CS-Insights: A System for Analyzing Computer Science Research

• URL: http://arxiv.org/abs/2210.06878v1
• Date: Thu, 13 Oct 2022 10:03:52 GMT
• Title: CS-Insights: A System for Analyzing Computer Science Research
• Authors: Terry Ruas and Jan Philip Wahle and Lennart K\"ull and Saif M. Mohammad and Bela Gipp
• Abstract summary: This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives. The dedicated interfaces allow its users to identify trends in research activity, productivity, accessibility, author's productivity, venues' statistics, topics of interest, and the impact of computer science research on other fields.
• Score: 25.523422468372114
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives. The dedicated interfaces allow its users to identify trends in research activity, productivity, accessibility, author's productivity, venues' statistics, topics of interest, and the impact of computer science research on other fields. CS-Insightsis publicly available, and its modular architecture can be easily adapted to domains other than computer science.

Related papers

• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics [1.2374541748245838]
  The study aims to analyze the synergy of Artificial Intelligence (AI) with scientometrics, webometrics, and bibliometrics. Our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication.
  arXiv  Detail & Related papers  (2024-02-22T15:10:02Z)
• The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny? [1.0985060632689174]
  We show that a compute divide has coincided with a reduced representation of academic-only research teams in compute intensive research topics. To address the challenges arising from this trend, we recommend approaches aimed at thoughtfully expanding academic insights.
  arXiv  Detail & Related papers  (2024-01-04T01:26:11Z)
• Mapping Computer Science Research: Trends, Influences, and Predictions [0.0]
  We employ advanced machine learning techniques, including Decision Tree and Logistic Regression models, to predict trending research areas. Our analysis reveals that the number of references cited in research papers (Reference Count) plays a pivotal role in determining trending research areas. The Logistic Regression model outperforms the Decision Tree model in predicting trends, exhibiting higher accuracy, precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2023-08-01T16:59:25Z)
• PyRCA: A Library for Metric-based Root Cause Analysis [66.72542200701807]
  PyRCA is an open-source machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps) It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents.
  arXiv  Detail & Related papers  (2023-06-20T09:55:10Z)
• Literature Review: Computer Vision Applications in Transportation Logistics and Warehousing [58.720142291102135]
  Computer vision applications in transportation logistics and warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential.
  arXiv  Detail & Related papers  (2023-04-12T17:33:41Z)
• Analyzing the State of Computer Science Research with the DBLP Discovery Dataset [0.0]
  We conduct a scientometric analysis to uncover the implicit patterns hidden in CS metadata. We introduce the CS-Insights system, an interactive web application to analyze CS publications with various dashboards, filters, and visualizations. Both D3 and CS-Insights are open-access, and CS-Insights can be easily adapted to other datasets in the future.
  arXiv  Detail & Related papers  (2022-12-01T16:27:42Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
  We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives. The proposed framework can be applied in several domains in humanities and social sciences.
  arXiv  Detail & Related papers  (2022-04-05T12:19:24Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

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.