Reducing metadata complexity for faster table summarization

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.