The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

References in zbMATH (referenced in 134 articles )

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  1. Dejun Teng, Yanhui Liang, Hoang Vo, Jun Kong, Fusheng Wang: Efficient 3D Spatial Queries for Complex Objects (2022) not zbMATH
  2. Jin, Zhao; Zhang, Bowen; Zhang, Lei; Cao, Yongzhi; Wang, Hanpin: An adaptation-complete proof system for local reasoning about cloud storage systems (2022)
  3. Li, Yanting: A cloud computing-based intelligent forecasting method for cross-border e-commerce logistics costs (2022)
  4. Nanongkai, Danupon; Scquizzato, Michele: Equivalence classes and conditional hardness in massively parallel computations (2022)
  5. Apishev, M. A.: Effective implementations of topic modeling algorithms (2021)
  6. Becker, Florent; Montealegre, Pedro; Rapaport, Ivan; Todinca, Ioan: The role of randomness in the broadcast congested clique model (2021)
  7. Chelly, Dagdia Zaineb; Zarges, Christine: A detailed study of the distributed rough set based locality sensitive hashing feature selection technique (2021)
  8. Czumaj, Artur; Davies, Peter; Parter, Merav: Simple, deterministic, constant-round coloring in congested clique and MPC (2021)
  9. Fernandez-Basso, Carlos; Ruiz, M. Dolores; Martin-Bautista, Maria J.: Spark solutions for discovering fuzzy association rules in big data (2021)
  10. Ramon-Cortes, Cristian; Alvarez, Pol; Lordan, Francesc; Alvarez, Javier; Ejarque, Jorge; Badia, Rosa M.: A survey on the distributed computing stack (2021)
  11. Zhu, Xuening; Li, Feng; Wang, Hansheng: Least-square approximation for a distributed system (2021)
  12. Ahmadi, Saba; Khuller, Samir; Purohit, Manish; Yang, Sheng: On scheduling coflows (2020)
  13. Czumaj, Artur; Łącki, Jakub; Mądry, Aleksander; Mitrović, Slobodan; Onak, Krzysztof; Sankowski, Piotr: Round compression for parallel matching algorithms (2020)
  14. Feldman, Dan; Schmidt, Melanie; Sohler, Christian: Turning big data into tiny data: constant-size coresets for (k)-means, PCA, and projective clustering (2020)
  15. Montealegre, P.; Perez-Salazar, S.; Rapaport, I.; Todinca, I.: Graph reconstruction in the congested clique (2020)
  16. Wang, Zairong; Tang, Xuan; Liu, Haohuai; Peng, Lingxi: Artificial immune intelligence-inspired dynamic real-time computer forensics model (2020)
  17. Zhang, Longxin; Zhou, Liqian; Salah, Ahmad: Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments (2020)
  18. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  19. Farruggia, Andrea; Ferragina, Paolo; Frangioni, Antonio; Venturini, Rossano: Bicriteria data compression (2019)
  20. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)

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