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  • Open Access

    ARTICLE

    An Ui Design Optimization Strategy for General App in Big Data Environment

    Hangjun Zhou1, Jieyu Zhou1,*, Guang Sun2,3, Wangdong Jiang3, Chuntian Luo1, Xiaoping Fan1, Haowen Zhang1, Haoran Zhang1

    Journal of Quantum Computing, Vol.1, No.2, pp. 65-80, 2019, DOI:10.32604/jqc.2019.07238

    Abstract Due to the huge amount of increasing data, the requirements of people for electronic products such as mobile phones, tablets, and notebooks are constantly improving. The development and design of various software applications attach great importance to users’ experiences. The rationalized UI design should allow a user not only enjoy the visual design experience of the new product but also operating it more pleasingly. This process is to enhance the attractiveness and performance of the new product and thus to promote the active usage and consuming conduct of users. In this paper, an UI design More >

  • Open Access

    ARTICLE

    A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

    Hangjun Zhou1,2,*, Guang Sun1,3, Sha Fu1, Wangdong Jiang1, Juan Xue1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 179-192, 2019, DOI:10.32604/cmc.2019.05214

    Abstract With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach More >

  • Open Access

    ARTICLE

    Knowledge Composition and Its Influence on New Product Development Performance in the Big Data Environment

    Chuanrong Wu1,*, Veronika Lee1, Mark E. McMurtrey2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 365-378, 2019, DOI:10.32604/cmc.2019.06949

    Abstract Product innovation is regarded as a primary means for enterprises to maintain their competitive advantage. Knowledge transfer is a major way that enterprises access knowledge from the external environment for new product innovation. Knowledge transfer may face the risk of infringement of the intellectual property rights of other enterprises and the termination of licensing agreements by the knowledge source. Enterprises must develop independent innovation knowledge at the same time they profit from knowledge transfers. Therefore, new product development by an enterprise usually consists of three types of new knowledge: big data knowledge transferred from big… More >

  • Open Access

    ARTICLE

    A Scalable Method of Maintaining Order Statistics for Big Data Stream

    Zhaohui Zhang*,1,2,3, Jian Chen1, Ligong Chen1, Qiuwen Liu1, Lijun Yang1, Pengwei Wang1,2,3, Yongjun Zheng4

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 117-132, 2019, DOI:10.32604/cmc.2019.05325

    Abstract Recently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take the GK algorithm as the subroutine, which is not known to be mergeable. Another drawback is that they can’t maintain the correctness, which means the error will increase during the process of the window sliding. In this paper, we use a novel data structure to store the sketch that maintains the order statistics over sliding windows. Therefore three algorithms have… More >

  • Open Access

    ARTICLE

    Stream-Based Data Sampling Mechanism for Process Object

    Yongzheng Lin1, Hong Liu1, ∗, Zhenxiang Chen2, Kun Zhang2, Kun Ma2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 245-257, 2019, DOI:10.32604/cmc.2019.04322

    Abstract Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive More >

  • Open Access

    ARTICLE

    EIAS: An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

    Yong Xie1, Fang Xu2, Xiang Li1, Songsong Zhang1, Xiaodan Zhang1,*, Muhammad Israr3

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 903-924, 2019, DOI:10.32604/cmc.2019.05309

    Abstract Wireless sensor networks (WSNs) are the major contributors to big data acquisition. The authenticity and integrity of the data are two most important basic requirements for various services based on big data. Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs. However, the process of data acquisitions in WSNs are in open environments, data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence, such as coalition attack. Aimed to provide data authenticity and integrity protection for WSNs, an efficient and secure identity-based aggregate signature scheme (EIAS) More >

  • Open Access

    ARTICLE

    Development of Cloud Based Air Pollution Information System Using Visualization

    SangWook Han1, JungYeon Seo1, Dae-Young Kim2, SeokHoon Kim3, HwaMin Lee3,*

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 697-711, 2019, DOI:10.32604/cmc.2019.06071

    Abstract Air pollution caused by fine dust is a big problem all over the world and fine dust has a fatal impact on human health. But there are too few fine dust measuring stations and the installation cost of fine dust measuring station is very expensive. In this paper, we propose Cloud-based air pollution information system using R. To measure fine dust, we have developed an inexpensive measuring device and studied the technique to accurately measure the concentration of fine dust at the user’s location. And we have developed the smartphone application to provide air pollution More >

  • Open Access

    ARTICLE

    Quantitative Analysis of Crime Incidents in Chicago Using Data Analytics Techniques

    Daniel Rivera Ruiz1,*, Alisha Sawant1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 389-396, 2019, DOI:10.32604/cmc.2019.06433

    Abstract In this paper we aim to identify certain social factors that influence, and thus can be used to predict, the occurrence of crimes. The factors under consideration for this analytic are social demographics such as age, sex, poverty, etc., train ridership, traffic density and the number of business licenses per community area in Chicago, IL. A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area. More >

  • Open Access

    ARTICLE

    Security and Privacy Frameworks for Access Control Big Data Systems

    Paolina Centonze1,*

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 361-374, 2019, DOI:10.32604/cmc.2019.06223

    Abstract In the security and privacy fields, Access Control (AC) systems are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to analyze complex and distributed Big Data (BD) processing cluster frameworks, which are adopted to manage yottabyte of unstructured sensitive data. For instance, Big Data systems’ privacy and security restrictions are most likely to failure due to the malformed AC policy configurations. Furthermore, BD systems were initially developed toped to take care of some of the DB issues to address BD challenges and… More >

  • Open Access

    ARTICLE

    Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

    Ning Cao1,2, Shengfang Li1, Keyong Shen1, Sheng Bin3, Gengxin Sun3,*, Dongjie Zhu4, Xiuli Han5, Guangsheng Cao5, Abraham Campbell6

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 227-241, 2019, DOI:10.32604/cmc.2019.06125

    Abstract Monitoring, understanding and predicting Origin-destination (OD) flows in a city is an important problem for city planning and human activity. Taxi-GPS traces, acted as one kind of typical crowd sensed data, it can be used to mine the semantics of OD flows. In this paper, we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China. The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows. Then based on a novel complex network model, a semantics More >

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