Recently, low Earth orbit (LEO) satellites have been suggested as a promising solution for global coverage of 5G networks. However, long propagation delay makes obtained channel state information (CSI) (which is employed for efficient spectrum utilization) outdated before usage. Note that CSI is closely related to elevation angle and relative location. Although elevation angle and relative location could be estimated by terrestrial device position and LEO ephemeris, maintaining these two informations requires a high cost for most IoT devices. Due to regular and high-speed orbital movement of LEO, for an arbitrary terrestrial device, LEOs repeat the same behavioral pattern (e.g., satellite rise and satellite set) again and again. Satellite rise and set result in different temporal correlations in elevation angle and relative location. By the aid of the temporal correlations mentioned above, we proposed a CSI prediction scheme for satellite-terrestrial networks without terrestrial device position and LEO ephemeris. Simulation results show that our scheme has a rather low prediction error under various LEO altitude/obit, terrestrial device location/mobility, ground environments, weather, and elevation angle when handover.

Cloud computing has become increasingly popular model for delivering applications hosted in large data centers as subscription oriented services. Hadoop is a popular system supporting the MapReduce function, which plays a crucial role in cloud computing. The resources required for executing jobs in a large data center vary according to the job type. In Hadoop, jobs are scheduled by default on a first-come-first-served basis, which may unbalance resource utilization. This paper proposes a job scheduler called the job allocation scheduler (JAS), designed to balance resource utilization. For various job workloads, the JAS categorizes jobs and then assigns tasks to a CPU-bound queue or an I/O-bound queue. However, the JAS exhibited a locality problem, which was addressed by developing a modified JAS called the job allocation scheduler with locality (JASL). The JASL improved the use of nodes and the performance of Hadoop in heterogeneous computing environments. Finally, two parameters were added to the JASL to detect inaccurate slot settings and create a dynamic job allocation scheduler with locality (DJASL). The DJASL exhibited superior performance than did the JAS, and data locality similar to that of the JASL.

Improving PCM endurance is a fundamental issue when it is considered as an alternative to replace DRAM as main memory. Memory-based wear leveling (WL) is an effective way to improve PCM endurance, but its major challenge is how to efficiently determine the appropriate memory pages for allocation or swapping. In this article, we present a constant-cost WL design that is compatible with existing memory management. Two implementations, namely bucket-based and array-based WL, with constant-time (or nearly zero) search cost are proposed to be integrated into the OS layer and the hardware layer, respectively, as well as to trade between time and space complexity. The results of experiments conducted based on an implementation in Android, as well as simulations with popular benchmarks, to evaluate the effectiveness of the proposed design are very encouraging.

Financial statements of all listed and over the counter (OTC) companies collected by stock exchange represent valuable big open data. Therefore, automatic processing and analyzing such big data would create tremendous added value economically. In this paper, we design and implement a cloud service for evaluating company's financial health using XBRL-based financial statements collected by Taiwan Stock Exchange Corporation (TWSE). The XBRL-based financial statements are parsed and stored as key-value pairs into MongoDB, which is a kind of NoSQL database. The proposed system is designed using three-tier architecture for flexibility and maintainability. It also provides user-friendly interface with various charts. The proposed system indeed demonstrates powerful benefits of implementing interactive data using XBRL for financial disclosure documents in big data era.

Improving the endurance of PCM is a fundamental issue when the technology is considered as an alternative to main memory usage. In the design of memory-based wear leveling approaches, a major challenge is how to efficiently determine the appropriate memory pages for allocation or swapping. In this paper, we present an efficient wear-leveling design that is compatible with existing virtual memory management. Two implementations, namely, bucket-based and array-based wear leveling, with nearly zero search cost are proposed to tradeoff time and space complexity. The results of experiments conducted based on popular benchmarks to evaluate the efficacy of the proposed design are very encouraging.