Abstract: The method of the present invention aligns a set of N sequences, where N is large. The alignment brings out the best commonality of the N sequences. The method is performed in two stages. A first stage involving discovering motifs, and a second stage involve motif pruning and sequence alignment. The present invention also provides an additional constraint, K, as a user defined control parameter. The additional parameter constrains the alignment of the N sequences to have at least K of the N sequences agree on a character, whenever possible, in the alignment. The alignment number, K, provides a natural constraint for dealing with a large number of sequences in that a commonality across most, if not all sequences is required to be detected.

Abstract: The method of the present invention discovers patterns in a sequence of characters in two phases. In a sampling phase, preferably proper templates corresponding to the sequence of characters are generated. Patterns are then generated corresponding to the templates and stored in memory. In a convolution phase, the patterns stored in memory are combined to identify a set of maximal patterns.

Abstract: The present invention groups character sequences by identifying a sequence of characters. A set of internal repeats in said sequence of characters is identified by a pattern discovery technique. For at least one internal repeat belonging to the set of internal repeats, it is determined if the internal repeat corresponds to a group of character sequences; If so, first data that identifies the sequence of characters and second data that associates the sequence of characters with the group of character sequences is stored in persistent memory. The pattern discovery mechanism discovers patterns in a sequence of characters in two phases. In a sampling phase, preferably proper templates corresponding to the sequence of characters are generated. Patterns are then generated corresponding to the templates and stored in memory. In a convolution phase, the patterns stored in memory are combined to identify a set of maximal patterns.

Abstract: The method of the present invention discovers patterns in an sequence of characters in two phases. In a sampling phase, preferably proper templates corresponding to the sequence of characters are generated. Patterns are then generated corresponding to the templates and stored in memory. In a convolution phase, the patterns stored in memory are combined to identify a set of maximal patterns. A subset of the maximal patterns is selected. Compressed data representing the sequence of characters is generated. The compressed data includes first data data representing each selected pattern of the subset, and second data representing the sequence of characters wherein occurrences of each selected pattern within the sequence of characters is replaced by a reference to first data corresponding to the selected pattern. The method is useful in compressing information stored in a database or compressing information communicated over a communication link.

Abstract: A uniform distribution of affine invariants is produced for a plurality of one or more two-dimensional objects. Each of the two-dimensional objects is defined by a set of object points selected from an object feature domain. By selecting one or more five-point tuples of the object points, three of the object points divide the object feature domain into a region arrangement of seven regions while the fourth point of the four-point tuple lies in one of the seven regions and each of the four-point tuples further defines each of the seven regions as one of four non convex quadrilateral arrangements or one of three convex quadrilateral arrangements. A four-point tuple is said to belong to class I if the fourth point of the tuple resides in the I-th of the 7 regions defined by the first three points. A tagger identifies each of the four-point tuples as having one of the arrangements with one of the regions containing the fourth point of the four-point tuple.

Abstract: A uniform distribution of affine invariants (3-tuples) is produced for a plurality of one or more three-dimensional objects. Each of the three-dimensional objects, capable of a plurality of affine transformations, is defined by a set of object points (feature points) selected from an object feature domain. By selecting one or more five-point tuples of the object points, four of the object points divide the object feature domain into a region arrangement of fifteen regions while the fifth point of the five-point tuple lies in one of the fifteen regions and each of the five-point tuples further defines each of the fifteen regions as one of five reentrant polyhedral (RP) arrangements or one of ten non-reentrant polyhedral (NRP) arrangements. A five-point tuple is said to belong to class i if the fifth point of the tuple resides in the i-th of the 15 regions defined by the first four points.

Abstract: A reference storage process populates a data structure so that the data structure contains all of the molecular structures and/or rigid substructures in the data base classified according to attributes of tuples. In a preferred embodiment, the tuples are derived from sites (e.g. atomic sites) of the molecular structures and the attributes can be derived from geometric (and other) information related to the tuples. The attributes are used to define indices in the data structure that are associated with invariant vector information (e.g. information about rotatable bond(s) in skewed local coordinate frames created from tuples). These representations are invariant with respect to the rotation and translation of molecular structures and/or the rotation of substructures about attached rotatable bond(s). Accordingly, the invariant vector information is classified in the data structure with the respective tuple attributes in locations determined by the index derived from the respective tuple.

Abstract: A reference storage process populates a data structure so that the data structure contains all of the molecular structures and/or rigid substructures in the database classified according to attributes of tuples. In a preferred embodiment, the tuples are derived from sites (e.g. atomic sites) of the molecular structures and the attributes can be derived from geometric (and other) information related to the tuples. The attributes are used to define indices in the data structure that are associated with invariant vector information (e.g. information about rotatable bond(s) in skewed local coordinate frames created from tuples). These representations are invariant with respect to the rotation and translation of molecular structures and/or the rotation of substructures about attached rotatable bond(s). Accordingly, the invariant vector information is classified in the data structure with the respective tuple attributes in locations determined by the index derived from the respective tuple.