Heterogeneous data source integration for smart grid ecosystems based on metadata mining

The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-

Describing and Forecasting Video Access Patterns

Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems.We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. For each type of video we ask two questions: first, are there relatively simple models that can describe its daily access patterns? And second, can we use these simple models to predict the number of accesses that a video will have in the near future, as a tool for capacity planning? To answer these questions we develop two different frameworks for characterization and forecasting of access patterns. We show that for frequently-accessed videos, daily access patterns can be extracted via principal component analysis, and used efficiently for forecasting. For rarely-accessed videos, we demonstrate a clustering method that allows one to classify bursts of popularity and use those classifications for forecasting

Macroscopic Anisotropic Bone Material Properties in Children with Severe \u3cem\u3eOsteogenesis imperfecta\u3c/em\u3e

Children with severe osteogenesis imperfecta(OI) typically experience numerous fractures and progressive skeletal deformities over their lifetime. Recent studies proposed finite element models to assess fracture risk and guide clinicians in determining appropriate intervention in children with OI, but lack of appropriate material property inputs remains a challenge. This study aimed to characterize macroscopic anisotropic cortical bone material properties and investigate relationships with bone density measures in children with severe OI. Specimens were obtained from tibial or femoral shafts of nine children with severe OI and five controls. The specimens were cut into beams, characterized in bending, and imaged by synchrotron radiation X-ray micro-computed tomography. Longitudinal modulus of elasticity, yield strength, and bending strength were 32–65% lower in the OI group (p \u3c 0.001). Yield strain did not differ between groups (p ≥ 0.197). In both groups, modulus and strength were lower in the transverse direction (p ≤ 0.009), but anisotropy was less pronounced in the OI group. Intracortical vascular porosity was almost six times higher in the OI group (p \u3c 0.001), but no differences were observed in osteocyte lacunar porosity between the groups (p = 0.086). Volumetric bone mineral density was lower in the OI group (p \u3c 0.001), but volumetric tissue mineral density was not (p = 0.770). Longitudinal OI bone modulus and strength were correlated with volumetric bone mineral density (p ≤ 0.024) but not volumetric tissue mineral density (p ≥ 0.099). Results indicate that cortical bone in children with severe OI yields at the same strain as normal bone, and that their decreased bone material strength is associated with reduced volumetric bone mineral density. These results will enable the advancement of fracture risk assessment capability in children with severe OI

A genetic approach to Markovian characterisation of H.264 scalable video

We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios