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ACM Transactions on Knowledge Discovery from Data (TKDD)

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영향력 지수:
4.8
출판사:
ACM
ISSN:
1556-4681
조회:
36234
팔로우:
58

논문 모집

ACM Transactions on Knowledge Discovery from Data (TKDD) is an academic journal published by ACM. (ISSN 1556-4681, impact factor 4.8, CCF B).

ACM Transactions on Knowledge Discovery from Data (TKDD) welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited 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. This journal is published nine times a year. 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 the publication. 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. 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. 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 submission 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 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.
최종 수정: Dou Sun ()

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
Materials DiscoveryElsevier2352-9245
CACM Transactions on Computing for HealthcareACM2637-8051
ACM SIGMIS DatabaseACM0095-0033
AACM Transactions on Computer Systems1.8ACM0734-2071
BData Mining and Knowledge Discovery4.3Springer1384-5810
ACM Transactions on Social ComputingACM2469-7826
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition7.6Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255

관련 학회

CCFICORE약칭정식 명칭투고 마감통보일개최일
BDiscovery ScienceInternational Conference on Discovery Science2026-06-152026-07-212026-10-05
AA*KDDACM SIGKDD Conference on Knowledge Discovery and Data Mining2026-07-192026-11-142027-08-09
CIC3KInternational Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management2026-07-032026-07-172026-10-28
DMKDInternational Conference on Data Mining and Knowledge Discovery2024-04-082024-05-082024-06-07
CAACM ASIACCSACM ASIA Conference on Computer and Communications Security2026-12-112027-03-312027-07-12
BA*ICRAInternational Conference on Robotics and Automation2027-05-24
BA*IJCAIInternational Joint Conference on Artificial Intelligence2026-01-312026-08-15
BICASSPInternational Conference on Acoustics, Speech and Signal Processing2026-09-162027-01-132027-05-16
BA*PODSACM SIGMOD Conference on Principles of DB Systems2026-12-032027-03-012027-06-13
BAICMEInternational Conference on Multimedia and Expo2025-12-312026-03-132026-07-05

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