As sensor networks become increasingly popular, heterogeneous sensor networks are being interconnected into federated sensor networks and provide huge volumes of sensor data to large user communities for a variety of applications. Effective metadata management plays a crucial role in processing and properly interpreting raw sensor measurement data, and needs to be performed in a collaborative fashion. Previous data management work has concentrated on metadata and data as two separate entities and has not provided specific support for joint real-time processing of metadata and sensor data. In this paper we propose a framework that allows effective sensor data and metadata management based on real-time metadata creation and join processing over federated sensor networks. The framework is established on three key mechanisms: (i) distributed metadata joins to allow streaming sensor data to be efficiently processed with their associated metadata, regardless of their location in the network, (ii) automated metadata generation to permit users to define monitoring conditions or operations for extracting and storing metadata from streaming sensor data, (iii) advanced metadata search utilizing various techniques specifically designed for sensor metadata querying and visualization. This framework is currently deployed and used as the backbone of a concrete application in environmental science and engineering, the Swiss Experiment, which runs a wide variety of measurements and experiments for environmental hazard forecasting and warning.
Abstract-Data storage is one of today's fundamental services with companies, universities and research centers having the need of storing large amounts of data every day. Cloud storage services are emerging as strong alternative to local storage, allowing customers to save costs of buying and maintaining expensive hardware. Several solutions are available on the market, the most famous being Amazon S3. However it is rather difficult to access information about each service architecture, performance, and pricing. To shed light on storage services from the customer perspective, we propose a benchmarking methodology, apply it to four popular offers (Amazon S3, Amazon Glacier, Windows Azure Blob and Rackspace Cloud Files), and compare their performance. Each service is analysed as a black box and benchmarked through crafted workloads. We take the perspective of a customer located in Europe, looking for possible service providers and the optimal data center where to deploy its applications. At last, we complement the analysis by comparing the actual and forecast costs faced when using each service.According to collected results, all services show eventual weaknesses related to some workload, with no all-round eligible winner, e.g., some offers providing excellent or poor performance when exchanging large or small files. For all services, it is of paramount importance to accurately select the data center to where deploy the applications, with throughput that varies by factors from 2x to 10x. The methodology (and tools implementing it) here presented is instrumental for potential customers to identify the most suitable offer for their needs.
In this paper, we present our spreadsheet framework, which uses a spreadsheet-like interface for exploring biomedical datasets. The principles and advantages of this class of visualization systems are illustrated, and a case study for the analysis of hip joint congruity is presented. Throughout this use case, we see how end users can compare different datasets, apply parallel operations on data, create analysis templates, and how this helps them in the exploration process.
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