The data warehouse of newsgroups. Himanshu Gupta and Divesh Srivastava. Electronic newsgroups are one of the primary means for the dissemination, exchange and sharing of information. We argue that the current newsgroup model is unsatisfactory, especially when posted articles are relevant to multiple newsgroups. We demonstrate that considerable additional flexibility can be achieved by managing newsgroups in a data warehouse, where each article is a tuple of attribute-value pairs, and each newsgroup is a view on the set of all posted articles. Supporting this paradigm for a large set of newsgroups makes it imperative to efficiently support a very large number of views: this is the key difference between newsgroup data warehouses and conventional data warehouses. We identify two complementary problems concerning the design of such a newsgroup data warehouse. An important design decision that the system needs to make is which newsgroup views to eagerly maintain (i.e., materialize). We demonstrate the intractability of the general newsgroup-selection problem, consider various natural special cases of the problem, and present efficient exact/approximation algorithms and complexity hardness results for them. A second important task concerns the efficient incremental maintenance of the eagerly maintained newsgroups. The newsgroup-maintenance problem for our model of newsgroup definitions is a more general version of the classical point-location problem, and we design an I/O and CPU efficient algorithm for this problem.