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

在各种与运动相关的应用中产生了大量的复杂数据时间序列。这些数据需要为适当的数据存储、检索和分析定义有效和高效的服务。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，目前的研究计划解决了与使用电子科学视角处理大规模复杂数据时间序列收集的适当系统的规范和实施相关的研究问题。我们期望从体育分析和计算机科学的最新进展中受益。特别是，在特征提取、机器学习和数据融合方面，利用与足球比赛分析相关的体育科学应用中的数据集。

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.