This presentation is a review of the NoSQL spaces I did for the X Jornades de Programari Lliure in Barcelona.
You will see a complete review of the NoSQL movement, use cases, technology review, an special review of what are the Graph Databases. And more....
Special thanks to @Hagenburger, @sbitxu, @jannis and the inspiration of the big @jimwebber and the amazing community.
Exchange and Consumption of Huge RDF DataMario Arias
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.
The Best of Both Worlds: Unlocking the Power of (big) Knowledge Graphs with S...Gezim Sejdiu
Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies.
A major and yet unsolved challenge that research faces today is to perform scalable analysis of large scale knowledge graphs in order to facilitate applications like link prediction, knowledge base completion, and question answering.
Most machine learning approaches, which scale horizontally (i.e. can be executed in a distributed environment) work on simpler feature vector based input rather than more expressive knowledge structures.
On the other hand, the learning methods which exploit the expressive structures, e.g. Statistical Relational Learning and Inductive Logic Programming approaches, usually do not scale well to very large knowledge bases owing to their working complexity.
This talk gives an overview of the ongoing project Semantic Analytics Stack (SANSA) which aims to bridge this research gap by creating an out of the box library for scalable, in-memory, structured learning.
This presentation is a review of the NoSQL spaces I did for the X Jornades de Programari Lliure in Barcelona.
You will see a complete review of the NoSQL movement, use cases, technology review, an special review of what are the Graph Databases. And more....
Special thanks to @Hagenburger, @sbitxu, @jannis and the inspiration of the big @jimwebber and the amazing community.
Exchange and Consumption of Huge RDF DataMario Arias
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.
The Best of Both Worlds: Unlocking the Power of (big) Knowledge Graphs with S...Gezim Sejdiu
Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies.
A major and yet unsolved challenge that research faces today is to perform scalable analysis of large scale knowledge graphs in order to facilitate applications like link prediction, knowledge base completion, and question answering.
Most machine learning approaches, which scale horizontally (i.e. can be executed in a distributed environment) work on simpler feature vector based input rather than more expressive knowledge structures.
On the other hand, the learning methods which exploit the expressive structures, e.g. Statistical Relational Learning and Inductive Logic Programming approaches, usually do not scale well to very large knowledge bases owing to their working complexity.
This talk gives an overview of the ongoing project Semantic Analytics Stack (SANSA) which aims to bridge this research gap by creating an out of the box library for scalable, in-memory, structured learning.
Generating Executable Mappings from RDF Data Cube Data Structure DefinitionsChristophe Debruyne
Data processing is increasingly the subject of various internal and external regulations, such as GDPR which has recently come into effect. Instead of assuming that such processes avail of data sources (such as files and relational databases), we approach the problem in a more abstract manner and view these processes as taking datasets as input. These datasets are then created by pulling data from various data sources. Taking a W3C Recommendation for prescribing the structure of and for describing datasets, we investigate an extension of that vocabulary for the generation of executable R2RML mappings. This results in a top-down approach where one prescribes the dataset to be used by a data process and where to find the data, and where that prescription is subsequently used to retrieve the data for the creation of the dataset “just in time”. We argue that this approach to the generation of an R2RML mapping from a dataset description is the first step towards policy-aware mappings, where the generation takes into account regulations to generate mappings that are compliant. In this paper, we describe how one can obtain an R2RML mapping from a data structure definition in a declarative manner using SPARQL CONSTRUCT queries, and demonstrate it using a running example. Some of the more technical aspects are also described.
Reference: Christophe Debruyne, Dave Lewis, Declan O'Sullivan: Generating Executable Mappings from RDF Data Cube Data Structure Definitions. OTM Conferences (2) 2018: 333-350
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
This presentation is about applications of graph theory applications....it is updated version it was given at international conference at applications of graph theory at KAULALAMPUR MALYSIA 2OO7
Talk given at neo4j conference "Graph Connect" - discussing some graph theory (old and new), and why knowing your stuff can come in handy on a software project.
Introduction to graph of class 8th students. Find a new easy way to understand graph, histogram, double-bar graph, pie-chart etc....This ppt could lead to u a better picture of maths
Generating Executable Mappings from RDF Data Cube Data Structure DefinitionsChristophe Debruyne
Data processing is increasingly the subject of various internal and external regulations, such as GDPR which has recently come into effect. Instead of assuming that such processes avail of data sources (such as files and relational databases), we approach the problem in a more abstract manner and view these processes as taking datasets as input. These datasets are then created by pulling data from various data sources. Taking a W3C Recommendation for prescribing the structure of and for describing datasets, we investigate an extension of that vocabulary for the generation of executable R2RML mappings. This results in a top-down approach where one prescribes the dataset to be used by a data process and where to find the data, and where that prescription is subsequently used to retrieve the data for the creation of the dataset “just in time”. We argue that this approach to the generation of an R2RML mapping from a dataset description is the first step towards policy-aware mappings, where the generation takes into account regulations to generate mappings that are compliant. In this paper, we describe how one can obtain an R2RML mapping from a data structure definition in a declarative manner using SPARQL CONSTRUCT queries, and demonstrate it using a running example. Some of the more technical aspects are also described.
Reference: Christophe Debruyne, Dave Lewis, Declan O'Sullivan: Generating Executable Mappings from RDF Data Cube Data Structure Definitions. OTM Conferences (2) 2018: 333-350
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
This presentation is about applications of graph theory applications....it is updated version it was given at international conference at applications of graph theory at KAULALAMPUR MALYSIA 2OO7
Talk given at neo4j conference "Graph Connect" - discussing some graph theory (old and new), and why knowing your stuff can come in handy on a software project.
Introduction to graph of class 8th students. Find a new easy way to understand graph, histogram, double-bar graph, pie-chart etc....This ppt could lead to u a better picture of maths
How to Leverage the Social Graph with Facebook PlatformDave Olsen
Facebook is about more than just Pages and Groups. Facebook's set of powerful APIs, Facebook Platform, has made it easier than ever to create engaging social experiences on your own sites. We'll talk about why you will want to take advantage of Facebook Platform, share an example of using Facebook Platform to drive engagement and give you several strategies for how you can go back to your campus and quickly take advantage of Facebook Platform.
An introduction to Facebook Graph API and OAuth 2. This presentation covers basic example of Facebook Graph API, and including how OAuth 2 client-side flow works.
This presentation was made for "Facebook Dev Meetup Kathmandu" held on 3rd April, 2016.
In this presentation, we talk about Facebook's Social Graph, Facebook Open Graph v2.5 and How we can use the api to build our apps. We explore the Graph API using Facebook's Graph API Explorer.
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf
Abstract: How graphs became just another big data primitive
Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.
Data Integration at the Ontology Engineering GroupOscar Corcho
Presentation done on the work being done on Data Integration at OEG-UPM (http://www.oeg-upm.net/), for the CredIBLE workshop, in Sophia-Antipolis (October 15th, 2012).
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
1st in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Xavier Lopez (PM Senior Director) and Zhe Wu (Graph Architect) will share a brief intro to what property graphs can do for you, and take your questions - on property graphs or any other aspect of Oracle Database Spatial and Graph features. With property graphs, you can analyze relationships in Big Data like social networks, financial transactions, or IoT sensor networks; identify influencers; discover patterns of fraudulent behavior; recommend products, and much more -- right inside Oracle Database.
What is the "Big Data" version of the Linpack Benchmark?; What is “Big Data...Geoffrey Fox
Advances in high-performance/parallel computing in the 1980's and 90's was spurred by the development of quality high-performance libraries, e.g., SCALAPACK, as well as by well-established benchmarks, such as Linpack.
Similar efforts to develop libraries for high-performance data analytics are underway. In this talk we motivate that such benchmarks should be motivated by frequent patterns encountered in high-performance analytics, which we call Ogres.
Based upon earlier work, we propose that doing so will enable adequate coverage of the "Apache" bigdata stack as well as most common application requirements, whilst building upon parallel computing experience.
Given the spectrum of analytic requirements and applications, there are multiple "facets" that need to be covered, and thus we propose an initial set of benchmarks - by no means currently complete - that covers these characteristics.
We hope this will encourage debate
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
Data Processing over very Large Relational Databaseskvaderlipa
Final presentation of my dissertation thesis focused on orientation, analyzing and finding information in large or unknown relational databases and data visualisation
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Graph Theory and Databases
1. Graph ( Theory and Databases )
Pere Urbón Bayes
Senior Software Engineer
Independent
purbon@purbon.com
purbon.com
in/purbon
December of 2010
@purbon
2. Graph (Theory and Databases)
● Graph Theory ● Graph Databases
– Definitions – Definitions
– Applications – Facts
– Analytics
– Performance
– Vendors
Graph ( Theory and Databases ) 2
3. Graph
Definitions
● Graph G(V,E) where V = {v1,v2,...,vN) and E =
{E1,E2,...,EN)
– Directed / Undirected
– Mixed
– Multigraph
– Weighted
– ....
Graph ( Theory and Databases ) 3
4. Graph
Definitions
● Directed graphs
● Vertex
● Edges
● From V(N) to V(M)
Graph ( Theory and Databases ) 4
5. Graph
Definitions
Multigraph Labelling
● More than one edge ● The process of
between two nodes. assigning a label to a
● Loops, edges vertex and edges.
between the same
node.
Graph ( Theory and Databases ) 5
6. Graph Theory
Applications
● Task planning
● Scheduling
● Process assignation
● Routing
● Logistics
● League planning
Graph ( Theory and Databases ) 6
7. Graph Theory
Applications
● Pattern Recognition
● Dependency analysis
● Impact analysis
● Network flow
– Traffic analysis and optimization
– Delivery optimization
● Optimization of tasks
Graph ( Theory and Databases ) 7
8. Graph Theory
analytics
● Clustering (Communities)
● Social connexions
● Hubs
● Graph Mining
● Centrality measures
Graph ( Theory and Databases ) 8
9. Graph Like
Applications
● Recommendations
– Heuristics (PageRank)
– Local
● Shortest Paths
● Hammock Functions
● Walks
● Search algorithms
● Shooting stars
● K-nearest neighbours
Graph ( Theory and Databases ) 9
10. Graph Like
Applications
● Location based services
● Hubs
● Spatial databases
● Logical (multi-)index construction
Graph ( Theory and Databases ) 10
11. Web
Trending Topics
● Semantic web
– RDF (OWL) Store
– RDF-Sail
– SPARQL
● Linked data (Open Data)
● Link analysis
● Structure mining
Graph ( Theory and Databases ) 11
12. Graph databases
“A graph database is a database that uses graph
structures with nodes, edges, and properties to
represent and store information.
General graph databases that can store any
graph are distinct from specialized graph
databases such as triple stores and network
databases.”
Wikipedia
Graph ( Theory and Databases ) 12
13. Graph databases
Property graph
● Abstractions
– Nodes
– Relationships
– Properties on both.
John smith liked http://www.example.com at 01/10/11
Graph ( Theory and Databases ) 13
14. Graph databases
Facts
Connectivity
Everything
connected
RDF Ontologies
Linked Data
Tagging
Blogs Folksonomies
Social Networks
Text files
1990's 2010's 2020's Decades
Graph ( Theory and Databases ) 14
15. Graph databases
Facts
Size of
1990's 2010's 2020's Decades
Graph ( Theory and Databases ) 15
http://www.guardian.co.uk/business/2009/may/18/digital-content-expansion
16. Graph databases
Facts
Performance
Lists
Graph like structures
Semantic web
Semantic reasoning
Linked data
Performance slowdown
Unstructured
Graph ( Theory and Databases ) 16
17. Graph databases
Performance
Kernel DEX Neo4j Jena HyperGraphDB
Scale 15
Load(s) 7,44 697 141 +24h
Scan (s) 0,0010 2,71 0,689
2-Hops(s) 0,0120 0,0260 0,443
BC (s) 14,8 8,24 138
Size (MB) 30 17 207
Kernel DEX Neo4j Jena HyperGraph
Scale 20 DB
Load(s) 317 32.094 4.560 +24h
Scan (s) 0,005 751 18,6
2-Hops(s) 0,033 0,0230 0,4580
BC (s) 617 7027 59512
Size (MB) 893 539 6656
Graph ( Theory and Databases ) 17
HPC Scalable Graph Analysis Benchmark IWGD 2010
18. Graph databases
Vendors
● Neo4J: Open source database NoSQL graph.
● Dex: The high performance graph database.
● HyperGraphDB: An IA and semantic web graph
database.
● Infogrid: The Internet Graph database.
● Sones: SaaS dot Net Graph database.
● VertexDB: High performance database server.
Graph ( Theory and Databases ) 18
19. Graph ( Theory and Databases )
Thanks!
purbon@purbon.com
December of 2010
Graph ( Theory and Databases ) 19