This is the talk I gave at ECOWS'10. This work passed through an acceptance rate of 19% (http://goo.gl/Atqic)
If we want to create a system out of various stateful services, we have to cope up with their different interfaces and protocol/behavior. We already presented papers which tackled how to recognize these differences (http://goo.gl/z9CAX) and build upon them (http://goo.gl/y1aIH). Now we develop a scalable technique to discover these incompatible, yet useful, services
This document proposes a hypergraph-based approach for optimizing SPARQL queries on RDF data. It first transforms an RDF graph into a hypergraph by grouping subjects and objects connected by the same predicate under a hyperedge for that predicate. It then rearranges the patterns in a SPARQL query based on the size of corresponding hyperedges to build a query path for efficient processing. The query is executed by looping through the rearranged patterns and extracting required subjects and objects from the hypergraph representation to find matching triples. Experimental results show this approach performs better than existing systems like RDF-3x, Jena and AllegroGraph.
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET Journal
The document reviews existing methods for the k-means clustering algorithm. It discusses how k-means clustering works and some of its limitations when dealing with large datasets, such as being dependent on the initial choice of centroids. It then proposes using Hadoop to overcome big data challenges and calculate preliminary centroids for k-means clustering in a distributed manner. Finally, it reviews different techniques that have been proposed in other research to improve k-means clustering, such as methods for selecting better initial centroids or determining the optimal number of clusters.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
The document provides an overview of the relational data model and relational algebra. It discusses how the relational model represents data using tables of attribute-value pairs and allows standard logical operations. Key concepts covered include the relational operations of projection, selection, join, union, difference, and divide. SQL is introduced as the standard language for querying and manipulating relational data using these algebraic operations.
1) The document presents a model for privacy-preserving execution of data services. Data services are modeled as parameterized RDF views over domain ontologies.
2) Security and privacy policies are expressed using access control languages and are enforced during service composition and execution.
3) Data services are rewritten as compositions of other available services while ensuring privacy policies are followed. Filtering operations sanitize responses by removing restricted data.
This document proposes a hypergraph-based approach for optimizing SPARQL queries on RDF data. It first transforms an RDF graph into a hypergraph by grouping subjects and objects connected by the same predicate under a hyperedge for that predicate. It then rearranges the patterns in a SPARQL query based on the size of corresponding hyperedges to build a query path for efficient processing. The query is executed by looping through the rearranged patterns and extracting required subjects and objects from the hypergraph representation to find matching triples. Experimental results show this approach performs better than existing systems like RDF-3x, Jena and AllegroGraph.
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET Journal
The document reviews existing methods for the k-means clustering algorithm. It discusses how k-means clustering works and some of its limitations when dealing with large datasets, such as being dependent on the initial choice of centroids. It then proposes using Hadoop to overcome big data challenges and calculate preliminary centroids for k-means clustering in a distributed manner. Finally, it reviews different techniques that have been proposed in other research to improve k-means clustering, such as methods for selecting better initial centroids or determining the optimal number of clusters.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
The document provides an overview of the relational data model and relational algebra. It discusses how the relational model represents data using tables of attribute-value pairs and allows standard logical operations. Key concepts covered include the relational operations of projection, selection, join, union, difference, and divide. SQL is introduced as the standard language for querying and manipulating relational data using these algebraic operations.
1) The document presents a model for privacy-preserving execution of data services. Data services are modeled as parameterized RDF views over domain ontologies.
2) Security and privacy policies are expressed using access control languages and are enforced during service composition and execution.
3) Data services are rewritten as compositions of other available services while ensuring privacy policies are followed. Filtering operations sanitize responses by removing restricted data.
Presentation on the Data Cube vocabulary to support Linked Data publication of statistics and measurement data sets. Given at SemTech 2011, San Francisco.
Building a Spatial Database in PostgreSQLKudos S.A.S
The document discusses building a spatial database in PostgreSQL using PostGIS. It provides an introduction to spatial data and databases, and explains why PostGIS was created - to provide an open source spatial extension for PostgreSQL that is compliant with OpenGIS standards. It covers key topics like spatial data types, spatial relationships, spatial indexing and functions, and implementing the OpenGIS specification in PostgreSQL.
IEEE 2015 - 2016 | Combining Efficiency, Fidelity, and Flexibility in Resource...1crore projects
1 CRORE PROJECTS
chennai | kumbakonam
offers (2015-2016) M.E, BE, M. Tech, B. Tech, PhD, MCA, BCA, MSC & MBA projects and also a real time application projects.
Final Year Projects for BE, B. Tech - ECE, EEE, CSE, IT, MCA, ME, M. Tech, M SC (IT), BCA, BSC and MBA.
Project support:-
1.Abstract, Diagrams, Review Details, Relevant Materials, Presentation,
2.Supporting Documents, Software E-Books,
3.Software Development Standards & Procedure
4.E-Book, Theory Classes, Lab Working programs, Project design & Implementation
online support :
For other districts and states
1.we will help in skype and teamviewer support for project
For further details feel free to call us:
1 CRORE PROJECTS ,Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall), Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026.
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536 / +91 77081 50152
Automated Syntactic Mediation for Web Service IntegrationMartin Szomszor
The document discusses using ontologies and mappings to enable syntactic mediation between semantically compatible but syntactically incompatible web services. It proposes an architecture that uses OWL ontologies to represent data formats at a conceptual level, and a mapping language to describe relationships between XML schemas and OWL ontologies. A configurable mediator would consume mappings to transform documents between source and destination formats via an intermediate OWL representation. This approach aims to support automated syntactic mediation when integrating diverse web services.
The ‘discovery to delivery’ DLF reference modelAndy Powell
UKOLN is supported by JISC to develop a reference model for discovery to delivery (d2d) of digital resources. The document discusses work done by the Digital Library Federation Abstract Service Framework Working Group to develop a model describing library services as discrete components. It proposes applying this model to the d2d use case and represents key functions like search, delivery, and metadata as abstract services. Issues discussed include how well the model captures non-linear user workflows and how terminology could be improved.
The document discusses the REST (Representational State Transfer) architectural style. It defines key REST concepts like resources, representations, self-descriptive messages, and hypermedia as the engine of application state. It also outlines different REST sub-styles and constraints like client-server architecture, statelessness, and uniform interfaces. The document provides examples of how to design RESTful systems using services as resources and hiding domain models behind active resources.
The document discusses relational database management systems (RDBMS). It describes some key disadvantages of file processing systems like data redundancy and inconsistency. An RDBMS uses a database, DBMS, and application programs to allow for data storage in tables/relations with rows and columns. The document outlines important aspects of RDBMS like data models, database languages, database administrators, keys, relationships, and normalization.
Combining efficiency, fidelity, and flexibility in resource information servicesCloudTechnologies
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
This document discusses Service Oriented Architecture (SOA) and Representational State Transfer (REST) systems of systems. It describes how SOA has evolved over time to include grids, clouds, and systems of systems. REST is characterized as an architectural style for building distributed hypermedia systems and leverages existing web technologies like HTTP and XML. In a REST system, resources are addressable via URIs and clients interact with servers by transferring representations of resources through standardized interfaces and operations.
This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
This document outlines the course content for Oracle SOA and OSB, including introductions to concepts like service-oriented architecture, building blocks of SOA like XML and WSDL, installations of SOA servers, interaction patterns in SOA, SCA architecture, building and deploying SOA composites with BPEL, synchronous and asynchronous services, parallel processing, mediators, human workflow, business rules, fault handling, OSB, security, and transaction management. It also lists many hands-on practices and examples that will be covered related to these topics.
The document proposes a method called RAndom Space Perturbation (RASP) to provide secure and efficient range and k-nearest neighbor (kNN) query services for protected data hosted in the cloud. RASP combines order preserving encryption, dimensionality expansion, random noise injection, and random projection to transform data in a way that preserves the topology of multidimensional ranges, allowing for efficient query processing while providing strong confidentiality guarantees. The authors analyze attacks on the RASP-protected data and queries under a defined threat model and security assumptions. Experimental results demonstrate advantages of the RASP approach in efficiency and security for cloud-based query services.
Introduction to Service Oriented ArchitectureDATA Inc.
The document introduces SOA and discusses its key concepts. It describes why organizations adopt SOA, defines what SOA is, and outlines some of its benefits including reuse, flexibility and cost savings. It also discusses components of a SOA system like services, service contracts and an enterprise service bus.
Data services integrate and enable access to heterogeneous data sources so that consumers see a single coherent data source rather than separate schemas and APIs. They support CRUD operations on data instances and relationships between instances. Methods can be public, internal, or private. Cloud data services offer pay-as-you-go scalability and availability. Models include key-value stores, sparse tables, and relational databases. Challenges include transactions across sources and updating sources consistently. Emerging areas include query tools, optimization, summaries, and security.
This presentation was provided by Ralph LeVan of OCLC, during the NISO event "Next Generation Discovery Tools: New Tools, Aging Standards," held March 27 - March 28, 2008.
A Case Elaboration Methodology for a Semantic Web Service Discovery System Ba...IJERA Editor
The Case Based Reasoning is a paradigm of intelligent reasoning which consists on reusing results of previously solved problems (Source Cases) to solve new problems (Target Cases). It has been formalized as a five-step process consisting of: "Elaboration", "Retrieve", "Reuse", "Revise" and "Retain". In this paper we focus on the first phase of the CBR cycle with all of the required modeling to formalize a Case in our CBR-based system for semantic Web service discovery (CBR4WSD). This phase consists in formalizing the problem description and its structuring before launching the “Retrieve” phase and select the most appropriate Source Cases from the Case Base. We identify a set of basic descriptors to formalize Cases handled in our CBR4WSD system. In this conduct and in accordance with CBR policies, we put forward our Case representation model.
This document provides an overview of relational databases and the emergence of alternative database technologies like NoSQL. It discusses the dominance and stability of relational databases but also some of their limitations for certain use cases. It introduces NoSQL databases and why they emerged, focusing on their scalability and flexibility compared to relational databases. The document describes different types of NoSQL databases and how they handle concepts like schemas, transactions and scaling. It provides examples of when different database types may be more suitable and discusses additional concepts like aggregates, consistency models and sharding.
The document provides an overview of NoSQL databases and MongoDB. It discusses:
- What NoSQL is and why it was created
- The different categories of NoSQL databases, including key-value stores, document databases, column family stores, and graph databases
- MongoDB specifically, including its flexible schema, horizontal scalability, replication support, and data modeling approach
- Comparisons between relational and NoSQL databases
Presentation on the Data Cube vocabulary to support Linked Data publication of statistics and measurement data sets. Given at SemTech 2011, San Francisco.
Building a Spatial Database in PostgreSQLKudos S.A.S
The document discusses building a spatial database in PostgreSQL using PostGIS. It provides an introduction to spatial data and databases, and explains why PostGIS was created - to provide an open source spatial extension for PostgreSQL that is compliant with OpenGIS standards. It covers key topics like spatial data types, spatial relationships, spatial indexing and functions, and implementing the OpenGIS specification in PostgreSQL.
IEEE 2015 - 2016 | Combining Efficiency, Fidelity, and Flexibility in Resource...1crore projects
1 CRORE PROJECTS
chennai | kumbakonam
offers (2015-2016) M.E, BE, M. Tech, B. Tech, PhD, MCA, BCA, MSC & MBA projects and also a real time application projects.
Final Year Projects for BE, B. Tech - ECE, EEE, CSE, IT, MCA, ME, M. Tech, M SC (IT), BCA, BSC and MBA.
Project support:-
1.Abstract, Diagrams, Review Details, Relevant Materials, Presentation,
2.Supporting Documents, Software E-Books,
3.Software Development Standards & Procedure
4.E-Book, Theory Classes, Lab Working programs, Project design & Implementation
online support :
For other districts and states
1.we will help in skype and teamviewer support for project
For further details feel free to call us:
1 CRORE PROJECTS ,Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall), Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026.
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536 / +91 77081 50152
Automated Syntactic Mediation for Web Service IntegrationMartin Szomszor
The document discusses using ontologies and mappings to enable syntactic mediation between semantically compatible but syntactically incompatible web services. It proposes an architecture that uses OWL ontologies to represent data formats at a conceptual level, and a mapping language to describe relationships between XML schemas and OWL ontologies. A configurable mediator would consume mappings to transform documents between source and destination formats via an intermediate OWL representation. This approach aims to support automated syntactic mediation when integrating diverse web services.
The ‘discovery to delivery’ DLF reference modelAndy Powell
UKOLN is supported by JISC to develop a reference model for discovery to delivery (d2d) of digital resources. The document discusses work done by the Digital Library Federation Abstract Service Framework Working Group to develop a model describing library services as discrete components. It proposes applying this model to the d2d use case and represents key functions like search, delivery, and metadata as abstract services. Issues discussed include how well the model captures non-linear user workflows and how terminology could be improved.
The document discusses the REST (Representational State Transfer) architectural style. It defines key REST concepts like resources, representations, self-descriptive messages, and hypermedia as the engine of application state. It also outlines different REST sub-styles and constraints like client-server architecture, statelessness, and uniform interfaces. The document provides examples of how to design RESTful systems using services as resources and hiding domain models behind active resources.
The document discusses relational database management systems (RDBMS). It describes some key disadvantages of file processing systems like data redundancy and inconsistency. An RDBMS uses a database, DBMS, and application programs to allow for data storage in tables/relations with rows and columns. The document outlines important aspects of RDBMS like data models, database languages, database administrators, keys, relationships, and normalization.
Combining efficiency, fidelity, and flexibility in resource information servicesCloudTechnologies
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
This document discusses Service Oriented Architecture (SOA) and Representational State Transfer (REST) systems of systems. It describes how SOA has evolved over time to include grids, clouds, and systems of systems. REST is characterized as an architectural style for building distributed hypermedia systems and leverages existing web technologies like HTTP and XML. In a REST system, resources are addressable via URIs and clients interact with servers by transferring representations of resources through standardized interfaces and operations.
This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
This document outlines the course content for Oracle SOA and OSB, including introductions to concepts like service-oriented architecture, building blocks of SOA like XML and WSDL, installations of SOA servers, interaction patterns in SOA, SCA architecture, building and deploying SOA composites with BPEL, synchronous and asynchronous services, parallel processing, mediators, human workflow, business rules, fault handling, OSB, security, and transaction management. It also lists many hands-on practices and examples that will be covered related to these topics.
The document proposes a method called RAndom Space Perturbation (RASP) to provide secure and efficient range and k-nearest neighbor (kNN) query services for protected data hosted in the cloud. RASP combines order preserving encryption, dimensionality expansion, random noise injection, and random projection to transform data in a way that preserves the topology of multidimensional ranges, allowing for efficient query processing while providing strong confidentiality guarantees. The authors analyze attacks on the RASP-protected data and queries under a defined threat model and security assumptions. Experimental results demonstrate advantages of the RASP approach in efficiency and security for cloud-based query services.
Introduction to Service Oriented ArchitectureDATA Inc.
The document introduces SOA and discusses its key concepts. It describes why organizations adopt SOA, defines what SOA is, and outlines some of its benefits including reuse, flexibility and cost savings. It also discusses components of a SOA system like services, service contracts and an enterprise service bus.
Data services integrate and enable access to heterogeneous data sources so that consumers see a single coherent data source rather than separate schemas and APIs. They support CRUD operations on data instances and relationships between instances. Methods can be public, internal, or private. Cloud data services offer pay-as-you-go scalability and availability. Models include key-value stores, sparse tables, and relational databases. Challenges include transactions across sources and updating sources consistently. Emerging areas include query tools, optimization, summaries, and security.
This presentation was provided by Ralph LeVan of OCLC, during the NISO event "Next Generation Discovery Tools: New Tools, Aging Standards," held March 27 - March 28, 2008.
A Case Elaboration Methodology for a Semantic Web Service Discovery System Ba...IJERA Editor
The Case Based Reasoning is a paradigm of intelligent reasoning which consists on reusing results of previously solved problems (Source Cases) to solve new problems (Target Cases). It has been formalized as a five-step process consisting of: "Elaboration", "Retrieve", "Reuse", "Revise" and "Retain". In this paper we focus on the first phase of the CBR cycle with all of the required modeling to formalize a Case in our CBR-based system for semantic Web service discovery (CBR4WSD). This phase consists in formalizing the problem description and its structuring before launching the “Retrieve” phase and select the most appropriate Source Cases from the Case Base. We identify a set of basic descriptors to formalize Cases handled in our CBR4WSD system. In this conduct and in accordance with CBR policies, we put forward our Case representation model.
This document provides an overview of relational databases and the emergence of alternative database technologies like NoSQL. It discusses the dominance and stability of relational databases but also some of their limitations for certain use cases. It introduces NoSQL databases and why they emerged, focusing on their scalability and flexibility compared to relational databases. The document describes different types of NoSQL databases and how they handle concepts like schemas, transactions and scaling. It provides examples of when different database types may be more suitable and discusses additional concepts like aggregates, consistency models and sharding.
The document provides an overview of NoSQL databases and MongoDB. It discusses:
- What NoSQL is and why it was created
- The different categories of NoSQL databases, including key-value stores, document databases, column family stores, and graph databases
- MongoDB specifically, including its flexible schema, horizontal scalability, replication support, and data modeling approach
- Comparisons between relational and NoSQL databases
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
1. Feature-Based Discovery of
Services with Adaptable Behaviour
J. Antonio Martín and Ernesto Pimentel
University of Málaga
Spain
3 of December, 2010 Ayia Napa, Cyprus ECOWS'10
2. Introduction
a:
We assume Web
services have
complex behavior
E.g., Web services
described as BPEL
processes
3. Mismatches
a:
Services might have
incompatibilities in signature and
behavior
These lead the orchestration
to a deadlock situation
We want to discover
both compatible services
and incompatible but b:
usefull services
Adaptable services
are usefull services
4. Adaptation
a:
Behavioral adaptation
deploys an adaptor in the
middle of the communication
which overcomes signature
and behavioral
incompatibilities
Adaptors are
described by b:
abstract
adaptation
contracts
5. Adaptation
Adaptation a:
Contract
<b:?find(S);a:!request(S,I)>
<b:!results(SS);a:?POIlist(SS)>
<a:!quit()>
Adaptors are generated from
adaptation contracs
Adaptation contracs can
b:
be designed or
automatically created
We focus the dicovery
on adaptability
requirements
7. Pros & Cons
Pros:
These approaches take into account all the behavioral
information of the services
Indexes allow fast narrowing of the results and offer
distance measures between the services so we can find
similar, but incompatible, services
Sub-isomorphism, in several steps, could allow us to find not
just a single service but several that can conform an
orchestration which complies with the expected behavior
Some related work is based on chemistry DB and they put a
lot of emphasis on scalability
Cons:
Although they are able to return similar services, these don't
take into account the adaptability of those services
8. Motivation
Behavior as a first class entity for service discovery
"I want to look up a service that can be orchestrated
within this business process"
Use indexes to narrow the search and quicken the
discovery
Adaptability as an integral part of the discovery
Indexes should support similarity searches which, in
turn, return adaptable services
The more adaptations are required (adaptation cost),
the more distant those services must be
Extreme case: avoid heuristics/similarity and search
for adaptable services, no matter the adaptation cost
10. What can be adapted?
Restriction:
The adaptor is a mere "router" of data with a protocol
Data and side-effects are features provided and
consumed by services at appropriate times
The adaptor does not make decisions nor generates
any new feature
Cases:
Message names,
extra argument which is not needed,
an extra optional branch,
some "superfluous" messages,
data and message reordering,
message splitting and merging,
data reutilization
11. What can be adapted?
Cases:
Message names,
extra argument which is not needed,
an extra optional branch,
some "superfluous" messages,
data and message reordering,
message splitting and merging,
data reutilization
<a:!request(S,T);b:?command(S)>
Adaptation
Contract
12. What can be adapted?
Cases:
Message names,
extra argument which is not needed,
an extra optional branch,
some "superfluous" messages,
data and message reordering,
message splitting and merging,
data reutilization.
<a:!request();b:?request()>
13. What can be adapted?
Cases:
Message names,
extra argument which is not needed,
an extra optional branch,
some "superfluous" messages,
data and message reordering,
message splitting and merging,
data reutilization.
<a:!request();b:?request()>
<b:!ack()>
14. What can be adapted?
Cases:
Message names,
extra argument which is not needed,
an extra optional branch,
some "superfluous" messages,
data and message reordering,
message splitting and merging,
data reutilization.
<b:!b()>
<a:!a(X,Y);b:?a(Y,X)>
<a:?b()>
15. What can be adapted?
Cases:
Message names,
extra argument which is not needed,
an extra optional branch,
some "superfluous" messages,
data and message reordering,
message splitting and merging,
data reutilization.
<a:!login(P)>
<a:!request(R);b:?request(R,P)>
<a:?reply(D);b:!reply(D)>
16. Adaptation Example
a:
<b:?find(S);a:!request(S,I)>
<b:!results(SS);a:?POIlist(SS)>
<a:!quit()>
I is ignored.
!quit() is ignored.
?noPOI() is not required. b:
Adaptor
17. Adaptation Example
a:
<b:?find(S);a:!request(S,I)>
<b:!results(SS);a:?POIlist(SS)>
<a:!quit()>
This is control driven adaptation
but focused on features
Data dependencies steam from
the behavior
b:
Message names
are irrelevant in
adaptation terms
18. Discovery through feature deps.
Let's abstract the behavior of a WS by the minimum
amount of required features and the maximum amount of
provided features
Features can be reused and services have a finite set of
features, therefore we can unfold behaviours into trees
with the first occurrences of the features
We unfold the different traces of adaptation dependencies
(AD) and compose them together in an AD-tree
Local choices (IF,WHILE,FOR) are AND nodes
External choices (PICK) are OR nodes
Any service which complies with the complementary AD-
tree of another service can be adapted without generating
new data or deadlocks
21. Tree Traversal
Matching between two AD-trees
OR: A match exists with any
(possibly several) branches
AND: A match exists for every
branch
!-nodes can be skipped
?-nodes require their data from
previous !-nodes in the other
side
Final nodes match with final
nodes
Sent argumens
are carried throughout the trace and
received arguments can be reused
22. Naive Discovery
This index is the "union" of AD-
trees with a root OR-node
We must merge equivalent
nodes, though
Some optimizations are
possible for having less
nodes
We might have both AND-results b:
and OR-results. AND-results
must be intersected to obtain
feasible results a:
This naive approach has high
complexity per query b: a:
23. FD-Rules
Abstract the behavior into a set of rules
which specify which features must be
provided before being offered a certain AD-Tree
feature
?S is needed before !SS
... and which further features are needed to
guarantee that an stable state is reached
afterwards:
end(SS) requires {D,I,S}
FD-rule: SS <- {S} | {I,D}
With OR nodes we keep the least
demanding rule and with AND nodes we
merge the requirements
Discovery request are composed with the
duals of these rules, e.g.:
S <- {} | {SS} ; {I,D} <- {SS} | {}
24. AD Search Tree
c:
b: a:
SS <- {S} | {I,D}; SS<- {S} | {}; {S,I} <- {} | {}
25. AD Search Tree
A query to the AD Search Tree has
an time complexity of O(q.f.s.log(s))
q = # of AD-rules in the query
f = # of features
s = # of services in the registry
We have lost the "intra-session
dependencies" among several
emissions (e.g., !S,!SS and !I).
Therefore adaptation with this
approach might need to invoke
several instances of a WS
26. Compositional Discovery
Compositional discovery is to return
several services that, together, can
fulfil the query
There are two straighforward ways
to perform compositional discovery
with AD Search Tree:
Each FD-rule in the query is
answered by a different service
Obtain !S from a: and !SS
from b:
We can go further down in the
tree if we delegate in other
services
We can use a: to satisfy c: to
deliver !SS
27. Future Work
We abstracted a lot of behavioral information, hence
further comparisons are needed to refine and rank the
candidate services.
Semantic information is missing.
Loop tests over AD-Trees would be desirable.
Use in parallel with another behavioral index (graph-based
or RE-based) and intersect their results.
Final pruning and ranking of the candidates with an in-
deep comparison* but without penalizing adaptation.
* See Kozlenkov et al. "Architecture-driven Service Discovery for Service
Centric Systems", IJSR, 2007.
28. Open Questions
We need a set of relevant features
Enough arguments to alleviate semantic mismatches
Not so many to hamper adaptation and efficiency
What to do with structured or custom data types?
Decompose them into a set of elemental data types?
Use the 47 built-in datatypes in XML Schema as an
starting point?
Are AD search trees expressive enough to
quickly differentiate WSs?
Are AD search trees space-efficient?
What is the complexity of creating and updating AD
search trees?
There are no behavioral WS, how to evaluate this
proposal?
Synthetic examples? TravelAgency?
31. Trace Index
1. Encode service behavior as regular expressions (RE) of
observable communication.
2. Include those RE into a RE-Index [1]. This will be the
registry of services.
3. Identify the query as a set of relevant traces.
4. Look up those traces in the index.
5. The intersection of the services found are candidates for
further comparisons.
[1] C. Y. Chan et al. "RE-tree: an efficient index structure for regular
expressions". The VLDB Journal. Springer, 2004.
32. Behavior Index
1. Encode service behavior as regular expressions (RE) of
observable communication.
2. Include those RE into a RE-Index [1]. This will be the
registry of services.
3. Identify the query as the RE which represents the
expected behavior.
4. "Insert" the query in the index.
5. The leaf-node where the query-RE is going to be inserted
contains the candidate services.
1. More candidates can be found in the descendants of ancestor
nodes. The farther, the worse (more dissimilar).
[1] C. Y. Chan et al. "RE-tree: an efficient index structure for regular
expressions". The VLDB Journal. Springer, 2004.
33. Behavior Graph Index
Encode service behavior as graphs, Labelled Transitions
Systems with specially marked nodes for start and final
nodes.
Introduce these graphs in a graph index [2,3,...].
Queries are composed as "expected behavior" encoded
as graphs and using sub-isomorphism graph queries
over the index.
Several similarity metrics allow us to rank the candidates
found: size of the graphs, edit distance [3], common
features [2]...
[2] X. Yan et al. "Graph Indexing Based on Discriminative
Frequent Structure Analysis". TODS. ACM, 2005
[3] D. W. Williams et al. "Graph Database Indexing Using Structured Graph
Decomposition". ICDE. IEEE, 2007.