StorageEdge for SharePoint optimizes SharePoint storage and performance. It externalizes all BLOBs from SharePoint content database and reduces the database size by 95%.
StorageEdge for SharePoint optimizes SharePoint storage and performance. It externalizes all BLOBs from SharePoint content database and reduces the database size by 95%.
2013 GISCO Track, Quality Assessment and Improvement for Addressed Locations ...GIS in the Rockies
ISO 19157 Geographic information - Data quality provides a structure for organizing comprehensive data quality assessment measures. What it doesn't provide is a priority of data quality elements for a specific dataset and jurisdiction. Over the past year, the Colorado Address Data Quality subgroup has developed a prioritized list of data quality measures for addressed locations, in an effort to establish common criteria and a scorecard. These will provide a means to describe the data compiled from multiple jurisdictions with varying origins in an objective manner so users of the data can determine their fitness for use. It also provides feedback for local jurisdictions to increase their level of quality according to their need and discretion.
In addition, the State of Colorado in coordination with the US Postal Service, the US Census Bureau, and state and local agencies will begin to provide feedback to local jurisdictions on possible discrepancies in comparison to Master Street Address Guides (MSAGs), the Coding Accuracy Support System (CASS), Statewide Colorado Voter Registration and Election System (SCORE), the Colorado Motorist Insurance Identification Database MIDB, and other datasets that contain addresses. These comparisons are particularly helpful in identifying possible omissions but also in confirming and completing georeferenced address data content. This presentation will describe the value of these comparisons and progress in developing and measuring data quality using common criteria and objective measures.
Cloud Computing course presentation, Tarbiat Modares University
By: Sina Ebrahimi, Mohammadreza Noei
Advisor: Sadegh Dorri Nogoorani, PhD.
Presentation Data: 1397/03/07
Video Link in Aparat: https://www.aparat.com/v/N5VbK
Video Link on TMU Cloud: http://cloud.modares.ac.ir/public.php?service=files&t=9ecb8d2dd08df6f990a3eb63f42011f7
This presenation's pptx file (some animations may be lost in slideshare) : http://cloud.modares.ac.ir/public.php?service=files&t=f62282dbd205abaa66de2512d9fdfc83
This is a introduction to PostgreSQL that provides a brief overview of PostgreSQL's architecture, features and ecosystem. It was delivered at NYLUG on Nov 24, 2014.
http://www.meetup.com/nylug-meetings/events/180533472/
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.
A Day in the Life of a Druid Implementor and Druid's RoadmapItai Yaffe
Benjamin Hopp (Solutions Architect) @ Imply:
Druid is an emerging standard in the data infrastructure world, designed for high-performance slice-and-dice analytics (“OLAP”-style) on large data sets.
This talk is for you if you’re interested in learning more about pushing Druid’s analytical performance to the limit.
Perhaps you’re already running Druid and are looking to speed up your deployment, or perhaps you aren’t familiar with Druid and are interested in learning the basics.
Some of the tips in this talk are Druid-specific, but many of them will apply to any operational analytics technology stack.
The most important contributor to a fast analytical setup is getting the data model right.
The talk will center around various choices you can make to prepare your data to get best possible query performance.
We’ll look at some general best practices to model your data before ingestion such as OLAP dimensional modeling (called “roll-up” in Druid), data partitioning, and tips for choosing column types and indexes.
We’ll also look at how more can be less: often, storing copies of your data partitioned, sorted, or aggregated in different ways can speed up queries by reducing the amount of computation needed.
We’ll also look at Druid-specific optimizations that take advantage of approximations; where you can trade accuracy for performance and reduced storage.
You’ll get introduced to Druid’s features for approximate counting, set operations, ranking, quantiles, and more.
And we will finish with the latest and greatest Druid news, including details about the latest roadmap and releases.
2013 GISCO Track, Quality Assessment and Improvement for Addressed Locations ...GIS in the Rockies
ISO 19157 Geographic information - Data quality provides a structure for organizing comprehensive data quality assessment measures. What it doesn't provide is a priority of data quality elements for a specific dataset and jurisdiction. Over the past year, the Colorado Address Data Quality subgroup has developed a prioritized list of data quality measures for addressed locations, in an effort to establish common criteria and a scorecard. These will provide a means to describe the data compiled from multiple jurisdictions with varying origins in an objective manner so users of the data can determine their fitness for use. It also provides feedback for local jurisdictions to increase their level of quality according to their need and discretion.
In addition, the State of Colorado in coordination with the US Postal Service, the US Census Bureau, and state and local agencies will begin to provide feedback to local jurisdictions on possible discrepancies in comparison to Master Street Address Guides (MSAGs), the Coding Accuracy Support System (CASS), Statewide Colorado Voter Registration and Election System (SCORE), the Colorado Motorist Insurance Identification Database MIDB, and other datasets that contain addresses. These comparisons are particularly helpful in identifying possible omissions but also in confirming and completing georeferenced address data content. This presentation will describe the value of these comparisons and progress in developing and measuring data quality using common criteria and objective measures.
Cloud Computing course presentation, Tarbiat Modares University
By: Sina Ebrahimi, Mohammadreza Noei
Advisor: Sadegh Dorri Nogoorani, PhD.
Presentation Data: 1397/03/07
Video Link in Aparat: https://www.aparat.com/v/N5VbK
Video Link on TMU Cloud: http://cloud.modares.ac.ir/public.php?service=files&t=9ecb8d2dd08df6f990a3eb63f42011f7
This presenation's pptx file (some animations may be lost in slideshare) : http://cloud.modares.ac.ir/public.php?service=files&t=f62282dbd205abaa66de2512d9fdfc83
This is a introduction to PostgreSQL that provides a brief overview of PostgreSQL's architecture, features and ecosystem. It was delivered at NYLUG on Nov 24, 2014.
http://www.meetup.com/nylug-meetings/events/180533472/
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.
A Day in the Life of a Druid Implementor and Druid's RoadmapItai Yaffe
Benjamin Hopp (Solutions Architect) @ Imply:
Druid is an emerging standard in the data infrastructure world, designed for high-performance slice-and-dice analytics (“OLAP”-style) on large data sets.
This talk is for you if you’re interested in learning more about pushing Druid’s analytical performance to the limit.
Perhaps you’re already running Druid and are looking to speed up your deployment, or perhaps you aren’t familiar with Druid and are interested in learning the basics.
Some of the tips in this talk are Druid-specific, but many of them will apply to any operational analytics technology stack.
The most important contributor to a fast analytical setup is getting the data model right.
The talk will center around various choices you can make to prepare your data to get best possible query performance.
We’ll look at some general best practices to model your data before ingestion such as OLAP dimensional modeling (called “roll-up” in Druid), data partitioning, and tips for choosing column types and indexes.
We’ll also look at how more can be less: often, storing copies of your data partitioned, sorted, or aggregated in different ways can speed up queries by reducing the amount of computation needed.
We’ll also look at Druid-specific optimizations that take advantage of approximations; where you can trade accuracy for performance and reduced storage.
You’ll get introduced to Druid’s features for approximate counting, set operations, ranking, quantiles, and more.
And we will finish with the latest and greatest Druid news, including details about the latest roadmap and releases.
Traditional approaches in anti-money laundering involve simple matching algorithms and a lot of human review. However, in recent years this approach has proven to not scale well with the ever increasingly strict regulatory environment. We at Bayard Rock have had much success at applying fancier approaches, including some machine learning, to this problem. In this talk I walk you through the general problem domain and talk about some of the algorithms we use. I’ll also dip into why and how we leverage typed functional programming for rapid iteration with a small team in order to out-innovate our competitors.
Bayard Rock, LLC, is a private research and software development company with headquarters in the Empire State Building. It is a leader in the filed in the research and development of tools for improving the state of the art in anti-money laundering and fraud detection. As you might imagine, these tools rely heavily on mathematics and graph algorithms. In this talk, Richard Minerich will discuss the research activities of Bayard Rock and its approaches to build tools to find the “bad guys”. Richard Minerich is Bayard Rock’s Director of Research and Development. Rick has expertise in F#, C#, C, C++, C++/CLI,. NET (1.1, 2.0, 3.0, 3.5, 4.0, and 4.5), Object Oriented Design, Functional Design, Entity Resolution, Machine Learning, Concurrency, and Image Processing. He is interested in working on algorithmically, mathematically complex projects and remains open to explore new ideas.
Rick holds 2 patents. The first one, co-invented with a colleague, is titled “Method of Image Analysis Using Sparse Hough Transform.” The other independently held is known as “Method for Document to Template Alignment.”
Geospatial data appears to be simple right up until the part when it becomes intractable. There are many gotcha moments with geospatial data in spark and we will break those down in our talk. Users who are new to geospatial analysis in spark will find this portion useful as projections, geometry types, indices, and geometry storage can cause issues.
Hybrid Databases - PHP UK Conference 22 February 2019Dave Stokes
The introduction of a JSON data type allows for relational databases that can also function as schemaless NoSQL JSON document stores. This also let you reduce expensive and nasty many-to-many table joins as well as providing data mutability in an environment known for having very ridgid structures
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
Similar to Colorado State Address Dataset Automated Processing (20)
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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.
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.
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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Colorado State Address Dataset Automated Processing
1. Colorado State Address Dataset
Automated Processing
Nathan Lowry, GIS Outreach Coordinator
State of Colorado
September 23, 2014
2.
3. Common Data Model
● Allows local and state-wide querying, analysis, and integration …
● Accommodates information exchanges
▪ Hierarchical - City to County, County to Region, Region to State
▪ Among neighboring jurisdictions (eg. County to County, etc.)
● Allows profiles to provide data in standard forms for specific
objectives
▪ NENA CLDXF for NG-911
▪ USPS Pub-28 for CASS
▪ ArcGIS Geocoding (for quality comparisons, etc.)
● It’s more efficient (less work) and assures more quality (less loss)
4. FGDC-STD-016-2011
United States Thoroughfare, Landmark, and Postal Address Data Standard
Of Greatest Significance:
1.Everything* is ‘fully explicit’ (fully spelled‐out)
No abbreviations allowed; No Ambiguity
*The only exception is two‐letter state postal codes (eg. “CO” = Colorado)
●2.You will express exactly how each address will be parsed
Parsing is no longer subject to interpretation
The break‐down is stored in the data for each record
3.Each Address must be assigned a Unique Identifier (UID)
Multiple representations of the same address can be “tied
together” if and only if (iff) addresses are assigned UIDs.
These are big changes that few have yet implemented
•Our common data model is designed to accommodate both:
‒your current state and
‒this “to be” state
5. Presuppositions:
● SQL Server Integration Services (SSIS)
o Parallel processing - fast translations - True.
o Most Compatible with SQL Server - Irrelevant*
o Developed by DBAs for DBAs - No, developed by app
developers for app developers
▪ (ie. Normalization tools) - Hah, hah, hah, hah,
hah!
o No Additional Cost - (This one bore out)
o I learned French instead of Spanish - (SSIS instead of
Python)
● No Parsing
o I will translate, but it’ll be the locals’ responsibility to
pre-parse... - No parsing, no geocoding*
o In addition, no last lines, no geocoding*
● 6-8 Weeks Processing - 6-8 Months of Processing
9. Observations
● SQL Server Integration Services (SSIS)
○ SSIS is quirky
○ SSIS Expression Language is Swahili
○ A modeling canvas may be more effective for design
○ SSIS can integrate with many other server processes (FTP)
● Parsing and “Last Lining” will give CO jurisdictions a
leg up
○ The level of effort can be significant
○ CLDXF Street Naming and Address Numbering Conventions
● Standards
○ Jurisdictional pretypes, sequencers - minor tweaks
○ Subaddress conventions need ... something
10. Opportunities
● Standards
○ Improvement via implementation
○ Coalescence on Subaddresses
● Common implementations of data models
○ Reduce the cost of development
○ Makes sharing of code useful and possible
● Common code
○ Shared parsing tools
○ Shared applications
