MapReduce is a new parallel programming model initially developed for large-scale web content processing. Data analysis meets the issue of how to do calculation over extremely large datasets. The arrival of MapReduce provides a chance to utilize commodity hardware for massively parallel data analysis applications. The translation and optimization from relational algebra operators to MapReduce programs is still an open and dynamic research field. In this paper, we focus on a special type of data analysis query, namely multiple group by query. We first study the communication cost of the MapReduce model, then we give an initial implementation of multiple group by query. We then propose an optimized version which addresses and improves the communication cost issues. Our optimized version shows a better accelerating ability and a better scalability than the other version

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  1. Afzal, Asif; Ansari, Zahid; Rimaz Faizabadi, Ahmed; Ramis, M.K.: Parallelization strategies for computational fluid dynamics software: state of the art review (2017)
  2. Annoni, Jennifer; Seiler, Peter: A method to construct reduced-order parameter-varying models (2017)
  3. Chen, Cong; Xu, Yinfeng; Zhu, Yuqing; Sun, Chengyu: Online MapReduce scheduling problem of minimizing the makespan (2017)
  4. Fabisiak, Tomasz; Danilecki, Arkadiusz: Browser-based harnessing of voluntary computational power (2017)
  5. Fuerst, Carlo; Pacut, Maciej; Schmid, Stefan: Data locality and replica aware virtual cluster embeddings (2017)
  6. Lanza, Daniel; Chávez, F.; Fernandez, Francisco; Garcia-Valdez, M.; Trujillo, Leonardo; Olague, Gustavo: Profiting from several recommendation algorithms using a scalable approach (2017)
  7. Luo, Taibo; Zhu, Yuqing; Wu, Weili; Xu, Yinfeng; Du, Ding-Zhu: Online makespan minimization in MapReduce-like systems with complex reduce tasks (2017)
  8. Masegosa, Andrés R.; Martinez, Ana M.; Langseth, Helge; Nielsen, Thomas D.; Salmerón, Antonio; Ramos-López, Darío; Madsen, Anders L.: Scaling up Bayesian variational inference using distributed computing clusters (2017)
  9. Stewart, Iain A.: On the combinatorial design of data centre network topologies (2017)
  10. Zhu, Yao; Gleich, David F.; Grama, Ananth: Erasure coding for fault-oblivious linear system solvers (2017)
  11. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)
  12. Bermanis, Amit; Salhov, Moshe; Wolf, Guy; Averbuch, Amir: Measure-based diffusion grid construction and high-dimensional data discretization (2016)
  13. Chawla, Priyanka; Chana, Inderveer; Rana, Ajay: Cloud-based automatic test data generation framework (2016)
  14. Choi, Woohyuk; Hong, Sumin; Jeong, Won-Ki: Vispark: GPU-accelerated distributed visual computing using Spark (2016)
  15. Cota, Giuseppe; Zese, Riccardo; Bellodi, Elena; Riguzzi, Fabrizio; Lamma, Evelina: Distributed parameter learning for probabilistic ontologies (2016)
  16. Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud; Sharma, Shantanu: Security and privacy aspects in MapReduce on clouds: a survey (2016)
  17. Grandi, Umberto; Loreggia, Andrea; Rossi, Francesca; Saraswat, Vijay: A Borda count for collective sentiment analysis (2016)
  18. Hameed, Abdul; Khoshkbarforoushha, Alireza; Ranjan, Rajiv; Jayaraman, Prem Prakash; Kolodziej, Joanna; Balaji, Pavan; Zeadally, Sherali; Malluhi, Qutaibah Marwan; Tziritas, Nikos; Vishnu, Abhinav; Khan, Samee U.; Zomaya, Albert: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016) ioport
  19. Kärkkäinen, Juha; Kempa, Dominik: LCP array construction in external memory (2016)
  20. Liu, Zhihong; Zhang, Qi; Ahmed, Reaz; Boutaba, Raouf; Liu, Yaping; Gong, Zhenghu: Dynamic resource allocation for MapReduce with partitioning skew (2016)

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