一个精心策划的多频道电视节目日程表数据集,用于优化和基准测试
Kadri Sylejmani1, Shefket Bylygbashi1, Uran Lajçi1
1Faculty of Electrical and Computer Engineering, University of Prishtina, Bregu i Diellit p.n, 10000 Prishtina, Kosova.
Data in brief
|February 24, 2026
概括
本研究引入了来自不同来源的515个智能电视调度实例的新数据集. 该资源有助于对比优化和基于约束的调度方法,以提高程序指南的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 智能电视程序指南需要高效的调度算法.
- 现有的数据集可能缺乏多样性或现实世界的复杂性.
- 自动数据收集和标准化对于大规模分析至关重要.
研究的目的:
- 提供515个智能电视调度实例的综合数据集.
- 为基准测试调度算法提供标准化格式.
- 促进优化和基于约束的调度研究.
主要方法:
- 数据收集自IPTV EPG,EPG.PW和YouTube数据API. 这些数据来自IPTV EPG,EPG.PW和YouTube数据API.
- 用于数据采集和处理的自动化Python脚本.
- 实例标准化为JSON格式,具有一致的调度期,频道,程序详细信息和质量评分.
主要成果:
- 现在可以使用515个不同的智能电视调度实例的数据集.
- 每个实例包括调度周期,频道信息,不重叠的节目时段,类别和质量评分.
- 实例包含严格的可用性规则和灵活的时间偏好.
结论:
- 数据集作为优化和基于约束的调度的有价值的基准.
- 它可以在依赖时间的约束和客观表述下测试算法.
- 这些数据有助于特征提取,用于数据集的表征和缩小维度.
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