37 research outputs found
Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation
Increasingly, business projects are ephemeral. New Business Intelligence
tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds
are a popular community-driven visualization technique. Hence, we investigate
tag-cloud views with support for OLAP operations such as roll-ups, slices,
dices, clustering, and drill-downs. As a case study, we implemented an
application where users can upload data and immediately navigate through its ad
hoc dimensions. To support social networking, views can be easily shared and
embedded in other Web sites. Algorithmically, our tag-cloud views are
approximate range top-k queries over spontaneous data cubes. We present
experimental evidence that iceberg cuboids provide adequate online
approximations. We benchmark several browser-oblivious tag-cloud layout
optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou
Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes
Bitmap indexes must be compressed to reduce input/output costs and minimize
CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use
techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid
(WAH) compression. These techniques are sensitive to the order of the rows: a
simple lexicographical sort can divide the index size by 9 and make indexes
several times faster. We investigate reordering heuristics based on computed
attribute-value histograms. Simply permuting the columns of the table based on
these histograms can increase the sorting efficiency by 40%.Comment: To appear in proceedings of DOLAP 200
Unasssuming View-Size Estimation Techniques in OLAP
Even if storage was infinite, a data warehouse could not materialize all possible views due to the running time and update requirements. Therefore, it is necessary to estimate quickly, accurately, and reliably the size of views. Many available techniques make particular statistical assumptions and their error can be quite large. Unassuming techniques exist, but typically assume we have independent hashing for which there is no known practical implementation. We adapt an unassuming estimator due to Gibbons and Tirthapura: its theoretical bounds do not make unpractical assumptions. We compare this technique experimentally with stochastic probabilistic counting, LogLog probabilistic counting, and multifractal statistical models. Our experiments show that we can reliably and accurately (within 10%, 19 times out 20) estimate view sizes over large data sets (1.5 GB) within minutes, using almost no memory. However, only Gibbons-Tirthapura provides universally tight estimates irrespective of the size of the view. For large views, probabilistic counting has a small edge in accuracy, whereas the competitive sampling-based method (multifractal) we tested is an order of magnitude faster but can sometimes provide poor estimates (relative error of 100%). In our tests, LogLog probabilistic counting is not competitive. Experimental validation on the US Census 1990 data set and on the Transaction Processing Performance (TPC H) data set is provided
Unasssuming View-Size Estimation Techniques in OLAP
Even if storage was infinite, a data warehouse could not materialize all possible views due to the running time and update requirements. Therefore, it is necessary to estimate quickly, accurately, and reliably the size of views. Many available techniques make particular statistical assumptions and their error can be quite large. Unassuming techniques exist, but typically assume we have independent hashing for which there is no known practical implementation. We adapt an unassuming estimator due to Gibbons and Tirthapura: its theoretical bounds do not make unpractical assumptions. We compare this technique experimentally with stochastic probabilistic counting, LogLog probabilistic counting, and multifractal statistical models. Our experiments show that we can reliably and accurately (within 10%, 19 times out 20) estimate view sizes over large data sets (1.5 GB) within minutes, using almost no memory. However, only Gibbons-Tirthapura provides universally tight estimates irrespective of the size of the view. For large views, probabilistic counting has a small edge in accuracy, whereas the competitive sampling-based method (multifractal) we tested is an order of magnitude faster but can sometimes provide poor estimates (relative error of 100%). In our tests, LogLog probabilistic counting is not competitive. Experimental validation on the US Census 1990 data set and on the Transaction Processing Performance (TPC H) data set is provided
Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation
Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations