SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos

In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances $\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201

Memory hierarchy characterization of NoSQL applications through full-system simulation

In this work, we conduct a detailed memory characterization of a representative set of modern data-management software (Cassandra, MongoDB, OrientDB and Redis) running an illustrative NoSQL benchmark suite (YCSB). These applications are widely popular NoSQL databases with different data models and features such as in-memory storage. We compare how these data-serving applications behave with respect to other well-known benchmarks, such as SPEC CPU2006, PARSEC and NAS Parallel Benchmark. The methodology employed for evaluation relies on state-of-the-art full-system simulation tools, such as gem5. This allows us to explore configurations unattainable using performance monitoring units in actual hardware, being able to characterize memory properties. The results obtained suggest that NoSQL application behavior is not dissimilar to conventional workloads. Therefore, some of the optimizations present in state-of-the-art hardware might have a direct benefit. Nevertheless, there are some common aspects that are distinctive of conventional benchmarks that might be sufficiently relevant to be considered in architectural design. Strikingly, we also found that most database engines, independently of aspects such as workload or database size, exhibit highly uniform behavior. Finally, we show that different data-base engines make highly distinctive demands on the memory hierarchy, some being more stringent than others.This work was supported in part by the Spanish Government (Secretarıa de Estado de Investigacion, Desarrollo e Innovacion) under Grants TIN2015-66979-R and TIN2016-80512-R

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

Performance assessment of RDF graph databases for smart city services

Abstract Smart cities are providing advanced services aggregating and exploiting data from different sources. Cities collect static data such as road graphs, service description, as well as dynamic/real time data like weather forecast, traffic sensors, bus positions, city sensors, events, emergency data, flows, etc. RDF stores may be used to set up knowledge bases integrating heterogeneous information for web and mobile applications to use the data for new advanced services to citizens and city administrators, thus exploiting inferential capabilities, temporal and spatial reasoning, and text indexing. In this paper, the needs and constraints for RDF stores to be used for smart cities services, together with the currently available RDF stores are evaluated. The assessment model allows a full understanding of whether an RDF store is suitable to be used as a basis for Smart City modeling and applications. The RDF assessment model is also supported by a benchmark which extends available RDF store benchmarks at the state the art. The comparison of the RDF stores has been applied on a number of well-known RDF stores as Virtuoso, GraphDB (former OWLIM), Oracle, StarDog, and many others. The paper also reports the adoption of the proposed Smart City RDF Benchmark on the basis of Florence Smart City model, data sets and tools accessible as Km4City Http://www.Km4City.org , and adopted in the European Commission international smart city projects named RESOLUTE H2020, REPLICATE H2020, and in Sii-Mobility National Smart City project in Italy