Titlebook: Data Warehousing and Knowledge Discovery; 14th International C Alfredo Cuzzocrea,Umeshwar Dayal Conference proceedings 2012 Springer-Verlag

arsenal

AMOR

联想

J. Nielsen,K. Nikolajsen,J. Villadsend navigate into a multidimensional structure of precomputed measures, which is referred to as a.. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration fo

ADOPT

MIME

anaerobic

M. Banta,M. Mäntylä,M. Inuit,F. Kimurae a significant impact on the warehouse performance. This interaction has been largely exploited in solving isolated problems like (i) the multiple-query optimization, (ii) the materialized view selection, (iii) the buffer management, (iv) the query scheduling, etc. Recently, some research efforts s

钩针织物

M. Banta,M. Mäntylä,M. Inuit,F. Kimura. cuboids. To build ROLAP(Relational OLAP) data cubes efficiently, existing algorithms (e.g., GBLP, PipeSort, PipeHash, BUC, etc) use several strategies sharing sort cost and input data scan, reducing data computation, and utilizing parallel processing techniques. On the other hand, MapReduce is rec

博爱家

modish

Susumu Furukawa,Shinji Mukap,Mitsuru Kurodaressing incremental update problem generally propose incremental itemset mining methods based on Apriori and FP-Growth algorithms. Besides inheriting the disadvantages of base algorithms, incremental itemset mining has challenges such as handling i) increments without re-running the algorithm, ii) s

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