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 ...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.展开更多
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 da...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.展开更多
Radio frequency identification(RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing(CEP) based RFID-enabled retail store ma...Radio frequency identification(RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing(CEP) based RFID-enabled retail store management is studied, particularly focusing on automated shelf replenishment decisions. We define different types of event queries to describe retailer store workflow action over the RFID data streams on multiple tagging levels(e.g., item level and container level). Non-deterministic finite automata(NFA)based evaluation models are used to detect event patterns. To manage pattern match results in the process of event detection, optimization algorithm is applied in the event model to share event detection results. A simulated RFID-enabled retail store is used to verify the effectiveness of the method, experiment results show that the algorithm is effective and could optimize retail store management workflow.展开更多
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 offer...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.展开更多
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 broad...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.展开更多
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...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.展开更多
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(Po...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。展开更多
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(materializatio...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.展开更多
文摘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.
基金supported by two research grants provided by the Karachi Institute of Economics and Technology(KIET)the Big Data Analytics Laboratory at the Insitute of Business Administration(IBAKarachi)。
文摘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.
基金supported by National Social Science Fund (No. 16CTQ013)the Application Fundamental Research Foundation of Sichuan Province, China (No. 2017JY0011)the Key Project of Sichuan Provincial Department of Education, China (No. 2017GZ0333)
文摘Radio frequency identification(RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing(CEP) based RFID-enabled retail store management is studied, particularly focusing on automated shelf replenishment decisions. We define different types of event queries to describe retailer store workflow action over the RFID data streams on multiple tagging levels(e.g., item level and container level). Non-deterministic finite automata(NFA)based evaluation models are used to detect event patterns. To manage pattern match results in the process of event detection, optimization algorithm is applied in the event model to share event detection results. A simulated RFID-enabled retail store is used to verify the effectiveness of the method, experiment results show that the algorithm is effective and could optimize retail store management workflow.
文摘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.
文摘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.
文摘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.
文摘大数据具有规模大、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.
