MapReduce and parallel DBMSs: friends or foes?
Communications of the ACM - Amir Pnueli: Ahead of His Time
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Database systems research on data mining
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Fundamentals of Database Systems
Fundamentals of Database Systems
Statistical Model Computation with UDFs
IEEE Transactions on Knowledge and Data Engineering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Parallel data processing with MapReduce: a survey
ACM SIGMOD Record
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Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.