102,995 research outputs found
A Query, a Minute: Evaluating Performance Isolation in Cloud Databases
Several cloud providers offer reltional databases as part of their portfolio. It is however not obvious how resource virtualization and sharing, which is inherent to cloud computing, influence performance and predictability of these cloud databases. Cloud providers give little to no guarantees for consistent execution or isolation from other users. To evaluate the performance isolation capabilities of two commercial cloud databases, we ran a series of experiments over the course of a week (a query, a minute) and report variations in query response times. As a baseline, we ran the same experiments on a dedicated server in our data center. The results show that in the cloud single outliers are up to 31 times slower than the average. Additionally, one can see a point in time after which the average performance of all executed queries improves by 38 %
Point Cloud Quality Assessment using 3D Saliency Maps
Point cloud quality assessment (PCQA) has become an appealing research field
in recent days. Considering the importance of saliency detection in quality
assessment, we propose an effective full-reference PCQA metric which makes the
first attempt to utilize the saliency information to facilitate quality
prediction, called point cloud quality assessment using 3D saliency maps
(PQSM). Specifically, we first propose a projection-based point cloud saliency
map generation method, in which depth information is introduced to better
reflect the geometric characteristics of point clouds. Then, we construct point
cloud local neighborhoods to derive three structural descriptors to indicate
the geometry, color and saliency discrepancies. Finally, a saliency-based
pooling strategy is proposed to generate the final quality score. Extensive
experiments are performed on four independent PCQA databases. The results
demonstrate that the proposed PQSM shows competitive performances compared to
multiple state-of-the-art PCQA metrics
Is Distributed Database Evaluation Cloud-Ready?
The database landscape has significantly evolved over the last decade as cloud computing enables to run distributed databases on virtually unlimited cloud resources. Hence, the already non-trivial task of selecting and deploying a distributed database system becomes more challenging. Database evaluation frameworks aim at easing this task by guiding the database selection and deployment decision. The evaluation of databases has evolved as well by moving the evaluation focus from performance to distribution aspects such as scalability and elasticity. This paper presents a cloud-centric analysis of distributed database evaluation frameworks based on evaluation tiers and framework requirements. It analysis eight well adopted evaluation frameworks. The results point out that the evaluation tiers performance, scalability, elasticity and consistency are well supported, in contrast to resource selection and availability. Further, the analysed frameworks do not support cloud-centric requirements but support classic evaluation requirements
Infrared composition of the Large Magellanic Cloud
The evolution of galaxies and the history of star formation in the Universe
are among the most important topics in today's astrophysics. Especially, the
role of small, irregular galaxies in the star-formation history of the Universe
is not yet clear. Using the data from the AKARI IRC survey of the Large
Magellanic Cloud at 3.2, 7, 11, 15, and 24 {\mu}m wavelengths, i.e., at the
mid- and near-infrared, we have constructed a multiwavelength catalog
containing data from a cross-correlation with a number of other databases at
different wavelengths. We present the separation of different classes of stars
in the LMC in color-color, and color-magnitude, diagrams, and analyze their
contribution to the total LMC flux, related to point sources at different
infrared wavelengths
Evaluating Point Cloud Quality via Transformational Complexity
Full-reference point cloud quality assessment (FR-PCQA) aims to infer the
quality of distorted point clouds with available references. Merging the
research of cognitive science and intuition of the human visual system (HVS),
the difference between the expected perceptual result and the practical
perception reproduction in the visual center of the cerebral cortex indicates
the subjective quality degradation. Therefore in this paper, we try to derive
the point cloud quality by measuring the complexity of transforming the
distorted point cloud back to its reference, which in practice can be
approximated by the code length of one point cloud when the other is given. For
this purpose, we first segment the reference and the distorted point cloud into
a series of local patch pairs based on one 3D Voronoi diagram. Next, motivated
by the predictive coding theory, we utilize one space-aware vector
autoregressive (SA-VAR) model to encode the geometry and color channels of each
reference patch in cases with and without the distorted patch, respectively.
Specifically, supposing that the residual errors follow the multi-variate
Gaussian distributions, we calculate the self-complexity of the reference and
the transformational complexity between the reference and the distorted sample
via covariance matrices. Besides the complexity terms, the prediction terms
generated by SA-VAR are introduced as one auxiliary feature to promote the
final quality prediction. Extensive experiments on five public point cloud
quality databases demonstrate that the transformational complexity based
distortion metric (TCDM) produces state-of-the-art (SOTA) results, and ablation
studies have further shown that our metric can be generalized to various
scenarios with consistent performance by examining its key modules and
parameters
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