Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Integrating K-Means Clustering with a Relational DBMS Using SQL
IEEE Transactions on Knowledge and Data Engineering
MapReduce and parallel DBMSs: friends or foes?
Communications of the ACM - Amir Pnueli: Ahead of His Time
Statistical Model Computation with UDFs
IEEE Transactions on Knowledge and Data Engineering
Comparing SQL and MapReduce to compute Naive Bayes in a single table scan
CloudDB '10 Proceedings of the second international workshop on Cloud data management
Relational versus non-relational database systems for data warehousing
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
A data mining system based on SQL queries and UDFs for relational databases
Proceedings of the 20th ACM international conference on Information and knowledge management
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Data mining remains an important research area in database systems. We present a review of processing alternatives, storage mechanisms, algorithms, data structures and optimizations that enable data mining on large data sets. We focus on the computation of well-known multidimensional statistical and machine learning models. We pay particular attention to SQL and MapReduce as two competing technologies for large scale processing. We conclude with a summary of solved major problems and open research issues.