Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Global Intermedia Agenda Setting: A Big Data Analysis of International News Flow

This study contributes to international news flow literature methodologically, by significantly expanding its scope, and theoretically, by incorporating intermedia agenda-setting theory, through which we reveal how news media in different countries influence each other in covering international news. With a big data analysis of 4,708 online news sources from 67 countries in 2015, the study shows that wealthier countries not only continue to attract most of the world news attention, they are also more likely to decide how other countries perceive the world. However, international news flow is not as hierarchical and U.S.-centric as found earlier. Online-only, emerging media in core countries are not necessarily more impactful in setting the world news agenda than those in (semi)peripheral countries.     

Mobile contact synchronous with cloud server

Mobile industry promotes new products every recent year. It is reported that consumers in high-income countries typically replace their mobile phones at intervals of between 12 and 18 months. How to transfer data from one mobile to another and how to share data between different mobile apps are taken into consideration. We build a cloud service to combine contacts from different mobiles and synchronize contacts in different mobiles. Some algorithms and data structures are designed to meet the needs of data combination and synchronization. The result shows that our method can provide more efficient result than other solutions.     

Constructing gazetteers from volunteered Big Geo-Data based on Hadoop

Traditional gazetteers are built and maintained by authoritative mapping agencies. In the age of Big Data, it is possible to construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI) from the Web. In this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem to harvest crowd-sourced gazetteer entries. Using experiments based on geotagged datasets in Flickr, we find that the MapReduce-based workflow running on the spatially enabled Hadoop cluster can reduce the processing time compared with traditional desktop-based operations by an order of magnitude. We demonstrate how to use such a novel spatial-computing infrastructure to facilitate gazetteer research. In addition, we introduce a provenance-based trust model for quality assurance. This work offers new insights on enriching future gazetteers with the use of Hadoop clusters, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analytics.     

Large-scale incremental processing with MapReduce

An important property of today’s big data processing is that the same computation is often repeated on datasets evolving over time, such as web and social network data. While repeating full computation of the entire datasets is feasible with distributed computing frameworks such as Hadoop, it is obviously inefficient and wastes resources. In this paper, we present HadUP (Hadoop with Update Processing), a modified Hadoop architecture tailored to large-scale incremental processing with conventional MapReduce algorithms. Several approaches have been proposed to achieve a similar goal using task-level memoization. However, task-level memoization detects the change of datasets at a coarse-grained level, which often makes such approaches ineffective. Instead, HadUP detects and computes the change of datasets at a fine-grained level using a deduplication-based snapshot differential algorithm (D-SD) and update propagation. As a result, it provides high performance, especially in an environment where task-level memoization has no benefit. HadUP requires only a small amount of extra programming cost because it can reuse the code for the map and reduce functions of Hadoop. Therefore, the development of HadUP applications is quite easy.     

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

Abstract—This article presents an efficient multi-objective optimization approach based on the supervised big bang–big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The MathWorks, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.     

实时超高清电影模式检测算法及其实现

为了解决当帧率上变换算法遇见超高清电影模式序列时,由于重复帧导致运动矢量场无法收敛到真实轨迹,设计了一种高效率的电影模式检测与实现的方法.将视频图像进行分块不规则采样,对各个子块进行像素直方图统计,根据统计结果来进行电影模式处理,最后经过FPGA硬件验证及RC综合.实验结果证明,该算法设计在实时处理50/60 Hz超高清序列大数据流量时仅需很小的存储器资源,并且可以快捷准确地对电影模式进行实时检测处理,特别是局部运动的序列检测.     

带虚拟LED功能的多屏控制系统设计

本文设计了一种带虚拟LED功能的多屏控制系统。介绍了虚拟LED功能的实现原理和实现方法;阐述了系统启动和虚拟LED功能的实现流程。将虚拟LED功能集成到多屏控制系统中,可以解决指挥中心大屏幕和LED显示屏两套设备、两套软件的问题。带虚拟LED功能的多屏控制器使得大屏幕系统的安装更方便、操作更简便、稳定性更高。     

运营商IT支撑系统“去IOE”思路探讨

结合移动互联网时代电信运营商发展流量经营的背景,从业务驱动的角度出发,提出了运营商IT支撑系统“去IOE”的初步思路,即采用新型互联网化系统架构,在建立云平台与大数据平台的基础上,利用核心系统轻量化、分布式重构等技术手段,逐步摆脱“IOE”的束缚。     

基于电信运营商固网DPI系统的大数据清洗方案探讨

随着大数据时代的到来,传统电信运营商也加入到大数据业务开展的庞大阵营中来。固网DPI系统采集到的HTTP等信息为运营商开展对内、对外的大数据业务提供了重要数据支撑。 由于DPI采集信息中含有大量的垃圾数据,在使用数据前对其进行清洗是非常必要的。文章提出一种针对电信运营商固网HTTP信息的清洗方案,经过现网试点部署验证可到80%以上的清洗率,大大节省了存储空间和网络传输带宽,对运营商开展固网大数据业务具有重要的借鉴意义。