Using Genetic Algorithm as Test Data Generator for Stored PL/SQL Program Units

PL/SQL is the most common language for ORACLE database application. It allows the developer to create stored program units (Procedures, Functions, and Packages) to improve software reusability and hide the complexity of the execution of a specific operation behind a name. Also, it acts as an interface between SQL database and DEVELOPER. Therefore, it is important to test these modules that consist of procedures and functions. In this paper, a new genetic algorithm (GA), as search technique, is used in order to find the required test data according to branch criteria to test stored PL/SQL program units. The experimental results show that this was not fully achieved, such that the test target in some branches is not reached and the coverage percentage is 98%. A problem rises when target branch is depending on data retrieved from tables;in this case, GA is not able to generate test cases for this branch.

Big Data Analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.

基于MQTT协议的轻量化文本信息分发技术研究

随着卫星通信网络的发展,基于卫星通信网络实现手机、平板电脑等移动通信设备之间的通信成为研究的热点。由于其采用无线电波进行信号传输,且需要经过多个中继站进行信号的转发和处理,导致存在网络延时大、丢包率高以及信道狭窄等问题。针对文本信息在卫星通信网络下的分发过程存在效率低下、可靠性不高的问题,设计一种基于MQTT(Message Queuing Telemetry Transport)协议的轻量化文本信息分发技术。该技术使用MQTT协议作为消息传输协议,在文本信息分发前对MQTT协议进行主题设计、发布订阅机制设计、设备连接设计以及设备心跳设计,确保设备之间的连通性;在文本信息的分发过程中,设计数据校验加密算法、文本信息轻量化处理方法和离线消息存储机制,保证文本信息分发的安全性、可靠性。实验结果表明:相较于传统的基于JSON数据的文本信息分发技术,该技术在提高文本信息分发效率的同时,可确保信息分发的安全性、完整性和稳定性。

具有高价值密度的农业物联网数据区块链压缩存储方案

针对现有区块链物联网体系主要采用链上存储摘要链下保留原始数据的存储方式中链上无法直接获得真实数据的问题,该研究提出了一种具有高价值密度的农业物联网数据区块链压缩存储方案。首先,边缘服务器将收集的同一批数据聚合到同一批事务中;其次,设计离群值可处理的自适应有损压缩方法,在满足用户保真度要求的前提下降低数据冗余;最后,压缩数据上链,实现区块链上物联网数据的高价值密度存储。与现有方案相比,该方案能提高链上存储真实物联网数据的效率。试验结果表明,在使用自适应压缩方法压缩数据时,针对不同的数据集会自适应选取k值,在满足用户重建精度的前提下,获得良好压缩效果,减少链上存储空间,提高了数据价值密度;相对于风速之类频率较高的数据,温湿度、二氧化碳等变化频率较低的数据可以获得较高的压缩比;当假设正常数据压缩比为10:1时,该方案可节省约85%的链上存储空间,相应的价值密度提高了约85%。该研究可为农产品溯源过程中的物联网数据高效存储提供解决方案,为实现分布式农业物联网数据压缩存储提供技术支持。

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

时序数据库关键技术综述

随着工业物联网(industrial Internet of things,IIoT)的不断发展,越来越多的设备和传感器开始连接到网络中,产生了大量的时间序列数据(简称“时序数据”),时序数据爆炸式的增长给数据库管理系统带来了新的挑战:持续高吞吐量数据摄取、低延迟多维度数据查询、高性能时间序列索引以及低成本数据存储.近年来时序数据库技术已经成为一个研究热点,一些学者对时序数据库技术进行了深入的研究,同时出现了一些专门用于管理时序数据的时序数据库,并且已经被应用在多个领域,成为工业物联网中不可缺少的关键组成.现有的时序数据库相关综述侧重于时序数据库的功能和性能比较,以及在特定领域中对时序数据库的选择建议,缺少对时序数据库持久化存储、查询、计算和索引等关键技术的研究,同时这些综述工作出现的时间较早,缺少对现代时序数据库关键技术的研究.对学术界时序数据存储研究和工业界时序数据库进行了全面的调查和研究,凝练了时序数据库的4类关键技术:1)时间序列索引优化技术;2)内存数据组织技术;3)高吞吐量数据摄取和低延迟数据查询技术;4)海量历史数据低成本存储技术.同时分析总结了时序数据库评测基准.最后,展望了时序数据库关键技术在未来的发展方向.

Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments

Digital broadcasting is a novel paradigm for the next generation broadcasting. Its goal is to provide not only better quality of pictures but also a variety of services that is impossible in traditional airwaves broadcasting. One of the important factors for this new broadcasting environment is the interoperability among broadcasting applications since the environment is distributed. Therefore the broadcasting metadata becomes increasingly important and one of the metadata standards for a digital broadcasting is TV-Anytime metadata. TV-Anytime metadata is defined using XML schema, so its instances are XML data. In order to fulfill interoperability, a standard query language is also required and XQuery is a natural choice. There are some researches for dealing with broadcasting metadata. In our previous study, we have proposed the method for efficiently managing the broadcasting metadata in a service provider. However, the environment of a Set-Top Box for digital broadcasting is limited such as low-cost and low-setting. Therefore there are some considerations to apply general approaches for managing the metadata into the Set-Top Box. This paper proposes a method for efficiently managing the broadcasting metadata based on the Set-Top Box and a prototype of metadata management system for evaluating our method. Our system consists of a storage engine to store the metadata and an XQuery engine to search the stored metadata and uses special index for storing and searching. Our two engines are designed independently with hardware platform therefore these engines can be used in any low-cost applications to manage broadcasting metadata.

电子商务数据分析在网店运营中的应用研究

随着互联网的快速发展,电子商务已成为一种不可替代的商业模式。在电商领域,数据分析不仅可帮助企业更好地了解市场需求和消费者行为,还可优化产品和服务,提高营销效果。本研究主要探讨电子商务数据分析在网店运营中的应用。通过对商务数据分析的意义、作用的分析,对比常见的商务数据分析工具,明确商务数据分析的方法与步骤,通过案例列举电子商务数据分析在网店运营的作用,并总结其中的难点以及网店运营中电子商务数据分析报告的撰写步骤与技巧。

基于高密度计算的多核处理器电力芯片低功耗设计系统

多核处理器电力芯片是目前多种系统的重要组成部分,设计低功耗电力芯片,能够更好地保证系统正常运行。目前设计的电力芯片低功耗系统运行速度较慢,功耗难以达到用户要求,为此该文应用高密度计算设计了一种多核处理器电力芯片低功耗系统。兼容系统多核处理器与层次化AHB总线,探索处理器电力芯片的整体结构,集中处理存储数据信息,不断调整系统算法参数,通过高密度分析引入矩阵进行数据解析,确保运行过程的安全性。在分析处理器调度性能的基础上,利用高密度处理对数据进行层次化处理,避免数据冗余造成的系统运行故障。实验结果表明,引入所设计系统后电力芯片功耗减少了60%,加速比达到3.992,可以有效提高电力芯片运行性能。

存储式交通气象传感器数据采集系统

针对交通气象监测等领域对数据采集系统数据安全性与低成本的要求,设计一种基于存储式的气象数据采集系统,系统利用RS485自动收发电路采集前端气象传感器输出的气象监测区域采集时间、能见度、风速、风向、降水、气压、温度、相对湿度等数据信号;以STM32F103主控芯片进行数据处理,通过FatFS文件系统移植、SPI通信实现数据存储,提升对低信号区域气象灾害的监测和预警能力。试验结果表明,此系统能实时采集、解析、存储气象传感器数据,具有数据存储量大、兼容性强、成本低、稳定性高的优势,便于对交通气象传感器进行长期采集、存储、分析。

连锁门店数据驱动运营发展策略

近年来,随着社会新零售模式的发展,连锁门店经营新模式向智能技术、多渠道销售和外卖服务方向转型,传统的管理方法不易处理经营管理中的新问题,而数据可视化技术可以有效地分析大量数据,并从中提取有效信息。文章就连锁门店运营管理分析,目的在于探索当前新零售背景下,连锁企业门店经营发展策略及新模式选择,结合企业具体经营方式,寻找连锁门店数据化运营发展的方法。

Modeling the Physical Parameters of Solenoids for MEMS Applications

The paper is devoted to study of the electrical parameters of the motion parts of the MEMS such as solenoids. The analytical background is given in order to describe the influence of the electrical field components on the forces, which are result of interaction of the electromagnetic (EM) field components with the parts of motion devices of MEMS. The given analytical formulas open the ability to calculate the self-inductance of the microsolenoids of the different kind, as well as the stored energy of such motion devices, that could be used for the modeling and optimization of parameters of running devices of MEMS such as actuators, sensors etc.

基于SATA的嵌入式高速大容量数据存储系统设计

在高清晰油气管道检测等应用场合,需要快速存储大量数据,使用传统的传输总线和Flash存储器已经难以满足要求,为了解决这个问题,本文介绍了一种应用于嵌入式系统中、基于SATA接口的高速大容量数据存储系统的设计。本设计基于高速SATA接口技术,以高速DSP为微控制器,以大容量电子盘为存储介质。本设计的存储速度可以达到32Mbit/s,存储容量高达120GByte,很好地解决了嵌入式检测系统中大量数据的快速存储问题。

面向物联网传感器信息的数据分配策略

物联网作为国内外新兴的热门技术,正在深刻地影响着人们的生产生活,它在带来诸多好处的同时也给信息存储领域带来挑战.物联网信息存储中心需要根据其数据特性结合分布式实时数据库信息存储管理的优点,设计与之相适应的数据存储方案,而数据分配策略作为数据存储方案的关键技术是研究的重点.根据物联网传感器信息的海量性、时空相关性、访问失衡性和连续变化性,需要一种基于时域的数据分配模型与之相适应,以此设计出基于自适应时域负载反馈的动态数据分配策略(adaptive time domain data allocation,ATDA).策略根据数据特征,将静态数据分配问题归约成简单线性规划问题,同时采用自适应时域对负载信息进行反馈,最后设置动态负载门限函数实现数据的动态分配.实验表明,该策略与同类Random、Bubba算法相比,在系统短时域负载均衡(LBST)、系统数据迁移量(DM)方面具有更好的性能.

一种基于Java的ODS与Excel数据交换方法

Microsoft Excel文档是ODS(Operational Data Store,操作型数据仓)的重要数据来源,同时ODS中的数据也需要按照Excel文件格式输出,而Excel的专有文件格式使其与ODS进行数据交换时存在一定困难。在分析Excel文件结构和Jakarta POI-HSSF(Poor Obfuscation Implementation&Horrible Spread Sheet Format)功能基础上,详细描述了基于Java的Excel文档与ODS之间进行数据交换的方法,并介绍了实际实现过程中应注意的事项。

400MSPS嵌入式虚拟数字存储示波器

介绍了一种嵌入式虚拟数字存储示波器的设计原理与实现。该示波器是以高速数据采集技术为基础,具有示波和数据采集的功能。它由预处理电路、A/D转换器、同步动态存储器(SDRAM)、高频时钟发生器和集成于FPGA芯片的SDRAM控制器、逻辑控制器、接口控制器组成,通过PC/104总线与嵌入式CPU主板连接,可完成400MByte/s实时数据采集、每通道64兆字节实时数据存储。

基于PCI总线的高速大容量数据采集卡

介绍了一种基于 PCI总线的高速大容量数据采集卡的设计原理与实现。该采集卡由预处理电路、A/D转换器、同步动态随机存储器 (SDRAM)、高频时钟发生器、集成于 FPGA芯片的 PCI接口控制器和 SDRAM控制器组成。它通过 PCI总线接口与计算机连接 ,可完成 40 0 MHz/s实时数据采集、5 1 2 MB实时数据存储。

基于列存储的大数据分析系统物化策略研究

大数据具有规模大、深度大、宽度大、处理时间短、硬件系统普通化和软件系统开源化特点.针对当前传统数据库在对大数据进行分析时系统性能严重下降、计算效率提升有限的问题,提出一种基于列存储的大数据分析系统物化策略(materialization strategies in MapReduce based on column-store,MSMC).首先,通过引入MapReduce物化代价估计模型,深入分析影响物化效率的各个因素.在此基础上设计了MapReduce分布式环境下的列存储文件格式(MapReduce column-store file,MCF),并在数据加载过程中采用协同定位策略实现对物化数据的存储优化.其次,分别针对不同的物化时机,构建了MapReduce早期物化策略(MapReduce early materialization strategy,MEMS)、MapReduce延迟物化策略(MapReduce late materialization strategy,MLMS)和MapReduce混合物化策略(MapReduce early-late materialization strategy,MELMS).利用自适应物化调整策略对其做了进一步优化.实验结果在证明算法有效的同时,也显示出算法在存储空间和负载能力上都有很好的表现.

多通道高精度实时数据采集存储系统设计

文中设计的记录器是一种具有对现场实时数据进行采集、存储及显示分析等功能的数据采集系统。该系统采用FPGA芯片XC2S200作为系统的核心控制器件来实现对信号的采集和数据的编帧以及存储等。此采集系统已配合测试系统地面测试台应用于某飞行器试验,完成了对其试验参数的采集、记录及监测。

基于ArcEngine和C/S构架的矿产资源管理系统开发

基于组件式GIS(Geographical Information System)思想,在Java平台下,以Oracle为后台数据库,通过以下技术的实施:ArcEngine实现地图表现、Java存储过程实现Oracle远程逻辑备份与恢复、ArcSDE实现空间数据存储,成功开发了攀枝花市矿产资源管理系统,并取得良好应用效果。系统的应用和实践促进了该市矿政管理信息化建设进程。