On the Behaviour of General-Purpose Applications on Cloud Storages

Managing data over cloud infrastructures raises novel challenges with respect to existing and well studied approaches such as ACID and long running transactions. One of the main requirements is to provide availability and partition tolerance in a scenario with replicas and distributed control. This comes at the price of a weaker consistency, usually called eventual consistency. These weak memory models have proved to be suitable in a number of scenarios, such as the analysis of large data with Map-Reduce. However, due to the widespread availability of cloud infrastructures, weak storages are used not only by specialised applications but also by general purpose applications. We provide a formal approach, based on process calculi, to reason about the behaviour of programs that rely on cloud stores. For instance, one can check that the composition of a process with a cloud store ensures `strong' properties through a wise usage of asynchronous message-passing

Scalable transactions in the cloud: partitioning revisited

Lecture Notes in Computer Science, 6427Cloud computing is becoming one of the most used paradigms to deploy highly available and scalable systems. These systems usually demand the management of huge amounts of data, which cannot be solved with traditional nor replicated database systems as we know them. Recent solutions store data in special key-value structures, in an approach that commonly lacks the consistency provided by transactional guarantees, as it is traded for high scalability and availability. In order to ensure consistent access to the information, the use of transactions is required. However, it is well-known that traditional replication protocols do not scale well for a cloud environment. Here we take a look at current proposals to deploy transactional systems in the cloud and we propose a new system aiming at being a step forward in achieving this goal. We proceed to focus on data partitioning and describe the key role it plays in achieving high scalability.This work has been partially supported by the Spanish Government under grant TIN2009-14460-C03-02 and by the Spanish MEC under grant BES-2007-17362 and by project ReD Resilient Database Clusters (PDTC/EIA-EIA/109044/2008)

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

Cetaceans in the Mediterranean Sea. Encounter rate, dominant species, and diversity hotspots

We investigated the presence and diversity of cetaceans in the Mediterranean Sea, analysing the data collected by 32 different research units, over a period of 15 years (2004–2018), and shared on the common web-GIS platform named Intercet. We used the encounter rate, the species prevalence, and the Shannon diversity index as parameters for data analysis. The results show that cetacean diversity, in the context of the Mediterranean basin, is generally quite low when compared with the eastern Atlantic, as few species, namely the striped dolphin, the bottlenose dolphin, the fin whale, and the sperm whale, dominate over all the others. However, some areas, such as the Alboran Sea or the north-western Mediterranean Sea, which includes the Pelagos Sanctuary (the Specially Protected Area of Mediterranean Interest located in the northern portion of the western basin), show higher levels of diversity and should be considered hotspots to be preserved. Primary production and seabed profile seem to be the two main drivers influencing the presence and distribution of cetaceans, with the highest levels of diversity observed in areas characterized by high levels of primary production and high bathymetric variability and gradient. This collective work underlines the importance of data sharing to deepen our knowledge on marine fauna at the scale of the whole Mediterranean Sea and encourages greater efforts in the networking process, also to accomplish the requirements of the Marine Strategy Framework Directive, with particular reference to Descriptor 1: biological diversity is maintained

Improvement of Human-Plant Interactivity via Industrial Cloud-Based Supervisory Control and Data Acquisition System

Part 1: Knowledge-Based Performance ImprovementInternational audienceIndustrial companies look for the best way to be perfectly optimized, failure resistant, how to handle increasing amounts of information, and have more open and reliable union with their customers and suppliers. This paper focuses on the improvement of industry performance through the integration of SCADA/HMI (Supervisory Control and Data Acquisition/Human Machine Interface), SOA (Serviced Oriented Architecture) and cloud computing. This paper’s contribution is in a failover and high availability solution for small and medium industry companies. The contribution is based on the integration of the cloud and SCADA/HMI, and contains a developed SOA load balancer for better accessibility to the cloud. This whole solution improves performance, reliability and availability. It is cost-effective and includes a mobile device implementation for controlling and monitoring systems