66 research outputs found
Towards Open Smart Services Platform
The landscape of services in the enterprise has changed significantly for both service providers and service clients over the last few years. In the IT services domain, the mega IT outsourcing service deals with a sole provider are diminishing fast. A typical service client is now consuming multiple IT services, from specialized providers, and services contracts has become smaller in size and duration. More importantly the line of business, not the IT, owns the decisions and the relationship for consuming services. This has also shifted the service consumption input from IT requirements into the business requirements. This new world is posing a new and unique set of opportunities and challenges for service providers in offering services, which include third party providers, to their clients, and for service clients to consume services from multiple providers. To facilitate offering and consuming such multi-vendor services, in this paper, we present a conceptual architecture for an open services platform which enables a given server provider (a service integrator) to offer services to its clients that are a mixture of its own and other services from third party providers. It also enables service clients to look for and choose services from multiple vendors in a seamless, integrated and consistent manner
Assisted mashup development: On the discovery and recommendation of mashup composition knowledge
Over the past few years, mashup development has been made more accessible with tools such as Yahoo! Pipes that help in making the development task simpler through simplifying technologies. However, mashup development is still a difficult task that requires knowledge about the functionality of web APIs, parameter settings, data mappings, among other development efforts. In this work, we aim at assisting users in the mashup process by recommending development knowledge that comes in the form of reusable composition knowledge. This composition knowledge is harvested from a repository of existing mashup models by mining a set of composition patterns, which are then used for interactively providing composition recommendations while developing the mashup. When the user accepts a recommendation, it is automatically woven into the partial mashup model by applying modeling actions as if they were performed by the user. In order to demonstrate our approach we have implemented Baya, a Firefox plugin for Yahoo! Pipes that shows that it is indeed possible to harvest useful composition patterns from existing mashups, and that we are able to provide complex recommendations that can be automatically woven inside Yahoo! Pipes' web-based mashup editor
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
Generative Pre-trained Transformer (GPT) is a state-of-the-art machine
learning model capable of generating human-like text through natural language
processing (NLP). GPT is trained on massive amounts of text data and uses deep
learning techniques to learn patterns and relationships within the data,
enabling it to generate coherent and contextually appropriate text. This
position paper proposes using GPT technology to generate new process models
when/if needed. We introduce ProcessGPT as a new technology that has the
potential to enhance decision-making in data-centric and knowledge-intensive
processes. ProcessGPT can be designed by training a generative pre-trained
transformer model on a large dataset of business process data. This model can
then be fine-tuned on specific process domains and trained to generate process
flows and make decisions based on context and user input. The model can be
integrated with NLP and machine learning techniques to provide insights and
recommendations for process improvement. Furthermore, the model can automate
repetitive tasks and improve process efficiency while enabling knowledge
workers to communicate analysis findings, supporting evidence, and make
decisions. ProcessGPT can revolutionize business process management (BPM) by
offering a powerful tool for process augmentation, automation and improvement.
Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting
data engineers in maintaining data ecosystem processes within large bank
organizations. Our scenario highlights the potential of this approach to
improve efficiency, reduce costs, and enhance the quality of business
operations through the automation of data-centric and knowledge-intensive
processes. These results underscore the promise of ProcessGPT as a
transformative technology for organizations looking to improve their process
workflows.Comment: Accepted in: 2023 IEEE International Conference on Web Services
(ICWS); Corresponding author: Prof. Amin Beheshti ([email protected]
An Approach for Reducing the Gap between BPMN Models and Implementation Artifacts
The need for using high-level modeling tools (e.g. BPMN) isincreasing considerably. The proliferation of the service oriented architectures (SOA) is also apparent. In this context, there is a gap between the developed model and its execution. This work introduces the MoSC Translator which translates models produced in BPMN into executable WS-BPEL processes
Towards Generating Richer Code by Binding Security Abstractions to BPMN Task Types
This paper presents an approach for binding security requirements to different BPMN task types to create secure executable business processes.This paper presents an approach for binding security requirements to different BPMN task types to create secure executable business processes
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