A MOLECULAR CONCEPT OF MANAGING DATA

Christoph Schommer

2010

Abstract

The following (position) paper follows the concept of the field of Artificial Life and argues that the (relational) management of data can be understood as a chemical model. Whereas each data itself is consistent with atomic entities, each combination of data corresponds to a (artificial) molecular structure. For example, an attribute D inside a relational system can be represented by a nucleus αD sharing a cloud of values, which consists of so-called valectrons (the values for the column D). By using reaction rules like the selection of tuples or projection of attributes, a retrieve of molecules can be achieved quite easily. Advantages of the chemical model are no data types, a fast data access, and the associative nature of the molecules: this automatically supports a direct identification of patterns in the sense of data mining. A disadvantage is the need for restructuring that must eventually be done, because the incoming data stream is allowed to influence the chemical model. With this position paper, we present our basic concept.

References

  1. Dittrich, P., Ziegler, J., and Banzhaf, W. (2001). Artificial chemistries-a review. Artificial Life, 7(3):225-275.
  2. Fernández-Baizán, M. C., García, A., González, M. M., Pérez-Llera, C., Portaencasa, R., and Santos, E. (1996). Analysis and design of a relational database management system and implementation of its nucleus. Computers and Artificial Intelligence, 15(4).
  3. Gerrilsan, R. (1975). The application of artificial intelligence of data base management. In IJCAI, pages 521- 527.
  4. Hutton, T. J. (2002). Evolvable self-replicating molecules in an artificial chemistry. Artificial Life, 8(4):341-356.
  5. Kelemen, J. and Sosík, P., editors (2001). Advances in Artificial Life, 6th European Conference, ECAL 2001, Prague, Czech Republic, September 10-14, 2001, Proceedings, volume 2159 of Lecture Notes in Computer Science. Springer.
  6. Leach, A. (2001). Molecular Modelling - Principles and Applications. Prentice Hall, 2nd edition.
  7. Schommer, C. (2009). An artificial molecular model to foster communities. In Knowledge Discovery and Information Retrieval (KDIR). IEEE Computer Society.
  8. Skusa, A., Banzhaf, W., Busch, J., Dittrich, P., and Ziegler, J. (2000). Künstliche chemie. KI, 14(1):12-19.
  9. Tominaga, K., Watanabe, T., Kobayashi, K., Nakamura, M., Kishi, K., and Kazuno, M. (2007). Modeling molecular computing systems by an artificial chemistry - its expressive power and application. Artificial Life, 13(3):223-247.
  10. von Luck, K. and Marburger, H., editors (1994). Management and Processing of Complex Data Structures, Third Workshop on Information Systems and Artificial Intelligence, Hamburg, Germany, February 28 - March 2, 1994, Proceedings, volume 777 of Lecture Notes in Computer Science. Springer.
  11. Ziegler, J. and Banzhaf, W. (2001). Evolving control metabolisms for a robot. Artificial Life, 7(2):171-190.
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Paper Citation


in Harvard Style

Schommer C. (2010). A MOLECULAR CONCEPT OF MANAGING DATA . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 411-415. DOI: 10.5220/0002758304110415


in Bibtex Style

@conference{icaart10,
author={Christoph Schommer},
title={A MOLECULAR CONCEPT OF MANAGING DATA},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={411-415},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002758304110415},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A MOLECULAR CONCEPT OF MANAGING DATA
SN - 978-989-674-021-4
AU - Schommer C.
PY - 2010
SP - 411
EP - 415
DO - 10.5220/0002758304110415