Design, implementation, and evaluation of the linear road bnchmark on the stream processing core
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
Experiences with MapReduce, an abstraction for large-scale computation
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the VLDB Endowment
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Continuous mapreduce for In-DB stream analytics
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems
Experience in Continuous analytics as a Service (CaaaS)
Proceedings of the 14th International Conference on Extending Database Technology
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The massively growing data volume and the pressing need for low latency are pushing the traditional store-first-query-later data warehousing technologies beyond their limits. Many enterprise applications are now based on continuous analytics of data streams. While integrating stream processing with query processing takes advantage of SQL's expressive power and DBMS's data management capability, it raises serious challenges in dealing with complex dataflow, applying queries to unbounded stream data, and providing highly scalable, dynamically configurable, elastic infrastructure. To solve these problems, we model the general graph-structured, continuous dataflow analytics as a SQL Streaming Process with multiple connected and stationed continuous queries; then we extend the query engine to support cycle-based query execution for processing unbounded stream data chunk-wise with sound semantics; and finally, we develop the Query Engine Net (QE-Net) over the Distributed Caching Platforms (DCP) as a dynamically configurable elastic infrastructure for parallel and distributed execution of SQL Streaming Processes. We extended the PostgreSQL engines for building the QE-Net infrastructure. Our experience shows its merit in leveraging SQL and query processing to analyze real-time, graph-structured and unbounded streams. Integrating it with a commercial and proprietary MPP based database cluster is being investigated.