大数据分析与隐私保护

大数据为商业创新和社区服务带来了巨大利益.然而,由于大数据分析技术挖掘出的信息可能超出人们想象,隐私问题备受关注.介绍大数据分析方法及支撑架构,剖析大数据的安全与隐私保护相关技术,并提出一种基于云存储的大数据隐私保护方案.     

A Data Quality in Use model for Big Data

Beyond the hype of Big Data, something within business intelligence projects is indeed changing. This is mainly because Big Data is not only about data, but also about a complete conceptual and technological stack including raw and processed data, storage, ways of managing data, processing and analytics. A challenge that becomes even trickier is the management of the quality of the data in Big Data environments. More than ever before the need for assessing the Quality-in-Use gains importance since the real contribution–business value–of data can be only estimated in its context of use. Although there exists different Data Quality models for assessing the quality of regular data, none of them has been adapted to Big Data. To fill this gap, we propose the “3As Data Quality-in-Use model”, which is composed of three Data Quality characteristics for assessing the levels of Data Quality-in-Use in Big Data projects: Contextual Adequacy, Operational Adequacy and Temporal Adequacy. The model can be integrated into any sort of Big Data project, as it is independent of any pre-conditions or technologies. The paper shows the way to use the model with a working example. The model accomplishes every challenge related to Data Quality program aimed for Big Data. The main conclusion is that the model can be used as an appropriate way to obtain the Quality-in-Use levels of the input data of the Big Data analysis, and those levels can be understood as indicators of trustworthiness and soundness of the results of the Big Data analysis.     

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

Lately, the Internet of Things (IoT) application requires millions of structured and unstructured data since it has numerous problems, such as data organization, production, and capturing. To address these shortcomings, big data analytics is the most superior technology that has to be adapted. Even though big data and IoT could make human life more convenient, those benefits come at the expense of security. To manage these kinds of threats, the intrusion detection system has been extensively applied to identify malicious network traffic, particularly once the preventive technique fails at the level of endpoint IoT devices. As cyberattacks targeting IoT have gradually become stealthy and more sophisticated, intrusion detection systems (IDS) must continually emerge to manage evolving security threats. This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning (IDMBOA-DL) algorithm. In the presented IDMBOA-DL model, the Hadoop MapReduce tool is exploited for managing big data. The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets. Finally, the sine cosine algorithm (SCA) with convolutional autoencoder (CAE) mechanism is utilized to recognize and classify the intrusions in the IoT network. A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm. The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.     

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.     

基于云模型理论面向大数据的协作联盟决策评价

针对联盟决策评价中存在较强的不确定性,提出基于云模型理论面向大数据的协作联盟决策评价方法.首先,构建面向大数据的多任务协作联盟多层决策评价架构,依托大数据处理分析平台获取联盟成员的基本评价指标的评价数据,应用逆向云发生器算法生成相应的评价云,并运用综合云运算产生联盟评价指标的云数字特征.然后,结合联盟评价指标权重和任务权重,运用云加权算术平均数算子进行云集结,分别产生单任务联盟决策评价云和多任务协作联盟决策评价云.再对多任务协作联盟备选方案进行决策评价和选优,以确定最优的联盟方案.最后通过实例与D-S证据理论联盟评价方法进行对比,验证文中方法的有效性.     

大数据探讨

大数据的重大意义正逐步被人们认识到。简要介绍大数据,从技术和工具、解决方案和应用案例等方面对大数据进行研究。并对大数据给计算机科学带来的若干问题进行探讨。     

大数据背景下的民政公共服务信息平台研究

大数据具有规模大、种类多、生成速度快、价值巨大但密度低的特点。民政大数据应用就是利用数据分析的方法,从大数据中挖掘有效信息,为民政提供辅助决策,实现大数据价值的过程。主要介绍民政公共服务模型、技术框架、大数据的联机分析和大数据挖掘模型,对民政公共服务数据处理具有一定的参考价值。     

大型零售企业数据传输模型的实现

随着大型零售企业的规模越来越大,数据传输成为大型零售企业MIS系统的核心模块,为了满足大型零售企业数据传输的不同要求,本文提出并实现了一种扩展性很强的数据传输模型.     

平行数据:从大数据到数据智能*

为了解决实际问题,大数据分析处理系统需要获取数据,然而实际场景中收集到的实际数据通常不完备.另外,大多数问题的解决方案通常是由问题引导或者仅仅进行数据分析,运行参数调整和设定带有较大的盲目性,难以达到应用的智能性.为此,文中提出平行数据的概念和框架,根据实际数据经计算实验产生真正的虚拟大数据,结合默顿定律,以期待的解决方案与问题进行广义对偶,引导大数据聚焦到实际问题.实际数据与虚拟数据动态互动,平行演化,形成一个虚实相生、数据动态变化的过程,最终使数据具备智能,进而解决未知的问题.平行数据不但是一种数据表示形式,更是一种数据演化机制与方式,其特色是虚实互动,所有数据的动力学轨迹构成了数据动力学系统.平行数据为数据处理、表示、挖掘和应用提供了一个新的范式.     

大数据环境下的数据迁移技术研究

数据是信息系统运行的基础和核心,是机构稳定发展的宝贵资源。随着信息系统数据量成几何级数增加,特别是在当前大数据环境和信息技术快速发展情况下,海量数据迁移是企业解决存储空间不足、新老系统切换和信息系统升级改造等过程中必须面对的一个现实问题。如何在业务约束条件下,快速、正确、完整地实现海量数据迁移,保障数据的完整性、一致性和继承性,是一个关键研究课题。从海量数据管理的角度,阐述了海量数据迁移方法,比较了不同数据迁移的方案特点。     

大数据时代下计算机专业教学的探索

大数据时代的到来引起了教育领域人士的关注,各高校逐步开始重视相关教学及研究.根据大数据概念及计算机学科联系紧密的特点,能够帮助学习者整理和分析信息,结合实际教学经验,引入计算机专业教学的教育模式、创新教学方法、与时俱进的学习意识等内容来探索大数据时代对计算机专业教学作用及方法,为该专业教学研究人员提供一定参考,也为培养大数据研究人才奠定基础.     

From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

The quality of the data is directly related to the quality of the models drawn from that data. For that reason, many research is devoted to improve the quality of the data and to amend errors that it may contain. One of the most common problems is the presence of noise in classification tasks, where noise refers to the incorrect labeling of training instances. This problem is very disruptive, as it changes the decision boundaries of the problem. Big Data problems pose a new challenge in terms of quality data due to the massive and unsupervised accumulation of data. This Big Data scenario also brings new problems to classic data preprocessing algorithms, as they are not prepared for working with such amounts of data, and these algorithms are key to move from Big to Smart Data. In this paper, an iterative ensemble filter for removing noisy instances in Big Data scenarios is proposed. Experiments carried out in six Big Data datasets have shown that our noise filter outperforms the current state-of-the-art noise filter in Big Data domains. It has also proved to be an effective solution for transforming raw Big Data into Smart Data.     

大数据时代数据分析与信息安全

大数据的价值不仅仅局限于它的初始收集目的,而在于收集后可以用于其他用途并可重复使用。目前,包括美国在内的许多国家,都将大数据分析管理上升到国家战略层面,从国家层面通盘考虑其发展战略。     

对等P2P网络中大数据关键特征挖掘模型仿真

在网络数据管理优化问题的研究中,对等P2P为点对点网络通信.由于数据特征受到主观因素的影响较大,无法形成固定的关联特征,使得关键特征定位往往需要进行较大规模的大数据对比,传统的关联规则方法应用到此网络特征搜索过程时,建立的规则往往较为混乱甚至无规则可言,造成数据特征挖掘耗时,无效挖掘行为较多,效率较低.为此,提出利用Apriori算法的对等P2P网络中大数据关键特征挖掘方法.筛选对等p2p网络中大数据特征,选取聚类中心,并针对聚类中心进行关联性计算,删除关联性较差的特征.根据Apriori算法相关理论,对数据进行连接和剪枝处理,建立大数据关键特征挖掘模型.实验结果表明,利用改进算法进行对等p2p网络中大数据关键特征挖掘,能够提高挖掘的准确性,满足p2p网络的实际需求.     

Editorial for FGCS special issue: Big Data in the cloud

Research associated with Big Data in the Cloud will be important topic over the next few years. The topic includes work on demonstrating architectures, applications, services, experiments and simulations in the Cloud to support the cases related to adoption of Big Data. A common approach to Big Data in the Cloud to allow better access, performance and efficiency when analysing and understanding the data is to deliver Everything as a Service. Organisations adopting Big Data this way find the boundaries between private clouds, public clouds and Internet of Things (IoT) can be very thin. Volume, variety, velocity, veracity and value are the major factors in Big Data systems but there are other challenges to be resolved.The papers of this special issue address a variety of issues and concerns in Big Data, including: searching and processing Big Data, implementing and modelling event and workflow systems, visualisation modelling and simulation and aspects of social media.     

Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

A Data Model for Data Integration

Data integration systems provide a uniform query interface (UQI) to multiple, autonomous data sources [Alon Y. Halevy. Answering queries using views: A survey. The VLDB Journal, 10(4):270–294, 2001]. This paper presents the universal data model (UDM) that captures the semantically salient aspects of relational, entity-relationship, and XML data models. As a consequence, UDM — including its accompanying query language — provides a simple and elegant UQI for integrating data represented in some of the most widely adopted data models.     

一种基于XML的半结构数据模型

半结构数据的模型是对半结构数据进行了有效管理的基础，也是基于XML半结构数据管理系统的基础，首先探讨了半结构数据的表示形式，然后对XML数据模型进行了研究，最后，在以上研究的基础实现了一种基于XML的半结构数据模型。     

大数据时代对高校人才培养模式的影响——以信息系统专业为例

大数据具有数据量巨大、数据形式多样化等特点,大数据时代为教育和学习提供了丰富的信息资源,但也给教育模式和人才培养带来挑战。首先具体说明大数据时代的特点及对高校人才培养的影响,分析大数据时代对信息系统及相应人才的要求,结合教学实践研究大数据背景下信息系统专业的人才培养模式。     

大数据在智慧城市中的应用

伴随智慧城市的发展,多源、异构、冗余的大数据应运而生。利用数据挖掘、决策分析等技术,实现大数据转化为数据资产,必将成为大数据时代智慧城市智能化、互联化的必要手段。阐述了智慧城市概念、智慧城市数据资源及大数据处理技术,分析了智慧城市中的大数据综合应用,阐明了大数据在智慧城市重点行业领域的应用现状,并对其进行展望,为未来智慧城市中大数据的深入应用提供解决方案和思路。