To reflect upon feedback from previous years we will advice reviewers to extend the constructive feedback given within each paper review, in accordance with the IEEE conference guidelines. We aim at a fair, objective and transparent review process. To increase transparency for both reviewers and authors and to enhance the quality of submissions, please find the review criteria that serve as guidelines for reviewers and authors alike published below.
Reviewers can find guidelines and technical instructions here.
Papers will be evaluated for relevance to ICDATA, originality, significance, information content, clarity, and soundness on an international level. Each aspect will be evaluated on a scale of 1 (bad – reject) to 10 (excellent – accept) or 10%-100%. Papers need to achieve at least 50% overall score to be accepted without mandatory revisions. Each paper will be refereed by at least two researchers in the topical area. All reviews will be considered for the acceptance / rejection decision. Each reviewer will indicate their expertise as an indicator for confidence in a particular topic area and hence review. The camera-ready papers will be reviewed by one person.
We particularly encourage submissions of industrial applications and case studies from practitioners. To reflect the requirements of an application or project centric case study presentation, these will be subject to different review criteria. In particular, they will not be evaluated using predominantly theoretical research criteria of originality etc., but will take general interest and presentation stronger into consideration. The camera-ready papers will be reviewed by one person.
Instructions used in the review process
|Relevance||Is the topic of the paper relevant to the scope of ICDATA and its participants? (or related conferences of CSCE such as ICAI etc) Does it show the potential to stimulate interactive discussion?|
|Originality||How novel and innovative is the paper? A paper presenting methods or application domains not frequently discussed will receive a high mark. This also takes into consideration whether the topic has been published in similar form before. If the paper contains mostly known material, i.e. established methods and well understood application domains, it is not considered very original. Empirical case studies of a particular application domain are often highly original, but may have only limited significance to the field.|
|Significance||Does the paper make a valuable contribution to the theory or the practice of data mining? A high significance indicates a high influence of this research on following publications in the field or applications, implications for practices, policies and future research etc. It represents an indicator of the importance of the findings, regardless of their degree of originality.|
|Content||What is the information content of the paper? Does the paper allow non-experts in the field to comprehend its research objective? ICDATA as part of CSCE is inherently interdisciplinary. Therefore a balanced literature review of relevant aspects, sufficient description of the application domain, methods and established best practices will be considered as good information content.|
|Is the paper technically correct (considering its submission category)? What is the technical quality?
For research papers:
Quality of literature review and statement of research goals. Appropriate use of the most relevant references to indicates orientation within the field. Appropriately chosen and documented methods, logical presentation and analysis of results, findings, inferences and conclusions. Were all technical and technological aspects of the experiments well documented? (reliability) Were results compared to established benchmark practices, methods etc.? Were the results evaluated taking care of established standard procedures (validity)?
For application papers:
Creativity, leadership and excellence in professional practice, demonstrated in teaching, staff development, program or institutional development, educational media or services developments, or learning skills services.
|Clarity||Is the paper well presented and organised? A well presented paper enhances the understanding of the presented content also to non experts in the field. It often shows clear and logical presentation, appropriate style, the standard of English, freedom from errors, ease of reading, correct grammar and spelling, appropriate abstract, adequate use of graphical materials and tables to support ideas & findings, conformance with ICDATA specifications for referencing, length and format details. ICDATA is a highly international conference, so English quality may be substandard. Please indicate mandatory revisions and the need for corrections through a native English speaker, if the content of the paper is still comprehensible. Indicate it if the level of English prohibits an understanding of the thoughts presented.|
|Overall rating||All aspects will be evaluated and combined to an overall rating, providing a suggestion for acceptance or rejection of the paper.
The individual aspects are not all of the same importance and may be weighted to provide a final score.
expertise & confidence
|The combined overall ranking will be weighted with each reviewers expertise in the area. A reviewer’s expertise for a topic indicates how familiar he is with current research, publications, best practices and applications in the field. Is he familiar with the references? Reviewers with a high confidence will be able to evaluate a paper more accurate then a reviewer with little expertise in the field.|
The score may be weighted by reviewer expertise in comparison to the other reviews.
|Detailed comments||Try to provide constructive criticism that allows feedback on what to change for a resubmission or even future submission to other conferences. No arrogance even for abysmal papers, very bad English language etc. You may not need to comment on all aspects. Think of a student learning to ski – just indicate the next steps to alleviate the paper to a higher level. Please indicate spelling mistakes and inconsistencies in equations if there are not too many.
In your comments, please pay particular attention to