Efficient resumption of interrupted warehouse loads
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Fault-tolerance in the Borealis distributed stream processing system
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fault-Tolerant Distributed Stream Processing System
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
Distributed Stream Processing Analysis in High Availability Context
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
Towards Reliability and Fault-Tolerance of Distributed Stream Processing System
DEPCOS-RELCOMEX '07 Proceedings of the 2nd International Conference on Dependability of Computer Systems
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Not so long ago data warehouses were used to process data sets loaded periodically during ETL process (Extraction, Transformation and Loading). We could distinguish two kinds of ETL processes: full and incremental. Now we often have to process real-time data and analyse them almost on-the-fly, so the analyses are always up to date. There are many possible applications for real-time data warehouses. In most cases two features are important: delivering data to the warehouse as quick as possible, and not losing any tuple in case of failures. In this paper we describe an architecture for gathering and processing data from geographically distributed data sources and we define a method for analysing properties of the connections structure, finding the weakest points in case of single and multiple node failures. At the end of the paper our future plans are described briefly.