TKDD歡迎關(guān)于知識發(fā)現(xiàn)和各種形式數(shù)據(jù)分析的全方位研究的論文。這些主題包括但不限于：數(shù)據(jù)挖掘和大數(shù)據(jù)分析的可擴(kuò)展和有效算法、挖掘大腦網(wǎng)絡(luò)、挖掘數(shù)據(jù)流、挖掘多媒體數(shù)據(jù)、挖掘高維數(shù)據(jù)、挖掘文本、Web和半結(jié)構(gòu)化數(shù)據(jù)、挖掘時(shí)空數(shù)據(jù)、社區(qū)生成的數(shù)據(jù)挖掘、社會網(wǎng)絡(luò)分析。分析和圖形結(jié)構(gòu)化數(shù)據(jù)、數(shù)據(jù)挖掘中的安全和隱私問題、可視化、交互式和在線數(shù)據(jù)挖掘、數(shù)據(jù)挖掘的預(yù)處理和后處理、健壯和可擴(kuò)展的統(tǒng)計(jì)方法、數(shù)據(jù)挖掘語言、數(shù)據(jù)挖掘的基礎(chǔ)、KDD框架和過程，以及利用DAT的新型應(yīng)用程序和基礎(chǔ)設(shè)施。包括大規(guī)模并行處理和云計(jì)算平臺的挖掘技術(shù)。TKDD鼓勵(lì)在計(jì)算機(jī)、并行或多處理計(jì)算機(jī)或新數(shù)據(jù)設(shè)備的大型分布式網(wǎng)絡(luò)環(huán)境中探討上述主題的論文。TKDD還鼓勵(lì)那些描述當(dāng)前數(shù)據(jù)挖掘技術(shù)無法滿足的新興數(shù)據(jù)挖掘應(yīng)用程序的論文。TKDD歡迎那些既為數(shù)據(jù)挖掘、大數(shù)據(jù)奠定理論基礎(chǔ)，又為大規(guī)模數(shù)據(jù)挖掘系統(tǒng)和工具、數(shù)據(jù)挖掘接口工具和與整體信息處理基礎(chǔ)設(shè)施集成的數(shù)據(jù)挖掘工具的設(shè)計(jì)和實(shí)現(xiàn)提供新見解的論文。TKDD還接受描述用戶和數(shù)據(jù)挖掘開發(fā)人員以及大型現(xiàn)實(shí)數(shù)據(jù)挖掘應(yīng)用程序中的管理經(jīng)驗(yàn)和問題的論文。強(qiáng)調(diào)理論與實(shí)踐的結(jié)合是鼓勵(lì)理論論文的作者考慮理論結(jié)果的適用性和/或可實(shí)現(xiàn)性，同時(shí)鼓勵(lì)系統(tǒng)論文的作者反思可能用于構(gòu)建系統(tǒng)和/或就問題提供建議的理論結(jié)果。這可能需要理論上的處理。TKDD還要求對與TKDD相關(guān)的主題進(jìn)行重點(diǎn)調(diào)查。這些應(yīng)該很深，有時(shí)會很窄，但應(yīng)該有助于我們理解數(shù)據(jù)庫的一個(gè)重要領(lǐng)域或子領(lǐng)域。針對廣泛的計(jì)算機(jī)科學(xué)受眾或可能影響其他計(jì)算研究領(lǐng)域的調(diào)查的更一般的調(diào)查應(yīng)繼續(xù)進(jìn)行ACM計(jì)算調(diào)查。對數(shù)據(jù)挖掘研究最新進(jìn)展的簡要調(diào)查更適合于ACM Sigkdd的勘探。TKDD調(diào)查應(yīng)該通過提供一個(gè)相對成熟的數(shù)據(jù)庫研究機(jī)構(gòu)來教育數(shù)據(jù)庫的讀者。有關(guān)TKDD將接受的論文類型的更多信息，請參閱編輯指南。國際編輯委員會由該領(lǐng)域各子領(lǐng)域的公認(rèn)專家組成，所有這些專家都承諾將TKDD作為該領(lǐng)域的首要出版物。論文應(yīng)以電子方式提交給ACM TKDD手稿中心。編委會與ACM的知識發(fā)現(xiàn)和數(shù)據(jù)挖掘特別興趣小組（SIGKDD）以及其他協(xié)會保持聯(lián)系，鼓勵(lì)提交高級和原始論文。在適當(dāng)情況下，可以將簡明的結(jié)果作為技術(shù)說明提交；也歡迎對早期出版物的技術(shù)評論。該雜志出現(xiàn)在ACM數(shù)字圖書館，因此可供許多個(gè)人和機(jī)構(gòu)的數(shù)字圖書館用戶使用。TKDD也將被收錄在sigkdd選集和sigkdd數(shù)字研討會的cdrom出版物中。這些分散的媒體（打印、web、cdrom、dvdrom）廣泛分布，確保知識發(fā)現(xiàn)和數(shù)據(jù)挖掘研究人員可以輕松獲得TKDD文章。TKDD的存在有助于定義知識發(fā)現(xiàn)和數(shù)據(jù)挖掘研究領(lǐng)域。它包括抽象和模型的開發(fā)、形式化和驗(yàn)證，以描述數(shù)據(jù)挖掘應(yīng)用程序，以及用于知識發(fā)現(xiàn)和自動分析大量數(shù)據(jù)的設(shè)計(jì)和實(shí)現(xiàn)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.