for each job rather than relying on a single vendor. Examples are given of projects that use different technology combinations including Esri, OpenGeo, Microsoft, and Google tools. The presentation encourages embracing ongoing changes and mixing technologies rather than taking an "all or nothing" approach.
Tracking Task Context To Support ResumptionDamien Clauzel
The document discusses tracking user activity and context on computer systems to enable task resumption. It proposes wanting to follow what users are doing and why, and manipulate systems by storing and restoring context. To do so, activity and action tracking is needed, as well as the ability to remotely control platforms. Possible approaches include screen mapping, spyware tools, and modifying applications to send event data. The conclusion notes the difficulty of collecting data and that solutions depend on the environment. Customizing applications is posed as the most promising solution.
Introducing GeoPySpark, a Big Data GeoSpatial LibraryJacob Bouffard
This document introduces GeoPySpark, an open source geospatial library for big data. It discusses how GeoPySpark can be used to process geospatial raster and vector data at scale, provides examples of local operations, focal operations, and distance calculations that can be performed. The document concludes with a demo of GeoPySpark capabilities and outlines future work including improving performance by integrating with Apache Arrow.
From the Introduction to GeoTools workshop!
Are you new to GeoSpatial? This GeoTools session is back by popular demand with Java 8 examples. Offering a visual introduction for Java developers we will exploring how you can integrate GIS services into your next project.
For those new to the GeoSpatial scene we provide an introduction to spatial concepts and how to avoid common pitfalls.
The workshop offers a steady series of workbooks introducing:
Feature creation
Geometry, Coordinate Reference Systems and Re-projection
Spatial Queries
Handling large format rasters
Working with Style
Raster Operations
Covering both the concepts and the science of map making the workbooks serve as an excellent reference, but the focus is always on you and the code you need to get the job done.
Digital Graph tour Rome: "Connect the Dots, Lorenzo SperanzoniNeo4j
This document provides information about a GraphTour Rome 2020 event hosted by LARUS, including:
- LARUS is an Italian company founded in 2004 that is a leader in graph database development and Neo4j partner.
- The event will discuss how viewing business problems as networks can provide benefits, as well as examples of how customers like banks and telecom companies are using Neo4j for applications like fraud detection and recommendations.
- LARUS' typical customer success roadmap for graph database projects is presented, outlining the assessment, use case identification, prototype, and production phases of a project.
This document provides an overview of geospatial analytics in Spark. It discusses the challenges of geospatial analysis including projections, indexing, data curation, and system libraries. It then presents case studies on large-scale geospatial joins, spatial disaggregation, and pattern of life analysis. Live demos are shown for each case study. Key lessons learned are to standardize data formats, leverage datalakes, use domain-driven design, test scaling, and leverage existing work from others.
Dirty data? Clean it up! - Datapalooza Denver 2016Dan Lynn
Dan Lynn (AgilData) & Patrick Russell (Craftsy) present on how to do data science in the real world. We discuss data cleansing, ETL, pipelines, hosting, and share several tools used in the industry.
OpenGeoData Italia 2014 - Marco Fago "Infrastrutture di dati territoriali, IN...giovannibiallo
(1) The document discusses GetLOD, an open source solution for publishing geographic data as Linked Open Data. It allows publishing geospatial data and metadata from traditional cartographic web services as open, linkable RDF data.
(2) GetLOD is integrated with Spatial Data Infrastructures through OGC standards and allows publishing geographic open data in RDF and other formats like Shapefile and GML.
(3) The Region Emilia-Romagna uses GetLOD and Moka to organize their SDI and build applications, while also publishing open data through GetLOD's services. SDI and open data infrastructures will interoperate through Moka.
This presentation is about best practices when using hibernate and Spring Data JPA. I'm sharing commons problems and possible solutions to them, and also the demo project is in Github, I hope you like.
This document provides tips and tricks for using Google Maps, definition modifier files, and asynchronous processes in Logi Info. It discusses overlaying state/province polygons on Google Maps, using definition modifier files to dynamically modify element definitions at runtime for purposes like translations or adding elements, and how asynchronous processes can be triggered through HTTP requests to perform tasks without blocking the user interface. The document demonstrates these techniques with examples and encourages attendees to try them out.
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
This document provides a summary of a project report on big data Twitter data retrieval and text mining. The project involved creating a Twitter application, installing and loading R packages for Twitter API access and text analysis, authenticating with Twitter via OAuth, extracting text from Twitter timelines, transforming and analyzing the text through techniques like stemming words and finding frequent terms and word associations, and showcasing results with a word cloud. The project was completed as part of a summer training program at the GEOPIC center of ONGC in India under the guidance of a chief manager.
Similar to Geoposicionamiento Big Data o It's bigger on the inside Commit conf 2018 (20)
El documento presenta una charla sobre cómo hacer que los datos sean atractivos mediante el uso de técnicas de machine learning como K-means. Explica conceptos como el entrenamiento de algoritmos, su ejecución a gran escala y la representación de datos. También describe las tecnologías Docker, Apache Spark, Jupyter Notebook y Apache Toree que se pueden utilizar para analizar y visualizar datos de forma interactiva.
Este documento presenta cómo optimizar y monitorear trabajos de Spark con la Spark Web. Explica la terminología básica de Spark como aplicaciones, trabajos, etapas y tareas. Luego describe cómo la Spark Web proporciona información sobre aplicaciones, trabajos, etapas, caché y contadores que ayuda a optimizar el DAG. También cubre cómo la Spark Web monitorea trabajos de Spark SQL y Spark Streaming.
Talk about add proxy user in Spark Task execution time given in Spark Summit East 2017 by Jorge López-Malla and Abel Ricon
full video:
https://www.youtube.com/watch?v=VaU1xC0Rixo&feature=youtu.be
Meetup de Spark y su interacción con Kerberos, para verlo como animación: https://docs.google.com/presentation/d/1DCjp_-s9J647Vydt5ltmqfXpS2PrJDo3KzoVz0C9T7Q/edit?usp=sharing
La problemática Big Data ha dejado de ser una nueva moda y se ha asentado como una nueva realidad en nuestro día a día, y la tecnolgía se ha adaptado a esta nueva realidad permitiendonos afortar problemas complejos de una manera sencilla y casi transaparete.
Pero, ¿y nosotros, hemos cambiado la forma de ver los proyectos y de atacar la solución?¿seguimos tratando de solucionar esta nueva problemática con la misma metodología?¿Seguimos creyendo que el Big Data nos va a solucionar todos nuestros problemas por arte de magia?
Esta charla versará sobre como, según la experiencia del ponente en distintos proyectos de distintas áreas de negocio, se han cambiado la forma de afrontar estos y de como se han solucionado los distintos problemas a la hora de afrontar un proyecto Big Data.
Meetup de Apache Spark Madrid sobre los errores que todos cometemos en proyectos Big Data.
Como las animaciones no van muy bien podeis verla en el siguiente enlace:
https://docs.google.com/presentation/d/1W4Foy9u0NkZziQ36I5_00b_e-JlwhSshSFv-hcxaBpM/edit?usp=sharing
Apache Big Data Europa- How to make money with your own dataJorge Lopez-Malla
This document discusses how Stratio used big data technologies like Apache Spark to help a middle eastern telecommunications company with data challenges. It describes Stratio as the first Spark-based big data platform and discusses how they helped the telco process over 9.5 million daily events from 9.2 million customers. Specifically, Stratio used Spark and its machine learning library MLLib to build models from millions of data points to recognize patterns and improve network coverage, gather customer insights, and monetize data.
Meetup Spark y la Combinación de sus Distintos MódulosJorge Lopez-Malla
El documento presenta una introducción a Spark y sus distintos módulos. Explica brevemente Spark Core, que incluye RDD, transformaciones y acciones; Spark SQL, que permite consultas SQL sobre RDD; y cómo se relacionan estos módulos. También menciona Spark Streaming y MLlib, pero se enfoca principalmente en describir Spark Core y SQL, y cómo pueden combinarse mediante la creación de DataFrames a partir de RDD o realizando operaciones de join.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
2. Operations Strategy in a Global Environment.ppt
Geoposicionamiento Big Data o It's bigger on the inside Commit conf 2018
1. Big Data y geoposicionamiento
or
it’s bigger on the inside
Jorge López-Malla Matute
Senior Data Engineer
2. 1. Presentation
2. What does Key Value means and why does it matters so
much?
3. Why do we need Geopositioning analytics?
4. How can we merge these two worlds?
5. Q&A
Index
4. SKILLS
JORGE LÓPEZ-MALLA
@jorgelopezmalla
Arquitecto Big Data, certificado número
13 de Spark, riojano y miope.
Después de años tratando de solventar
problemas modernos con tecnologías
tradicionales lo intenté con el Big Data
y, ¡vi que lo resolvían!
5. What we do
Geoblink is the ultimate location
Intelligence solution that helps companies
of any size make strategic, location-related
decisions on an easy-to-use platform
6. COLLECTING
DATA
We combine our
client’s internal data
with external data and
Geoblink’s proprietary
location data
TRANSFORMING
DATA
We process and analyze
data using advanced
analytics (big data) and
artificial intelligence
techniques
PROVIDING
INSIGHTS
We present insights on a
user-friendly platform to
help companies make
powerful, data-driven
decisions
How we do it
7. What does “Key Value” mean
and why does it matters so
much?
8. ● Big Data was born in the early 2000s
● Data is no longer small enough to fit in a single commodity
machine
● Data grows exponentially
● Vertical scaling is both dangerous and expensive
A little bit of history
● Solutions?
13. Processing & Storing
● Choosing a proper key is not only critical in a stored system but
also very important in distributed processing frameworks
● Spark, is probably the most important distributed processing
framework right now, is no exception
● Both important in streaming and batch processing
15. The Five Ws are questions whose answers are considered basic in
information gathering or problem solving
● Who was involved?
● What happened?
● Why did that happen?
● When did it take place?
● Where did it take place?
Five W
16. ● Digital society needs immediate reactions
● “Slows” responses are not useful anymore
● Big Data allows us to answer 4 of the 5 W questions
● Geospatial problem is not just an enterprise problem
The where matters
20. ● Knowing both the problem to solve and technology should be
enough
● Obtaining the proper key is the “key” in every Big Data project
● In geospatial projects it is fundamental to obtain the results
exactly where we want
● Taking this in mind we should find the key to each record of our
dataset, easy … or not?
Merging worlds
23. ● Remember: We should assign a key to a value using as few
logic as possible
● All geospatial logic must be understandable by humans
● The intuitive behaviour is to assign each point to a knowing
geospatial cardinality
The real problem
25. Intersection
● Each coordinate is not relevant by itself
● To assign each coordinate to a recognizable area we need both
geometries
● So we need to intersect the coordinates with the areas
27. Intersection
● The intersect operation has a high computational cost
● We need to do this operation only in the cases that a
intersection is probable
● We need to find a key to reduce the operation cost
28. ● First of all, there is no silver bullet
● The “key” problem is worse in the Geospatial world
● Both storing and processing technologies have similar problems
● Geospatial indexes help a lot
Finding a proper Key
30. ● Some Geospatial tech has been grouped by Eclipse in
locationtech
● Geospark and Magellan are spatial modules for Spark
● Although we only talk about Spark, other processing engines
have this functionality
● We have tested only processing engines but researched for
storage techs
Big Data initiatives
32. ● Both Magellan and Geospark offer geospatial functionality
powered by Apache Spark
● Both allow us to use SparkSQL for Geospatial queries
● Both optimize the queries in Spark
● Geospark’s documentation is better than Magellan Spark
Processing engines
34. ● Spatial joins allows us to assign several geometries to a
geometry
● Remember intersect operations came with a high cost
● In most use cases you only want a 1:1 mapping
● You can use Broadcast variables!
Do you really need a join?
35. Geomesa-Big Data storing
● Geomesa is an open-source project that allows performing
geospatial operations against several datasources and
processing engines
● Has connectors with visual tools (like Geoserver)
● We only tested Geomesa with Hbase and as a POC (yet
● We only have tested Geomesa as a POC
39. Takeaways
● We really need to give the insights in the proper location
● Big Data requires finding suitable key to our problem
● When dealing with big amount of data we have to aggregate it
● Spatial indexes are adecuate keys but they are not perfect
● If you only need to assign one geometry to another, a spatial
join is not a good idea