Characterstics of soa
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Services in a SOA should be independent and self-contained so that changes to one service do not impact others. Each service should define its own operations, data types, and responses. Services should publish their descriptions to a shared directory and be able to communicate asynchronously and reliably with each other through standard platform-independent messages to ensure requests reach the correct destination and secure responses are obtained.
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Characteristics of services
Services are economic activities that create value and benefits for customers. Main characteristics of services include:
1) Intangibility - Services cannot be seen, touched, or physically possessed by customers. Customers can only experience the benefits of a service.
2) Perishability - Services cannot be stored and perish or lose value if not used when produced. Unused services represent an economic waste.
3) Inseparability - Services are generally created and consumed simultaneously. The service provider and customer must interact during service delivery and consumption.
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1) Services which implement discrete pieces of application logic and are autonomous, reusable modules accessible via messages.
2) Service contracts which define the messages and interfaces that a service supports.
3) Endpoints which are addresses where services can be accessed.
4) Messages which are the units of communication between services in the form of requests and responses.
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The document discusses Service Oriented Architecture (SOA), defining it as an architectural style for building systems based on interacting coarse-grained autonomous components called services. It outlines the basic SOA components - services, contracts, endpoints, messages, and policies. Services expose behavior through contracts composed of messages sent to discoverable endpoints. Behavior is governed by externally defined policies. The document also discusses best practices, the past and future of SOA, and introduces the concept of Cloud Service Oriented Architecture (C-SOA).
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Services are economic activities that create value and benefits for customers. Main characteristics of services include:
1) Intangibility - Services cannot be seen, touched, or physically possessed by customers. Customers can only experience the benefits of a service.
2) Perishability - Services cannot be stored and perish or lose value if not used when produced. Unused services represent an economic waste.
3) Inseparability - Services are generally created and consumed simultaneously. The service provider and customer must interact during service delivery and consumption.
4) Heterogeneity - The quality of services can vary and is difficult to standardize due to human involvement in service delivery and differences in customer expectations.
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The document discusses the key components of Service Oriented Architecture (SOA). SOA is based on coarse-grained services that expose their capabilities through standardized interfaces and messages. The core SOA components are:
1) Services which implement discrete pieces of application logic and are autonomous, reusable modules accessible via messages.
2) Service contracts which define the messages and interfaces that a service supports.
3) Endpoints which are addresses where services can be accessed.
4) Messages which are the units of communication between services in the form of requests and responses.
5) Policies which govern a service's behavior and are set externally to the service.
6) Service consumers which interact with
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Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
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This document discusses the evolution from monolithic applications to service-oriented architectures (SOA) and microservices. It begins by describing the monolith approach of one giant application that does everything. It then explains that SOA breaks applications into services that communicate with each other. Microservices take this further by making each service independent and focused on doing one thing well. The document outlines some key differences between SOA and microservices, such as microservices having no shared schema/contract and being independently deployable. Overall, it provides an overview of moving from monolithic applications to microservices.
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- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
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An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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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.
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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.
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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!
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Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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Characterstics of soa
- 1. Characteristics of SOA •In SOA, Services should be independent of other services. Altering a service should not affect calling service. •Services should be self-contained. When we talk about a RegisterCusomer service it means, service will do all the necessary work for us, we are not required to care about anything. •Services should be able to define themselves. Services should be able to answer a question what is does? It should be able to tell client what all operations it does, what all data types it uses and what kind of responses it will return. •Services should be published into a location (directory) where anyone can search for it. •As I said, SOA comprises of collection services which communicate via standard Messages. Standard messages make them platform independent. (Here standard doesn•t mean standard across Microsoft it means across all programming languages and technologies.) •Services should be able to communicate with each other asynchronously. •Services should support reliable messaging. Means there should be a guarantee that request will be reached to correct destination and correct response will be obtained. •Services should support secure communication