大数据参考文献
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
关于大数据技术的毕业论文
关于大数据技术的毕业论文
简介
这篇毕业论文将研究大数据技术的发展、应用和挑战。
大数据技术已成为当今信息时代的重要组成部分，对各个领域的发展产生了巨大影响。
本文将对大数据技术的基本概念和原理进行阐述，并探讨其在商业、科学、医疗等领域的应用。
同时，还将分析大数据技术面临的挑战和可能的解决方案。
主要内容
1. 大数据技术的基本概念和原理
- 大数据定义和特点
- 大数据处理框架和技术架构
2. 大数据技术在商业领域的应用
- 大数据分析与决策支持
- 大数据驱动的营销和销售
3. 大数据技术在科学研究中的应用
- 大数据在生态学、天文学等领域的应用案例
- 大数据分析对科学研究的影响
4. 大数据技术在医疗领域的应用
- 大数据在疾病预测与治疗中的应用
- 大数据对医疗管理和政策制定的影响
5. 大数据技术面临的挑战和解决方案
- 隐私保护和数据安全性
- 大数据分析方法的改进和优化
结论
本文通过对大数据技术的研究和分析，发现其在商业、科学和医疗领域的应用潜力巨大。
然而，大数据技术也面临着隐私保护、数据安全性和分析方法的挑战。
为了更好地应用大数据技术，需要进一步研究和改进相关的技术和方法。
参考文献
- [1] 蔡银龙. 大数据技术与应用[M]. 清华大学出版社, 2017.
- [2] 陈立辉, 孙剑平. 大数据：理论与算法[M]. 清华大学出版社, 2015.
- [3] 李明. 大数据时代的隐私保护[M]. 清华大学出版社, 2016.。
APA格式参考文献示例
APA格式参考文献示例期刊文章1.一位作者写的文章Hu, L. X. [胡莲香]. (2014). 走向大数据知识服务: 大数据时代图书馆服务模式创新. 农业图书情报学刊(2): 173-177.Olsher, D. (2014). Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding. Neural Networks, 58, 131-147.2.两位作者写的文章Li, J. Z., & Liu, X. M. [李建中, 刘显敏]. (2013). 大数据的一个重要方面: 数据可用性. 计算机研究与发展(6): 1147-1162.Mendel, J. M., & Korjani, M. M. (2014). On establishing nonlinear combinations of variables from small to big data for use in later processing. Information Sciences, 280, 98-110.3. 三位及以上的作者写的文章Weichselbraun, A. et al. (2014). Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge-Based Systems, 69, 78-85.Zhang, P. et al. [张鹏等]. (2013). 云计算环境下适于工作流的数据布局方法. 计算机研究与发展(3): 636-647.专著1.一位作者写的书籍Rossi, P. H. (1989). Down and out in America: The origins of homelessness. Chicago: University of Chicago Press.Wang, B. B. [王彬彬]. (2002).文坛三户：金庸·王朔·余秋雨——当代三大文学论争辨析. 郑州: 大象出版社.2.两位作者写的书籍Plant, R., & Hoover, K. (2014). Conservative capitalism in Britain and the United States: A critical appraisal. London: Routledge.Yin, D., & Shang, H. [隐地, 尚海]. (2001).到绿光咖啡屋听巴赫读余秋雨. 上海: 上海世界图书出版公司.3. 三位作者写的书籍Chen, W. Z. et al. [陈维政等]. (2006).人力资源管理. 大连: 大连理工大学出版社. Hall, S. et al. (1991). Culture, media, language: Working papers in cultural studies, 1972-79 (Cultural studies Birmingham ). London: Routledge.4. 新版书Kail, R. (1990). Memory development in children (3rd ed.). New York: Freeman.编著1. 一位主编编撰的书籍Loshin, D. (Ed.). (2013a). Big data analytics. Boston: Morgan Kaufmann.Zhong, L. F. [钟兰凤] (编). (2014). 英文科技学术话语研究. 镇江: 江苏大学出版社.2. 两位主编编撰的书籍Hyland, K., & Diani, G. (Eds.). (2009). Academic evaluation: Review genres in university settings. London: Palgrave Macmillan.Zhang, D. L., & Zhang, G. [张德禄, 张国] (编). (2011). 英语文体学教程. 北京: 高等教育出版社.3. 三位及以上主编编撰的书籍Zhang, K. D. et al. [张克定等] (编). (2007). 系统评价功能. 北京: 高等教育出版社.Campbell, C. M. et al. (Eds.). (2003). Groups St Andrews 2001 in Oxford: Volume 2.New York: Cambridge University Press.4.书中的文章De la Rosa Algarín, A., & Demurjian, S. A. (2014). An approach to facilitate security assurance for information sharing and exchange in big-data applications. In B.Akhgar & H. R. Arabnia (Eds.), Emerging trends in ICT security(pp. 65-83).Boston: Morgan Kaufmann.He, J. M., & Yu, J. P. [何建敏, 于建平]. (2007). 学术论文引言部分的经验功能分析.张克定等. (编). 系统功能评价(pp. 93-101). 北京: 高等教育出版社.翻译的书籍Bakhtin, M. M. (1981). The dialogic imagination: Four essays(C. Emerson & M.Holquist, Trans.). Austin: University of Texas Press.Le, D. L. [勒代雷]. (2001).释意学派口笔译理论(刘和平译). 北京: 中国对外翻译出版公司.Kontra, M. et al. (2014).语言: 权利和资源(李君, 满文静译). 北京: 外语教学与研究出版社.Wang, R. D., & Yu, Q. Y. [王仁定, 余秋雨]. (2001).吴越之间——余秋雨眼里的中国文化（彩图本）(梁实秋, 董乐天译). 上海: 上海文化出版社.硕博士论文Huan, C. P. (2015). Journalistic stance in Chinese and Australian hard news.Unpublished doctorial dissertation, Macquarie University, Sydney.Wang, X. Z. [王璇子]. (2014). 功能对等视角下的英语长句翻译.南京大学硕士学位论文.注：1.APA格式参考文献中的文章标题、书籍名称，冒号后第一个单词，括号里第一个单词和专有名词的首字母大写，其余单词首字母均小写。
“大数据”时代背景论文计算机信息处理论文
“大数据”时代背景论文计算机信息处理论文摘要：在这个大数据的背景时代下，大数据在计算机信息处理技术中的应用可以有效的提高计算信息处理工作质量与效率，满足计算机用户的使用需求。
前言随着社会不断的发展，联网信息技术的快速发展，大数据的背景时代已经到来，并给人们的日常生活带来了巨大的变化。
并在各个领域中得到了广泛的应用，我们平时所应用的技术软件都于大数据有着重要的关系。
大数据可以做好网络计算机信息的处理与管理工作，只为人们提供一个全新的计算机网络环境，保证计算机信息的处理工作可以顺利进行下去，提高计算机的安全性与稳定性。
一、大数据与计算机信息处理技术的概述随着社会不断的发展，我国互联网技术水平逐渐提高，实现了全球化的发展，互联网信息技术在各个领域中得到了广泛的应用，已经成为了人们日常生活中中要组成部分。
随着互联网信息技术的普及，网络信息数量也逐渐增加，大数据时代已经到来，这对于各行各业的发展管理来说产生了巨大的影响，对于社会的发展更是有着非常重要的意义[1]。
大数据主要以计算机技术为主对一些大规模的数据信息进行处理、分析、存储、使用，满足计算机用户的使用需求。
另外，大数据具有规模较大结构多样化，可以对视频、文字等相关数据信息进行处理，并将其中的信息以一个全新的形式呈现出来，供给计算机用户使用。
在这个大数据的背景时代下在计算机信息处理技术中的应用将原有的处理方式创新、完善，提高信息处理工作质量与效率。
计算机信息处理技术在各个领域中得到了广泛的应用，可以做好数据的收集、传输、分析、应用工作，保证数据信息的科学性与合理性，并通过统一的形式对数据信息进行管理。
而计算机信息处理技术是现代化科学技术中重要组成部分，在现代社会中得到了广泛的应用，主要体现在各个企业的办公管理中，可以满足计算机用户的使用需求，并提高信息处理工作质量与效率，促进企业快速发展[2]。
二、大数据时代下的计算机信息处理技术在这个大数据的背景时代下，大数据是计算机信息处理技术中的应用可以有效的保证数据信息的使用安全，并数据信息中真正的价值体现出来。
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
关于大数据的参考文献
关于大数据的参考文献以下是关于大数据的一些参考文献，这些文献涵盖了大数据的基本概念、技术、应用以及相关研究领域。
请注意，由于知识截至日期为2022年，可能有新的文献发表，建议查阅最新的学术数据库获取最新信息。
1.《大数据时代》作者：维克托·迈尔-舍恩伯格、肯尼思·库克斯著，李智译。
出版社：中信出版社，2014年。
2.《大数据驱动》作者：马克·范·雷尔、肖恩·吉福瑞、乔治·德雷皮译。
出版社：人民邮电出版社，2015年。
3.《大数据基础》作者：刘鑫、沈超、潘卫国编著。
出版社：清华大学出版社，2016年。
4.《Hadoop权威指南》作者：Tom White著，陈涛译。
出版社：机械工业出版社，2013年。
5.《大数据：互联网大规模数据管理与实时分析》作者：斯图尔特·赫哈特、乔·赖赫特、阿什拉夫·阿比瑞克著，侯旭翔译。
出版社：电子工业出版社，2014年。
6.《Spark快速大数据分析》作者：Holden Karau、Andy Konwinski、Patrick Wendell、Matei Zaharia著，贾晓义译。
出版社：电子工业出版社，2015年。
7.《大数据时代的商业价值》作者：维克托·迈尔-舍恩伯格著，朱正源、马小明译。
出版社：中国人民大学出版社，2016年。
8.《数据密集型应用系统设计》作者：Martin Kleppmann著，张宏译。
出版社：电子工业出版社，2018年。
9.《大数据：互联网金融大数据风控模型与实证》作者：李晓娟、程志强、陈令章著。
出版社：机械工业出版社，2017年。
10.《数据科学家讲数据科学》作者：杰夫·希尔曼著，林巍巍译。
出版社：中信出版社，2013年。
这些参考文献覆盖了大数据领域的多个方面，包括理论基础、技术实践、应用案例等。
你可以根据具体的兴趣和需求选择阅读。
大数据参考文献(20201022214159)
大数据研究综述陶雪娇，胡晓峰，刘洋（国防大学信息作战与指挥训练教研部，北京100091）研究机构Gartne:的定义:大数据是指需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。
维基百科的定义：大数据指的是所涉及的资料量规模巨大到无法通过目前主流软件工具，在合理时间内达到撷取、管理、处理并整理成为帮助企业经营决策目的的资讯。
麦肯锡的定义：大数据是指无法在一定时间内用传统数据库软件工具对其内容进行采集、存储、管理和分析的赞据焦合。
图多处理阶段模型2009 2014 1011 mi血5 ^020图1 IDC全球数拯使用量预测数据挖掘的焦点集中在寻求数据挖掘过程中的可视化方法，使知识发现过程能够被用户理解，便于在知识发现过程中的人机交互；研究在网络环境卜的数据挖掘技术，特别是在In ternet上建立数据挖掘和知识发现((DMKD)服务器，与数据库服务器配合，实现数据挖掘；加强对各种非结构化或半结构化数据的挖掘，如多媒体数据、文本数据和图像数据等。
5.1数据量的成倍增长挑战数据存储能力大数据及其潜在的商业价值要求使用专门的数据库技术和专用的数据存储设备，传统的数据库追求高度的数据一致性和容错性，缺乏较强的扩展性和较好的系统可用性，小能有效存储视频、音频等非结构化和半结构化的数据。
目前，数据存储能力的增长远远赶小上数据的增长，设计最合理的分层存储架构成为信息系统的关键。
5.2数据类型的多样性挑战数据挖掘能力数据类型的多样化，对传统的数据分析平台发出了挑战。
从数据库的观点看，挖掘算法的有效性和可伸缩性是实现数据挖掘的关键，而现有的算法往往适合常驻内存的小数据集，大型数据库中的数据可能无法同时导入内存，随着数据规模的小断增大，算法的效率逐渐成为数据分析流程的瓶颈。
要想彻底改变被动局面，需要对现有架构、组织体系、资源配置和权力结构进行重组。
5.3 对大数据的处理速度挑战数据处理的时效性随着数据规模的小断增大，分析处理的时间相应地越来越长，而大数据条件对信息处理的时效性要求越来越高。
大数据参考文献
大数据研究综述陶雪娇，胡晓峰，刘洋(国防大学信息作战与指挥训练教研部，北京100091)研究机构Gartne:的定义:大数据是指需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。
维基百科的定义:大数据指的是所涉及的资料量规模巨大到无法通过目前主流软件工具，在合理时间内达到撷取、管理、处理并整理成为帮助企业经营决策目的的资讯。
麦肯锡的定义:大数据是指无法在一定时间内用传统数据库软件工具对其内容进行采集、存储、管理和分析的赞据焦合。
数据挖掘的焦点集中在寻求数据挖掘过程中的可视化方法，使知识发现过程能够被用户理解，便于在知识发现过程中的人机交互;研究在网络环境卜的数据挖掘技术，特别是在Internet上建立数据挖掘和知识发现((DMKD)服务器，与数据库服务器配合，实现数据挖掘;加强对各种非结构化或半结构化数据的挖掘，如多媒体数据、文本数据和图像数据等。
5.1数据量的成倍增长挑战数据存储能力大数据及其潜在的商业价值要求使用专门的数据库技术和专用的数据存储设备，传统的数据库追求高度的数据一致性和容错性，缺乏较强的扩展性和较好的系统可用性，小能有效存储视频、音频等非结构化和半结构化的数据。
目前，数据存储能力的增长远远赶小上数据的增长，设计最合理的分层存储架构成为信息系统的关键。
5.2数据类型的多样性挑战数据挖掘能力数据类型的多样化，对传统的数据分析平台发出了挑战。
从数据库的观点看，挖掘算法的有效性和可伸缩性是实现数据挖掘的关键，而现有的算法往往适合常驻内存的小数据集，大型数据库中的数据可能无法同时导入内存，随着数据规模的小断增大，算法的效率逐渐成为数据分析流程的瓶颈。
要想彻底改变被动局面，需要对现有架构、组织体系、资源配置和权力结构进行重组。
5.3对大数据的处理速度挑战数据处理的时效性随着数据规模的小断增大，分析处理的时间相应地越来越长，而大数据条件对信息处理的时效性要求越来越高。
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大数据研究综述陶雪娇，胡晓峰，刘洋(国防大学信息作战与指挥训练教研部，北京100091)研究机构Gartne:的定义:大数据是指需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。
维基百科的定义:大数据指的是所涉及的资料量规模巨大到无法通过目前主流软件工具，在合理时间内达到撷取、管理、处理并整理成为帮助企业经营决策目的的资讯。
麦肯锡的定义:大数据是指无法在一定时间内用传统数据库软件工具对其内容进行采集、存储、管理和分析的赞据焦合。
数据挖掘的焦点集中在寻求数据挖掘过程中的可视化方法，使知识发现过程能够被用户理解，便于在知识发现过程中的人机交互;研究在网络环境卜的数据挖掘技术，特别是在Internet上建立数据挖掘和知识发现((DMKD)服务器，与数据库服务器配合，实现数据挖掘;加强对各种非结构化或半结构化数据的挖掘，如多媒体数据、文本数据和图像数据等。
5.1数据量的成倍增长挑战数据存储能力大数据及其潜在的商业价值要求使用专门的数据库技术和专用的数据存储设备，传统的数据库追求高度的数据一致性和容错性，缺乏较强的扩展性和较好的系统可用性，小能有效存储视频、音频等非结构化和半结构化的数据。
目前，数据存储能力的增长远远赶小上数据的增长，设计最合理的分层存储架构成为信息系统的关键。
5.2数据类型的多样性挑战数据挖掘能力数据类型的多样化，对传统的数据分析平台发出了挑战。
从数据库的观点看，挖掘算法的有效性和可伸缩性是实现数据挖掘的关键，而现有的算法往往适合常驻内存的小数据集，大型数据库中的数据可能无法同时导入内存，随着数据规模的小断增大，算法的效率逐渐成为数据分析流程的瓶颈。
要想彻底改变被动局面，需要对现有架构、组织体系、资源配置和权力结构进行重组。
5.3对大数据的处理速度挑战数据处理的时效性随着数据规模的小断增大，分析处理的时间相应地越来越长，而大数据条件对信息处理的时效性要求越来越高。
传统的数据挖掘技术在数据维度和规模增大时，需要的资源呈指数增长，面对PB级以上的海量数据，N1ogN甚至线性复杂度的算法都难以接受，处理大数据需要简单有效的人工智能算法和新的问题求解方法。
5.4数据跨越组织边界传播挑战信息安全随着技术的发展，大量信息跨越组织边界传播，信息安全问题相伴而生，不仅是没有价值的数据大量出现，保密数据、隐私数据也成倍增长，国家安全、知识产权、个人信息等等都面临着前所未有的安全挑战。
大数据时代，犯罪分子获取信息更加容易，人们防范、打击犯罪行为更加困难，这对数据存储的物理安全性以及数据的多副本与容灾机制提出了更高的要求。
要想应对瞬息万变的安全问题，最关键的是算法和特征，如何建立相应的强大安全防御体系来发现和识别安全漏洞是保证信息安全的重要环节。
5.5大数据时代的到来挑战人才资源从大数据中获取价值至少需要三类关键人才队伍:一是进行大数据分析的资深分析型人才;二是精通如何申请、使用大数据分析的管理者和分析家;三是实现大数据的技术支持人才。
此外，由于大数据涵盖内容广泛，所需的高端专业人才小仅包括程序员和数据库工程师，同时也需要天体物理学家、生态学家、数学和统计学家、社会网络学家和社会行为心理学家等。
可以预测，在未来几年，资深数据分析人才短缺问题将越来越突显。
同时，需要具有前瞻性思维的实干型领导者，能够基于从大数据中获得的见解和分析，制定相应策略并贯彻执行。
大数据分析与处理方法分析孔志文(广东省民政职业技术学校，广州510310)二、大数据分析的基本方面大数据分析可以划分为五个基本方而。
一是具有预测性分析能力。
分析员可以通过数据挖掘来更好地理解数据，而预测性分析是分析员在数据挖掘的基础上结合可视化分析得到的结果做出一些预测性的判断。
二是具有数据质量和数据管理能力。
数据管理和数据质量是数据分析的重点，是应用在管理方而的最佳实践，通过数据的标准化流程和工具，可以达到一个预先设定好的高质量的分析结果。
三是具有可视化分析能力。
可视化是服务于分析专家和使用用户的，数据可视化是数据分析的基木要求，它可以通过屏幕显示器直观地展示数据，提供给使用者，还可以让数据自己说话，让使用者听到结果。
四是具有数据挖掘算法。
可视化是给数据专家和使用用户提供的，数据挖掘是给机器使用的，通过集群、分割、孤立点分析等算法，深入数据内部，挖掘使用价值，数据挖掘算法不仅要处理大量的大数据，也要保持处理大数据的运行速度。
五是具有语义引擎。
语义引擎能从“文档”中只能提取信息，解决了非结构化数据多样性带来的数据分析困扰，通过语义引擎，能解析、提取、分析数据，完成使用者所需要的信息提取。
三、大数据处理方法1.大数据处理流程大数据整个处理流程可概括为四步。
一是大数据采集过程。
用户端数据通过多个数据库来接收，用户可以通过这些数据进行简单的查询和处理，在大数据采集过程中，可能有大量的用户来进行访问和操作，并发访问和使用量高，有时可峰值可达上百万，需要采集端部署大量的数据库才能支持止常运行。
二是进行大数据统计和分析过程。
统计和分析是通过对分布式计算集群内存储的数据进行分析和分类汇总，通过大数据处理方法，以满足使用者需求，统计与分析主要特点和挑战是分析所涉及的数据量大，极大地占用系统资源。
三是大数据导入和预处理过程。
因为采集端木身有很多数据库，在统计和分析数据时，如果对这些海量数据进行有效分析，还应该把来自各个前端数据导入集中的大型分布式数据库，也可以导入分布式存储集群，导入后在集群基础上再进行简单的清洗和预处理工作，导入和预处理环节主要特点是导入数据量大，每秒导入量经常达到几百兆，有时会达到千兆级别。
四是大数据挖掘过程。
数据挖掘与统计分析过程不同的是数据挖掘没有预先设定好的主题，主要在依据现有的数据进行计算，从而实现一些高级别数据分析的需求，达到预测效果。
2.大数据处理技术(1) Hadoop架构。
Hadoop是一个能够对大量数据进行分布式处理的软件框架。
Hadoop具有可靠性，能维护多个工作数据副木，可以对存储失败的节点重新分布处理。
它具有高效性，通过并行处理加快处理速度。
具有可伸缩性，能够处理PB级数据。
Hadoop架构的关键点是借助大量PC构成一个PC群难以实现对数据的处理。
处理数据时，现分析数据，后结合分配的相应电脑处理数据，最后整合数据处理结果。
浅谈数据挖掘技术及其应用舒正渝<1.西北师范大学数信学院计算机系，甘肃兰州730070; 2.兰州理工中等专业学校，甘肃兰州730050)摘要:科技的进步，特别是信息产业的发展，把我们带入了一个崭新的信息时代。
数据库管理系统的应用领域涉及到了各行各业，但目前所能做到的只是对数据库中已有的数据进行存储、查询、统计等功能，通过这些数据获得的信息量仅占整个数据库信息量的一小部分，如何才能从中提取有价值的知识，进一步提高信息量利用率，因此需要新的技术来自动、智能和快速地分析海量的原始数据，以使数据得以充分利用，由此引发了一个新的研究方向:数据挖掘与知识发现的理论与技术研究。
数据挖掘技术在分析大量数据中具有明显优势，基于数据挖掘的分析技术在金融、保险、电信等有大量数据的行业已有着广泛的应用。
2数据挖掘的定义数据挖掘(Data Mining)，又称数据库中的知识发现(Knowledge Discovery in Database，简称KDD)，比较公认的定义是由U. M. Fayyad等人提出的:数据挖掘就是从大量的、小完全的、有噪声的、模糊的、随机的数据集中，提取隐含在其中的、人们事先小知道的、但又是潜在的有用的信息和知识的过程，提取的知识表示为概念(Concepts)、规则(Rules)、规律(Regularities)、模式(Patterns)等形式。
数据挖掘是一种决策支持过程，分析各组织原有的数据，做出归纳的推理，从中挖掘出潜在的模式，为管理人员决策提供支持。
3数据挖掘的过程KDD的整个过程包括在指定的数据库中用数据挖掘算法提取模型，以及围绕数据挖掘所进行的预处理和结果表达等一系列的步骤，是一个需要经过反复的多次处理的过程。
整个知识发现过程是由若干挖掘步骤组成的，而数据挖掘仅是其中的一个主要步骤。
整个知识发现的主要步骤有以下几点。
3. 1目标定义阶段要求定义出明确的数据挖掘目标。
目标定义是否适度将影响到数据挖掘的成败，因此往往需要具有数据挖掘经验的技术人员和具有应用领域知识的专家以及最终用户紧密协作，一方面明确实际工作中对数据挖掘的要求，另一方面通过对各种学习算法的对比进而确定可用的算法。
3. 2数据准备阶段数据准备在整个数据挖掘过程中占的比例最大，通常达到60%左右。
这个阶段又可以进一步划分成三个子步骤:数据选择(DataSelection)，数据预处理(Data Processing)和数据变换(Data Transformation)。
数据选择主要指从已存在的数据库或数据仓库中提取相关数据，形成目标数据(Target Data)。
数据预处理对提取的数据进行处理，使之符合数据挖掘的要求。
数据变换的主要目的是精减数据维数，即从初始特征中找出真正有用的特征以减少数据挖掘时要考虑的特征或变量个数。
3. 3数据挖掘阶段这一阶段进行实际的挖掘工作。
首先是算法规划，即决定采用何种类型的数据挖掘方法。
然后，针对该挖掘方法选择一种算法。
完成了上述的准备工作后，就可以运行数据挖掘算法模块了。
这个阶段是数据挖掘分析者和相关领域专家最关心的阶段，也可以称之为真正意义上的数据挖掘。
3. 4结果解释和评估阶段根据最终用户的决策目的对提取的信息进行分析，把最有价值的信息提取出来。
对于数据挖掘阶段发现的模式还要经过用户或机器的评估，对于存在冗余或无关的模式要将其删除;对于小能满足用户要求的模式，则需要退回到上一阶段。
另外，数据挖掘面对的最终用户是人，因此要对发现的模式进行可视化，或者把结果转换为用户易懂的其他方式。
4数据挖掘的研究方向目前研究主要从以卜几个方面开展:<1)针对小同的数据挖掘任务开发专用的数据挖掘系统。
一个功能很强的数据挖掘系统要能够处理各种类型的数据是小现实的，应当根据特定类型数据的挖掘任务构造专用的数据挖掘系统，如关系数据库挖掘，空问数据库挖掘等。
<2)高效率的挖掘算法。
数据挖掘算法必须是高效的，即算法的运行时问必须是可预测的和可接受的，带有指数甚至是中阶多项式的算法，没有实际使用价值。
<3)提高数据挖掘结果的有效性、确定性和可表达性。
对已发现的知识应能准确地描述数据库中的内容，并能用于实际领域。
对有缺陷的数据应当根据小确定性度量，以近似规律或定量规则形式表示出来。
还应能很好地处理和抑制噪声数据和小希望的数据。