Collaborative Problem Solving on a Data Platform Kaggle

• URL: http://arxiv.org/abs/2107.11929v1
• Date: Mon, 26 Jul 2021 02:28:01 GMT
• Title: Collaborative Problem Solving on a Data Platform Kaggle
• Authors: Teruaki Hayashi, Takumi Shimizu, Yoshiaki Fukami
• Abstract summary: Data exchange ecosystem is developed by platform services that facilitate data and knowledge exchange. In this study, we investigate Kaggle, a data analysis competition platform.
• Score: 0.4511923587827301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data exchange across different domains has gained much attention as a way of creating new businesses and improving the value of existing services. Data exchange ecosystem is developed by platform services that facilitate data and knowledge exchange and offer co-creation environments for organizations to promote their problem-solving. In this study, we investigate Kaggle, a data analysis competition platform, and discuss the characteristics of data and the ecosystem that contributes to collaborative problem-solving by analyzing the datasets, users, and their relationships.

Related papers

• Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
  We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions. Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
  arXiv  Detail & Related papers  (2023-12-23T03:31:46Z)
• Collaborative business intelligence virtual assistant [1.9953434933575993]
  This study focuses on the applications of data mining within distributed virtual teams through the interaction of users and a CBI Virtual Assistant. The proposed virtual assistant for CBI endeavors to enhance data exploration accessibility for a wider range of users and streamline the time and effort required for data analysis.
  arXiv  Detail & Related papers  (2023-12-20T05:34:12Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations [1.5029560229270191]
  Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. We conduct 15 semi-structured interviews with industry experts. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.
  arXiv  Detail & Related papers  (2023-02-03T13:09:57Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)
• A Proactive Management Scheme for Data Synopses at the Edge [20.711789781518753]
  The Internet of Things (IoT) with numerous processing nodes present at the Edge Computing ecosystem opens up new pathways to support intelligent applications. Such applications can be provided upon humongous volumes of data collected by IoT devices being transferred to the edge nodes through the network. Various processing activities can be performed on the discussed data and multiple collaborative opportunities between EC nodes can facilitate the execution of the desired tasks. In this paper, we recommend the exchange of data synopses than real data between EC nodes to provide them with the necessary knowledge about peer nodes owning similar data.
  arXiv  Detail & Related papers  (2021-07-22T10:22:37Z)
• 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)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)
• Variable-Based Network Analysis of Datasets on Data Exchange Platforms [0.15229257192293197]
  We apply a network approach with a novel variable-based structural analysis to the metadata of datasets on two data platform services. It was noted that the structures of the data networks are locally dense and highly assortative, similar to human-related net-works.
  arXiv  Detail & Related papers  (2020-03-11T04:42:30Z)

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.