Abstract: A method and structure for analyzing a database having non-text data in data fields and text in text fields. The invention first selects a subset of the database based upon criteria. The subset includes data field(s) and associated text field(s). The invention searches for data matching the criteria within structured data fields of the database. If the invention searches multiple databases, the invention creates shared dimensions for databases that do not share common attributes. The invention automatically selects a relatively short text phrase from the text fields that helps to explain the underlying meaning (i.e. unique text content) of a data subset selected using the non-text data fields.

Abstract: A relational database system and method to perform a method of temporal summarization of a data set comprising data that are initially unevenly distributed with respect to a period of time in the set and have attributed data categories and dates, wherein the method comprises creating N different buckets in a hashing table, wherein each of the buckets comprises a substantially same number of data elements, wherein each of the data elements uniquely corresponds with a particular bucket, and wherein each of the buckets corresponds with a unique time period; and plotting, for each data category, a number corresponding to the number of data elements in the data category, as a function of the time periods corresponding to the N different buckets. The method may also comprise plotting respective line graphs corresponding to the plotted number of data elements, wherein each line graph corresponds with a particular data category.

Abstract: A method and system for interesting relationships in text documents includes generating a dictionary of keywords in the text documents, forming categories of the text documents using the dictionary and an automated algorithm, counting occurrences of the structured variables, categories and structured variable/category combinations in the text documents, and calculating probabilities of occurrences of the structured variable/category combinations.

Abstract: A user initially analyzes a statistically significant sample of documents randomly drawn from a data warehouse to create a cached feature space and text classifier, which can then be used to establish a classification dimension in the data warehouse for in depth and detailed text analysis of the entire data set.