R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
Multi-thread processing of long aggregates lists
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Hybrid index for spatio-temporal OLAP operations
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Optimization of operator partitions in stream data warehouse
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
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Data processing computer systems store and process large volumes of data. The volumes tend to grow very quickly, especially in data warehouse systems. A few years ago data warehouses were used only for supporting strictly business decisions but nowadays they find their application in many domains of everyday life. New and very demanding field is stream data warehousing. Car traffic monitoring, cell phones tracking or utilities meters integrated reading systems generate stream data. In a stream data warehouse the ETL process is a continuous one. Stream data processing poses many new challenges to memory management and data processing algorithms. The most important aspects concern efficiency and scalability of the designed solutions. In this paper we present an example of a stream data warehouse and then, basing on the presented example and our previous work results, we discuss a solution for stream data parallel processing. We also show, how to integrate the presented solution with a spatial aggregating index.