Systems Interoperability Types: A Tertiary Study

• URL: http://arxiv.org/abs/2310.19999v1
• Date: Mon, 30 Oct 2023 20:32:55 GMT
• Title: Systems Interoperability Types: A Tertiary Study
• Authors: Rita S. P. Maciel and Pedro H. Valle and K\'ecia S. Santos and Elisa Y. Nakagawa
• Abstract summary: This work presents an updated panorama of software-intensive systems interoperability with particular attention to its types. We conducted a tertiary study that scrutinized 37 secondary studies published from 2012 to 2023. We found 36 interoperability types associated with 117 different definitions, besides 13 interoperability models and six frameworks in various domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4.0 brings not only new interaction possibilities but also challenges for interoperability. Moreover, confusion and ambiguity in the current understanding of interoperability types exist, hampering stakeholders' communication and decision making. This work presents an updated panorama of software-intensive systems interoperability with particular attention to its types. For this, we conducted a tertiary study that scrutinized 37 secondary studies published from 2012 to 2023, from which we found 36 interoperability types associated with 117 different definitions, besides 13 interoperability models and six frameworks in various domains. This panorama reveals that the concern with interoperability has migrated from technical to social-technical issues going beyond the software systems' boundary and still requiring solving many open issues. We also address the urgent actions and also potential research opportunities to leverage interoperability as a multidisciplinary research field to achieve low-coupled, cost-effective, and interoperable systems.

Related papers

• Uni-Inter: Unifying 3D Human Motion Synthesis Across Diverse Interaction Contexts [59.78384600454231]
  We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios.<n>Uni-Inter introduces the Unified Interactive Volume (UIV), a volumetric representation that encodes heterogeneous interactive entities into a shared spatial field.
  arXiv  Detail & Related papers  (2025-11-17T06:32:38Z)
• Enhancing Systematic Interoperability: Convergences and Mismatches between Web 3.0 and the EU Data Act [7.292771695228123]
  This paper compares the technological interoperability in Web 3.0 with the theoretical framework of legal interoperability established by the EU Data Act.<n>The goal is to align technical interoperability with legal concepts of interoperability, thereby enhancing the practical implementation of systematic interoperability in the next generation of the Web.
  arXiv  Detail & Related papers  (2025-08-10T14:14:46Z)
• Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models [8.08979200534563]
  Real-world applications demand sophisticated multi-turn interactions. Recent advancements in large language models (LLMs) have revolutionized their ability to handle single-turn tasks.
  arXiv  Detail & Related papers  (2025-04-07T04:00:08Z)
• Towards deployment-centric multimodal AI beyond vision and language [67.02589156099391]
  We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions. We identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact.
  arXiv  Detail & Related papers  (2025-04-04T17:20:05Z)
• A Survey on Multi-Turn Interaction Capabilities of Large Language Models [47.05742294162551]
  Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction.
  arXiv  Detail & Related papers  (2025-01-17T05:21:49Z)
• Towards Interoperability Testing of Smart Energy Systems -- An Overview and Discussion of Possibilities [22.57425103682572]
  With smart energy systems, interoperability faces challenges due to integrating differ-ent approaches and technologies. This work analyses existing interoperability test concepts, identi-fies enablers and the potential for harmonisation of procedures.
  arXiv  Detail & Related papers  (2024-11-05T10:42:44Z)
• A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions [1.1458366773578277]
  Research interest in autonomous agents is on the rise as an emerging topic. The challenge lies in enabling these agents to learn, reason, and navigate uncertainties in dynamic environments. Context awareness emerges as a pivotal element in fortifying multi-agent systems.
  arXiv  Detail & Related papers  (2024-02-03T00:27:22Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• A Review of Cooperation in Multi-agent Learning [5.334450724000142]
  Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines. This paper provides an overview of the fundamental concepts, problem settings and algorithms of multi-agent learning.
  arXiv  Detail & Related papers  (2023-12-08T16:42:15Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• Collective Relational Inference for learning heterogeneous interactions [8.215734914005845]
  We propose a novel probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. Overall the proposed model is data-efficient and generalizable to large systems when trained on smaller ones.
  arXiv  Detail & Related papers  (2023-04-30T19:45:04Z)
• Attention Diversification for Domain Generalization [92.02038576148774]
  Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. When applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. We propose a novel Attention Diversification framework, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated.
  arXiv  Detail & Related papers  (2022-10-09T09:15:21Z)
• Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction [97.40338982628094]
  We propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN) which predicts trajectories of heterogeneous agents in multiply categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously. A hierarchical graph attention module is proposed to obtain category-tocategory interaction and agent-to-agent interaction.
  arXiv  Detail & Related papers  (2021-07-31T13:36:04Z)
• Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches [0.0]
  We present the most common multiagent problem representations and their main challenges. We identify five research areas that address one or more of these challenges. We suggest that, for multiagent reinforcement learning to be successful, future research addresses these challenges with an interdisciplinary approach.
  arXiv  Detail & Related papers  (2021-06-29T19:53:15Z)
• Coordination Among Neural Modules Through a Shared Global Workspace [78.08062292790109]
  In cognitive science, a global workspace architecture has been proposed in which functionally specialized components share information. We show that capacity limitations have a rational basis in that they encourage specialization and compositionality.
  arXiv  Detail & Related papers  (2021-03-01T18:43:48Z)
• A Survey on Synchronous Augmented, Virtual and Mixed Reality Remote Collaboration Systems [81.0723729946659]
  The focus of this work is clearly on synchronised collaboration from a distance. A total of 82 unique systems for remote collaboration are discussed, including more than 100 publications and 25 commercial systems.
  arXiv  Detail & Related papers  (2021-02-11T13:33:51Z)

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.