{'fraction_non_alphanumeric': 0.022888713496448304, 'fraction_numerical': 0.0, 'mean_word_length': 5.436548223350254, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A distributed OLAP infrastructure for e-commerce

Warehousing and mining sales transaction data to generate summary information, customer profiles, and business rules has become increasingly important in e-commerce. Such summary information and rules have to be extracted from very large collections of transaction data gathered at many distributed sites. This is challenging data mining, both in terms of the magnitude of data involved, and the need to incrementally adapt the mined patterns and rules as new data is collected. This paper describes a distributed and cooperative data warehousing, OLAP, and data mining infrastructure that addresses these challenges. Our contributions are as follows. First, we define various new classes of multidimensional and multi-level association rules (scoped multidimensional, with conjoint items, and functional) that can be extracted from customer profiles and are used for e-commerce applications. Then, we show how customer profiles and different classes of association rules can be computed in a distributed, cooperative manner using OLAP tools. Finally, we show how the summaries, profiles, and rules can be incrementally updated as new transaction data is collected. This infrastructure has been prototyped at HP Labs.