A metaphoric trajectory data warehouse for Olympic athlete follow‐up

Abstract

Sport science is a research discipline that aims to understand exercise and apply scientific methods in support of increasing an athlete's performance. In this paper, we present initial results on modeling, managing and analyzing an athlete's data gathered by sport scientists. An Olympic data warehouse is designed initially to support the monitoring of an athlete's biochemical data. A trajectory data model is extended to represent the athlete's measurements along his/her training states, referred to here as metaphoric trajectories. Furthermore, a data warehouse for metaphoric trajectories is designed and two analysis approaches -a relational and a multidimensional one -are evaluated. We compare both approaches and discuss their benefits to the athlete's follow-up analyses applied by sport scientists.At this stage of the project, Sportomics scientists are interested in evaluating the variation on amino acid concentration in an athlete's blood during a training cycle. The latter plans the athlete's activity by organizing it according to training states (such as rest, training, recovery, etc.). At each training cycle, a set of variables is observed, including glucose, lactate, arterial pressure, etc., and their measures captured at each planned training state. In fact, the process of monitoring athletes' biophysical conditions during their training cycles is considered the athletes' follow-up activity. This paper focuses on proposing a strategy for managing and analyzing the data collected during this process.The measures obtained during athletes' training cycles are interpolated to produce a curve representative of an athlete's performance. The analyses of curves therein give insights about the athlete's physical condition and support for his/her training plan. The volume of data to be analyzed is, however, increasing and will soon hamper current manual analytical procedures. If we consider that other research areas are expected to join the project, managing and supporting the athlete's biophysical measure analyses will become impractical in a nonautomated scenario.In this context, a data warehouse for storing, managing and analyzing an Olympic athlete's followup data has been designed. The system aims to fulfill the following requirements: (i) to represent the continuous nature of a follow-up procedure; (ii) to express analytical queries over follow-up data; (iii) to permit data from different disciplines to be represented; and (iv) to provide an acceptable response time when running analytical queries.The data warehouse implements a multidimensional model [2] whose dimensions qualify an athlete's biophysical and biochemical measures. These measures collected during a training cycle, are modeled using a trajectory conceptualization, in which each trajectory point, a stop, corresponds to a measurement value, and the trajectory curve describes the behavior of the athlete between two measurements.The trajectory model allows scientists to express queries on the continuous nature of their measures, such as identifying ...

Middleware for Clouds and e‐Science

“…Porto et al , present a trajectory data model that has recently been adopted as a semantic enrichment of traditional time‐series values. This paper applies this model to the follow‐up of Olympic athlete's data.…”

“…Relevant contributions have been provided by Beloglazov and Buyya , Veiga et al , Klôh et al , Mathias and Baude , de Alencar et al , Porto et al , Ogasawara et al , and Oliveira et al . These contributions focus on: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers; A checkpointing‐enabled and resource‐aware Java Virtual Machine (VM) for efficient and reliable e‐science applications in grid environments; A bicriteria scheduling process with Class of Services support on Grids and Clouds; A component‐based middleware for hybrid Grid/Cloud computing platforms; A peer‐to‐peer scheduling mechanism for workflows in grid computing; A metaphoric trajectory data warehouse for Olympic follow‐up; A provenance management system for scientific workflows; and An adaptive parallel execution strategy for Cloud‐based scientific workflows.…”

A sportomic follow-up of a muscle injury succeeded by acetaminophen hepatotoxicity

“…It can also be used for observational research. Generally speaking, our study is holistic and topdown, treating data in a systematic way and generating a large amount of data with a large computational effort 5 . Back pain is one of the most prevalent injuries in high-level cyclists [6][7][8] .…”

“…In fact, the use of Met combined with APAP has been proposed and defended as 'safe' APAP 17 . Here, we describe the use of sportomics methodology combined with a wide sports data bank 5 to diagnose, produce an intervention and follow the evolution of hepatotoxicity caused by a large APAP dose to treat back pain. This approach allowed …”