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Modele architektury biznesowej administracji publicznej w warunkach przetwarzania danych masowych

Author

Listed:
  • Małgorzata Pańkowska

    (Uniwersytet Ekonomiczny w Katowicach, Wydział Informatyki i Komunikacji)

  • Mariusz Żytniewski

    (Uniwersytet Ekonomiczny w Katowicach, Wydział Informatyki i Komunikacji)

Abstract

Urzędy administracji publicznej stanęły wobec wyzwania jakim jest smart city, czyli inteligentne miasto lub inteligentna gmina. Celem artykułu jest prezentacja modelu architektury inteligentnego miasta, ze wskazaniem rozwiązań w technologii Big Data. Został również przedstawiony przykład odkrywania wiedzy na temat procesów biznesowych na podstawie logów rejestrowanych w nieustrukturalizowanych bazach danych.

Suggested Citation

  • Małgorzata Pańkowska & Mariusz Żytniewski, 2019. "Modele architektury biznesowej administracji publicznej w warunkach przetwarzania danych masowych," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 56, pages 171-183.
  • Handle: RePEc:sgh:annals:i:56:y:2019:p:171-183
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    References listed on IDEAS

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    1. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
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