[
  {
    "id": "10.1007/978-981-97-5786-2_17",
    "type": "paper-conference",
    "title": "Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings",
    "author": [
      {
        "family": "Ajibesin",
        "given": "Adeyimi Abel"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      },
      {
        "family": "Joel",
        "given": "Ernest"
      },
      {
        "family": "Rakshit",
        "given": "Sandip"
      }
    ],
    "editor": [
      {
        "literal": "Frank Lin"
      },
      {
        "literal": "David Pastor"
      },
      {
        "literal": "Nishtha Kesswani"
      },
      {
        "literal": "Ashok Patel"
      },
      {
        "literal": "Sushanta Bordoloi"
      },
      {
        "literal": "Chaitali Koley"
      }
    ],
    "container-title": "Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023)",
    "collection-title": "Lecture Notes in Networks and Systems",
    "issued": {
      "date-parts": [
        [
          2024,
          10,
          17
        ]
      ]
    },
    "volume": "1072",
    "page": "221-231",
    "publisher": "Springer Nature Singapore",
    "ISSN": "2367-3370",
    "ISBN": "9789819757862",
    "DOI": "10.1007/978-981-97-5786-2_17",
    "URL": "https://www.narasimharao.net/research/predictive-web-prefetching-clustering-weka-high-traffic/",
    "abstract": "Network congestion poses challenges for internet users, diminishing the utility of accessed information due to reduced speeds. To mitigate this, various strategies such as web mining, caching, and server prefetching have been proposed to enhance internet performance. We introduced a novel prefetching approach tailored for high-traffic environments with minimal server idle times. This method constructs an online navigation graph from preprocessed log data, offering insights into user navigation patterns across the web. Our primary objective was to refine web prefetching techniques for congested digital landscapes. We evaluated the model using log files from specific domain client groups. Our proposed web clustering method surpasses conventional prefetching techniques, especially after a short-lived acceleration when server idle times are sufficient for prefetching across all user predictions. This method, when synergized with web caching, can predict subsequent related web items following a particular web object request.",
    "keyword": "Predictive web prefetching, Network congestion, Clustering algorithms, WEKA toolkit, High-traffic environments, Online navigation graph, Server idle times, Web caching synergy, Domain client groups, web prefetching, web caching, network congestion, clustering algorithms, WEKA, web usage mining, web log analysis, user navigation patterns, Internet performance, Internet of Things",
    "language": "en"
  }
]