Constraints on water vapor content in the lower atmosphere of Mars

<p>By the utilisation of a novel synergistic approach, we constrain the vertical distribution of water vapor on Mars with nadir-pointing instruments. Water vapor column abundances were retrieved simultaneously from PFS (sensing the thermal infrared range) and SPICAM (sensing the near-infrared range) on Mars Express, yielding distinct yet complementary sensitivity to different parts of the atmospheric column. This approach offers a way to study the vertical partitioning of water, which has remained out of the scope of nadir observations made by single instruments covering a specific spectral range. Here we present a global dataset covering all seasons and latitudes, with measurements taken over 8 Martian years. Special focus is given to the high-latitude regions in spring and summer during the sublimation of the seasonal polar cap. A significant deviance was discovered between the retrieved vertical distribution water vapor, and the predicted profile from the Mars Climate Database, which is used as prior. We also show that by exploiting a synergistic retrieval approach, we obtain more accurate water vapor column abundances compared to when only one instrument is used. The synergistic retrieval has a tendency to obtain a lower total column abundance compared to the prior, albeit with a stronger vertical partitioning. This indicates a more prominent low-altitude layer, with more water vapor contained close to the surface than predicted by models.</p>

An empirical study on the evaluation of the RDF storage systems

In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieves highly competitive computational performance against other state-of-the-art methods in terms of load time and query response time.

An Empirical Study on the Evaluation of the RDF Storage Systems

In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieve highly competitive computational performance against other state-of-the-art methods in terms of Load Time and Query Response Time.

Localization of Data Sets in Distributed Database Systems Using Slope-Based Vertical Fragmentation

This chapter explains an algorithm that can perform vertical partitioning of database tables dynamically on distributed database systems. After vertical partitioning, a new algorithm is developed to allocate that fragments to the proper sites. To accomplish this, three major tasks are performed in this chapter. The first task is to develop a partitioning algorithm, which can partition the relation in such a way that it would perform better than most of the existing algorithms. The second task is to allocate the fragments to the appropriate sites where allocating the fragments will incur low communication cost with respect to other sites. The third task is to monitor the change in frequency of queries at different sites as well as same site. If the change in frequency of queries at different sites as well as the same site exceeds the threshold, the re-partitioning and re-allocation are performed.