SIAM Data Mining (SDM), 2014: Review Guidelines

Review Guidelines
All accepted papers should be good scientific papers, regardless of their specific area. Each paper will be judged according to the four criteria listed below. A list of questions is also provided under each criterion to help reviewers assess the papers. Reviewers are encouraged to provide their response to the questions under the four criteria listed below in the Detailed Comments section of the review form:

Is the paper technically sound? Are the claims well-supported by theoretical analysis or experimental results? Is this a complete piece of work, or merely a position paper? Are the authors careful (and honest) about evaluating both the strengths and weaknesses of the work?

Is the paper clearly written? Is it well-organized? (If not, reviewers are encoutaged to make suggestions to improve the manuscript.) Does it adequately inform the reader? (A superbly written paper should provide enough information for the expert reader to reproduce its results.)

Are the problems or approaches new? Is this a novel combination of familiar techniques? Is it clear how this work differs from previous contributions? Is related work adequately referenced?

Are the results important? Will other people (practitioners or researchers) likely to use these ideas or build on them? Does the paper address a difficult problem in a better way than previous research? Does it advance the state of the art in a demonstrable way? Does it provide unique data, unique conclusions on existing data, or a unique theoretical or pragmatic approach?

Additional Qualitative Evaluation: Application Papers
Applications require the development of significant domain knowledge, understanding and preparing suitable datasets, evaluating results based on domain specific metrics/skills, and providing suitable perspective on realized as well as potential impact. These aspects are as important as methodological novelty in traditional data mining papers. As a result, the application papers will be evaluated using the following additional criteria:

Problem description
Is the problem clearly described along with a discussion of the state-of-the-art? Is the problem well-motivated? Does the paper fit the scope of the conference?

Has the work been evaluated using domain/specific metrics/skills? Has the method been compared to suitable domain specific baselines? Are there new domain-specific insights due to the new methodology described in the paper?

Impact: Realized and Potential
Does the paper clearly describe the impact of the realized and potential impact of the work? Will the paper motivate other data mining researchers or practitioners to pursue their research in this area?