Data-Driven Approaches for Soccer Match Analysis:an e-Science Perspective

在各种与体育相关的应用中，已经产生了大量的复杂数据时间序列。这些数据需要定义有效和高效的服务，以进行适当的数据存储、检索和分析。术语复杂数据指的是潜在的非结构化、多模态数据，因此涵盖了各种数据类型，如文本、声音、图像、视频、图形或位置信息。因此，本研究建议解决的规范和实施适当的系统，以处理大规模的复杂数据时间序列收集使用电子科学的角度相关的研究问题。我们希望从体育分析和计算机科学的最新进展中受益。特别是，关于利用来自与足球比赛分析相关的体育科学应用的数据集的特征提取、机器学习和数据融合。

Huge volumes of Complex Data time series have been generated in various sport-related applications. These data require the definition of effective and efficient services for appropriate data storage, retrieval, and analysis. The term Complex Data refers to the universe of potentially non-structured, multimodal data, and therefore covers a wide variety of data types such as text, sound, image, video, graphs or positional information. Therefore, the present research proposal addresses research issues related to the specification and implementation of appropriate systems to handle large-scale Complex Data time series collections using an e-Science perspective. We expect to benefit from recent advances in the state of art on sport analysis and computer science. In particular, with respect to feature extraction, machine learning, and data fusion making use of data sets from sport science applications related to soccer match analysis.