New Generation Computing

, 29:223 | Cite as

Overview of ERATO Minato Project: The Art of Discrete Structure Manipulation between Science and Engineering

  • Shin-ichi Minato
Invited Paper


Discrete structure manipulation is a fundamental technique for solving many kinds of problems. Recently, BDD (Binary Decision Diagram) and ZDD (Zero-suppressed BDD) attract a great deal of attention, because they efficiently represent and manipulate large-scale combinational logic data, which are the basic discrete structures in various fields of applications, including system verification/optimization, knowledge discovery, statistical analysis, etc. Last year, the author proposed a new research project to focus on BDDs/ZDDs. In this proposal, as a new viewpoint of BDD/ZDD-based techniques, we intended to organize an integrated method of algebraic operations for manipulating various types of discrete structures, and to construct standard techniques for efficiently solving large-scale and practical problems. Fortunately, the proposal was accepted by JST (Japan Science and Technology Agency) as an ERATO (Exploratory Research for Advanced Technology) project, one of the prestigious projects in Japan. In this article, we present an overview of our research project. Our project aims to develop “The Art” of discrete structure manipulation between Science and Engineering.


Discrete Structure BDD ZDD ERATO Project Algorithm Data Structure Algebra 


  1. 1.
    Agrawal, R., Imielinski, T. and Swami, A. N., “Mining association rules between sets of items in large databases,” in Proc. of the 1993 ACM SIGMOD International Conference on Management of Data (Buneman, P. and Jajodia, S. eds.), 22, 2, SIGMOD Record, pp. 207–216, 1993.Google Scholar
  2. 2.
    Bryant, R. E., “Graph-based algorithms for Boolean function manipulation,” IEEE Transactions on Computers, C-35, 8, pp. 677–691, 1986.Google Scholar
  3. 3.
    Coudert, O., “Solving graph optimization problems with ZBDDs,” in Proc. of ACM/IEEE European Design and Test Conference (ED &TC '97), pp. 224–228, 1997.Google Scholar
  4. 4.
    Denzumi, S., Arimura, H. and Minato, S., “Substring indices based on sequence bdds,” TCS technical Reports, TCS-TR-A-10-42, Hokkaido University, Division of Computer Science, 2010.Google Scholar
  5. 5.
    Goethals, B., “Survey on frequent pattern mining,” 2003.
  6. 6.
    Ishihata, M., Kameya, Y., Sato, T., Minato, S., “Propositionalizing the em algorithm by bdds,” in Proc. of 18th International Conference on Inductive Logic Programming (ILP 2008), Sep., 2008.Google Scholar
  7. 7.
    Knuth D.E., The Art of Computer Programming: Bitwise Tricks & Techniques; Binary Decision Diagrams 4, 1, Addison-Wesley, 2009.Google Scholar
  8. 8.
    Loekito, E. and Bailey, J., “Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams,” in Proc. The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2006), pp. 307–316, 2006.Google Scholar
  9. 9.
    Loekito, E., Bailey, J. and Pei, J., “A binary decision diagram based approach for mining frequent subsequences,” Knowledge and Information Systems, 24, 2, pp. 235–268, 2010.CrossRefGoogle Scholar
  10. 10.
    Minato, S., “Zero-suppressed BDDs for set manipulation in combinatorial problems,” in Proc. of 30th ACM/IEEE Design Automation Conference, pp. 272–277, 1993.Google Scholar
  11. 11.
    Minato, S., “Zero-suppressed BDDs and their applications,” International Journal on Software Tools for Technology Transfer (STTT), 3, 2, Springer, pp.156–170, 2001.Google Scholar
  12. 12.
    Minato, S., “VSOP (Valued-Sum-Of-Products) calculator for knowledge processing based on zero-suppressed BDDs,” in Federation over the Web (K. P. Jantke, et al. eds.), LNAI 3847, pp. 40–58, Feb., 2006.Google Scholar
  13. 13.
    Minato, S., Satoh, K. and Sato, T., “Compiling bayesian networks by symbolic probability calculation based on zero-suppressed bdds,” in Proc. of 20th International Joint Conference of Artificial Intelligence (IJCAI-2007), pp. 2550–2555, 2007.Google Scholar
  14. 14.
    Minato, S., Uno, T. and Arimura, H., “LCM over ZBDDs: Fast generation of very large-scale frequent itemsets using a compact graph-based representation,” in Proc. of 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008), LNAI 5012, Springer, pp. 234–246, May, 2008.Google Scholar
  15. 15.
    Japan Science and Technology Agency (JST), Basic research programs,
  16. 16.
    Uno, T., Uchida, Y., Asai, T. and Arimura, H., “LCM: an efficient algorithm for enumerating frequent closed item sets,” in Proc. Workshop on Frequent Itemset Mining Implementations (FIMI'03), 2003.
  17. 17.
    Zaki, M. J., “Scalable algorithms for association mining,” IEEE Trans. Knowl. Data Eng., 12, 2, pp. 372–390, 2000.MathSciNetCrossRefGoogle Scholar

Copyright information

© Ohmsha and Springer Japan jointly hold copyright of the journal. 2011

Authors and Affiliations

  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan
  2. 2.ERATO MINATO Discrete Structure Manipulation System Project, Japan Science and Technology AgencyKawaguchi-shiJapan

Personalised recommendations