Search

International audience; 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.

This article addresses 2-dimensional layout of high-dimensional biomedical datasets, which is useful for browsing them efficiently. We employ the Isomap technique, which is based on classical MDS (multi-dimensional scaling) but seeks to preserve the intrinsic geometry of the data, as captured in the geodesic manifold distances between all pairs of data points while classical approaches can see just the Euclidean structure. According to first two of Isomap's coordinates, the high-dimensional data points are arranged in a plane. Experimental results with images of marine creatures' shapes and 3D bone renderings are presented.

This article addresses visualization of deformation or shape differences between bones while conventional visualization techniques are often about a single bone such as its 3D reconstruction. Given a pair of bones with a set of corresponding anatomical landmarks, we compute displacement vectors describing the deformation from one bone to the other at the landmark points on one of the bones. Out of these prescribed ones, a displacement vector at each vertex on the bone surface is derived using a multi-level approximation technique of scattered data. Based on the value of inner product between a displacement vector and a surface normal at each vertex, color is mapped. Considering error-prone estimation of the landmark location in real applications, approximations at different levels are realized instead of exact interpolation as usually done in elastic image registration. Experimental results on a pair of femoral bones are presented.