An overview of data warehousing and OLAP technology
ACM SIGMOD Record
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Relational Database Compression Using Augmented Vector Quantization
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Supporting Imprecision in Multidimensional Databases Using Granularities
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
TabSum: A Flexible and Dynamic Table Summarization Approach
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
Improving table compression with combinatorial optimization
Journal of the ACM (JACM)
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
General purpose database summarization
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A quad-tree based multiresolution approach for two-dimensional summary data
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Syntactic Similarities Between XML Documents
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
CP/CV: concept similarity mining without frequency information from domain describing taxonomies
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Ontology summarization based on rdf sentence graph
Proceedings of the 16th international conference on World Wide Web
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Measuring the structural similarity of semistructured documents using entropy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Supporting OLAP operations over imperfectly integrated taxonomies
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
AlphaSum: size-constrained table summarization using value lattices
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
The summary abox: cutting ontologies down to size
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Building data warehouses with semantic web data
Decision Support Systems
Towards an automatic construction of Contextual Attribute-Value Taxonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Since the visualization real estate puts stringent constraints on how much data can be presented to the users at once, table summarization is an essential tool in helping users quickly explore large data sets. An effective summary needs to minimize the information loss due to the reduction in details. Summarization algorithms leverage the redundancy in the data to identify value and tuple clustering strategies that represent the (almost) same amount of information with a smaller number of data representatives. It has been shown that, when available, metadata, such as value hierarchies associated to the attributes of the tables, can help greatly reduce the resulting information loss. However, table summarization, whether carried out through data analysis performed on the table from scratch or supported through already available metadata, is an expensive operation. We note that the table summarization process can be significantly sped up when the metadata used for supporting the summarization itself is pre-processed to reduce the unnecessary details. The pre-processing of the metadata, however, needs to be performed carefully to ensure that it does not add significant amounts of additional loss to the table summarization process. In this paper, we propose a tRedux algorithm for value hierarchy pre-processing and reduction. Experimental evaluations show that, depending on the table and taxonomy complexity, metadata summarization can provide gains in table summarization time that can range (in absolute values) from seconds to 10s-of-1000s of seconds. Consequently, while resulting in only an extra ~ 20% reduction in table quality, tRedux can provide ~ 2x speedups in table summarization time. Experiments also show that tRedux has a better performance than alternative metadata reduction strategies in supporting table summarization; and, as the taxonomy complexity increases, the absolute gains of tRedux also increase.