Extracting k most important groups from data efficiently

We study an important data analysis operator, which extracts the k most important groups from data (i.e., the k groups with the highest aggregate values). In a data warehousing context, an example of the above query is "find the 10 combinations of product-type and month with the largest sum of sales". The problem is challenging as the potential number of groups can be much larger than the memory capacity. We propose on-demand methods for efficient top-k groups processing, under limited memory size. In particular, we design top-k groups retrieval techniques for three representative scenarios as follows. For the scenario with data physically ordered by measure, we propose the write-optimized multi-pass sorted access algorithm (WMSA), that exploits available memory for efficient top-k groups computation. Regarding the scenario with unordered data, we develop the recursive hash algorithm (RHA), which applies hashing with early aggregation, coupled with branch-and-bound technique...

• Type: Journal
• Year: 2008
• Where: DKE
• Authors: Man Lung Yiu, Nikos Mamoulis, Vagelis Hristidis
• Keywords: DKE 2008, Efficient Top-k Groups, Important Data Analysis, Top-k Groups