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  1. Introduction
    1. problem statement, motivation
    2. precise definitions and terminology (define terms),
    3. define one goal and list consequent objectives
  2. Stateof-the-art
    1. Overview
    2. Inductive Logic Programming
    3. Statistical relational learning
    4. SVM, Tree, Neural, Distance based
    5. Graph Mining
    6. Multi view
    7. Propositionalisation
  3. Objectives
    1. The goal is to get an automated solution into industry. Hurdles: money & time.
      1. Saves time, takes little time
      2. Simple or well known mechanism (SQL) → minimal training
      3. Compatible with current processes (do not fix what ain't broken) → minimal training
      4. Scalable (SQL can be executed)
      5. Simple deployment (no need to install databases…)
      6. Well documented
      7. Make it viral: easily extensible (like evolutionary algorithm)
  4. Contribution
    1. approach,
    2. implementation
    3. experimentation
      1. describe selection of databases (# of references, # columns, #rows, field)
      2. describe selection of comparison algorithms (draw graph with references)
      3. describe HW and SW for time comparison
      4. describe accuracy evaluation (Wilcoxon test)
  5. Conclusions
  6. bibliografie, seznam obrazku, tabulek, seznam patentu, publikovanych clanku, list of abbreviations