Applying update streams in a soft real-time database system
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Shrinking the warehouse update Window
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Synchronizing a database to improve freshness
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimizing refresh of a set of materialized views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Travel time estimation using NiagaraST and latte
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Enabling Real-Time Querying of Live and Historical Stream Data
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Meshing Streaming Updates with Persistent Data in an Active Data Warehouse
IEEE Transactions on Knowledge and Data Engineering
RiTE: Providing On-Demand Data for Right-Time Data Warehousing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Stream warehousing with DataDepot
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Peta-scale data warehousing at Yahoo!
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Performance Evaluation and Benchmarking
A Performance Study of Event Processing Systems
Performance Evaluation and Benchmarking
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
Continuous analytics over discontinuous streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data Stream Management
R-MESHJOIN for near-real-time data warehousing
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Scheduling to Minimize Staleness and Stretch in Real-Time Data Warehouses
Theory of Computing Systems - Special Issue: Parallelism in Algorithms and Architectures
Scalable Scheduling of Updates in Streaming Data Warehouses
IEEE Transactions on Knowledge and Data Engineering
A sequence-oriented stream warehouse paradigm for network monitoring applications
PAM'12 Proceedings of the 13th international conference on Passive and Active Measurement
Temporal Analytics on Big Data for Web Advertising
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
International Journal of Data Warehousing and Mining
HYBRIDJOIN for Near-Real-Time Data Warehousing
International Journal of Data Warehousing and Mining
Proceedings of the 21st ACM international conference on Information and knowledge management
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Data management systems are facing two challenges driven by the requirements of emerging data-intensive applications: more data and less time to process the data. Data volumes continue to increase as new sources and data collecting mechanisms appear. At the same time, these sources tend to be highly dynamic and generate data in the form of a stream, which requires quick reaction to newly arrived data. Traditional data warehouses enable scalable data storage and analytics, including the ability to define nested levels of materialized views. However, views are typically refreshed during downtimes---e.g., every night---which does not meet the latency requirements of many applications. Stream data warehousing is a new data management technology that allows nearly-continuous view refresh as new data arrive, which enables seamless integration of real-time monitoring and business intelligence with long-term data mining. In this paper, we argue that a new benchmark is required for stream warehouses, which should focus on measuring the property that determines the utility of these systems, namely how well they can keep up with the incoming data and guarantee the "freshness" of materialized views.