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Data Mining for Business Applications pp 209–223Cite as

DAG Mining for Code Compaction

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In order to reduce cost and energy consumption, code-size optimization is an important issue for embedded systems. Traditional instruction saving techniques recognize code duplications only in exactly the same order within the program. As instructions can be reordered with respect to their data dependencies, Procedural Abstraction achieves better results on data flow graphs that reflect these dependencies. Since these graphs are always directed acyclic graphs (DAGs), a special mining algorithm for DAGs is presented in this chapter. Using a new canonical representation that is based on the topological order of the nodes in a DAG, the proposed algorithm is faster and uses less memory than the general graph mining algorithm gSpan. Due to its search lattice expansion strategy, an efficient pruning strategy is applied to the algorithm while using it for Procedural Abstraction. Its search for unconnected graph fragments outperforms traditional approaches for code-size reduction.

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Authors and Affiliations

  1. Programming Systems Group, Computer Science Department, University of Erlangen—Nuremberg, Germany

    T. Werth, M. Wörlein, A. Dreweke & M. Philippsen

  2. Nycomed Chair for Bioinformatics and Information Mining, University of Konstanz, Germany

    I. Fischer

Authors
  1. T. Werth
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  2. M. Wörlein
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  3. A. Dreweke
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  4. I. Fischer
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  5. M. Philippsen
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Corresponding author

Correspondence to T. Werth .

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Editors and Affiliations

  1. School of Software Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Sydney, Broadway, NSW 2007, Australia

    Longbing Cao  & Huaifeng Zhang  & 

  2. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Chicago, IL, 60607

    Philip S. Yu

  3. Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Sydney, Broadway, NSW 2007, Australia

    Chengqi Zhang

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Werth, T., Wörlein, M., Dreweke, A., Fischer, I., Philippsen, M. (2009). DAG Mining for Code Compaction. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_15

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  • DOI: https://doi.org/10.1007/978-0-387-79420-4_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-79419-8

  • Online ISBN: 978-0-387-79420-4

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