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A Performance Evaluation of Online Warehouse Update Algorithms. Alexandros Labrinidis. Nick Roussopoulos. November 1998.
Data warehouse maintenance algorithms usually work off-line, making the warehouse unavailable to users. However, since most organizations require continuous operation, we need be able to perform the updates online, concurrently with user queries. To guarantee that user queries access a consistent view of the warehouse, online update algorithms introduce redundancy in order to store multiple versions of the data objects that are being changed. In this paper, we present an online warehouse update algorithm, that stores multiple versions of data as separate rows (vertical redundancy). We compare our algorithm to another online algorithm that stores multiple versions within each tuple by extending the table schema (horizontal redundancy). We have implemented both algorithms on top of an Informix Dynamic Server and measured their performance under varying workloads, focusing on their impact on query response times. Our experiments show that, except for a limited number of cases, vertical redundancy is a better choice, with respect to storage, implementation overhead, and query performance. (Also cross-referenced as UMIACS TR-98-66) University of Maryland Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland,
Reduction of Materialized View Staleness Using Online Updates. Alexandros Labrinidis. Nick Roussopoulos. February 1998.
Updating the materialized views stored in data warehouses usually implies making the warehouse unavailable to users. We propose MAUVE, a new algorithm for online incremental view updates that uses timestamps and allows consistent read-only access to the warehouse while it being updated. The algorithm propagates the updates to the views more often than the typical once a day in order to reduce view staleness. We have implemented MAUVE top of the Informix Universal Server and used a synthetic workload generator to experiment with various update workloads and different view update frequencies. Our results show that, all kinds of update streams benefit from more frequent view updates, instead of just once a day. However, there is a clear maximum for the view update frequency, for which view staleness is minimal. (Also cross-referenced as UMIACS-TR-98-13) University of Maryland Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland,
Quantifiable Data Mining Using Principal Component Analysis. Flip Korn. Alexandros Labrinidis. Yannis Kotidis. Christos Faloutsos. Alex Kaplunovich. Dejan Perkovic. February 1997.
Association Rule Mining algorithms operate on a data matrix (e.g., customers x products) to derive rules. We propose a single-pass algorithm for mining linear rules in such a matrix based on Principal Component Analysis. PCA detects correlated columns of the matrix, which correspond to, e.g., products that sell together. The first contribution of this work is that we propose to quantify the ``goodness'' of a set of discovered rules. We define the ``guessing error'': the root-mean-square error of the reconstructed values of the cells of the given matrix, when we pretend that they are unknown. The second contribution is a novel method to guess missing/hidden values from the linear rules that our method derives. For example, if somebody bought $10 of milk and $3 of bread, our rules can ``guess'' the amount spent on, say, butter. Thus, we can perform a variety of important tasks such as forecasting, `what-if' scenarios, outlier detection, and visualization. Moreover, we show that we can compute the principal components with a single pass over the dataset. Experiments on real datasets (e.g., NBA statistics) demonstrate that the proposed method consistently achieves a ``guessing error'' of up to 5 times lower than the straightforward competitor. (Also cross-referenced as UMIACS-TR-97-13) University of Maryland Institute for Advanced Computer Studies, Dept. of Computer Science, Univ. of Maryland,
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