Responsible Composition and Optimization of Integration Processes under
Correctness Preserving Guarantees
- URL: http://arxiv.org/abs/2305.19196v2
- Date: Sat, 17 Feb 2024 17:25:35 GMT
- Title: Responsible Composition and Optimization of Integration Processes under
Correctness Preserving Guarantees
- Authors: Daniel Ritter, Fredrik Nordvall Forsberg, Stefanie Rinderle-Ma
- Abstract summary: Enterprise Application Integration deals with the problem of connecting heterogeneous applications.
We formalize compositions of integration patterns based on their characteristics.
We describe optimization strategies that help to reduce the model complexity.
- Score: 0.7366405857677227
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Enterprise Application Integration deals with the problem of connecting
heterogeneous applications, and is the centerpiece of current on-premise, cloud
and device integration scenarios. For integration scenarios, structurally
correct composition of patterns into processes and improvements of integration
processes are crucial. In order to achieve this, we formalize compositions of
integration patterns based on their characteristics, and describe optimization
strategies that help to reduce the model complexity, and improve the process
execution efficiency using design time techniques. Using the formalism of timed
DB-nets - a refinement of Petri nets - we model integration logic features such
as control- and data flow, transactional data storage, compensation and
exception handling, and time aspects that are present in reoccurring solutions
as separate integration patterns. We then propose a realization of optimization
strategies using graph rewriting, and prove that the optimizations we consider
preserve both structural and functional correctness. We evaluate the
improvements on a real-world catalog of pattern compositions, containing over
900 integration processes, and illustrate the correctness properties in case
studies based on two of these processes.
Related papers
- AutoLayout: Closed-Loop Layout Synthesis via Slow-Fast Collaborative Reasoning [102.71841660031065]
Auto is a fully automated method that integrates a closed-loop self-validation process within a dual-system framework.<n>The effectiveness of Auto was validated across 8 distinct scenarios, where it demonstrated a significant 10.1% improvement over SOTA methods.
arXiv Detail & Related papers (2025-07-06T08:35:22Z) - RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes [14.031370458128068]
Next activity prediction is a challenge for optimizing business processes in service-oriented architectures.<n>We introduce RLHGNN, a novel framework that transforms event logs into heterogeneous process graphs.<n>We show that RLHGNN consistently outperforms state-of-the-art approaches.
arXiv Detail & Related papers (2025-07-03T15:01:08Z) - Architectural Fusion Through Contextual Partitioning in Large Language Models: A Novel Approach to Parameterized Knowledge Integration [0.0]
This paper introduces an innovative approach to enhancing the architectural design of large-scale computational models through the dynamic segmentation of parameters into context-aware regions.
Experimental evaluations demonstrate substantial improvements in accuracy, perplexity, and contextual coherence across a variety of linguistic tasks.
The findings collectively demonstrate the potential for Contextual Partitioning to redefine the scalability and adaptability of computational language architectures in diverse and complex domains.
arXiv Detail & Related papers (2025-01-22T14:21:04Z) - Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems [61.580419063416734]
A recent stream of structured learning approaches has improved the practical state of the art for a range of optimization problems.
The key idea is to exploit the statistical distribution over instances instead of dealing with instances separately.
In this article, we investigate methods that smooth the risk by perturbing the policy, which eases optimization and improves the generalization error.
arXiv Detail & Related papers (2024-07-24T12:00:30Z) - Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence [51.54175067684008]
This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks.
We first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes.
Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods.
arXiv Detail & Related papers (2024-03-17T07:02:55Z) - MUSIC: Accelerated Convergence for Distributed Optimization With Inexact
and Exact Methods [6.800113478497425]
In this paper, we propose an accelerated framework named as MUSIC allowing each agent to perform multiple local updates and a single combination in each iteration.
We equip inexact and exact distributed optimization methods into this framework, thereby developing two new algorithms that exhibit accelerated linear convergence and high communication efficiency.
arXiv Detail & Related papers (2024-03-05T02:02:00Z) - Analyzing and Enhancing the Backward-Pass Convergence of Unrolled
Optimization [50.38518771642365]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form.
This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method.
A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
arXiv Detail & Related papers (2023-12-28T23:15:18Z) - Constrained Bayesian Optimization Under Partial Observations: Balanced
Improvements and Provable Convergence [6.461785985849886]
We endeavor to design an efficient and provable method for expensive POCOPs under the framework of constrained Bayesian optimization.
We present an improved design of the acquisition functions that introduces balanced exploration during optimization.
We propose a Gaussian process embedding different likelihoods as the surrogate model for a partially observable constraint.
arXiv Detail & Related papers (2023-12-06T01:00:07Z) - Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver.
This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
arXiv Detail & Related papers (2023-01-28T01:50:42Z) - Accelerated Federated Learning with Decoupled Adaptive Optimization [53.230515878096426]
federated learning (FL) framework enables clients to collaboratively learn a shared model while keeping privacy of training data on clients.
Recently, many iterations efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings.
This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs)
arXiv Detail & Related papers (2022-07-14T22:46:43Z) - Automated Evolutionary Approach for the Design of Composite Machine
Learning Pipelines [48.7576911714538]
The proposed approach is aimed to automate the design of composite machine learning pipelines.
It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them.
The software implementation on this approach is presented as an open-source framework.
arXiv Detail & Related papers (2021-06-26T23:19:06Z) - Real-Time Optimization Meets Bayesian Optimization and Derivative-Free
Optimization: A Tale of Modifier Adaptation [0.0]
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes.
The proposed schemes embed a physical model and rely on trust-region ideas to minimize risk during the exploration.
The benefits of using an acquisition function, knowing the process noise level, or specifying a nominal process model are illustrated.
arXiv Detail & Related papers (2020-09-18T12:57:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.