TY - GEN
T1 - Towards stream data parallel processing in spatial aggregating index
AU - Gorawski, Marcin
AU - Malczok, Rafal
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Parallel algorithms
KW - Stream data warehouse
KW - Stream processing
UR - https://www.scopus.com/pages/publications/45449108218
U2 - 10.1007/978-3-540-68111-3_23
DO - 10.1007/978-3-540-68111-3_23
M3 - Conference contribution
AN - SCOPUS:45449108218
SN - 3540681051
SN - 9783540681052
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 209
EP - 218
BT - Parallel Processing and Applied Mathematics - 7th International Conference, PPAM 2007, Revised Selected Papers
T2 - 7th International Conference on Parallel Processing and Applied Mathematics, PPAM 2007
Y2 - 9 September 2007 through 12 September 2007
ER -