OpenStreetMap SOTM-US Conference presentation, "Working with Institutional Data in OSM". Presentation at the 1st annual State of the Map-US conference in Atlanta GA
Unparalleled Graph Database Scalability Delivered by Neo4j 4.0GraphAware
GraphAware provides scalable graph database solutions and machine learning capabilities powered by Neo4j. It discusses how graph databases and machine learning can be combined to address challenges like data storage, model storage, and communication. Graph sharding is presented as a way to simplify database creation, improve isolation, increase performance, and separate training and model data. Example applications discussed include mobile user modeling, recommendation systems, and organizing coronavirus research.
How Boston Scientific Improves Manufacturing Quality Using Graph AnalyticsGraphAware
Tracking end of line manufacturing issues to their source can be a daunting task. Boston Scientific, in partnership with GraphAware, has used the Neo4j platform to build a manufacturing quality tool that offers dramatic improvements to the time, quality, and quantity of investigations. In this talk we will review a manufacturing value stream in a graph and discuss the analysis methods available, which result in striking increases in business efficiencies, for this unique application. We will also present how the system was implemented within the existing data architecture and then scaled from a laptop investigational tool to an enterprise-grade solution with Neo4j Server.
*Talk at GraphConnect NYC 2018*
Graph-Powered Machine Learning - Meetup Paris - March 5, 2018
Graph -based machine learning is becoming an important trend in artificial intelligence, transcending a lot of other techniques. Using graphs as a basic representation of data for multiple purposes:
- the data is already modeled for further analysis
- graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs;
- improving computation performances and quality. The talk will present these advantages and present applications in the context of recommendation engines and natural language processing.
Speaker: Dr. Vlasta Kus (@VlastaKus) is a Data Scientist at GraphAware, specializing in graph-based Natural Language Processing and related topics, including deep learning techniques. He speaks English, Czech and some French and currently lives in Prague.
Graph-Powered machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
Efficient processing of Rank-aware queries in Map/ReduceSpiros Oikonomakis
Through the experimental part and the execution of three different algorithms, aims to show the disadvantages of the default operation of the Map/Reduce programming model in Top-K queries, as well as the recommended solution and the effective processing of such query types. Two of the major shortcomings that occur will be managed, namely the Early Termination and the Load Balancing. There is a code which is implemented for this solution.
The document discusses QI Macros for Excel, which provides easy capability analysis tools for Excel. It allows users to select data with a mouse click and access templates for histograms, Weibull analysis, and capability analysis with Cp and Cpk metrics. The macros simplify capability analysis in Excel without requiring programming knowledge.
Serverless Days Amsterdam - Choosing a Serverless Monitoring PlatformJosh Carlisle
- Logs provide deep diagnostics but can be a "haystack" that requires developer effort to find answers. Metrics give insights but complexity increases with correlation across systems. Tracing provides end-to-end visibility but gaps may exist.
- The three pillars of observability are logs, metrics, and traces. Avoid directly coding for monitoring to reduce technical debt and decrease visibility over time. Consider bytecode instrumentation to monitor without source code changes.
- Choosing a monitoring platform is difficult due to ephemeral compute, high distribution, and limitations of serverless platforms and cloud vendors. Look to logs for exceptions, metrics for baselines and AI, and traces for issues across upstream and downstream systems.
Unparalleled Graph Database Scalability Delivered by Neo4j 4.0GraphAware
GraphAware provides scalable graph database solutions and machine learning capabilities powered by Neo4j. It discusses how graph databases and machine learning can be combined to address challenges like data storage, model storage, and communication. Graph sharding is presented as a way to simplify database creation, improve isolation, increase performance, and separate training and model data. Example applications discussed include mobile user modeling, recommendation systems, and organizing coronavirus research.
How Boston Scientific Improves Manufacturing Quality Using Graph AnalyticsGraphAware
Tracking end of line manufacturing issues to their source can be a daunting task. Boston Scientific, in partnership with GraphAware, has used the Neo4j platform to build a manufacturing quality tool that offers dramatic improvements to the time, quality, and quantity of investigations. In this talk we will review a manufacturing value stream in a graph and discuss the analysis methods available, which result in striking increases in business efficiencies, for this unique application. We will also present how the system was implemented within the existing data architecture and then scaled from a laptop investigational tool to an enterprise-grade solution with Neo4j Server.
*Talk at GraphConnect NYC 2018*
Graph-Powered Machine Learning - Meetup Paris - March 5, 2018
Graph -based machine learning is becoming an important trend in artificial intelligence, transcending a lot of other techniques. Using graphs as a basic representation of data for multiple purposes:
- the data is already modeled for further analysis
- graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs;
- improving computation performances and quality. The talk will present these advantages and present applications in the context of recommendation engines and natural language processing.
Speaker: Dr. Vlasta Kus (@VlastaKus) is a Data Scientist at GraphAware, specializing in graph-based Natural Language Processing and related topics, including deep learning techniques. He speaks English, Czech and some French and currently lives in Prague.
Graph-Powered machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
Efficient processing of Rank-aware queries in Map/ReduceSpiros Oikonomakis
Through the experimental part and the execution of three different algorithms, aims to show the disadvantages of the default operation of the Map/Reduce programming model in Top-K queries, as well as the recommended solution and the effective processing of such query types. Two of the major shortcomings that occur will be managed, namely the Early Termination and the Load Balancing. There is a code which is implemented for this solution.
The document discusses QI Macros for Excel, which provides easy capability analysis tools for Excel. It allows users to select data with a mouse click and access templates for histograms, Weibull analysis, and capability analysis with Cp and Cpk metrics. The macros simplify capability analysis in Excel without requiring programming knowledge.
Serverless Days Amsterdam - Choosing a Serverless Monitoring PlatformJosh Carlisle
- Logs provide deep diagnostics but can be a "haystack" that requires developer effort to find answers. Metrics give insights but complexity increases with correlation across systems. Tracing provides end-to-end visibility but gaps may exist.
- The three pillars of observability are logs, metrics, and traces. Avoid directly coding for monitoring to reduce technical debt and decrease visibility over time. Consider bytecode instrumentation to monitor without source code changes.
- Choosing a monitoring platform is difficult due to ephemeral compute, high distribution, and limitations of serverless platforms and cloud vendors. Look to logs for exceptions, metrics for baselines and AI, and traces for issues across upstream and downstream systems.
Data cleansing and prep with synapse data flowsMark Kromer
This document provides resources for data cleansing and preparation using Azure Synapse Analytics Data Flows. It includes links to videos, documentation, and a slide deck that explain how to use Data Flows for tasks like deduplicating null values, saving data profiler summary statistics, and using metadata functions. A GitHub link shares a tutorial document for a hands-on learning experience with Synapse Data Flows.
Meetup in Prague (CZ), 31st May 2018
Abstract:
Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending other techniques and technologies. Using graphs as a basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between “things” and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and iteratively enhance/learn over them, creating Knowledge Graphs; (iii) improving computation performance and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
Bio:
Dr. Alessandro Negro (https://twitter.com/alessandronegro?lang=en) is Chief Scientist at GraphAware. He has been a long-time member of the graph community and is the main author of the original Neo4j-based recommendation engine. At GraphAware, Alessandro specializes in recommendation engines, graph-aided search, and NLP.
Cloudlytics Helps You analyze Amazon Cloud Logs -
- Amazon S3
- Amazon CloudFront
- Amazon ELB
This Presentation Gives a Basic overview of Cloudlytics Features, Pricing Details, Offers to AWS Activate Customers, AWS Marketplace Info & A Sneak Preview of All the Analytics ( The Reports Section will be covered in Detail in our Next Presentation.)
The document discusses automated hyperparameter tuning and model selection. It describes using Bayesian parameter optimization to learn from previous model training attempts and select promising hyperparameters for future models. The talk covers metric selection, dangers of naive cross-validation, selecting the best model while considering factors like retraining and speed, and techniques like fusions that combine multiple models. Caveats of model selection are noted, such as needing substantial data.
Automated machine learning (AutoML) can automate time-consuming tasks in the machine learning lifecycle like data preprocessing, model training, and tuning. This allows data scientists to focus on higher-level work. The presentation demonstrated AutoML on the Titanic dataset in Microsoft Azure Machine Learning service. It showed how AutoML can iterate through various algorithms and hyperparameters, measure model performance, enable model interpretability, facilitate model hosting and drift detection, and support code-based MLOps workflows. AutoML aims to make machine learning more accessible and productive.
Lviv MD Day 2015 Малаховський Віталій "Архітектура компонентів обробки даних ...Lviv Startup Club
This document discusses different architectures for data processing components in mobile applications, specifically Active Record and Data Mapper patterns.
Active Record maps objects directly to database tables, making it easy to use but difficult to test. Data Mapper separates concerns by using a mapper class to manage database interactions independently from business objects, improving testability but adding complexity.
While both have pros and cons depending on the application, the document concludes that Data Mapper is generally better for maintaining separation of concerns and allowing for easier testing, in line with the principle that architecture should prioritize intent over specific frameworks.
This document discusses methods for detecting bad or fraudulent data in online studies. It identifies sources of data problems such as technical errors, missing data, and response fraud. Specific detection techniques are presented, including duplicate detection, univariate and multivariate outlier analysis, and autocorrelation analysis to identify unusual response patterns. Common missing data mitigation strategies like imputation are also covered. Examples of Excel functions for analyzing and working with data are provided.
The document discusses how Experient contracts can provide value to clients by including beneficial contract clauses. It outlines 7 key clauses: 1) attrition, 2) cancellation, 3) lowest rate guarantee, 4) unavailability of facilities, 5) room audit, 6) outside vendors, and 7) extra charges. For each clause, it compares Experient contract language, which aims to save clients time, money, and liability, against non-Experient contracts, which often lack protections for clients. The document advocates that these "U-CLEAR-O" clauses demonstrate the value Experient contracts provide to clients.
The teachers at the school use technology like interactive whiteboards, computers, and word processing to make their lessons more fun and easy to teach. Some teachers use the technology to plan lessons, create spreadsheets, or work on projects for students. All of the tools help the teachers engage and assist the students.
This document discusses methods for detecting bad or fraudulent data in online studies. It identifies sources of data problems such as technical errors, missing data, and response fraud. Specific detection techniques are presented, including duplicate detection, univariate and multivariate outlier analysis, and autocorrelation analysis to identify unusual response patterns. Common missing data mitigation strategies like imputation are also covered. Examples of Excel functions for analyzing and working with data are provided.
Funding in Israel - What do local ecosystem investors are looking formyshivuk
This document discusses what local investors in Israel's startup ecosystem look for in potential investments. It notes that while it may seem easy for startups to raise money, only 30% actually succeed in subsequent funding rounds. The main reason for failure is not reaching significant milestones. Investors want to back companies pursuing large existing markets that can become "unicorns" (valued over $1 billion). Common traits of past unicorns included addressing competitive markets in ways that evolve existing behaviors, having untested founders, and no proven business model or revenue initially. The document outlines the types of questions investors ask about teams, products, competition, marketing, and risks. It provides examples of narratives investors prefer and ways to turn them off, concluding on
This document provides instructions for installing and using R-Studio. It describes R-Studio as an integrated development environment for R with four panes - code, console, workspace, and file/plots. It outlines downloading and installing R-Studio after first installing R. It then demonstrates creating a simple MyMode function to calculate the mode, and improving it through multiple iterations to properly handle duplicate values and return the correct mode. The document encourages testing the function on sample data and trying to "break" it to find flaws.
This document introduces Yariv Levski from the Israeli startup scene. It discusses trends in Tel Aviv including large amounts of capital raised by Israeli high-tech companies, mostly from Israeli VC funds and US investors. A large percentage of funding goes towards building big companies with high valuations, exemplified by Waze being acquired by Google for $1 billion. The secret to Israel's success includes commercializing technologies from day one globally, a supportive government and venture capital environment, and cultural factors like failure not being seen as failure and risk-taking balanced with quality and punctuality. The document argues that Germany and Israel are a perfect match when innovation and risk-taking meet quality and punctuality.
This document provides an introduction to advanced data analytics. It discusses [1] how organizations lose millions annually due to inefficient use of data, [2] the sources and types of big data being generated, and [3] the multi-disciplinary nature of data analytics, drawing on fields like database technology, statistics, machine learning, and visualization. The key steps of analytics projects are outlined, including understanding the domain, preprocessing data, reducing and transforming it, selecting analytical approaches, communicating results, and deploying and evaluating new systems.
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
MapReduce is a programming model that allows processing of large datasets across clusters of machines. It involves specifying map and reduce functions - map processes key-value pairs to generate intermediate pairs, and reduce merges all intermediate values with the same key. Hadoop is an open-source implementation of MapReduce that uses a distributed file system to spread data across machines and push processing to the data. Cascading provides an abstraction layer on top of Hadoop to more easily define multi-step logic without worrying about mapping and reducing. It can help with testing and avoids coding overhead of Hadoop's data structures.
Frustration-Reduced PySpark: Data engineering with DataFramesIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames in Python. For DataFrames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics.
Data cleansing and prep with synapse data flowsMark Kromer
This document provides resources for data cleansing and preparation using Azure Synapse Analytics Data Flows. It includes links to videos, documentation, and a slide deck that explain how to use Data Flows for tasks like deduplicating null values, saving data profiler summary statistics, and using metadata functions. A GitHub link shares a tutorial document for a hands-on learning experience with Synapse Data Flows.
Meetup in Prague (CZ), 31st May 2018
Abstract:
Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending other techniques and technologies. Using graphs as a basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between “things” and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and iteratively enhance/learn over them, creating Knowledge Graphs; (iii) improving computation performance and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
Bio:
Dr. Alessandro Negro (https://twitter.com/alessandronegro?lang=en) is Chief Scientist at GraphAware. He has been a long-time member of the graph community and is the main author of the original Neo4j-based recommendation engine. At GraphAware, Alessandro specializes in recommendation engines, graph-aided search, and NLP.
Cloudlytics Helps You analyze Amazon Cloud Logs -
- Amazon S3
- Amazon CloudFront
- Amazon ELB
This Presentation Gives a Basic overview of Cloudlytics Features, Pricing Details, Offers to AWS Activate Customers, AWS Marketplace Info & A Sneak Preview of All the Analytics ( The Reports Section will be covered in Detail in our Next Presentation.)
The document discusses automated hyperparameter tuning and model selection. It describes using Bayesian parameter optimization to learn from previous model training attempts and select promising hyperparameters for future models. The talk covers metric selection, dangers of naive cross-validation, selecting the best model while considering factors like retraining and speed, and techniques like fusions that combine multiple models. Caveats of model selection are noted, such as needing substantial data.
Automated machine learning (AutoML) can automate time-consuming tasks in the machine learning lifecycle like data preprocessing, model training, and tuning. This allows data scientists to focus on higher-level work. The presentation demonstrated AutoML on the Titanic dataset in Microsoft Azure Machine Learning service. It showed how AutoML can iterate through various algorithms and hyperparameters, measure model performance, enable model interpretability, facilitate model hosting and drift detection, and support code-based MLOps workflows. AutoML aims to make machine learning more accessible and productive.
Lviv MD Day 2015 Малаховський Віталій "Архітектура компонентів обробки даних ...Lviv Startup Club
This document discusses different architectures for data processing components in mobile applications, specifically Active Record and Data Mapper patterns.
Active Record maps objects directly to database tables, making it easy to use but difficult to test. Data Mapper separates concerns by using a mapper class to manage database interactions independently from business objects, improving testability but adding complexity.
While both have pros and cons depending on the application, the document concludes that Data Mapper is generally better for maintaining separation of concerns and allowing for easier testing, in line with the principle that architecture should prioritize intent over specific frameworks.
This document discusses methods for detecting bad or fraudulent data in online studies. It identifies sources of data problems such as technical errors, missing data, and response fraud. Specific detection techniques are presented, including duplicate detection, univariate and multivariate outlier analysis, and autocorrelation analysis to identify unusual response patterns. Common missing data mitigation strategies like imputation are also covered. Examples of Excel functions for analyzing and working with data are provided.
The document discusses how Experient contracts can provide value to clients by including beneficial contract clauses. It outlines 7 key clauses: 1) attrition, 2) cancellation, 3) lowest rate guarantee, 4) unavailability of facilities, 5) room audit, 6) outside vendors, and 7) extra charges. For each clause, it compares Experient contract language, which aims to save clients time, money, and liability, against non-Experient contracts, which often lack protections for clients. The document advocates that these "U-CLEAR-O" clauses demonstrate the value Experient contracts provide to clients.
The teachers at the school use technology like interactive whiteboards, computers, and word processing to make their lessons more fun and easy to teach. Some teachers use the technology to plan lessons, create spreadsheets, or work on projects for students. All of the tools help the teachers engage and assist the students.
This document discusses methods for detecting bad or fraudulent data in online studies. It identifies sources of data problems such as technical errors, missing data, and response fraud. Specific detection techniques are presented, including duplicate detection, univariate and multivariate outlier analysis, and autocorrelation analysis to identify unusual response patterns. Common missing data mitigation strategies like imputation are also covered. Examples of Excel functions for analyzing and working with data are provided.
Funding in Israel - What do local ecosystem investors are looking formyshivuk
This document discusses what local investors in Israel's startup ecosystem look for in potential investments. It notes that while it may seem easy for startups to raise money, only 30% actually succeed in subsequent funding rounds. The main reason for failure is not reaching significant milestones. Investors want to back companies pursuing large existing markets that can become "unicorns" (valued over $1 billion). Common traits of past unicorns included addressing competitive markets in ways that evolve existing behaviors, having untested founders, and no proven business model or revenue initially. The document outlines the types of questions investors ask about teams, products, competition, marketing, and risks. It provides examples of narratives investors prefer and ways to turn them off, concluding on
This document provides instructions for installing and using R-Studio. It describes R-Studio as an integrated development environment for R with four panes - code, console, workspace, and file/plots. It outlines downloading and installing R-Studio after first installing R. It then demonstrates creating a simple MyMode function to calculate the mode, and improving it through multiple iterations to properly handle duplicate values and return the correct mode. The document encourages testing the function on sample data and trying to "break" it to find flaws.
This document introduces Yariv Levski from the Israeli startup scene. It discusses trends in Tel Aviv including large amounts of capital raised by Israeli high-tech companies, mostly from Israeli VC funds and US investors. A large percentage of funding goes towards building big companies with high valuations, exemplified by Waze being acquired by Google for $1 billion. The secret to Israel's success includes commercializing technologies from day one globally, a supportive government and venture capital environment, and cultural factors like failure not being seen as failure and risk-taking balanced with quality and punctuality. The document argues that Germany and Israel are a perfect match when innovation and risk-taking meet quality and punctuality.
This document provides an introduction to advanced data analytics. It discusses [1] how organizations lose millions annually due to inefficient use of data, [2] the sources and types of big data being generated, and [3] the multi-disciplinary nature of data analytics, drawing on fields like database technology, statistics, machine learning, and visualization. The key steps of analytics projects are outlined, including understanding the domain, preprocessing data, reducing and transforming it, selecting analytical approaches, communicating results, and deploying and evaluating new systems.
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
MapReduce is a programming model that allows processing of large datasets across clusters of machines. It involves specifying map and reduce functions - map processes key-value pairs to generate intermediate pairs, and reduce merges all intermediate values with the same key. Hadoop is an open-source implementation of MapReduce that uses a distributed file system to spread data across machines and push processing to the data. Cascading provides an abstraction layer on top of Hadoop to more easily define multi-step logic without worrying about mapping and reducing. It can help with testing and avoids coding overhead of Hadoop's data structures.
Frustration-Reduced PySpark: Data engineering with DataFramesIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames in Python. For DataFrames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics.
Apache Hadoop India Summit 2011 talk "Making Hadoop Enterprise Ready with Am...Yahoo Developer Network
1) Amazon Elastic MapReduce enables customers to easily process vast amounts of data by launching Hadoop clusters across AWS infrastructure.
2) It provides features for managing, monitoring, and debugging Hadoop jobs and clusters without the operational complexities of Hadoop.
3) New features were announced that provide more flexibility for enterprises including expanding and shrinking running clusters, using spot instances to reduce costs, and additional support options.
Sawmill - Integrating R and Large Data CloudsRobert Grossman
This document discusses using R for large-scale data analysis on distributed data clouds. It recommends splitting large datasets into segments using MapReduce or UDFs, then building separate models for each segment in R. PMML can be used to combine the separate models into an ensemble model. The Sawmill framework is proposed to preprocess data in parallel, build models for each segment using R, and combine the models into a PMML file for deployment. Running R on each segment sequentially allows scaling to large datasets, with examples showing processing times for different numbers of segments.
When it comes to dealing with large, complex, and disparate data sets, traditional database technologies are unable to keep pace with the rich analytics necessary to power today’s data-driven applications. Graph analytics databases are becoming the underlying infrastructure for AI and machine learning. These databases allow users to ask complex questions across complex data, which is not always practical or even possible at scale using other approaches. They also enable faster insights against massive data sets when combined with pattern recognition, statistical analysis, and AI/ machine learning. And in the case of standards-based graph databases, they connect with popular visualization tools like Graphileon, allowing users to easily explore their data stores and quickly build compelling graph-based applications.
Geo-Enabling Retail and Property (with emergent location solutions)
Steven Eglinton, GeoEnable
First presented at The SLA Forum 2013
In this presentation I will examine how Location Analytics, as a technology solution and a term, is very rapidly becoming a mainstream concept in IT and GIS.
With examples, what one thought of as specialised tools, such as GIS, are now being ‘democratised’ and embedded in easy-to-use business processes and workflows.
With examples of how this could benefit the property and retail sector, I will examine the main and most important trends in, ‘Geospatial’ and Location Analytics that affect anyone involved in spatial analysis or GIS. These trends really are ‘game changers’ compared with the last 35 years of GIS and Location Technologies and need to be understood to leverage the potential benefits.
1. Cloud GIS – off-premise hosted mapping and location analytics tools. This can dramatically reduce costs and complexity of implementation.
2. ‘Big Data’ – analysing and visualising vast quantities of near real-time data
Location Analytics – the use of, what would have been called GIS technologies, embedded in systems for NON-specialist users.
3. Dynamic (Real-time) Mapping
4. Open Data – Open data from the UK and US for use in business context. This includes postcode data, which is now free to use
5. Mobility – Real-time maps in people’s pockets! with the ability to edit and capture new data
6. Embedding Location – In Processes and integration of Location in enterprise solutions – esp for Asset Management / ERP
7. Location for all – Location Analytics is becoming part of peoples’ jobs as part of a workflow. Typically non-specialist users are now leveraging ‘GIS-like’ technologies without even knowing.
The document provides an overview of AvisMap GIS Technologies and its GIS products. It discusses AvisMap's GIS architecture including its GIS engine, desktop software, data sources, data formats, and various analysis and visualization tools. It also outlines the company's focus on providing GIS platform solutions and its positioning in markets like government, commercial, and military sectors.
This document discusses big data, including the large amounts of data being collected daily, challenges with traditional DBMS solutions, the need for new approaches like Hadoop and Aster Data to handle large volumes of structured and unstructured data, techniques for analyzing big data, and case studies of companies like Mobclix and Yahoo using big data solutions.
Data Science Meetup: DGLARS and Homotopy LASSO for Regression ModelsColleen Farrelly
Short overview of two regression model extensions using differential geometry and homotopy continuation. Case study involves an open-source dataset that can be found on my ResearchGate page, along with the R code used in the analysis. Contains a short reference section for readers interested in learning more about the methods.
BigData: My Learnings from data analytics at Uber
Reference (highly recommended):
* Designing Data-Intensive Applications http://bit.ly/big_data_architecture
* Big Data and Machine Learning using Python tools http://bit.ly/big_data_machine_learning
* Uber Engineering Blog http://eng.uber.com
* Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
http://bit.ly/hadoop_guide_bigdata
The document discusses next generation data warehousing and business intelligence (BI) analytics. It outlines some of the challenges with scaling traditional BI systems to handle large and growing volumes of data. It then proposes using a massively parallel processing (MPP) database like Greenplum to enable scalable dataflow and embed analytics processing directly into the data warehouse. This would help address issues of data volume, processing time, and refreshing aggregated data for analytics servers. It presents an application profile for typical BI systems and discusses Greenplum's scaling technology using parallel queries and data streams. Finally, it introduces the draft gNet API for implementing parallel dataflows and analytics procedures directly in the MPP database.
The document provides an overview of distributed computing and related technologies. It discusses the history of distributed computing including local, parallel, grid and distributed computing. It then discusses applications of distributed computing like web indexing and recommendations. The document introduces Hadoop and its core components HDFS and MapReduce. It also discusses related technologies like HBase, Mahout and challenges in designing distributed systems. It provides examples of using Mahout for machine learning tasks like classification, clustering and recommendations.
Ultra Fast Deep Learning in Hybrid Cloud using Intel Analytics Zoo & AlluxioAlluxio, Inc.
The document discusses using Intel Analytics Zoo and Alluxio for ultra fast deep learning in hybrid cloud environments. Analytics Zoo provides an end-to-end deep learning pipeline that can prototype on a laptop using sample data and experiment on clusters with historical data, while Alluxio enables zero-copy access to remote data for accelerated analytics. Performance tests showed Alluxio providing up to a 1.5x speedup for data loading compared to accessing data directly from cloud storage. Real-world customers are using the combined Analytics Zoo and Alluxio solution for deep learning, recommendation systems, computer vision, and time series applications.
Geographic information systems (GIS) are great for capturing, storing, analyzing, and managing data and
associated attributes of spatially referenced points on earth. In the strictest sense, GIS displays only geographical
information. But have you ever considered other information being included? How about documents and assets
– they have locations on earth. We can understand a wealth of information by adding spatial components.
Bridging Between CAD & GIS: 6 Ways to Automate Your Data IntegrationSafe Software
Converting between CAD and GIS is a common requirement for projects involving infrastructure, buildings, city plans, and more. Unfortunately, the workflow presents many challenges, like translating geometry, attributes, annotations, symbology, geolocation, and other elements.
So how do you allow data to flow freely between these disparate data types, without losing the precision offered by CAD and the spatial context offered by GIS?
This webinar will explore the power of automated data integration workflows for CAD and GIS.
First, we’ll discuss challenges and scenarios for CAD-to-GIS translations, and demo how to use FME to power a digital plan submission portal that validates CAD data and integrates it into the central GIS repository. Next, we’ll discuss challenges and scenarios for GIS-to-CAD conversions, and demo how to build an automated FME workflow for requesting CAD data from GIS.
At the end of the webinar, you'll know how to achieve harmony between CAD & GIS by automating its integration.
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
Converting between CAD and GIS is a common requirement for projects involving infrastructure, buildings, city plans, and more. Unfortunately, the workflow presents many challenges, like translating geometry, attributes, annotations, symbology, geolocation, and other elements.
So how do you allow data to flow freely between these disparate data types, without losing the precision offered by CAD and the spatial context offered by GIS?
This webinar will explore the power of automated data integration workflows for CAD and GIS.
First, we’ll discuss challenges and scenarios for CAD-to-GIS translations, and demo how to use FME to power a digital plan submission portal that validates CAD data and integrates it into the central GIS repository. Next, we’ll discuss challenges and scenarios for GIS-to-CAD conversions, and demo how to build an automated FME workflow for requesting CAD data from GIS.
At the end of the webinar, you'll know how to achieve harmony between CAD &GIS by automating its integration.
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
Converting between CAD and GIS is a common requirement for projects involving infrastructure, buildings, city plans, and more. Unfortunately, the workflow presents many challenges, like translating geometry, attributes, annotations, symbology, geolocation, and other elements.
So how do you allow data to flow freely between these disparate data types, without losing the precision offered by CAD and the spatial context offered by GIS?
This webinar will explore the power of automated data integration workflows for CAD and GIS.
First, we’ll discuss challenges and scenarios for CAD-to-GIS translations, and demo how to use FME to power a digital plan submission portal that validates CAD data and integrates it into the central GIS repository. Next, we’ll discuss challenges and scenarios for GIS-to-CAD conversions, and demo how to build an automated FME workflow for requesting CAD data from GIS.
At the end of the webinar, you'll know how to achieve harmony between CAD & GIS by automating its integration.
This document provides an overview of using open source tools for web mapping, including databases, map servers, and web servers. It discusses setting up a PostgreSQL/PostGIS database and loading spatial data. It then covers using a map server to display the spatial data on a web page and perform spatial queries. Hands-on tasks guide working with these open source tools to create a basic web mapping application.
Similar to Working with Institutional Data in OSM (20)
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
2. Relax Big Itch to Contribute Adjust / Correct Data Topology Check for Necessity Obtain Data Attribute Mapping Conflation Exclude Existing Data Check Output Upload Merge New Data