Abstract: A data-triggered process definition language is employed, wherein each activity specified in a preferred process definition language is permitted to be enacted whenever a specified combination of data conditions is met, regardless of which activities have previously been enacted. A data-triggered workflow engine utilizes a current state of a process instance, the permitted and schedule rules, an activity network, and additional attributes of activities to schedule the enactment of activities. The activity network does not completely prescribe the enactment order, but rather controls what enactment order the data-triggered workflow engine will suggest to a participant. A participant, however, may select a different order based on other information, and can even enact activities that have not been scheduled.

Abstract: A method of using a group similarity measure, with an analyst, on a set containing a plurality of groups, the groups containing software objects, for providing a decision on which groups to combine, and wherein certain combined pairs are disapproved, comprises the following steps:(a) starting with a set of groups, each containing software objects, repeatedly identifying the two most similar groups according to the group similarity measure;(b) when an identified pair results, combining the identified pair by one of (A) merging the two groups and (B) making a supergroup containing the two groups as subgroups; and(c) stop repeating steps (a) and (b) when the current resulting set of groups is satisfactory.

Abstract: Given a set of objects (A, B, C, . . . ), each described by a set of attribute values, and given a classification of these objects into categories, a similarity function accounts well for this classification when only a small number of objects are not correctly classified.

Abstract: A method for computing the similarity between two groups of objects wherein the similarity between any pair of objects can be computed by a similarity function, the method being for use in software clustering, when it is known that only objects that are "near neighbors" to a given object are to be considered when computing similarity, that is, for some positive integer "Y", only the Y-nearest neighbors are to be considered, wherein for some smaller integer "X" when an object is in the same group as at least X of its Y nearest neighbors, none of the other neighbors matter, and wherein the input comprises a set of groups containing software objects, two specific groups, A and B, and two integer values for X and Y, the method comprising:(a) for each object, identifying its X nearest neighbors (hereafter called its X-neighbors);(b) for each object, identifying its Y nearest neighbors, hereinafter called its Y-neighbors;(c) for each pair of objects for which neither is a Y-neighbor of the other, redefining the sim

Abstract: A method of reoptimizing the coefficients of a similarity function coefficient estimation as mavericks are resolved in a maverick analysis comprises computing initial weights for each feature and passing the similarity function to an estimation procedure, along with software objects, their group assignments, a peer parameter K and a confidence parameter N. Receiving as output and using updated values for the coefficients to obtain lists of misclassified and poor-confidence mavericks and placing them in a Current Maverick Set.

Abstract: In a software system, a method for computing the similarity between first and second software objects, comprises the steps of using a monotonic, matching, symmetric function of the common distinctive features and including a term to account for linking.

Abstract: A method for identifying software objects that have been assigned to a wrong group, in which the similarity between objects is known, such as by evaluating a similarity function, comprises the steps of checking each object to see whether it belongs to its current group with K peers and confidence N, checking whether each object belongs to another group with a lower and therefore better confidence rating, and identifying as misclassified those objects having a lower confidence rating in said another group.