期刊信息

The VLDB Journal (VLDBJ)

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影响因子:
3.8
出版商:
Springer
ISSN:
1066-8888
浏览:
26533
关注:
21

征稿

The VLDB Journal (VLDBJ) is an academic journal published by Springer. (ISSN 1066-8888, impact factor 3.8, CCF A).

Aims and scope Aims The VLDB Journal is a bi-monthly journal published on behalf of the VLDB Endowment. Launched in July 1992, the journal is now published both in electronic and printed edition by Springer-Verlag. In addition to its goal as an outlet for high quality and timely research and development results, the journal has two important commitments: to low cost so that it is widely affordable; to a quick publication of accepted papers, so as to publish the most recent and timely results. High quality and timely publication is achieved by employing a large editorial board of internationally known researchers and a thorough review procedure. Each editor handles a relatively small number of papers at any one time, and can pay more attention to quality and timeliness of reviews. Papers are available electronically to subscribers as soon as they are accepted, regardless of the schedule of the paper version. The VLDB Endowment Inc. http://www.vldb.org Scope The journal is dedicated to the publication of scholarly contributions in areas of data management such as database system technology and information systems, including their architectures and applications. Further, the journal’s scope is restricted to areas of data management that are covered by the combined expertise of the journal’s editorial board. Submissions with a substantial theory component are welcome, but the VLDB Journal expects such submissions also to embody a systems component. In relation to data mining, the journal will handle submissions where systems issues play a significant role. Factors that we use to determine whether a data mining paper is within scope include: The submission targets systems issues in relation to data mining, e.g., by covering integration with a database engine or with other data management functionality. The submission’s contributions build on (rather than simply cite) work already published in database outlets, e.g., VLDBJ, ACM TODS, PVLDB, ACM SIGMOD, IEEE ICDE, EDBT. The journal's editorial board has the necessary expertise on the submission's topic. Traditional, stand-alone data mining papers that lack the above or similar characteristics are out of scope for this journal. Criteria similar to the above are applied to submission from other areas, e.g., information retrieval and geographical information systems.
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