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suppose that a data warehouse contains 20 dimensions each with about five levels of granularitya users are mainly
a data cube c has n dimensions and each dimension has exactly p distinct values in the base cuboid assume that there
what are the differences between the three main types of data warehouse usage information processing analytical
the ranking cube was designed to support top-k ranking queries in relational database systems however ranking queries
there are several typical cube computation methods such as multi way zdn97 buc br99 and star-cubing xhlw03 briefly
suppose a data cube c has d dimensions and the base cuboid contains k distinct tuplesa present a formula to calculate
often the aggregate count value of many cells in a large data cuboid is zero resulting in a huge yet sparse
suppose that we want to compute an iceberg cube for the dimensions a b c d where we wish to materialize all cells that
discuss how you might extend the star-cubing algorithm to compute iceberg cubes where the iceberg condition tests for
the sampling cube was proposed for multidimensional analysis of sampling data eg survey data in many real applications
the ranking cube was proposed for efficient computation of top-k ranking queries in relational databases recently
propose and outline a level-shared mining approach to mining multilevel association rules in which each item is encoded
suppose as manager of a chain of stores you would like to use sales transactional data to analyze the effectiveness of
implementation project many techniques have been proposed to further improve the performance of frequent itemset mining
the following contingency table summarizes supermarket transaction data where hot dogs refers to the transactions
let c be a candidate itemset in ck generated by the apriori algorithm how many length-k - 1 subsets do we need to check
a database has five transactions let min sup 60 and min conf 80a find all frequent itemsets using apriori and
implementation project using a programming language that you are familiar with such as c or java implement three
the apriori algorithm makes use of prior knowledge of subset support propertiesa prove that all nonempty subsets of a
an itemset x is called a generator on a data set d if there does not exist a proper sub-itemset y sub x such that
suppose you have the set c of all frequent closed itemsets on a data set d as well as the support count for each
discovery-driven cube exploration is a desirable way to mark interesting points among a large number of cells in a data
multifeature cubes allow us to construct interesting data cubes based on rather sophisticated query conditions can you
the prediction cube is a good example of multidimensional data mining in cube spacea propose an efficient algorithm
the following table consists of training data from an employee database the data have been generalized for example 31