GLADE: Big data analytics made easy

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2012, Pages 697-700

  Cheng, Yu ^a   Qin, Chengjie ^a   Rusu, Florin ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

gla; hadoop; uda

Indexed keywords

ANALYTICAL FUNCTIONS; BIG DATUM; COMPUTING NODES; DATA ANALYTICS; DISTRIBUTED SYSTEMS; GLA; HADOOP; INPUT DATAS; JAVA CODES; LARGE SCALE DATA; MAP-REDUCE; POSTGRESQL; RELATIONAL DATABASE; RUNNING TIME; RUNTIMES; UDA; USER CODES; USER-DEFINED AGGREGATES;

CLUSTER COMPUTING;

RELATIONAL DATABASE SYSTEMS;

EID: 84862686492     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2213836.2213936     Document Type: Conference Paper

References (9)

1. Hadoop. http://hadoop.apache.org/.
2. PostgreSQL. http://www.postgresql.org/.
3. S. Arumugam and al. The DataPath System: A Data-Centric Analytic Processing Engine for Large Data Warehouses. In SIGMOD 2010.
4. J. Cohen and al. MAD Skills: New Analysis Practices for Big Data. In VLDB 2009.
5. J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI 2004.
6. M. Isard and Y. Yu. Distributed Data-Parallel Computing Using a High-Level Programming Language. In SIGMOD 2009.
7. C. Olston and al. Pig Latin: A Not-So-Foreign Language for Data Processing. In SIGMOD 2008.
8. A. Pavlo and al. A Comparison of Approaches to Large-Scale Data Analysis. In SIGMOD 2009.
9. F. Rusu and A. Dobra. GLADE: A Scalable Framework for Efficient Analytics. In LADIS 2011.