Huge RDF datasets are currently exchanged on textual RDF formats, hence consumers need to post-process them using RDF stores for local consumption, such as indexing and SPARQL query. This results in a painful task requiring a great effort in terms of time and compu- tational resources. A first approach to lightweight data exchange is a compact (binary) RDF serialization format called HDT. In this paper, we show how to enhance the exchanged HDT with additional structures to support some basic forms of SPARQL query resolution without the need of "unpacking" the data. Experiments show that i) with an exchanging ef- ficiency that outperforms universal compression, ii) post-processing now becomes a fast process which iii) provides competitive query performance at consumption.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This slidedeck is about PoolParty Semantic Suite (http://www.poolparty.biz/), especially about features included by releases 5.2 and 5.3.
See how taxonomy management based on SKOS can be extended with SKOS-XL, all based on W3C's Semantic Web standards. See how SKOS-XL can be combined with ontologies like FIBO.
PoolParty's built in reference corpus analysis based on powerful text mining helps to continuously extend taxonomies. Its built-in co-occurence analysis supports taxonomists with the identification of candidate concepts.
PoolParty Semantic Integrator can be used for deep data analytics tasks and semantic search. See how this can be integrated with various graph databases and search engines.
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
I used these slides for an introductory lecture (90min) to a seminar on SPARQL. This slideset introduces the RDF query language SPARQL from a user's perspective.
RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics or meaning of those pieces. The point is to have a method so simple that it can express any fact, and yet so structured that computer applications can do useful things with knowledge expressed in RDF.
1h SPARQL tutorial given at the "Practical Cross-Dataset Queries on the Web of Data" tutorial at WWW2012. Supported by the LATC FP7 Project. http://latc-project.eu/
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Data deduplication, or entity resolution, is a common problem for anyone working with data, especially public data sets. Many real world datasets do not contain unique IDs, instead, we often use a combination of fields to identify unique entities across records by linking and grouping. This talk will show how we can use active learning techniques to train learnable similarity functions that outperform standard similarity metrics (such as edit or cosine distance) for deduplicating data in a graph database. Further, we show how these techniques can be enhanced by inspecting the structure of the graph to inform the linking and grouping processes. We will demonstrate how to use open source tools to perform entity resolution on a dataset of campaign finance contributions loaded into the Neo4j graph database.
En esta charla miraremos al futuro introduciendo Spark como alternativa al clásico motor de Hadoop MapReduce. Describiremos las diferencias más importantes frente al mismo, se detallarán los componentes principales que componen el ecosistema Spark, e introduciremos conceptos básicos que permitan empezar con el desarrollo de aplicaciones básicas sobre el mismo.
This slidedeck is about PoolParty Semantic Suite (http://www.poolparty.biz/), especially about features included by releases 5.2 and 5.3.
See how taxonomy management based on SKOS can be extended with SKOS-XL, all based on W3C's Semantic Web standards. See how SKOS-XL can be combined with ontologies like FIBO.
PoolParty's built in reference corpus analysis based on powerful text mining helps to continuously extend taxonomies. Its built-in co-occurence analysis supports taxonomists with the identification of candidate concepts.
PoolParty Semantic Integrator can be used for deep data analytics tasks and semantic search. See how this can be integrated with various graph databases and search engines.
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
I used these slides for an introductory lecture (90min) to a seminar on SPARQL. This slideset introduces the RDF query language SPARQL from a user's perspective.
RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics or meaning of those pieces. The point is to have a method so simple that it can express any fact, and yet so structured that computer applications can do useful things with knowledge expressed in RDF.
1h SPARQL tutorial given at the "Practical Cross-Dataset Queries on the Web of Data" tutorial at WWW2012. Supported by the LATC FP7 Project. http://latc-project.eu/
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Data deduplication, or entity resolution, is a common problem for anyone working with data, especially public data sets. Many real world datasets do not contain unique IDs, instead, we often use a combination of fields to identify unique entities across records by linking and grouping. This talk will show how we can use active learning techniques to train learnable similarity functions that outperform standard similarity metrics (such as edit or cosine distance) for deduplicating data in a graph database. Further, we show how these techniques can be enhanced by inspecting the structure of the graph to inform the linking and grouping processes. We will demonstrate how to use open source tools to perform entity resolution on a dataset of campaign finance contributions loaded into the Neo4j graph database.
En esta charla miraremos al futuro introduciendo Spark como alternativa al clásico motor de Hadoop MapReduce. Describiremos las diferencias más importantes frente al mismo, se detallarán los componentes principales que componen el ecosistema Spark, e introduciremos conceptos básicos que permitan empezar con el desarrollo de aplicaciones básicas sobre el mismo.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
Presentation about opportunities and challenges concerning Linked Data at the Open Science Data Cloud NSF PIRE Workshop [1] on 18 July 2012 in Edinburgh, UK.
[1] http://www.opensciencedatacloud.org/osdc-edinburgh-workshop-71612-71712/
Applying large scale text analytics with graph databasesData Ninja API
Data Ninja Services collaborated with Oracle to reach a major milestone in the integration of text analytics with Oracle Spatial and Graph. The Data Ninja Services client in Java can be used to analyze free texts, extract entities, generate RDF semantic graphs, and choose from a number of graph analytics to infer entity relationships. We demonstrated two case studies involving mining health news and detecting anomalies in product reviews.
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)
Fiche Online: A Vision for Digitizing All Documents FicheChristopher Brown
Brown, Christopher C. Fiche Online: A Vision for Digitizing All Documents Fiche. Presentation given at the Fall 2012 Depository Library Conference, 15 October 2012, Arlington, VA.
Capturing Interactive Data Transformation Operations using Provenance WorkflowsAndre Freitas
The ready availability of data is leading to the increased opportunity of their re-use for new applications and for analyses. Most of these data are not necessarily in the format users want, are usually heterogeneous, and highly dynamic, and this necessitates data transformation efforts to re-purpose them. Interactive data transformation (IDT) tools are becoming easily available to lower these barriers to data transformation
efforts. This paper describes a principled way to capture data
lineage of interactive data transformation processes. We provide a formal
model of IDT, its mapping to a provenance representation, and its
implementation and validation on Google Refine. Provision of the data transformation process sequences allows assessment of data quality and
ensures portability between IDT and other data transformation platforms.
The proposed model showed a high level of coverage against a set of requirements used for evaluating systems that provide provenance
management solutions.
Jump Start on Apache Spark 2.2 with DatabricksAnyscale
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands-On Labs
How to Build Linked Data Sites with Drupal 7 and RDFascorlosquet
Slides of the tutorial Stéphane Corlosquet, Lin Clark and Alexandre Passant presented at SemTech 2010 in San Francisco http://semtech2010.semanticuniverse.com/sessionPop.cfm?confid=42& proposalid=2889
Similar to Exchange and Consumption of Huge RDF Data (20)
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
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 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Large Language Model (LLM) and it’s Geospatial Applications
Exchange and Consumption of Huge RDF Data
1. Digital Enterprise Research Institute www.deri.ie
Exchange and Consumption of
Huge RDF Data
Miguel A. Martínez-Prieto1,2 <migumar2@infor.uva.es>
Mario Arias1,3 <mario.arias@deri.org>
Javier D. Fernández1,2 <jfergar@infor.uva.es>
1. Department of Computer Science, Universidad de Valladolid (Spain)
2. Department of Computer Science, Universidad de Chile (Chile)
3. Digital Enterprise Research Institute, National University of Ireland Galway
Copyright 2010 Digital Enterprise Research Institute. All rights reserved.
2. Sharing RDF in the Web of Data.
Digital Enterprise Research Institute www.deri.ie
Parsing / Indexing
Reasoning
R
• Dataset analysis. I
• Setup a SPARQL server. P
• Vocabulary interlinking / integration.
• Browsing and Visualization.
sensor • Exchange between servers
• Data-intensive tasks.
dereferenceable URIs
RDF dump
SPARQL Endpoints/
APIs
3. Dataset Exchange Workflow
Digital Enterprise Research Institute www.deri.ie
1º 2º 3º
Publication Exchange Consumption
Convert Transfer Decompress
If RDF is meant to be machine processable,
Serialize Parse
Why are we using plain text serialization formats??
Compress Index
4. HDT: RDF Binary Format
Digital Enterprise Research Institute www.deri.ie
Compact Data Structure for RDF.
W3C Submission. http://www.w3.org/Submission/2011/03/
Open Source C++/Java library.
5. HDT Focused on Querying
Digital Enterprise Research Institute www.deri.ie
FoQ
Contribution of this paper:
A complementary Index to make the HDT fully queryable.
Analysis on how HDT reduces exchange and indexing time.
Evaluate in-memory query performance.
6. Dictionary
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Mapping of strings to correlative IDs. {1..n}
Lexicographically sorted, no duplicates.
Section compression explained at [8]
7. Triples Model
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Triples
S 1 2 3
126
132
213 P[ 2 3] [ 1 2 ] [4 ] 3
224
225 O[ 6 ][ 2] [ ][
3 4 ] [5 ] [1 ] 2
241
332
8. Adjacency Lists
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1 2 3
[ 2 , 3] [ ,
1 ,2 ] [4 ] 3
1 2 3 4 5 6
Array 2 3 1 2 4 3
Bitmap 1 0 1 0 0 1
Operations:
– access(g) = Given a global position, get the value. O(1)
– findList(g) = Given a global position, get the list number. O(1)
O(log log n)
– first(l), last(l), = Given a list, find the first and last.
9. Triples Model and Coding
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Triples
S 1 2 3
126
132
213 P 2 3 1 2 4 3
224
225 O 6 2 3 4 5 1 2
241 Array Y 2 3 1 2 4 3
332 Bitmap Y 1 0 1 0 0 1
Array Z 6 2 3 4 5 1 2
Bitmap Z 1 1 1 1 0 1 1
10. Searching by Subject
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Triples
S 1 ( 2, 2, ? ) 2 3
126
132
213 P 2 3 1 2 4 3
224
225 O 6 2 3 4 5 1 2
241 Array Y 2 3 1 2 4 3
332 Bitmap Y 1 0 1 0 0 1
SPO, SP? Array Z 6 2 3 4 5 1 2
S??, S?O Bitmap Z 1 1 1 1 0 1 1
11. Searching by Predicate
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Triples
S 1 ( ?, 2, ? ) 2 3
126
132
213 P 2 3 1 2 4 3
224
225 O 6 2 3 4 5 1 2
241 Array Y 2 3 1 2 4 3
332 Bitmap Y 1 0 1 0 0 1
?P? Array Z 6 2 3 4 5 1 2
Bitmap Z 1 1 1 1 0 1 1
12. Wavelet Tree
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Compact Sequence of Integers {0,σ}.
rank(3, 7) = 2
2 3 6 3 6 1 2
1 3 6 2 5 2 4 1 4 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
9 16
select(6, 3) = 9
access(position) = Value at position.
rank(entry, position) = Number of appearances of O(log σ)
O(log σ)
“entry” up to “position”. O(log σ)
select(entry, i) = Position where “entry” appears for the
i-th time.
13. Searching by Predicate w/ Wavelet
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Triples
S 1 ( ?, 2, ? ) 2 3
126
132
213 P 2 3 1 2 4 3
224
225 O 6 2 3 4 5 1 2
241
Wavelet Y 2 3 1 2 4 3
332 Bitmap Y 1 0 1 0 0 1
?P? Array Z 6 2 3 4 5 1 2
Bitmap Z 1 1 1 1 0 1 1
15. Data Structure Summary.
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From HDT to HDT-FoQ:
Convert Array Y to Wavelet.
Generate OP-Index.
Triple Patterns:
SPO, SP?, S??, S?O Original HDT
?P? Wavelet Tree
?PO, ??O OP-Index
17. Compression Ratio
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DBPedia
Geonames
hdt
gz
DBLP
lzma
hdt.gz
LinkedMDB
hdt.lzma
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Compression ratio (% against plain ntriples)
18. Publication Times
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NT+GZIP NT+LZMA HDT HDT+GZIP HDT+LZMA
linkedMDB 11,3 sec 14,7 min 1,05 min 1,09 min 1,52 min
DBLP 2,72 min 103 min 12 min 13,5 min 21,9 min
Geonames 3,28 min 244 min 25 min 26,4 min 38,9 min
DBPedia 15,9 min 466 min 56 min 60 min 121 min
dbpedia
geonames
dblp
linkedMDB
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
Times slower than Ntriples+GZIP
gz lzma hdt hdt.gz hdt.lzma
19. Publication Times2
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NT+GZIP NT+LZMA HDT HDT+GZIP HDT+LZMA
linkedMDB 11,3 sec 14,7 min 1,05 min 1,09 min 1,52 min
DBLP 2,72 min 103 min 12 min 13,5 min 21,9 min
Geonames 3,28 min 244 min 25 min 26,4 min 38,9 min
DBPedia 15,9 min 466 min 56 min 60 min 121 min
dbpedia
geonames
dblp
linkedMDB
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Times slower than Ntriples + GZIP
gz hdt hdt.gz hdt.lzma
20. Exchange & Decompression Time
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GZIP
LZMA
HDT+GZIP
HDT+LZMA Exchange
Decompress
0 50 100 150 200 250 300
Seconds (Geometric Mean of all datasets)
*Assuming a Network Bandwidth of 2MByte/s
21. Overall Client Time
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LZMA+Virtuoso
GZ+Virtuoso
Exchange
LZMA+RDF3x
Decompress
Index
GZ+RDF3x
LZMA+RDF3x HDT+LZMA
linkedMDB 2,1 min 9,21 sec
HDT+LZMA+FOQ
dblp 27 min 2,02 min
geonames 49,2 min 3,04 min
HDT+GZIP+FOQ dbpedia 159 min 14,3 min
0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600
Seconds (Geometric mean of all datasets)
22. In-memory Data Store.
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Triples Index Size (Mb)
Virtuoso Hexastore RDF3x HDT-FoQ
LinkedMDB 6,1M 518 6976 337 68
DBLP 46M 3982 - 3252 850
Geonames 112M 9216 - 6678 1435
DBPedia 258M - - 15802 5260
Less size = more data in memory = less I/O access!
25. Conclusions
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Data is ready to be consumed 10-15x faster.
Exchange time reduced.
Indexing burden on server = Lightweight client processing.
Competitive query performance.
Very fast on triple patterns.
Joins on the same scale of existing solutions.
This is useful to you:
If you need a fast, compact read-only in-memory RDF store.
If you want to share self-queryable RDF dumps.
If you need fast download & query.
Addresses the volume issue of Big Data.
26. Future work.
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Full SPARQL support.
UNION, Optional, Multiple Join.
Optimized query evaluation.
Integration:
Jena, Any23…
Diffussion.
Get more people to use it!
Additional services on top of HDT.
SPARQL Endpoint.
Distributed Stream Processing.
Mobile Applications.
Importance of exchange. The Web is for exchanging data. Data flows between nodes. We are in the “Big Data era” We need fast speed, from the network to the application layers.Role of providers / Consumers.Consumption =~ QueryingHow data is shared:Dereferenceable URIs.SPARQL Endpoints.Big datasets: RDF dump. ( Similar to XML, PDF ).Examples where RDF dumps are important: - Setup a mirror. - Overloaded SPARQL Server. - Data analysis. - Vocabulary integration. - Download instead of crawl. - Visualization.Opens new applications. - Processing intensive. - Cooperating applications.
Triples are sorted component by component.We represent them in a tree: - Each level represents S, P, O. - Each path / leave node represents one triple. How we encode the tree for 1 Space 2 Traverse. - Level by level. S implicit. P, O Array. - Relations with brackets / Bitmap. -
CPUs are fast, memory/bandwidth are precious.Variable-length.Compression.Compact In-memory representations.
Triples are sorted component by component.We represent them in a tree: - Each level represents S, P, O. - Each path / leave node represents one triple. How we encode the tree for 1 Space 2 Traverse. - Level by level. S implicit. P, O Array. - Relations with brackets / Bitmap. -
Triples are sorted component by component.We represent them in a tree: - Each level represents S, P, O. - Each path / leave node represents one triple. How we encode the tree for 1 Space 2 Traverse. - Level by level. S implicit. P, O Array. - Relations with brackets / Bitmap. -
Triples are sorted component by component.We represent them in a tree: - Each level represents S, P, O. - Each path / leave node represents one triple. How we encode the tree for 1 Space 2 Traverse. - Level by level. S implicit. P, O Array. - Relations with brackets / Bitmap. -
Triples are sorted component by component.We represent them in a tree: - Each level represents S, P, O. - Each path / leave node represents one triple. How we encode the tree for 1 Space 2 Traverse. - Level by level. S implicit. P, O Array. - Relations with brackets / Bitmap. -
Triples are sorted component by component.We represent them in a tree: - Each level represents S, P, O. - Each path / leave node represents one triple. How we encode the tree for 1 Space 2 Traverse. - Level by level. S implicit. P, O Array. - Relations with brackets / Bitmap. -