IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v14y2010i2p37-44.html
   My bibliography  Save this article

Some Considerations about Modern Database Machines

Author

Listed:
  • Manole VELICANU
  • Daniela LITAN
  • Aura-Mihaela MOCANU (VIRGOLICI)

Abstract

Optimizing the two computing resources of any computing system - time and space - has al-ways been one of the priority objectives of any database. A current and effective solution in this respect is the computer database. Optimizing computer applications by means of database machines has been a steady preoccupation of researchers since the late seventies. Several information technologies have revolutionized the present information framework. Out of these, those which have brought a major contribution to the optimization of the databases are: efficient handling of large volumes of data (Data Warehouse, Data Mining, OLAP – On Line Analytical Processing), the improvement of DBMS – Database Management Systems facilities through the integration of the new technologies, the dramatic increase in computing power and the efficient use of it (computer networks, massive parallel computing, Grid Computing and so on). All these information technologies, and others, have favored the resumption of the research on database machines and the obtaining in the last few years of some very good practical results, as far as the optimization of the computing resources is concerned.

Suggested Citation

  • Manole VELICANU & Daniela LITAN & Aura-Mihaela MOCANU (VIRGOLICI), 2010. "Some Considerations about Modern Database Machines," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(2), pages 37-44.
    • Handle: RePEc:aes:infoec:v:14:y:2010:i:2:p:37-44
    as

    Download full text from publisher

    File URL: http://revistaie.ase.ro/content/54/04%20Velicanu,%20Litan,%20Mocanu%202.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Catherine Combes & Celine Rivat, 2008. "A modelling environment based on data warehousing to manage and to optimize the running of international company," Post-Print halshs-00519262, HAL.
    2. Combes, C. & Rivat, C., 2008. "A modelling environment based on data warehousing to manage and to optimize the running of international company," International Journal of Production Economics, Elsevier, vol. 112(1), pages 294-308, March.
    3. Li, Xiao-Bai & Jacob, Varghese S., 2008. "Adaptive data reduction for large-scale transaction data," European Journal of Operational Research, Elsevier, vol. 188(3), pages 910-924, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    2. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    3. Mingzheng Wang & Zhengrui Jiang & Haifang Yang & Yu Zhang, 2018. "T -Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 438-453, August.
    4. Zhang, Juheng & Aytug, Haldun, 2016. "Comparison of imputation methods for discriminant analysis with strategically hidden data," European Journal of Operational Research, Elsevier, vol. 255(2), pages 522-530.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:14:y:2010:i:2:p:37-44. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.