Abstract: A system and method for receiving time series data, representing the time series data as vector data, generating a plurality of indices using the vector data, wherein each of the indices has a different resolution and independently searching each of the indices for a given event. The vector data may be Symbolic Aggregate approXimation (SAX) data and the indices are SAX indices.

Abstract: A system and method for creating a set of objects correlated with a query. For each element of the set, calculating one of a relevance or a correlation between the element and other elements of the set, constructing an archetype comprising a list of elements of the set for which the relevance or the correlation satisfies a threshold and associating the list of elements with a probability and sorting the list of elements by the probability, wherein the elements having a greater probability are first.

Abstract: Described is a system and method for storing a plurality of data points in a form Subject->Object and Object->Subject, where subject and object are differently typed entities, wherein the data points are stored in a plurality of segments, performing an expression search in each segment to identify an expression set of objects or subjects which can be viewed as the right hand side of the expression, determining, for each segment, actions corresponding to each of the data points in the expression set, determining a count of each of the actions and applying a metric to each of the expression set, the actions and the count to obtain a result.

Abstract: A document similarity detection and classification system is presented. The system employs a case-based method of classifying electronically distributed documents in which content chunks of an unclassified document are compared to the sets of content chunks comprising each of a set of previously classified sample documents in order to determine a highest level of resemblance between an unclassified document and any of a set of previously classified documents. The sample documents have been manually reviewed and annotated to distinguish document classifications and to distinguish significant content chunks from insignificant content chunks. These annotations are used in the similarity comparison process. If a significant resemblance level exceeding a predetermined threshold is detected, the classification of the most significantly resembling sample document is assigned to the unclassified document.

Abstract: A system and method is provided for assisting a human document annotator in recording semantic judgments about the contents of sample electronic documents. A system administrator first configures and stores a document annotation definition at a server computer, providing a precise and consistent structure for annotating documents and portions of documents. Documents intended to serve as sample documents for pattern matching against unknown documents are collected and stored at the server computer. A human annotator located at a client computer connected by a net-work to the server computer requests a display of a sample document to be annotated. A document is transmitted in an annotatable form from the server computer to the client computer. The human document annotator reviews the annotatable document, records semantic judgments about the document using interactive controls displayed with the document, and transmits a set of selected annotation values to the server computer.