A Data-driven Approach for Constructing Multilayer Network-based Service Ecosystem Models

• URL: http://arxiv.org/abs/2004.10383v1
• Date: Wed, 22 Apr 2020 03:21:38 GMT
• Title: A Data-driven Approach for Constructing Multilayer Network-based Service Ecosystem Models
• Authors: Mingyi Liu, Zhiying Tu, Xiaofei Xu, Zhongjie Wang
• Abstract summary: Service ecosystems have become a focus in both research and practice. "Events" are introduced to describe the triggers of service ecosystem evolution. Our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.
• Score: 5.211872784262557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Services are flourishing drastically both on the Internet and in the real world. Additionally, services have become much more interconnected to facilitate transboundary business collaboration to create and deliver distinct new values to customers. Various service ecosystems have become a focus in both research and practice. However, due to the lack of widely recognized service ecosystem models and sufficient data for constructing such models, existing studies on service ecosystems are limited to very narrow scope and cannot effectively guide the design, optimization, and evolution of service ecosystems. We propose a Multilayer network-based Service Ecosystem Model, which covers a variety of service-related elements, including stakeholders, channels, functional and nonfunctional features, and domains, and especially, structural and evolutionary relations between them. "Events" are introduced to describe the triggers of service ecosystem evolution. We propose a data-driven approach for constructing MSEM from public media news and external data sources. Qualitative comparison with state-of-the-art models shows that MSEM has a higher coverage degree of fine-grained elements/relations in service ecosystems and richer semantics for higher interpretability. Experiments conducted on real news corpora show that compared with other approaches, our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.

Related papers

• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Tree-based variational inference for Poisson log-normal models [47.82745603191512]
  hierarchical trees are often used to organize entities based on proximity criteria. Current count-data models do not leverage this structured information. We introduce the PLN-Tree model as an extension of the PLN model for modeling hierarchical count data.
  arXiv  Detail & Related papers  (2024-06-25T08:24:35Z)
• Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification [2.4213557151485396]
  quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. Ground truth labels, such as biodiversity, are very difficult and expensive to measure. proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem.
  arXiv  Detail & Related papers  (2024-06-24T21:38:13Z)
• Balancing Similarity and Complementarity for Federated Learning [91.65503655796603]
  Federated Learning (FL) is increasingly important in mobile and IoT systems. One key challenge in FL is managing statistical heterogeneity, such as non-i.i.d. data. We introduce a novel framework, textttFedSaC, which balances similarity and complementarity in FL cooperation.
  arXiv  Detail & Related papers  (2024-05-16T08:16:19Z)
• A Microservices Identification Method Based on Spectral Clustering for Industrial Legacy Systems [5.255685751491305]
  We propose an automated microservice decomposition method for extracting microservice candidates based on spectral graph theory. We show that our method can yield favorable results even without the involvement of domain experts.
  arXiv  Detail & Related papers  (2023-12-20T07:47:01Z)
• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• Simulating News Recommendation Ecosystem for Fun and Profit [13.980779134063853]
  SimuLine is a simulation platform to dissect the evolution of news recommendation ecosystems. We analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory.
  arXiv  Detail & Related papers  (2023-05-23T14:25:37Z)
• Ecosystem Graphs: The Social Footprint of Foundation Models [64.02855828418608]
  We propose Ecosystem Graphs as a documentation framework to transparently centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical (e.g. how Bing relies on GPT-4) and social (e.g. how Microsoft relies on OpenAI) relationships.
  arXiv  Detail & Related papers  (2023-03-28T07:18:29Z)
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning [54.55119659523629]
  Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere.
  arXiv  Detail & Related papers  (2020-10-15T14:12:26Z)
• Modeling Stakeholder-centric Value Chain of Data to Understand Data Exchange Ecosystem [0.12891210250935145]
  We propose a model describing the stakeholder-centric value chain (SVC) of data by focusing on the relationships among stakeholders in data businesses. The SVC model enables the analysis and understanding of the structural characteristics of the data exchange ecosystem.
  arXiv  Detail & Related papers  (2020-05-22T05:04:08Z)
• Community-Based Service Ecosystem Evolution Analysis [4.770906657995415]
  Service ecosystem is a complex dynamic system with continuous evolution. Existing studies on service ecosystem evolution are more about facilitating programmers to use services. We present a method for analyzing service ecosystem evolution patterns from the perspective of the service community.
  arXiv  Detail & Related papers  (2020-05-06T11:00:04Z)

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.