Knowledge Patterns for the Web: extraction, transformation, and reuseAndrea Nuzzolese
KPs are an abstraction of frames as introduced by Fillmore and Minsky. KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts (i.e., top-down defined artifacts that can be compared to KPs, such as FrameNet frames or Ontology Design Patterns) to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Unfortunately, type paths are not always available. In fact, Linked Data is a knowledge soup because of the heterogeneous semantics of its datasets and because of the limited intentional as well as extensional coverage of ontologies (e.g., DBpedia ontology, YAGO) or other controlled vocabularies (e.g., SKOS, FOAF, etc.). Thus, we propose a solution for enriching Linked Data with additional axioms (e.g., rdf:type axioms) by exploiting the natural language available for example in annotations (e.g. rdfs:comment) or in corpora on which datasets in Linked Data are grounded (e.g. DBpedia is grounded on Wikipedia). Then we present K∼ore, a software architec- ture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. K∼ore is the architectural binding of a set of tools, i.e., K∼tools, which implements the methods for KP transformation and extraction. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.
The Open Knowledge Extraction Challenge focuses on the production of new knowledge aimed at either populating and enriching existing knowledge bases or creating new ones. This means that the defined tasks focus on extracting concepts, individuals, properties, and statements that not necessarily exist already in a target knowledge base, and on representing them according to Semantic Web standard in order to be directly injected in linked datasets and their ontologies. The OKE challenge, has the ambition to advance a reference framework for research on Knowledge Extraction from text for the Semantic Web by re-defining a number of tasks (typically from information and knowledge extraction) by taking into account specific SW requirements. The Challenge is open to everyone from industry and academia.
1) The document describes the SOPHIA project, which aims to build altmetric networks of researchers and institutions to understand how research impacts spread in society.
2) SOPHIA collects data from Scopus and social media sources to build a heterogeneous graph network, and analyzes the network using graph metrics to measure the influence and authority of researchers and institutions.
3) The project has developed visualization and search tools to explore the altmetric networks, annotated documents, and metrics within a software prototype called SOPHIA.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
Oles Petriv “Creating one concept embedding space for persons, brands and new...Lviv Startup Club
The document discusses creating an embedding space to represent entities like people, brands, and news authors mentioned in news articles. It involves training models on a dataset of over 300 million news articles from 20,000 sources in English, Ukrainian and Russian from 2000-2018 that have been parsed and had named entities recognized. The goal is to use the embedding space to supervise models for sentiment analysis and document representation. It describes preprocessing the data to create training data for the entity representations, and training word embedding models like FastText and Word2Vec on this data to learn the entity representations.
EKAW 2016 - TechMiner: Extracting Technologies from Academic PublicationsFrancesco Osborne
TechMiner is a new approach that combines natural language processing, machine learning, and semantic technologies to extract information about technologies (such as applications, systems, languages, and formats) from research publications. It generates an ontology describing technologies and their relationships to other research entities. The approach was evaluated on a gold standard of manually annotated publications and found to improve precision and recall over alternative natural language processing approaches. Future work includes enriching the approach to identify additional scientific objects and applying it to other research fields.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
This document outlines an approach to query formulation for similarity search using term extraction algorithms. It discusses the challenges of similarity search and constructing queries from documents. The solution involves preprocessing documents, extracting candidate terms, building an index, calculating statistical features, executing term extraction algorithms, and postprocessing outputs. Evaluation on a plagiarism detection dataset found TF-IDF and RIDF performed best among algorithms tested. The code is available on GitHub and further improvements could integrate topic modeling.
Knowledge Patterns for the Web: extraction, transformation, and reuseAndrea Nuzzolese
KPs are an abstraction of frames as introduced by Fillmore and Minsky. KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts (i.e., top-down defined artifacts that can be compared to KPs, such as FrameNet frames or Ontology Design Patterns) to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Unfortunately, type paths are not always available. In fact, Linked Data is a knowledge soup because of the heterogeneous semantics of its datasets and because of the limited intentional as well as extensional coverage of ontologies (e.g., DBpedia ontology, YAGO) or other controlled vocabularies (e.g., SKOS, FOAF, etc.). Thus, we propose a solution for enriching Linked Data with additional axioms (e.g., rdf:type axioms) by exploiting the natural language available for example in annotations (e.g. rdfs:comment) or in corpora on which datasets in Linked Data are grounded (e.g. DBpedia is grounded on Wikipedia). Then we present K∼ore, a software architec- ture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. K∼ore is the architectural binding of a set of tools, i.e., K∼tools, which implements the methods for KP transformation and extraction. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.
The Open Knowledge Extraction Challenge focuses on the production of new knowledge aimed at either populating and enriching existing knowledge bases or creating new ones. This means that the defined tasks focus on extracting concepts, individuals, properties, and statements that not necessarily exist already in a target knowledge base, and on representing them according to Semantic Web standard in order to be directly injected in linked datasets and their ontologies. The OKE challenge, has the ambition to advance a reference framework for research on Knowledge Extraction from text for the Semantic Web by re-defining a number of tasks (typically from information and knowledge extraction) by taking into account specific SW requirements. The Challenge is open to everyone from industry and academia.
1) The document describes the SOPHIA project, which aims to build altmetric networks of researchers and institutions to understand how research impacts spread in society.
2) SOPHIA collects data from Scopus and social media sources to build a heterogeneous graph network, and analyzes the network using graph metrics to measure the influence and authority of researchers and institutions.
3) The project has developed visualization and search tools to explore the altmetric networks, annotated documents, and metrics within a software prototype called SOPHIA.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
Oles Petriv “Creating one concept embedding space for persons, brands and new...Lviv Startup Club
The document discusses creating an embedding space to represent entities like people, brands, and news authors mentioned in news articles. It involves training models on a dataset of over 300 million news articles from 20,000 sources in English, Ukrainian and Russian from 2000-2018 that have been parsed and had named entities recognized. The goal is to use the embedding space to supervise models for sentiment analysis and document representation. It describes preprocessing the data to create training data for the entity representations, and training word embedding models like FastText and Word2Vec on this data to learn the entity representations.
EKAW 2016 - TechMiner: Extracting Technologies from Academic PublicationsFrancesco Osborne
TechMiner is a new approach that combines natural language processing, machine learning, and semantic technologies to extract information about technologies (such as applications, systems, languages, and formats) from research publications. It generates an ontology describing technologies and their relationships to other research entities. The approach was evaluated on a gold standard of manually annotated publications and found to improve precision and recall over alternative natural language processing approaches. Future work includes enriching the approach to identify additional scientific objects and applying it to other research fields.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
This document outlines an approach to query formulation for similarity search using term extraction algorithms. It discusses the challenges of similarity search and constructing queries from documents. The solution involves preprocessing documents, extracting candidate terms, building an index, calculating statistical features, executing term extraction algorithms, and postprocessing outputs. Evaluation on a plagiarism detection dataset found TF-IDF and RIDF performed best among algorithms tested. The code is available on GitHub and further improvements could integrate topic modeling.
This document summarizes a tutorial on practical cross-dataset queries on the Web of Data. The tutorial introduces link discovery tools like Silk that help publish new datasets and integrate existing ones by generating links between entities. It outlines the linking workflow including defining linkage rules, selecting and transforming values, calculating similarities, and aggregating results. The Silk Workbench provides a GUI for creating and evaluating linkage rules to discover links across datasets.
Este documento trata sobre la enseñanza de los derechos humanos. Brevemente discute que la educación es un derecho humano fundamental y que educar para la convivencia es una responsabilidad compartida entre la familia, la escuela y la sociedad. También menciona algunas características de los derechos humanos como que son innatos, universales e inalienables.
This document discusses Nikunj Vasoya's experience studying in the Pharmaceutical Manufacturing Engineering (PME) program at Stevens Institute of Technology. It describes his background in pharmacy in India and reasons for coming to the US for higher education. It outlines his coursework and involvement in the Stevens Pharmaceutical Research Center (SPRC) under the guidance of Professor Andrew Walsh. The SPRC provided hands-on research experience and opportunities to develop skills applicable to the pharmaceutical industry.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Skyworks Solutions is a manufacturer of analog semiconductors headquartered in Massachusetts. It has over 6,000 employees worldwide and supplies components for mobile devices, wireless infrastructure, and other electronics. The document provides an overview of Skyworks' management, products, clients, global operations, research spending, financials, industry trends, competitors, growth catalysts, risks, and stock performance.
Skyworks provides analog semiconductor solutions that enable mobile connectivity. It addresses high growth markets like mobile internet, smartphones, and embedded wireless applications. The company gains market share through technology leadership and economies of scale. This generates superior operating results. Mobile internet traffic is growing rapidly, driven by increases in video, audio, and social networking usage. The cellular handset and smartphone markets are also experiencing strong growth. This growth creates increasing opportunities for Skyworks in the power amplifier and front-end module semiconductor content area.
Model Runway, Part 3 Design Best Practices at Blue Cross BlueShieldRoger Snook
This is part 3 from the series: https://www.ibm.com/developerworks/mydeveloperworks/blogs/669242b1-dd91-4d63-a08f-231314c793bb/entry/model_runway_see_the_latest_design_best_practices_at_bluecross_blueshield24?lang=en
In operation research this is one of the intresting area which having lot of applications to apply in our real life. it can be used for both the service and manufacturing industry.
Decision making in management for large medical equipmentHTAi Bilbao 2012
This document discusses decision making for purchasing large medical equipment like MRI machines. It presents data showing differences in MRI availability between countries. There are challenges in decision making due to varying supplier policies, objectives of health facilities, and heterogeneous information. A methodology is proposed to help with rational choice involving developing a system for equipment selection and procurement. Experts would be evaluated and their competence indexed based on both objective and subjective criteria. Analytical hierarchy process (AHP) techniques could be applied to determine priority weights for MRI machine characteristics through pairwise comparisons. Predictions could also be made on how weights may change. This would help create a ranked list of recommended MRI machine options.
CIM to Modelica Factory - Automated Equation-Based Cyber-Physical Power Syste...Luigi Vanfretti
The Common Information Model (CIM) is described using the Unified Modeling Language (UML). UML can also describe data model of cyber-physical power system components and networks. However, there are several difficulties to transform the data model into a strictly defined mathematical model. A strictly defined mathematical model is one for which all-differential algebraic and discrete model equations are explicitly defined [1] (i.e. the equations are written in human readable form). This is known as equation-based modelling [2], and it is utilized in many areas such as the automotive and aerospace industry [3].
The automated generation of an unambiguous equation-based model would allow performing time-domain simulations of cyber-physical power systems [4] and the assessment of textual requirements, which could be defined from the UML model directly [5]. This flexibility would allow adopting model-based systems engineering practices within the power industry, such as those used in process control.
For the implementation of models in an equation-based language, the Modelica language [6] is the one of the best choices because it follows the Object Oriented Programming (OOP) notation, with a close relation with UML. Furthermore, the ModelicaML [5], an extended subset of the OMG Unified Modeling Language, enables integrated modelling and simulation of system requirements and design. Combining CIM, ModelicaML and Modelica models of cyber-physical power system components it is possible to automatically generate unambiguous mathematical models that can be used for simulation and requirements verification [7].
This CIM to Modelica Factory talk explores this possibility.
One of the main challenges that we face with power systems models defined using the Modelica language is the initialization of the dynamic states (in equilibrium condition) of the components within a model [8, 9]. However, objects and components modelled in CIM standard contain attributes for storing a power flow solution.
The purpose of the work described in this presentation is to develop a software tool capable to transform a CIM object model into a Modelica model that can be directly simulated using different Modelica engines. To this aim, we start from the CIM/UML representation of power system components and models, and exploit the ModelicaML profile to achieve a proper code representation of the power system in Modelica code. To confront issues with dynamic initialization, the power flow solution from CIM is linked to the Modelica component models and utilized within the initialization algorithms of the simulation engines. The result is a software tool that allows performing time domain simulations directly from a CIM/UML structure, while maintaining consistency in the resulting mathematical model within different simulation engines.
Farmacia ni Dok is a Pharmacy Franchising Company that carries Branded and Generic Medicines, Natural Supplements, and Consumer Products in a convenient and friendly store setup, complemented with trustworthy and reliable customer service.
It offers a breakthrough Franchise Format -- the 2-in-1 Combo Business Package: Retail Pharmacy + Distribution Center!
Backed by Francorp -- the world’s leader in franchising (Retail Franchise Consultant) and GMB Franchise Developers (Distribution Franchise Consultant), Farmacia ni Dok boasts of strong Management Team and Corporate Partners with over 10 years expertise in the Pharmaceutical Operation and Distribution, Importation, and Manufacturing (adhere to the GMP or Good Manufacturing Practice to “ensure that their products are safe, pure, and effective!
The document discusses several application domains for the Internet of Things including smart metering, industrial automation, building automation, eHealth, transportation, logistics, and remote monitoring. It provides examples of how connecting devices and sensors in healthcare, manufacturing, and factories can improve processes and operations by enabling tracking, monitoring, predictive maintenance, and real-time decision making. The Internet of Things involves many embedded devices without interfaces, so interactions are facilitated through wearables and embedded screens adapting to context.
Este documento presenta un estudio comparativo de precios entre las farmacias Walgreens y CVS. Los autores hipotetizaron que Walgreens sería más económica y recopilaron datos de precios de 31 productos iguales en ambas farmacias. Al aplicar una prueba t de hipótesis con un nivel de significancia de 0.05, el resultado no cayó dentro de la región crítica, por lo que no se puede rechazar la hipótesis nula de que los precios promedio son iguales entre las dos farmacias. Por lo tanto, los
The document provides an overview of business model components and strategies for direct and indirect sales channels. It discusses:
- The key components of a business model including customer segments, value propositions, channels, customer relationships, revenue streams, resources, activities and costs.
- Factors to consider for direct and indirect sales channels such as costs, target customers, partnerships, and balancing coverage between the two. Direct sales are suggested for high potential customers while indirect channels can help reach new customers.
- Examples of companies that use different balance of direct and indirect sales, such as Apple's mix of retail stores and partners, and Coca Cola relying entirely on indirect channels.
Case Study on Practical Applications of Lean Principles - Phillip Cain, Alcon...marcus evans Network
Phillip Cain, Alcon Laboratories, Inc. - Speaker at the Spring 2012 Medical Manufacturing Summit held in Las Vegas, NV, delivered his presentation entitled Case Study on Practical Applications of Lean Principles
The document discusses carving out Alcon from Nestle to increase company value. It finds that carving out Alcon and valuing it separately from Nestle's food business, using industry multiples, increases Nestle's total enterprise value by approximately 7% to over $104 million compared to its current $97500 million value. The best option for listing Alcon is the US market, which would provide high liquidity, regulation, and attract more pharmaceutical investors, despite some costs of restructuring. Carving out and listing Alcon in the US is recommended to realize increased value.
CVS Pharmacy faces high competition and low differentiation in the market. Their marketing plan aims to build customer loyalty through rewarding social media engagement with coupons, personalizing offers through relationship marketing like Extra Bucks rewards and local store recommendations on mobile, and allocating $1 million total across social, relationship, and mobile marketing channels over 6 months to increase coupon redemptions, Extra Bucks used, and mobile check-ins.
Where Is the Pharmaceutical Industry on Supply Chain Maturity? What Can They ...Lora Cecere
A presentation made on October 20th at the Integrichain Conference in Baltimore on the current state of the pharmaceutical industry and what industry leaders can learn from consumer products/retail collaboration.
Sales and distribution of pharmaceutical industryKrishna Bhawsar
This document discusses pharmaceutical distribution structures and perspectives in India. It provides details on different distribution models used by companies, including working with wholesalers, distributors, and retailers. It also compares various distributors and retailers based on metrics like employees, turnover, market coverage, credit terms, and incentives. Medical representative roles are described for government and private hospitals. The distribution network of Mankind Pharma is outlined. Insights are provided on blackmail of distributors and differences in retailer and wholesaler mindsets. Margin structures are shown for OTC and prescription drugs.
Journal presented at AlignmentTrack at ISWC2017.
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
This document summarizes a tutorial on practical cross-dataset queries on the Web of Data. The tutorial introduces link discovery tools like Silk that help publish new datasets and integrate existing ones by generating links between entities. It outlines the linking workflow including defining linkage rules, selecting and transforming values, calculating similarities, and aggregating results. The Silk Workbench provides a GUI for creating and evaluating linkage rules to discover links across datasets.
Este documento trata sobre la enseñanza de los derechos humanos. Brevemente discute que la educación es un derecho humano fundamental y que educar para la convivencia es una responsabilidad compartida entre la familia, la escuela y la sociedad. También menciona algunas características de los derechos humanos como que son innatos, universales e inalienables.
This document discusses Nikunj Vasoya's experience studying in the Pharmaceutical Manufacturing Engineering (PME) program at Stevens Institute of Technology. It describes his background in pharmacy in India and reasons for coming to the US for higher education. It outlines his coursework and involvement in the Stevens Pharmaceutical Research Center (SPRC) under the guidance of Professor Andrew Walsh. The SPRC provided hands-on research experience and opportunities to develop skills applicable to the pharmaceutical industry.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Skyworks Solutions is a manufacturer of analog semiconductors headquartered in Massachusetts. It has over 6,000 employees worldwide and supplies components for mobile devices, wireless infrastructure, and other electronics. The document provides an overview of Skyworks' management, products, clients, global operations, research spending, financials, industry trends, competitors, growth catalysts, risks, and stock performance.
Skyworks provides analog semiconductor solutions that enable mobile connectivity. It addresses high growth markets like mobile internet, smartphones, and embedded wireless applications. The company gains market share through technology leadership and economies of scale. This generates superior operating results. Mobile internet traffic is growing rapidly, driven by increases in video, audio, and social networking usage. The cellular handset and smartphone markets are also experiencing strong growth. This growth creates increasing opportunities for Skyworks in the power amplifier and front-end module semiconductor content area.
Model Runway, Part 3 Design Best Practices at Blue Cross BlueShieldRoger Snook
This is part 3 from the series: https://www.ibm.com/developerworks/mydeveloperworks/blogs/669242b1-dd91-4d63-a08f-231314c793bb/entry/model_runway_see_the_latest_design_best_practices_at_bluecross_blueshield24?lang=en
In operation research this is one of the intresting area which having lot of applications to apply in our real life. it can be used for both the service and manufacturing industry.
Decision making in management for large medical equipmentHTAi Bilbao 2012
This document discusses decision making for purchasing large medical equipment like MRI machines. It presents data showing differences in MRI availability between countries. There are challenges in decision making due to varying supplier policies, objectives of health facilities, and heterogeneous information. A methodology is proposed to help with rational choice involving developing a system for equipment selection and procurement. Experts would be evaluated and their competence indexed based on both objective and subjective criteria. Analytical hierarchy process (AHP) techniques could be applied to determine priority weights for MRI machine characteristics through pairwise comparisons. Predictions could also be made on how weights may change. This would help create a ranked list of recommended MRI machine options.
CIM to Modelica Factory - Automated Equation-Based Cyber-Physical Power Syste...Luigi Vanfretti
The Common Information Model (CIM) is described using the Unified Modeling Language (UML). UML can also describe data model of cyber-physical power system components and networks. However, there are several difficulties to transform the data model into a strictly defined mathematical model. A strictly defined mathematical model is one for which all-differential algebraic and discrete model equations are explicitly defined [1] (i.e. the equations are written in human readable form). This is known as equation-based modelling [2], and it is utilized in many areas such as the automotive and aerospace industry [3].
The automated generation of an unambiguous equation-based model would allow performing time-domain simulations of cyber-physical power systems [4] and the assessment of textual requirements, which could be defined from the UML model directly [5]. This flexibility would allow adopting model-based systems engineering practices within the power industry, such as those used in process control.
For the implementation of models in an equation-based language, the Modelica language [6] is the one of the best choices because it follows the Object Oriented Programming (OOP) notation, with a close relation with UML. Furthermore, the ModelicaML [5], an extended subset of the OMG Unified Modeling Language, enables integrated modelling and simulation of system requirements and design. Combining CIM, ModelicaML and Modelica models of cyber-physical power system components it is possible to automatically generate unambiguous mathematical models that can be used for simulation and requirements verification [7].
This CIM to Modelica Factory talk explores this possibility.
One of the main challenges that we face with power systems models defined using the Modelica language is the initialization of the dynamic states (in equilibrium condition) of the components within a model [8, 9]. However, objects and components modelled in CIM standard contain attributes for storing a power flow solution.
The purpose of the work described in this presentation is to develop a software tool capable to transform a CIM object model into a Modelica model that can be directly simulated using different Modelica engines. To this aim, we start from the CIM/UML representation of power system components and models, and exploit the ModelicaML profile to achieve a proper code representation of the power system in Modelica code. To confront issues with dynamic initialization, the power flow solution from CIM is linked to the Modelica component models and utilized within the initialization algorithms of the simulation engines. The result is a software tool that allows performing time domain simulations directly from a CIM/UML structure, while maintaining consistency in the resulting mathematical model within different simulation engines.
Farmacia ni Dok is a Pharmacy Franchising Company that carries Branded and Generic Medicines, Natural Supplements, and Consumer Products in a convenient and friendly store setup, complemented with trustworthy and reliable customer service.
It offers a breakthrough Franchise Format -- the 2-in-1 Combo Business Package: Retail Pharmacy + Distribution Center!
Backed by Francorp -- the world’s leader in franchising (Retail Franchise Consultant) and GMB Franchise Developers (Distribution Franchise Consultant), Farmacia ni Dok boasts of strong Management Team and Corporate Partners with over 10 years expertise in the Pharmaceutical Operation and Distribution, Importation, and Manufacturing (adhere to the GMP or Good Manufacturing Practice to “ensure that their products are safe, pure, and effective!
The document discusses several application domains for the Internet of Things including smart metering, industrial automation, building automation, eHealth, transportation, logistics, and remote monitoring. It provides examples of how connecting devices and sensors in healthcare, manufacturing, and factories can improve processes and operations by enabling tracking, monitoring, predictive maintenance, and real-time decision making. The Internet of Things involves many embedded devices without interfaces, so interactions are facilitated through wearables and embedded screens adapting to context.
Este documento presenta un estudio comparativo de precios entre las farmacias Walgreens y CVS. Los autores hipotetizaron que Walgreens sería más económica y recopilaron datos de precios de 31 productos iguales en ambas farmacias. Al aplicar una prueba t de hipótesis con un nivel de significancia de 0.05, el resultado no cayó dentro de la región crítica, por lo que no se puede rechazar la hipótesis nula de que los precios promedio son iguales entre las dos farmacias. Por lo tanto, los
The document provides an overview of business model components and strategies for direct and indirect sales channels. It discusses:
- The key components of a business model including customer segments, value propositions, channels, customer relationships, revenue streams, resources, activities and costs.
- Factors to consider for direct and indirect sales channels such as costs, target customers, partnerships, and balancing coverage between the two. Direct sales are suggested for high potential customers while indirect channels can help reach new customers.
- Examples of companies that use different balance of direct and indirect sales, such as Apple's mix of retail stores and partners, and Coca Cola relying entirely on indirect channels.
Case Study on Practical Applications of Lean Principles - Phillip Cain, Alcon...marcus evans Network
Phillip Cain, Alcon Laboratories, Inc. - Speaker at the Spring 2012 Medical Manufacturing Summit held in Las Vegas, NV, delivered his presentation entitled Case Study on Practical Applications of Lean Principles
The document discusses carving out Alcon from Nestle to increase company value. It finds that carving out Alcon and valuing it separately from Nestle's food business, using industry multiples, increases Nestle's total enterprise value by approximately 7% to over $104 million compared to its current $97500 million value. The best option for listing Alcon is the US market, which would provide high liquidity, regulation, and attract more pharmaceutical investors, despite some costs of restructuring. Carving out and listing Alcon in the US is recommended to realize increased value.
CVS Pharmacy faces high competition and low differentiation in the market. Their marketing plan aims to build customer loyalty through rewarding social media engagement with coupons, personalizing offers through relationship marketing like Extra Bucks rewards and local store recommendations on mobile, and allocating $1 million total across social, relationship, and mobile marketing channels over 6 months to increase coupon redemptions, Extra Bucks used, and mobile check-ins.
Where Is the Pharmaceutical Industry on Supply Chain Maturity? What Can They ...Lora Cecere
A presentation made on October 20th at the Integrichain Conference in Baltimore on the current state of the pharmaceutical industry and what industry leaders can learn from consumer products/retail collaboration.
Sales and distribution of pharmaceutical industryKrishna Bhawsar
This document discusses pharmaceutical distribution structures and perspectives in India. It provides details on different distribution models used by companies, including working with wholesalers, distributors, and retailers. It also compares various distributors and retailers based on metrics like employees, turnover, market coverage, credit terms, and incentives. Medical representative roles are described for government and private hospitals. The distribution network of Mankind Pharma is outlined. Insights are provided on blackmail of distributors and differences in retailer and wholesaler mindsets. Margin structures are shown for OTC and prescription drugs.
Journal presented at AlignmentTrack at ISWC2017.
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
SSONDE is a framework for calculating semantic similarity between ontology instances represented as linked data. It provides an asymmetric similarity score that emphasizes containment relationships between instances. SSONDE operates at the application layer and assumes integration steps like ontology alignment have already occurred. It has been applied to compare researchers based on publications and interests, and habitats based on hosted species. The framework supports configurable similarity contexts and caching to optimize performance on large linked datasets.
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Ontologies provide a shared understanding of a domain by formally defining concepts, properties, and relationships. An ontology introduces vocabulary relevant to a domain and specifies the meaning of terms. Ontologies are machine-readable and enable overcoming differences in terminology across complex, distributed applications. Examples include gene ontologies, pharmaceutical drug ontologies, and customer profile ontologies. Semantic technologies use ontologies to provide semantic search, integration, reasoning, and analysis capabilities.
This document outlines a course on Knowledge Representation (KR) on the Web. The course aims to expose students to challenges of applying traditional KR techniques to the scale and heterogeneity of data on the Web. Students will learn about representing Web data through formal knowledge graphs and ontologies, integrating and reasoning over distributed datasets, and how characteristics such as volume, variety and veracity impact KR approaches. The course involves lectures, literature reviews, and milestone projects where students publish papers on building semantic systems, modeling Web data, ontology matching, and reasoning over large knowledge graphs.
With the continuously increasing number of datasets published in the Web of Data and form part of the Linked Open Data Cloud, it becomes more and more essential to identify resources that correspond to the same real world object in order to interlink web resources and set the basis for large-scale data integration. This requirement becomes apparent in a multitude of domains ranging from science (marine research, biology, astronomy, pharmacology) to semantic publishing and cultural domains. In this context, instance matching is of crucial importance.
It is though essential at this point to develop, along with instance and entity matching systems, benchmarks to determine the weak and strong points of those systems, as well as their overall quality in order to support users in deciding the system to use for their needs. Hence, well defined, and good quality benchmarks are important for comparing the performance of the developed instance matching systems.
In this tutorial we aim at:
- Discussing the state-of-the-art instance matching benchmarks
- Presenting the benchmark design principles
- Providing an analysis of the performance results of instance matching systems for the presented benchmarks
- Presenting the research directions that should be exploited for the creation of novel benchmarks to answer the needs of the Linked Data paradigm.
Please click here for the Tutorial web-page: http://www.ics.forth.gr/isl/BenchmarksTutorial/
Sergey Nikolenko and Anton Alekseev User Profiling in Text-Based Recommende...AIST
This document summarizes a research paper on using distributed word representations and logistic regression to create user profiles for text-based recommender systems. The paper proposes clustering word embeddings to create document profiles, then using balanced logistic regression on individual users to assign weights to clusters for their profiles. This approach addresses issues of sparse data and uninformative clusters. An evaluation shows the regression-based recommender outperforms baselines on metrics like AUC, NDCG, and accuracy of top recommendations.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
Profile-based Dataset Recommendation for RDF Data Linking Mohamed BEN ELLEFI
This document summarizes Mohamed Ben Ellefi's PhD thesis defense on profile-based dataset recommendation for RDF data linking. The thesis proposes two approaches: a topic profile-based approach and an intensional profile-based approach. The topic profile-based approach models datasets as topics and recommends target datasets based on similarity between source and target topic profiles, achieving an average recall of 81% and reducing the search space by 86%. The approach shows better performance than baselines but needs improvement on precision.
The document discusses faceted search over ontology-enhanced RDF data. It formalizes faceted interfaces for querying RDF graphs that capture ontological information. It studies the expressivity and complexity of queries represented by faceted interfaces, and algorithms for generating and updating interfaces based on the underlying RDF and ontology information. The goal is to provide rigorous theoretical foundations for faceted search in the context of RDF and OWL 2 ontologies.
Abstract:
An increasing number of applications rely on RDF, OWL 2, and SPARQL for storing and querying data. SPARQL, however, is not targeted towards end-users, and suitable query interfaces are needed. Faceted search is a prominent approach for end-user data access, and several RDF-based faceted search systems have been developed. There is, however, a lack of rigorous theoretical underpinning for faceted search in the context of RDF and OWL 2. In this paper, we provide such solid foundations. We formalise faceted interfaces for this context, identify a fragment of first-order logic capturing the underlying queries, and study the complexity of answering such queries for RDF and OWL 2 profiles. We then study interface generation and update, and devise efficiently implementable algorithms. Finally, we have implemented and tested our faceted search algorithms for scalability, with encouraging results.
The document provides information about various conferences and workshops including ACL-IJCNLP 2015 which had 173 long papers presented orally and 68 as posters. It also summarizes several research papers related to natural language processing including automatic prediction of drunk texting, modeling argument strength in student essays, driving ROVER with segment-based ASR quality estimation, and multi-level translation quality prediction with QUEST++. Finally, it mentions an unsupervised method for decomposing multi-author documents and identifying age-appropriate ratings of song lyrics from text.
This document discusses using ontologies to make biological and biomedical data more interoperable and FAIR (Findable, Accessible, Interoperable, Reusable). It describes several ontology services and tools provided by EMBL-EBI to help with tasks like annotating data, mapping data to ontologies, searching and accessing ontologies, and publishing structured data. It also uses the example of the BioSamples database to illustrate challenges in working with large, heterogeneous datasets and how ontologies can help address issues like normalizing descriptions and attributes to enable better searching and data integration.
The document discusses the BioSamples Database (BioSD), which provides a reference system for searching and browsing information about biological samples used in biomedical experiments. It focuses on the sample context independently of specific assay types or technologies. BioSD allows for consistency in sample annotations and common interfaces to access sample information and links to other data repositories. By modeling BioSD as linked data, it enables integration with related datasets, exploitation of ontologies for standardization, and enhanced modeling of sample attributes. This can support applications and new ways of querying the data using SPARQL.
A paper presented at the 1st International Workshop on Benchmarking Linked Data (BLINK). We present experimental results with the instance matching benchmark generator LANCE that is developed in the context of HOBBIT.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Effective Semantics for Engineering NLP SystemsAndre Freitas
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
The Datalift Project aims to publish and interconnect government open data. It develops tools and methodologies to transform raw datasets into interconnected semantic data. The project's first phase focuses on opening data by developing an infrastructure to ease publication. The second phase will validate the platform by publishing real datasets. The goal of Datalift is to move data from its raw published state to being fully interconnected on the Semantic Web.
Similar to SPIMBENCH: A scalable, Schema-Aware Instance Matching Benchmark for the Semantic Publishing Domain (20)
LDBC 6th TUC Meeting conclusions by Peter BonczIoan Toma
The document discusses the conclusions and future perspectives of the Linked Data Benchmark Council (LDBC). It summarizes that the EU project is on track, four benchmarks were developed including for social networks and graph analytics, and there has been growing participation from academic and industry groups. It outlines plans for LDBC to transition to an independent council and develop additional benchmarks in areas like graph programming, temporal graphs, and ontology reasoning.
MODAClouds Decision Support System for Cloud Service SelectionIoan Toma
The document discusses MODAClouds' Decision Support System (DSS) for cloud service selection. Some key points:
- The DSS helps users select cloud services by considering multiple dimensions like cost, quality, risks, and technical/business constraints.
- It allows multiple stakeholders like architects, operators, managers to provide input on tangible/intangible assets and risks.
- The DSS performs risk analysis and generates requirements. It also considers issues around multi-cloud environments like interoperability and migration challenges.
- Other features include automatic data gathering from various sources, and progressive learning over time from user inputs and service selections.
This document outlines the process for auditing an implementation of the LDBC Social Network Benchmark (SNB). It describes downloading the necessary driver and data generator software. The main metrics for benchmarking are throughput at scale and query latency constraints during crash recovery testing. It provides guidelines for implementing the driver, loading data, running queries for 2 hours, and verifying crash recovery. Audit results will report throughput, load time, recovery time and system configuration. Rules prohibit hinted query plans, precomputed indexes, and require serializable consistency.
Social Network Benchmark Interactive WorkloadIoan Toma
The document summarizes the LDBC Social Network Benchmark (SNB) for interactive workloads on social network systems. It describes:
- The SNB data generator that produces synthetic social network data with characteristics similar to Facebook, including persons, groups, posts, and relationships. It scales to very large datasets.
- The types of queries in the benchmark - complex reads, short reads, and updates - that mimic real user interactions. Complex reads target specific system bottlenecks.
- The workload driver that schedules queries and updates over time based on the synthetic data, aiming to balance read and write loads and mimic user browsing behavior. It measures latency and throughput.
- Validation and execution rules to ensure fair
MarkLogic is an enterprise NoSQL database platform that can be used for semantic search, data integration, and intelligent recommendation engines. It natively stores XML, JSON, and RDF triples alongside documents. Triples provide context to documents and enable semantic queries over data. MarkLogic also supports full-text and geospatial search, transactions, and flexible indexing and replication of data at scale.
The LDBC Social Network Benchmark Interactive Workload - SIGMOD 2015Ioan Toma
The document discusses the LDBC Social Network Benchmark for evaluating database and graph processing systems. It describes the benchmark's social network data generator which produces realistic data following power law distributions and correlations. It also outlines the benchmark's three workloads: interactive, business intelligence, and graph analytics. The focus is on the interactive workload, which includes complex read queries, simple read queries, and concurrent updates. It aims to identify choke points and measure the acceleration factor a system can sustain for the query mix while meeting a maximum query latency. Parameter curation is used to select query parameters that produce stable performance. The parallel query driver respects dependencies between queries to evaluate a system's ability to handle the workload concurrently.
SADI: A design-pattern for “native” Linked-Data Semantic Web ServicesIoan Toma
Semantic Automated Discovery and Integration
A design-pattern for “native” Linked-Data
Semantic Web Services by Mark D. Wilkinson (Universidad Politécnica de Madrid)
The document discusses the UniProt SPARQL endpoint, which provides access to 20 billion biomedical data triples. It notes challenges in hosting large life science databases and standards-based federated querying. The endpoint uses Apache load balancing across two nodes each with 64 CPU cores and 256GB RAM. Loading rates of 500,000 triples/second are achieved. Usage peaks at 35 million queries per month from an estimated 300-2000 real users. Very large queries involving counting all IRIs can take hours. Template-based compilation and key-value approaches are proposed to optimize challenging queries. Public monitoring is also discussed.
Lighthouse: Large-scale graph pattern matching on GiraphIoan Toma
This document provides an overview of Lighthouse, a system for large-scale graph pattern matching on Giraph. It discusses the timeline of Lighthouse's development, its vertex-centric API, architecture based on BSP and Pregel, and execution algebra using operations like scan, select, project, and joins. Examples are provided of matching graph patterns in Cypher queries and finding the k-shortest paths between nodes while restricting to safe edges. The implementation uses two phases, first computing the routes for each top k path and then traversing back to build the full paths. Preliminary results are mentioned but not detailed.
HP Labs: Titan DB on LDBC SNB interactive by Tomer Sagi (HP)Ioan Toma
HP has a long history of innovation dating back to its founding in a Palo Alto garage in 1939. Some of its notable innovations include the first programmable calculator in 1968, the first pocket scientific calculator in 1972, launching the first inkjet printer in 1984, and being first to commercialize RISC technology in 1986. More recently, HP Labs has developed technologies like ePrint in 2010, 3D Photon technology in 2011, and Project Moonshot in 2013. Going forward, HP Labs is focusing its research on systems, networking, security, analytics, and printing to deliver the fastest and most efficient route from data to value.
LDBC Semantic Publishing Benchmark evolution. The Semantic Publishing Benchmark (SPB) needed to evolve in order to allow for retrieval of semantically relevant content based on rich metadata descriptions and diverse reference knowledge and demonstrate that triplestores offer simplified and more efficient querying proving it to be a challenging technology in the RDF and Graph arena. Read in the presentations all the changes.
FOSDEM2014 - Social Network Benchmark (SNB) Graph Generator - Peter BonczIoan Toma
This document describes the LDBC Social Network Benchmark (SNB). The SNB was created to benchmark graph databases and RDF stores using a social network scenario. It includes a data generator that produces synthetic social network data with correlations between attributes. It also defines workloads of interactive, business intelligence, and graph analytics queries. Systems are evaluated based on metrics like query response times. The SNB aims to uncover performance choke points for different database systems and drive improvements in graph processing capabilities.
GRAPH-TA 2013 - RDF and Graph benchmarking - Jose Lluis Larriba PeyIoan Toma
The document discusses an agenda for a meeting on benchmarking RDF and graph databases. It provides an overview of the LDBC benchmarking project including its objectives to create standardized benchmarks, spur industry cooperation, and push technological improvements. It outlines the working groups and task forces that will focus on specific types of benchmarks. It also discusses common issues to be addressed like suitable use cases, benchmark methodologies, and benchmark workloads for querying, updating, and integrating RDF and graph data. Open questions are raised about benchmark realism, benchmark rules, and technological convergence of RDF and graph databases.
The LDBC benchmark council develops industry-strength benchmarks for graph and RDF data management systems. It focuses on benchmarks for transactional updates, business intelligence queries, graph analytics, and complex RDF workloads. The benchmarks are designed based on choke points - difficulties in the workload that stimulate technical progress. Examples of choke points in TPC-H include dependent group by keys and sparse joins. A key choke point for graph benchmarks is structural correlation, where the structure of the graph depends on node attribute values. The LDBC benchmark includes a generator that produces synthetic graphs with correlated properties and edges to test how systems handle this challenge.
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
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
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.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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.
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
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
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.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
SPIMBENCH: A scalable, Schema-Aware Instance Matching Benchmark for the Semantic Publishing Domain
1. SPIMBENCH:
A Scalable, Schema-Aware
Instance Matching Benchmark
for the Semantic Publishing Domain
T. Saveta1, E. Daskalaki1, G. Flouris1, I. Fundulaki1,
M. Herschel2, A.-C. Ngonga Ngomo3
#1 FORTH-ICS, #2 University of Stuttgart, #3 University of Leipzig
2. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 2
Instance Matching in Linked Data
Data acquisition
Data
evolution
Data integration
Open/social data
How can we automatically recognize
multiple mentions of the same entity
across or within sources?
=
Instance Matching
3. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 3
Benchmarking
Instance matching research has led to the development of
various systems and algorithms.
How to compare these?
How can we assess their performance?
How can we push the systems to get better?
These systems need to be benchmarked
4. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 4
SPIMBENCH
• Based on Semantic Publishing Benchmark (SPB) of Linked
Data Benchmark Council (LDBC)
• Synthetic benchmark for the Semantic Publishing Domain
• Value-based, structure-based and semantics-aware
transformations [FMN+11, FLM08]
• Deterministic, scalable data generation in the order of
billion triples
• Weighted gold standard
5. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 5
Instance Matching Benchmark Ingredients [FLM08]
Benchmark
Datasets
Gold
Standard
Test
Cases
Metrics
7. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 7
Value & Structure Based Transformations
Value: Mainly typographical errors and the use of
different data formats.[FMN+11]
Structure: Changes that occur to the properties.
– Property Addition/Deletion
– Property Aggregation/Extraction
Blank Character Addition/Deletion Change Number
Random Character Addition/Deletion/Modification Synonym/Antonym
Token Addition/Deletion/Shuffle Abbreviation
Multi-linguality (65 supported languages) Stem of a Word
Date Format
10. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 10
Weighted Gold Standard
• Detailed GS for debugging reasons
• Final GS : Contains only URIs that we consider a match
and their similarity
spimbench:Match owl:Thing
spimbench:ValueTransf spimbench:StructureTransf spimbench:SemanticsAwareTransf
spimbench:Transformation
spimbench:VT1 spimbench:VTi
spimbench:ST1 spimbench:STi
spimbench:SAT1
…
spimbench:SATi
…
…
rdfs:subPropertyOf
rdfs:subClassOf
rdf:type
c
spimbench:source
spimbench:target
spimbench:weight xsd:string
spimbench:onProperty rdf:Property
spimbench:transformation
11. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 11
Scalability Experiments (1/2)
• Scalability experiments for datasets up to 500M triples
• 1000 triples ~ 36 entities
• Data generation along with data transformation is linear to the size
of triples
• Transformation overhead is negligible for value-based, structure-
based, semantics-aware and simple combinations
• Overhead for complex combinations is higher by one magnitude
13. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 13
Performance of LogMap [JG11]
Performance of LogMap for 10K triples Performance of LogMap for 25K triples
Performance of LogMap for 50K triples
14. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 14
Conclusions
• Schema aware variations
– Complex class definitions
– Property constraints
– Equivalence, Disjointness, etc.
• Combination of transformations
• Scalable data generation in order of billion triples
– Uses sampling
• Weighted gold standard
– Final gold standard
– Detailed gold standard for debugging reasons
15. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 15
Future Work
• SPIMBENCH will be used as one of the Ontology
Alignment Evaluation Initiative [OAEI]
benchmarks for 2015.
• Domain independent instance matching test
case generator.
• Definition of more sophisticated metrics that
takes into account the
difficulty (weight).
16. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 16
Acknowledgments
This work was partially supported by the ongoing FP7
European Project LDBC (Linked Data Benchmark Council)
(317548) and is done in collaboration with I. Fundulaki,
M. Herschel (University of Stuttgart), G. Flouris,
E. Daskalaki and A. C. Ngonga Ngomo (University of
Leipzig)
17. Semantic Publishing Instance Matching Benchmark (SPIMBENCH) 17
References
# Reference Abbreviation
1
A. Ferrara and D. Lorusso and S. Montanelli and G. Varese.
Towards a Benchmark for Instance Matching. In OM, 2008.
[FLM08]
2
A. Ferrara and S. Montanelli and J. Noessner and H. Stuckenschmidt.
Benchmarking Matching Applications on the Semantic Web. In ESWC, 2011.
[FMN+11]
3
M. Nickel and V. Tresp. Tensor Factorization for Multi-relational Learning.
Machine Learning and Knowledge Discovery in Databases. Springer Berlin
Heidelberg, 2013. 617-621.
[NV13]
4
J. M. Joyce . Kullback-Leibler Divergence. International Encyclopedia of
Statistical Science. Springer Berlin Heidelberg, 2011. 720-722.
[J11]
5
E. Jimenez-Ruiz and B. C. Grau. Logmap: Logic-based and scalable ontology
matching. In ISWC, 2011.
[JG11]
6
B. Fuglede and F. Topsoe. Jensen-Shannon divergence and Hilbert space
embedding, in IEEE International Symposium on Information Theory, 2004.
[FT04]
7
Ontology Alignment Evaluation Initiative, find at
http://oaei.ontologymatching.org/
[OAEI]
We are currently working on a domain-independent instance matching test case generator for Linked Data, whose aim is to take
any ontology and RDF dataset as source and produce a target dataset that will implement the test cases discussed earlier. We are
also studying how we can dene more sophisticated metrics that take into account the difficulty (weight) of the correctly
identified matches, to be used in tandem with the standard precision and recall metrics.
Also SPIMBENCH will be used as one of the OAEI benchmarks for 2015.
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Όσο αφορά την μελλοντική ανάπτυξη του συστήματος θα προσπαθήσουμε να κάνουμε τον SPIMBench τελείως ανεξάρτητο από οποιοδήποτε τομέα (domain). Ακόμα θα μπορεί να υποστηρίζει περισσοτέρους συνδυασμούς μετατροπών με πιο αυτόματο τρόπο. Ακόμα θα πρέπει να επανεξετάσουμε τις μετρικές (precision- recall) ώστε να μπορουν να λάβουν υπόψη και τα βάρη.
Wald method[ref] for sampling ?? -> provlepei kai poso tha einai to sfalma analoga to k
++++
koitaksame ola ta vasika tis owl lite kai owl rl kai auta pou kaname eixan mono noima alliws tha itan polu duskolo gia ta sustimata mpla mpla