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SEWEBAR-CMS: semantic analytical report authoring for data mining results

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SEWEBAR-CMS is a set of extensions for the Joomla! Content Management System (CMS) that extends it with functionality required to serve as a communication platform between the data analyst, domain expert and the report user. SEWEBAR-CMS integrates with existing data mining software through PMML. Background knowledge is entered via a web-based elicitation interface and is preserved in documents conforming to the proposed Background Knowledge Exchange Format (BKEF) specification. SEWEBAR-CMS offers web service integration with semantic knowledge bases, into which PMML and BKEF data are stored. Combining domain knowledge and mining model visualizations with results of queries against the knowledge base, the data analyst conveys the results of the mining through a semi-automatically generated textual analytical report to the end user. The paper demonstrates the use of SEWEBAR-CMS on a real-world task from the cardiological domain and presents a user study showing that the proposed report authoring support leads to a statistically significant decrease in the time needed to author the analytical report.

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  4. Current sphygmomanometers (blood pressure measuring devices) mostly do not use mercury. Some newer devices already give readings in kilopascals (kPa), the SI measure of pressure.

  5. The PMML 4.0 specification states: “This information is not directly needed by a PMML consumer, but in many cases it is helpful for maintenance and visualization of the model. The particular content structure of MiningBuildTask is not defined by PMML”.

  6. For completeness, additional setting (for explanation refer to Rauch and Šimůnek (2005)) was: (a) Coefficient setting: Family status: Subset, length 1-2; BMI: Interval, length 1-3; all other: Subset, length 1-1, (b) Cedent setting: conjunction with minimum length 0; for condition the minimum length was 1.

  7. This information could have been used already in the task setting to prevent all rules involving normal diastolic blood pressure from being generated.

  8. We chose OKS, because it is a commercial-grade software with many deployments, open sourced in 2009.

  9. Depending on practical needs, different tolog queries can adopt different, e.g. looser or stricter definitions of confirmation.

  10. This is a simplifying heuristic replacing focused constraints on negation affecting a(ω a ) and b(ω b ), which could in our experience severely affect the complexity and the execution time of the tolog query.

  11. The remaining students either did not attend the course at all, or left it early in the semester, and thus did not have competence to answer the questions.

  12. All tasks were assumed to be accomplished by team work, but it was not strictly enforced.

  13. As outliers we considered points located more than 1.5 interquartile ranges below the 1st or above the 3rd quartiles.

  14. In this survey we omit approaches that consider background knowledge in numerical form, such as prior probability estimates or expertise-driven parameter setting for mining tools.

  15. We also omit knowledge-intensive, computationally costly approaches to learning over first-logic representation, such as Inductive Logic Programming, where prior background knowledge is an indispensable part of the learning process. These approaches have never penetrated industrial data mining except for very specific, inherently structural task settings such as those in molecular biology.

  16. Frequent subgroup mining can roughly be seen as GUHA-style association mining with fixed consequent.

  17. XSLT transformations need to be customized to fit the required PMML Mining Model and the possible DM tool’s extensions to PMML.


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The work described here has been supported by Grant No. ME913 of Ministry of Education, Youth and Sports, of the Czech Republic, and by Grant No. 201/08/0802 of the Czech Science Foundation, and by Grant No. IGA 21/08 of the University of Economics, Prague. We would like to thank Marie Tomečková, who gave us a valuable feedback on the expert elicitation interface, and the following colleagues who significantly contributed to SEWEBAR-CMS: Jakub Balhar, Daniel Štastný, Vojtěch Jirkovský, Jan Nemrava, Stanislav Vojíř and Jan Zemánek. Last, but no least, we would like to thank teachers at the University of Economics, Prague, who devoted their time to the evaluation of the framework in the educational context.

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Kliegr, T., Svátek, V., Ralbovský, M. et al. SEWEBAR-CMS: semantic analytical report authoring for data mining results. J Intell Inf Syst 37, 371–395 (2011).

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