Parallel bottom-up processing of Datalog queries
Journal of Logic Programming
What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Optimizing joins in a map-reduce environment
Proceedings of the 13th International Conference on Extending Database Technology
Counting triangles and the curse of the last reducer
Proceedings of the 20th international conference on World wide web
A latency and fault-tolerance optimizer for online parallel query plans
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
The HaLoop approach to large-scale iterative data analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Communication steps for parallel query processing
Proceedings of the 32nd symposium on Principles of database systems
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Industry analysts describe Big Data in terms of three V's: volume, velocity, variety. The data is too big to process with current tools; it arrives too fast for optimal storage and indexing; and it is too heterogeneous to fit into a rigid schema. There is a huge pressure on database researchers to study, explain, and solve the technical challenges in big data, but we find no inspiration in the three Vs. Volume is surely nothing new for us, streaming databases have been extensively studied over a decade, while data integration and semistructured has studied heterogeneity from all possible angles.