ACM Transactions on Knowledge Discovery from Data

TKDD欢迎关于知识发现和各种形式数据分析的全方位研究的论文。这些主题包括但不限于：数据挖掘和大数据分析的可扩展和有效算法、挖掘大脑网络、挖掘数据流、挖掘多媒体数据、挖掘高维数据、挖掘文本、Web和半结构化数据、挖掘时空数据、社区生成的数据挖掘、社会网络分析。分析和图形结构化数据、数据挖掘中的安全和隐私问题、可视化、交互式和在线数据挖掘、数据挖掘的预处理和后处理、健壮和可扩展的统计方法、数据挖掘语言、数据挖掘的基础、KDD框架和过程，以及利用DAT的新型应用程序和基础设施。包括大规模并行处理和云计算平台的挖掘技术。TKDD鼓励在计算机、并行或多处理计算机或新数据设备的大型分布式网络环境中探讨上述主题的论文。TKDD还鼓励那些描述当前数据挖掘技术无法满足的新兴数据挖掘应用程序的论文。TKDD欢迎那些既为数据挖掘、大数据奠定理论基础，又为大规模数据挖掘系统和工具、数据挖掘接口工具和与整体信息处理基础设施集成的数据挖掘工具的设计和实现提供新见解的论文。TKDD还接受描述用户和数据挖掘开发人员以及大型现实数据挖掘应用程序中的管理经验和问题的论文。强调理论与实践的结合是鼓励理论论文的作者考虑理论结果的适用性和/或可实现性，同时鼓励系统论文的作者反思可能用于构建系统和/或就问题提供建议的理论结果。这可能需要理论上的处理。TKDD还要求对与TKDD相关的主题进行重点调查。这些应该很深，有时会很窄，但应该有助于我们理解数据库的一个重要领域或子领域。针对广泛的计算机科学受众或可能影响其他计算研究领域的调查的更一般的调查应继续进行ACM计算调查。对数据挖掘研究最新进展的简要调查更适合于ACM Sigkdd的勘探。TKDD调查应该通过提供一个相对成熟的数据库研究机构来教育数据库的读者。有关TKDD将接受的论文类型的更多信息，请参阅编辑指南。国际编辑委员会由该领域各子领域的公认专家组成，所有这些专家都承诺将TKDD作为该领域的首要出版物。论文应以电子方式提交给ACM TKDD手稿中心。编委会与ACM的知识发现和数据挖掘特别兴趣小组（SIGKDD）以及其他协会保持联系，鼓励提交高级和原始论文。在适当情况下，可以将简明的结果作为技术说明提交；也欢迎对早期出版物的技术评论。该杂志出现在ACM数字图书馆，因此可供许多个人和机构的数字图书馆用户使用。TKDD也将被收录在sigkdd选集和sigkdd数字研讨会的cdrom出版物中。这些分散的媒体（打印、web、cdrom、dvdrom）广泛分布，确保知识发现和数据挖掘研究人员可以轻松获得TKDD文章。TKDD的存在有助于定义知识发现和数据挖掘研究领域。它包括抽象和模型的开发、形式化和验证，以描述数据挖掘应用程序，以及用于知识发现和自动分析大量数据的设计和实现方法。

TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but not limite to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.TKDD welcomes papers that both lay theoretical foundations for data mining, big data and those that provide new insights into the design and implementation of large-scale data mining systems and tools, data mining interface tools, and data mining tools that integrate with the overall information processing infrastructure. TKDD also accepts papers that describe user and data mining developer and administration experiences and issues in large-scale real-world data mining applications. The emphasis on integration of theory and practice is an attempt to encourage authors of theory papers to consider applicability and/or implementability of the theoretical results, while encouraging authors of systems papers to reflect on the theoretical results that may have been used in building the systems and/or to offer suggestions on issues that may require theoretical treatment.TKDD also solicits focused surveys on topics relevant to TKDD. These should be deep and will sometimes be quite narrow, but should make a contribution to our understanding of an important area or subarea of databases. More general surveys that are intended for a broad-based Computer Science audience or surveys that may influence other areas of computing research should continue to go to ACM Computing Surveys. Brief surveys on recent developments in data mining research are more appropriate for ACM SIGKDD Explorations. TKDD surveys should be educational to the database audience by presenting a relatively well-established body of database research.For additional information on the types of papers TKDD will accept, see Editorial Guidelines.The international Editorial Board is composed of recognized experts in the various subareas of this field, all with a commitment to maintain TKDD as the premier publication in this active field. Papers should be submitted electronically to ACM TKDD manuscript center. The Editorial Board maintains contact with ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), as well as with other societies, to encourage submittal of advanced and original papers. When appropriate, concise results may be submitted as technical notes; technical comments on earlier publications are welcome as well.The journal appears in the ACM Digital Library and is thus available to the many individual and institutional DL subscribers. TKDD will be also included in the SIGKDD Anthology and SIGKDD Digital Symposium Collection CDROM publications. These disparate media (print, web, CDROM, DVDROM), widely distributed, ensure that TKDD articles are easily available to knowledge discovery and data mining researchers.The existence of TKDD has helped to define the field of knowledge discovery and data mining research. It encompasses the development, formalization, and validation of abstractions and models to describe data mining applications and the design and implementation methods for knowledge discovery and automated analysis of large amount of data.