Rethinking Online SPARQL Querying to Support Incremental Result VisualizationOlaf Hartig
These are the slides of my invited talk at the 5th Int. Workshop on Usage Analysis and the Web of Data (USEWOD 2015): http://usewod.org/usewod2015.html
The abstract of this talks is given as follows:
To reduce user-perceived response time many interactive Web applications visualize information in a dynamic, incremental manner. Such an incremental presentation can be particularly effective for cases in which the underlying data processing systems are not capable of completely answering the users' information needs instantaneously. An example of such systems are systems that support live querying of the Web of Data, in which case query execution times of several seconds, or even minutes, are an inherent consequence of these systems' ability to guarantee up-to-date results. However, support for an incremental result visualization has not received much attention in existing work on such systems. Therefore, the goal of this talk is to discuss approaches that enable query systems for the Web of Data to return query results incrementally.
LDQL: A Query Language for the Web of Linked DataOlaf Hartig
I used this slideset to present our research paper at the 14th Int. Semantic Web Conference (ISWC 2015). Find a preprint of the paper here:
http://olafhartig.de/files/HartigPerez_ISWC2015_Preprint.pdf
A lot of database related algorithms are more difficult to implement in a distributed environment. Quite often, the "distributed" version is far from the "classical" version : constraints are dropped (see the CAP theorem), only specific cases are supported (for example : the involved data needs to be co-located within the distributed system), etc. This talk focuses on joins. We start by presenting join implementations in "classical" relational databases than we lead the audience through the challenges and solutions to make these functions available in a distributed environment. While we start with a theoretical point of view, we finish by giving real life examples from implementations in ETL systems (known for joining heterogeneous databases and therefore quite advanced in this area, but often not real-time) and some modern NoSQL databases (where most systems choose to offer less features with respect to joins).
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
Rethinking Online SPARQL Querying to Support Incremental Result VisualizationOlaf Hartig
These are the slides of my invited talk at the 5th Int. Workshop on Usage Analysis and the Web of Data (USEWOD 2015): http://usewod.org/usewod2015.html
The abstract of this talks is given as follows:
To reduce user-perceived response time many interactive Web applications visualize information in a dynamic, incremental manner. Such an incremental presentation can be particularly effective for cases in which the underlying data processing systems are not capable of completely answering the users' information needs instantaneously. An example of such systems are systems that support live querying of the Web of Data, in which case query execution times of several seconds, or even minutes, are an inherent consequence of these systems' ability to guarantee up-to-date results. However, support for an incremental result visualization has not received much attention in existing work on such systems. Therefore, the goal of this talk is to discuss approaches that enable query systems for the Web of Data to return query results incrementally.
LDQL: A Query Language for the Web of Linked DataOlaf Hartig
I used this slideset to present our research paper at the 14th Int. Semantic Web Conference (ISWC 2015). Find a preprint of the paper here:
http://olafhartig.de/files/HartigPerez_ISWC2015_Preprint.pdf
A lot of database related algorithms are more difficult to implement in a distributed environment. Quite often, the "distributed" version is far from the "classical" version : constraints are dropped (see the CAP theorem), only specific cases are supported (for example : the involved data needs to be co-located within the distributed system), etc. This talk focuses on joins. We start by presenting join implementations in "classical" relational databases than we lead the audience through the challenges and solutions to make these functions available in a distributed environment. While we start with a theoretical point of view, we finish by giving real life examples from implementations in ETL systems (known for joining heterogeneous databases and therefore quite advanced in this area, but often not real-time) and some modern NoSQL databases (where most systems choose to offer less features with respect to joins).
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Detection of Related Semantic Datasets Based on Frequent Subgraph MiningMikel Emaldi Manrique
We describe an approach to find similarities between RDF datasets, which may be applicable to tasks such as link discovery, dataset summarization or dataset understanding. Our approach builds on the assumption that similar datasets should have a similar structure and include semantically similar resources and relationships. It is based on the combination of Frequent Subgraph Mining (FSM) techniques, used to synthesize the datasets and find similarities among them. The result of this work can be applied for easing the task of data interlinking and for promoting data reusing in the Semantic Web.
Full paper at: http://memaldi.github.io/pdf/iesd2015.pdf
eNanoMapper database, search tools and templatesNina Jeliazkova
A webinar given at the NCIP Hub https://nciphub.org/resources/1925
Nanomaterial safety assessment has become an important task following the production growth of engineered nanomaterials (ENMs) and the increased interest for ENMs from various academic, industry and regulatory parties. A number of challenges exist in nanomaterials data representation and integration mainly due to the data complexity and origination of ENM information from diverse sources. We have recently described eNanoMapper database [1] as part of the computational infrastructure for toxicological data management of engineered materials, developed within eNanoMapper project [2].
The eNanoMapper prototype database is publicly available at http://data.enanomapper.net, demonstrating the integration of data from multiple sources, using the common data model and Application Programming Interface. The supported import formats are IUCLID5 files (OECD HT), semantic format (RDF) and custom spreadsheet templates. The latter accommodates the preferred approach for data gathering for the majority of the NanoSafety Cluster projects and is enabled by a configurable parser mapping the the custom spreadsheet organization into the internal eNanoMapper storage components through external configuration file. Import of spreadsheet data and other data formats, generated by a number of NanoSafety Cluster projects is currently ongoing. The export formats have been extended with the new ISA JSON format, following the most recent ISA specification.
Defining templates for data gathering is a common activity for most of the NanoSafety Cluster projects usually resulting in modified Excel spreadsheets. In order to help avoiding the incompatibility issues, we present a tool for template generation, based on templates released under open license by JRC under the framework of the NANoREG project [3]. A number of physchem, in-vitro and in-vivo assays are supported and using feedback from users we added and extended existing information about different aspects of nanosafety, e.g. environmental exposure, cell culture assays, cellular and animal models, nanomaterial production features, and nanomaterial ageing.
Finally, the data can be accessed programmatically via the application programming interface as well as via user friendly search interface at https://search.data.enanomapper.net. The search application is powered by a free text search engine and eNanoMapper ontology and was improved over the last year based on user feedback.The search function allows now multiple filtering for information. It is possible to stack filters for e.g. nanomaterial type, cell model and assay.
eNanoMapper is supported by European Commission 7th Framework Programme for Research and Technological Development Grant (Grant agreement no: 604134).
This presentation looks in detail at SPARQL (SPARQL Protocol and RDF Query Language) and introduces approaches for querying and updating semantic data. It covers the SPARQL algebra, the SPARQL protocol, and provides examples for reasoning over Linked Data. We use examples from the music domain, which can be directly tried out and ran over the MusicBrainz dataset. This includes gaining some familiarity with the RDFS and OWL languages, which allow developers to formulate generic and conceptual knowledge that can be exploited by automatic reasoning services in order to enhance the power of querying.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
This presentation focuses on providing means for exploring Linked Data. In particular, it gives an overview of current visualization tools and techniques, looking at semantic browsers and applications for presenting the data to the end used. We also describe existing search options, including faceted search, concept-based search and hybrid search, based on a mix of using semantic information and text processing. Finally, we conclude with approaches for Linked Data analysis, describing how available data can be synthesized and processed in order to draw conclusions.
This presentation gives details on technologies and approaches towards exploiting Linked Data by building LD applications. In particular, it gives an overview of popular existing applications and introduces the main technologies that support implementation and development. Furthermore, it illustrates how data exposed through common Web APIs can be integrated with Linked Data in order to create mashups.
Adventures in Linked Data Land (presentation by Richard Light)jottevanger
"Adventures in Linked Data Land: bringing RDF to the Wordsworth Trust" is a paper given by RIchard Light (http://uk.linkedin.com/pub/richard-light/a/221/ba5) to a Linked Data meeting run by the Collections Trust in February 2010. He runs through the basics of LD, how it relates to cultural heritage, and some of his experiments with it, specifically with the data of the Wordsworth Trust, finally listing a series of challenges that face museums in trying to get on board the Linked Data bus.
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
2015 TaPP - Interoperability for Provenance-aware Databases using PROV and JSONBoris Glavic
Since its inception, the PROV standard has been widely adopted as a standardized exchange format for provenance information. Surprisingly, this standard is currently not supported by provenance- aware database systems limiting their interoperability with other provenance-aware systems. In this work we introduce techniques for exporting database provenance as PROV documents, importing PROV graphs alongside data, and linking outputs of an SQL operation to the imported provenance for its inputs. Our implementation in the GProM system offloads generation of PROV documents to the backend database. This implementation enables provenance tracking for applications that use a relational database for managing (part of) their data, but also execute some non-database operations.
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Slides from our tutorial on Linked Data generation in the energy domain, presented at the Sustainable Places 2014 conference on October 2nd in Nice, France
Linked Data for Information Extraction Challenge - Tasks and Results @ ISWC 2014Robert Meusel
The Linked Data for Information Extraction challenge explores aims at extracting structured data from Web pages. It is based on a subset of the Web Data Commons Microformats dataset.
For the challenge, original annotated pages are provided, as well as the triples extracted from them. Based on that information, participants have to design an Information extraction system for extracting that information from other web pages. In this year's challenge, we focus on hCard data, i.e., information about persons. The use case of such a system could be the assembly of a large database on person data.
The systems are evaluated on a test set of annotated web pages, from which all annotations have been removed. The participants have to extract triples from those pages and send in their resulting triple files. The submitted files are evaluated against the gold standard of the original triples, ranking the solutions by F-measure.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Detection of Related Semantic Datasets Based on Frequent Subgraph MiningMikel Emaldi Manrique
We describe an approach to find similarities between RDF datasets, which may be applicable to tasks such as link discovery, dataset summarization or dataset understanding. Our approach builds on the assumption that similar datasets should have a similar structure and include semantically similar resources and relationships. It is based on the combination of Frequent Subgraph Mining (FSM) techniques, used to synthesize the datasets and find similarities among them. The result of this work can be applied for easing the task of data interlinking and for promoting data reusing in the Semantic Web.
Full paper at: http://memaldi.github.io/pdf/iesd2015.pdf
eNanoMapper database, search tools and templatesNina Jeliazkova
A webinar given at the NCIP Hub https://nciphub.org/resources/1925
Nanomaterial safety assessment has become an important task following the production growth of engineered nanomaterials (ENMs) and the increased interest for ENMs from various academic, industry and regulatory parties. A number of challenges exist in nanomaterials data representation and integration mainly due to the data complexity and origination of ENM information from diverse sources. We have recently described eNanoMapper database [1] as part of the computational infrastructure for toxicological data management of engineered materials, developed within eNanoMapper project [2].
The eNanoMapper prototype database is publicly available at http://data.enanomapper.net, demonstrating the integration of data from multiple sources, using the common data model and Application Programming Interface. The supported import formats are IUCLID5 files (OECD HT), semantic format (RDF) and custom spreadsheet templates. The latter accommodates the preferred approach for data gathering for the majority of the NanoSafety Cluster projects and is enabled by a configurable parser mapping the the custom spreadsheet organization into the internal eNanoMapper storage components through external configuration file. Import of spreadsheet data and other data formats, generated by a number of NanoSafety Cluster projects is currently ongoing. The export formats have been extended with the new ISA JSON format, following the most recent ISA specification.
Defining templates for data gathering is a common activity for most of the NanoSafety Cluster projects usually resulting in modified Excel spreadsheets. In order to help avoiding the incompatibility issues, we present a tool for template generation, based on templates released under open license by JRC under the framework of the NANoREG project [3]. A number of physchem, in-vitro and in-vivo assays are supported and using feedback from users we added and extended existing information about different aspects of nanosafety, e.g. environmental exposure, cell culture assays, cellular and animal models, nanomaterial production features, and nanomaterial ageing.
Finally, the data can be accessed programmatically via the application programming interface as well as via user friendly search interface at https://search.data.enanomapper.net. The search application is powered by a free text search engine and eNanoMapper ontology and was improved over the last year based on user feedback.The search function allows now multiple filtering for information. It is possible to stack filters for e.g. nanomaterial type, cell model and assay.
eNanoMapper is supported by European Commission 7th Framework Programme for Research and Technological Development Grant (Grant agreement no: 604134).
This presentation looks in detail at SPARQL (SPARQL Protocol and RDF Query Language) and introduces approaches for querying and updating semantic data. It covers the SPARQL algebra, the SPARQL protocol, and provides examples for reasoning over Linked Data. We use examples from the music domain, which can be directly tried out and ran over the MusicBrainz dataset. This includes gaining some familiarity with the RDFS and OWL languages, which allow developers to formulate generic and conceptual knowledge that can be exploited by automatic reasoning services in order to enhance the power of querying.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
This presentation focuses on providing means for exploring Linked Data. In particular, it gives an overview of current visualization tools and techniques, looking at semantic browsers and applications for presenting the data to the end used. We also describe existing search options, including faceted search, concept-based search and hybrid search, based on a mix of using semantic information and text processing. Finally, we conclude with approaches for Linked Data analysis, describing how available data can be synthesized and processed in order to draw conclusions.
This presentation gives details on technologies and approaches towards exploiting Linked Data by building LD applications. In particular, it gives an overview of popular existing applications and introduces the main technologies that support implementation and development. Furthermore, it illustrates how data exposed through common Web APIs can be integrated with Linked Data in order to create mashups.
Adventures in Linked Data Land (presentation by Richard Light)jottevanger
"Adventures in Linked Data Land: bringing RDF to the Wordsworth Trust" is a paper given by RIchard Light (http://uk.linkedin.com/pub/richard-light/a/221/ba5) to a Linked Data meeting run by the Collections Trust in February 2010. He runs through the basics of LD, how it relates to cultural heritage, and some of his experiments with it, specifically with the data of the Wordsworth Trust, finally listing a series of challenges that face museums in trying to get on board the Linked Data bus.
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
2015 TaPP - Interoperability for Provenance-aware Databases using PROV and JSONBoris Glavic
Since its inception, the PROV standard has been widely adopted as a standardized exchange format for provenance information. Surprisingly, this standard is currently not supported by provenance- aware database systems limiting their interoperability with other provenance-aware systems. In this work we introduce techniques for exporting database provenance as PROV documents, importing PROV graphs alongside data, and linking outputs of an SQL operation to the imported provenance for its inputs. Our implementation in the GProM system offloads generation of PROV documents to the backend database. This implementation enables provenance tracking for applications that use a relational database for managing (part of) their data, but also execute some non-database operations.
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Slides from our tutorial on Linked Data generation in the energy domain, presented at the Sustainable Places 2014 conference on October 2nd in Nice, France
Linked Data for Information Extraction Challenge - Tasks and Results @ ISWC 2014Robert Meusel
The Linked Data for Information Extraction challenge explores aims at extracting structured data from Web pages. It is based on a subset of the Web Data Commons Microformats dataset.
For the challenge, original annotated pages are provided, as well as the triples extracted from them. Based on that information, participants have to design an Information extraction system for extracting that information from other web pages. In this year's challenge, we focus on hCard data, i.e., information about persons. The use case of such a system could be the assembly of a large database on person data.
The systems are evaluated on a test set of annotated web pages, from which all annotations have been removed. The participants have to extract triples from those pages and send in their resulting triple files. The submitted files are evaluated against the gold standard of the original triples, ranking the solutions by F-measure.
LOP – Capturing and Linking Open Provenance on LOD Cyclerogers.rj
Presentation of the paper "LOP – Capturing and Linking Open Provenance on LOD Cycle" at 5th Internacional Workshop on Semantic Web Information Management (SWIM 2013). New York, USA – June 23, 2013
Linked Data for the Masses: The approach and the SoftwareIMC Technologies
Title: Linked Data for the Masses: The approach and the Software
@ EELLAK (GFOSS) Conference 2010
Athens, Greece
15/05/2010
Creator: George Anadiotis (R&D Director)
An introduction deck for the Web of Data to my team, including basic semantic web, Linked Open Data, primer, and then DBpedia, Linked Data Integration Framework (LDIF), Common Crawl Database, Web Data Commons.
An Overview on PROV-AQ: Provenance Access and QueryOlaf Hartig
The slides which I used at the Dagstuhl seminar on Principles of Provenance (Feb.2012) for presenting the main contributions and open issues of the PROV-AQ document created by the W3C provenance working group.
Querying Trust in RDF Data with tSPARQLOlaf Hartig
With these slides I presented my paper on "Querying Trust in RDF Data with tSPARQL" at the European Semantic Web Conference 2009 (ESWC) in Heraklion, Crete. Actually, this slideset is an extended version of the slides I used for the talk (more examples and evaluation).
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.
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.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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…
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.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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.
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
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/
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.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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Tutorial "Linked Data Query Processing" Part 1 "Introduction" (WWW 2013 Ed.)
1. Linked Data Query Processing
Tutorial at the 22nd International World Wide Web Conference (WWW 2013)
May 14, 2013
http://db.uwaterloo.ca/LDQTut2013/
Olaf Hartig
University of Waterloo
2. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 2
Tutorial Outline
(1) Introduction
(2) Theoretical Foundations
(3) Source Selection Strategies
(4) Execution Process
(5) Query Planning and Optimization
3. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 3
Linked Data Query Processing
Tutorial at the 22nd International World Wide Web Conference (WWW 2013)
May 14, 2013
http://db.uwaterloo.ca/LDQTut2013/
1. Introduction
Olaf Hartig
University of Waterloo
4. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 4
Outline
The Linked Data Principles
Paradigms for Querying Linked Data
Characteristics of the “Database System”
5. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 5
The Traditional, Hypertext Web
MovieDB
Data exposed
to the Web
via HTML
CIA World
Factbook
6. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 6
Towards a Web of Linked Data
MovieDB
:
( Albania , unemployment rate , 13.2% )
:
Data model: RDF
( War Child , release date , 12 July 1999 )
( War Child , filming location , Albania )
( Michael Davie , directed , War Child )
:
CIA World
Factbook
7. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 7
Towards a Web of Linked Data
MovieDB
( http://...imdb.../WarChild , release date , 12 July 1999 )
( http://...imdb.../WarChild , filming location , http://cia.../Albania )
( http://...imdb.../MichaelDavie , directed , http://...imdb.../WarChild )
:
( http://cia.../Albania ,
unemployment rate , 13.2% )
:
Data model: RDF
Global identifier: URI
CIA World
Factbook
8. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 8
Towards a Web of Linked Data
MovieDB
( http://cia.../Albania ,
unemployment rate , 13.2% )
:
Data model: RDF
Global identifier: URI
Access mechanism: HTTP
( http://...imdb.../WarChild , release date , 12 July 1999 )
( http://...imdb.../WarChild , filming location , http://cia.../Albania )
( http://...imdb.../MichaelDavie , directed , http://...imdb.../WarChild )
:
CIA World
Factbook
9. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 9
Towards a Web of Linked Data
MovieDB
CIA World
Factbook
( http://...imdb.../WarChild , release date , 12 July 1999 )
( http://...imdb.../WarChild , filming location , http://cia.../Albania )
( http://...imdb.../MichaelDavie , directed , http://...imdb.../WarChild )
:
( http://cia.../Albania ,
unemployment rate , 13.2% )
:
Data model: RDF
Global identifier: URI
Access mechanism: HTTP
Connection: data links
10. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 10
Supplementary Access Methods
● RDF dump: the whole dataset provided as a big file
● SPARQL endpoint: Web service that allows for executing
SPARQL queries over the dataset
● Caveat: these access method cannot be assumed
to be available for all datasets
● Creating dumps is not feasible if data changes very frequently
● Dumps or endpoints may not be feasible if Linked Data
interface is simply a wrapper for some other data source
● Providing and maintaining a reliable SPARQL endpoint
is a significant additional effort
11. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 11
Outline
The Linked Data Principles
Paradigms for Querying Linked Data
Characteristics of the “Database System”
√
12. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 12
Traditional Paradigm 1: Warehousing
● Copy data into a centralized repository
● Query this repository
+ Almost instant results
– Misses unknown or new sources
– Collection possibly out of date
13. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 13
?
??
?
Traditional Paradigm 2: Federation
● Distribute query execution over a
federation of SPARQL endpoints
+ Current data
– Misses sources without
SPARQL endpoint
14. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 14
Principle 1: Rely on the Linked Data principles only
Principle 2: On-line execution
Linked Data Query Processing
15. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 15
Principle 1: Rely on the Linked Data principles only
Principle 2: On-line execution
Consequence: Obtain data for executing a given query by
looking up URIs during the query execution process itself
Linked Data Query Processing
16. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 16
“Ingredients” for LD Query Execution
Query-local data
17. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 17
“Ingredients” for LD Query Execution
● Data retrieval approach
● Data source selection
● Data source ranking
(optional, for optimization)
Query-local data
GET http://.../movie2449
18. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 18
“Ingredients” for LD Query Execution
● Data retrieval approach
● Data source selection
● Data source ranking
(optional, for optimization)
Query-local data
http://mdb.../Paul http://geo.../Berlin
http://mdb.../Ric http://geo.../Rome
?loc?actor
GET http://.../movie2449
● Result construction approach
● i.e., query-local data processing
19. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 19
“Ingredients” for LD Query Execution
● Data retrieval approach
● Data source selection
● Data source ranking
(optional, for optimization)
Query-local data
http://mdb.../Paul http://geo.../Berlin
http://mdb.../Ric http://geo.../Rome
?loc?actor
GET http://.../movie2449
● Result construction approach
● i.e., query-local data processing
● Combining data retrieval
and result construction
20. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 20
+ Current data
+ May make use of any Linked Data available on the Web
– Least efficient due to data shipping
Use cases: live querying where freshness and discovery of
results is more important than an almost instant answer
Properties of LD Query Processing
21. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 21
Combination with other Paradigms
● Linked Data query processing with a query-local dataset
● Query-local dataset contains additional data [LT11]
● Query-local dataset for caching [Har11b, HH11]
● Linked Data query processing with a SPARQL endpoint
● SPARQL endpoint exposes a cache of Linked Data [UKH+12]
22. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 22
Our Topic Today …
… pure Linked Data query processing
Linked Data query: a query that ranges over
data made available using
the Linked Data principles
Web of Linked Data: network of data that evolves
by publishing data according
to the Linked Data principles
23. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 23
Outline
The Linked Data Principles
Paradigms for Querying Linked Data
Characteristics of the “Database System”
√
√
24. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 24
An Analogy ...
25. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 25
Traditional, Central Database Systems
26. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 26
Distributed Database Systems
27. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 27
The Web of Linked Data
28. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 28
The Web of Linked Data
29. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 29
● Number of
potential data
sources infinite
The Web of Linked Data
30. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 30
● Number of
potential data
sources infinite
● No (a priori)
information
The Web of Linked Data
31. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 31
● Number of
potential data
sources infinite
● No (a priori)
information
The Web of Linked Data
32. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 32
● Number of
potential data
sources infinite
● No (a priori)
information
The Web of Linked Data
33. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 33
● Number of
potential data
sources infinite
● No (a priori)
information
The Web of Linked Data
34. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 34
● Number of
potential data
sources infinite
● No (a priori)
information
The Web of Linked Data
35. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 35
● Number of
potential data
sources infinite
● No (a priori)
information
● Number of
actual data
sources infinite
The Web of Linked Data
36. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 36
Issues due to the Openness
● Data quality issues
● Accuracy
● Freshness / timeliness
● Believability / trustworthiness
● Data source quality issues
● Availability
● Reliability
● Data integration issues
● Coreferences: Publishers may use different URIs
for denoting the same entity
● Schema heterogeneity: Publishers may use different
vocabularies for their data
For the purpose of discussing
execution of queries in this tutorial,
we largely ignore these issues.
37. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 37
Outline
The Linked Data Principles
Paradigms for Querying Linked Data
Characteristics of the “Database System”
√
√
√
Next part: 2. Theoretical Foundations ...
38. WWW 2013 Tutorial on Linked Data Query Processing [ Introduction ] 38
These slides have been created by
Olaf Hartig
for the
WWW 2013 tutorial on
Link Data Query Processing
Tutorial Website: http://db.uwaterloo.ca/LDQTut2013/
This work is licensed under a
Creative Commons Attribution-Share Alike 3.0 License
(http://creativecommons.org/licenses/by-sa/3.0/)