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Cole R.,ParAccel | Funke F.,TU Munich | Giakoumakis L.,Microsoft | Guy W.,Microsoft | And 10 more authors.
Proceedings of the 4th International Workshop on Testing on Database Systems, DBTest 2011, in Conjunction with the 2011 ACM SIGMOD/PODS Conference | Year: 2011

While standardized and widely used benchmarks address either operational or real-time Business Intelligence (BI) workloads, the lack of a hybrid benchmark led us to the definition of a new, complex, mixed workload benchmark, called mixed workload CH-benCHmark. This benchmark bridges the gap between the established single-workload suites of TPC-C for OLTP and TPC-H for OLAP, and executes a complex mixed workload: a transactional workload based on the order entry processing of TPC-C and a corresponding TPC-H-equivalent OLAP query suite run in parallel on the same tables in a single database system. As it is derived from these two most widely used TPC benchmarks, the CH-benCHmark produces results highly relevant to both hybrid and classic single-workload systems. Copyright © 2011 ACM. Source


Miner D.,Greenplum
ACM International Conference Proceeding Series | Year: 2012

Greenplum is using Hadoop and several other open source tools in interesting ways as part of a big data architecture with their Greenplum Database (a scale-out MPP SQL database). Copyright is held by author/owner(s). Source


Raghavan V.,Greenplum | Raghavan V.,Worcester Polytechnic Institute | Rundensteiner E.A.,Worcester Polytechnic Institute | Srivastava S.,Worcester Polytechnic Institute
Information Systems | Year: 2011

Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an add-on on top of a traditional Select-Project-Join query plan. In many real-world applications, the skyline dimensions can be anti-correlated such as the attribute pair price, mileage for cars and price, distance for hotels. Anti-correlated data are particularly challenging for skyline evaluation and therefore have commonly been ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anti-correlated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local within individual data sources and across disparate sources - to significantly reduce the above-mentioned costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions. © 2011 Elsevier B.V. Source


Trademark
Pivotal Software, Gopivotal Inc., Emc and Greenplum | Date: 2008-10-14

Computer software for creating searchable databases of information and data.


Trademark
Greenplum | Date: 2007-01-15

Computer software for creating searchable databases of information and data.

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