An optimal combination of mechanical means and microwave energy to break rock material could prove beneficial for space applications in terms of large scale production drilling or rock removal processes. In the present paper, application of low power microwave pulses was used to induce thermal cracks in rock samples, before use of mechanical breakage methods. The present investigation is conducted with the scope of possible space bound and terrestrial applications. Experimental studies were carried out to assess the low power (,150 W) microwave susceptibility of terrestrial basaltic rock samples (chosen for its close chemical similarity to lunar and Martian rocks). Point load tests were carried out on the microwaved rock samples in order to determine their strength before and after exposure to microwaves. The preliminary experimental results showed that the basalt rock samples used were quite susceptible to the exposure of low power microwaves. From the point load test results, a decreasing trend is observed in terms of strength of the rock samples with microwave exposure duration. Some rock samples even presented visible macro cracking and even splitting for the longest exposure duration used in this experimental program.
Multi-criteria decision support (MCDS) is crucial in many business and web applications such as web searches, B2B portals and on-line commerce. Such MCDS applications need to report results early; as soon as they are being generated so that they can react and formulate competitive decisions in near real-time. The ease in expressing user preferences in web-based applications has made Pareto-optimal (skyline) queries a popular class of MCDS queries. However, state-of-the-art techniques either focus on handling skylines on single input sets (i.e., no joins) or do not tackle the challenge of producing progressive early output results. In this work, we propose a progressive query evaluation framework ProgXe that transforms the execution of queries involving skyline over joins to be non-blocking, i.e., to be progressively generating results early and often. In ProgXe the query processing (join, mapping and skyline) is conducted at multiple levels of abstraction, thereby exploiting the knowledge gained from both input as well as mapped output spaces. This knowledge enables us to identify and reason about abstractlevel relationships to guarantee correctness of early output. It also provides optimization opportunities previously missed by current techniques. To further optimize ProgXe, we incorporate an ordering technique that optimizes the rate at which results are reported by translating the optimization of tuple-level processing into a job-sequencing problem. Our experimental study over a wide variety of data sets demonstrates the superiority of our approach over state-of-the-art techniques.
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Big Data analytics often include complex queries with similar or identical expressions, usually referred to as Common Table Expressions (CTEs). CTEs may be explicitly defined by users to simplify query formulations, or implicitly included in queries generated by business intelligence tools, financial applications and decision support systems. In Massively Parallel Processing (MPP) database systems, CTEs pose new challenges due to the distributed nature of query processing, the overwhelming volume of underlying data and the scalability criteria that systems are required to meet. In these settings, the effective optimization and efficient execution of CTEs are crucial for the timely processing of analytical queries over Big Data. In this paper, we present a comprehensive framework for the representation, optimization and execution of CTEs in the context of Orca-Pivotal's query optimizer for Big Data. We demonstrate experimentally the benefits of our techniques using industry standard decision support benchmark.
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