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The MapReduce model is becoming prominent for the large-scale data analysis in the cloud. In this paper, we present the benchmarking, evaluation and characterization of Hadoop, an open-source implementation of MapReduce. We first introduce HiBench, a new benchmark suite for Hadoop. It consists of a set of Hadoop programs, including both synthetic micro-benchmarks and real-world Hadoop applications. We then evaluate and characterize the Hadoop framework using HiBench, in terms of speed (i.e., job running time), throughput (i.e., the number of tasks completed per minute), HDFS bandwidth, system resource (e.g., CPU, memory and I/O) utilizations, and data access patterns.

When planning routes, drivers usually consider a multitude of different travel costs, e.g., distances, travel times, and fuel consumption. Different drivers may choose different routes between the same source and destination because they may have different driving preferences (e.g., time-efficient driving v.s. fuel-efficient driving). However, existing routing services support little in modeling multiple travel costs and personalization-they usually deliver the same routes that minimize a single travel cost (e.g., the shortest routes or the fastest routes) to all drivers.We study the problem of how to recommend personalized routes to individual drivers using big trajectory data. First, we provide techniques capable of modeling and updating different drivers' driving preferences from the drivers' trajectories while considering multiple travel costs. To recommend personalized routes, we provide techniques that enable efficient selection of a subset of trajectories from all trajectories according to a driver's preference and the source, destination, and departure time specified by the driver. Next, we provide techniques that enable the construction of a small graph with appropriate edge weights reflecting how the driver would like to use the edges based on the selected trajectories. Finally, we recommend the shortest route in the small graph as the personalized route to the driver. Empirical studies with a large, real trajectory data set from 52,211 taxis in Beijing offer insight into the design properties of the proposed techniques and suggest that they are efficient and effective.

Pollutant degradation via periodate (IO 4 − )-based advanced oxidation processes (AOPs) provides an economical, energy-efficient way for sustainable pollution control. Although single-atomic metal activation (SMA) can be exploited to optimize the pollution degradation process and understand the associated mechanisms governing IO 4 − -based AOPs, studies on this topic are rare. Herein, we demonstrated the first instance of using SMA for IO 4 − analysis by employing atomically dispersed Co active sites supported by N-doped graphene (N-rGO-CoSA) activators. N-rGO-CoSA efficiently activated IO 4 − for organic pollutant degradation over a wide pH range without producing radical species. The IO 4 − species underwent stoichiometric decomposition to generate the iodate (IO 3 − ) species. Whereas Co 2+ and Co 3 O 4 could not drive IO 4− activation; the Co−N coordination sites exhibited high activation efficiency. The conductive graphene matrix reduced the contaminants/electron transport distance/resistance for these oxidation reactions and boosted the activation capacity by working in conjunction with metal centers. The N-rGO-CoSA/IO 4 − system exhibited a substrate-dependent reactivity that was not caused by iodyl (IO 3• ) radicals. Electrochemical experiments demonstrated that the N-rGO-CoSA/IO 4 − system decomposed organic pollutants via electron-transfer-mediated nonradical processes, where N-rGO-CoSA/periodate* metastable complexes were the predominant oxidants, thereby opening a new avenue for designing efficient IO 4 − activators for the selective oxidation of organic pollutants.

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