Abstract: Disclosed herein is a computer implemented scheduling method. The method comprises processing project data to determine one or more team-specific scheduling solutions for work items, each team specific scheduling solution for the work item defining a particular team and one or more iterations of that particular team in which the work item can be completed; identifying from the one or more team-specific scheduling solutions an optimal scheduling solution for the work item, the optimal scheduling solution being selected based at least in part on the number of iterations defined by each of the one or more team-specific scheduling solutions; and scheduling the work item according to the optimal scheduling solution identified for the work item.

Abstract: Disclosed herein is a computer implemented scheduling method comprising: accessing project data from computer readable storage media. The project data comprises work item data defining a plurality of work items to be scheduled, and release data defining one or more releases into which one or more of the plurality of work items are to be scheduled. The method comprises processing, using a computer processor, the project data to schedule strictly assigned work items by identifying strictly assigned work items, a strictly assigned work item having a particular release into which the strictly assigned work item needs to be scheduled; and for each strictly assigned work item identified, scheduling the strictly assigned work item into the release specified for that strictly assigned work item.

Abstract: Disclosed herein is a computer implemented scheduling method comprising: accessing project data from computer readable storage media. The project data comprises work item data defining a plurality of work items to be scheduled, and release data defining one or more releases into which one or more of the plurality of work items are to be scheduled. The method comprises processing, using a computer processor, the project data to schedule strictly assigned work items by identifying strictly assigned work items, a strictly assigned work item having a particular release into which the strictly assigned work item needs to be scheduled; and for each strictly assigned work item identified, scheduling the strictly assigned work item into the release specified for that strictly assigned work item.

Abstract: Disclosed herein is a computer implemented scheduling method. The method comprises processing project data to determine one or more team-specific scheduling solutions for work items, each team specific scheduling solution for the work item defining a particular team and one or more iterations of that particular team in which the work item can be completed; identifying from the one or more team-specific scheduling solutions an optimal scheduling solution for the work item, the optimal scheduling solution being selected based at least in part on the number of iterations defined by each of the one or more team-specific scheduling solutions; and scheduling the work item according to the optimal scheduling solution identified for the work item.

Abstract: Described herein is a computer implemented method comprising processing, using a computer processor, project data to generate a set of solution candidates for an activity of a work item, selecting a solution candidate from the set of solution candidates, and processing the project data to attempt to identify an assignment of the activity to the selected solution candidate. A solution candidate comprises a subset of resources from a team of resources. Generating the a solution candidate comprises selecting a number of resources for inclusion in the subset of resources, and selecting a given resource for inclusion in a solution candidate subset comprises selecting a sampling method and using the selected sampling method to select a resource from the team of resources.

Abstract: A method of processing raw events to typed events, each raw event including data items containing data values, the method includes: providing a library of event type objects, each event type object relating to a given event type and including attributes of given data types, the attributes in each event type object structured according to a given structure; for each raw event, determining an event type object in the library which meets: (i) each of the data items in the raw event maps to an attribute in the event type object, and (ii) a data value in each of the data items is of a data type detected to match the given data type of the mapped attribute; and generating a typed event from each raw event, the typed event including the data values of the raw event structured according to the structure of the determined event type object.

Abstract: A method of visualizing sets of correlated events of a stream of events on a display, each event having a time of occurrence in the stream and at least one attribute indicating its correlation with the other events of a set, including the steps of plotting a glyph for each event on the display at a radial distance and an angular position with respect to an origin; the radial distance and the size of a glyph being proportional to the occurrence time of its event; the angular position of the glyph of a succeeding event in a set being chosen equal to that of the preceding event in the set unless these two glyphs would over-lap in which case the angular position of the glyph of said subsequent event is shifted in a given direction by an amount just sufficient to avoid such overlap; and the angular position of the glyph of the first event in a set being chosen so that overlapping of all glyphs is minimized.

Abstract: A method of detecting a reference sequence of events in a sample sequence of events, wherein each event is of a certain event type and holds a set of data attributes, includes the steps of: picking candidate combinations of events from said sample sequence so that the event types within each candidate combination match the event types in the reference sequence, calculating an overall similarity score for each candidate combination from at least (i) an event occurrence score based on occurrence deviations representing a count of non-matching events between the events of a candidate combination matching events of the reference sequence and (ii) an attribute match score based on similarity deviations between the data attributes of the events of a candidate combination and the data attributes of the matching events of the reference sequence, and identifying the candidate combination with the best overall similarity score as reference sequence detected.

Abstract: A method of detecting a reference sequence of events in a sample sequence of events, wherein each event is of a certain event type and holds a set of data attributes, includes the steps of: picking candidate combinations of events from said sample sequence so that the event types within each candidate combination match the event types in the reference sequence, calculating an overall similarity score for each candidate combination from at least (i) an event occurrence score based on occurrence deviations representing a count of non-matching events between the events of a candidate combination matching events of the reference sequence and (ii) an attribute match score based on similarity deviations between the data attributes of the events of a candidate combination and the data attributes of the matching events of the reference sequence, and identifying the candidate combination with the best overall similarity score as reference sequence detected.

Abstract: A method of detecting a reference sequence of events in a sample sequence of events, wherein each event is of a certain event type and holds a set of data attributes, includes the steps of: picking candidate combinations of events from said sample sequence so that the event types within each candidate combination match the event types in the reference sequence, calculating an overall similarity score for each candidate combination from at least (i) an event occurrence score based on occurrence deviations of the events of a candidate combination with respect to the matching events of the reference sequence and (ii) an attribute match score based on similarity deviations between the data attributes of the events of a candidate combination and the data attributes of the matching events of the reference sequence, and identifying the candidate combination with the best overall similarity score as reference sequence detected.

Abstract: A method of processing a source set of raw events of unknown event types to a target set of typed events, each raw event being a package of data items containing data values of detectable data types, includes the steps of: providing a library of event type objects each of which relates to a given event type and contains a structured set of attributes of given data types; for each raw event, determining at least one event type object in the library which meets the following criteria: (i) the data items of the raw event can each be mapped to one of the attributes of said event type object, and (ii) the detected data types of the raw event each match the given data type of the respective attribute mapped; and generating a typed event from the raw event by structuring the data values of the raw event in the form of the event type object determined.

Abstract: A method of detecting a reference sequence of events in a sample sequence of events, wherein each event is of a certain event type and holds a set of data attributes, includes the steps of: picking candidate combinations of events from said sample sequence so that the event types within each candidate combination match the event types in the reference sequence, calculating an overall similarity score for each candidate combination from at least (i) an event occurrence score based on occurrence deviations of the events of a candidate combination with respect to the matching events of the reference sequence and (ii) an attribute match score based on similarity deviations between the data attributes of the events of a candidate combination and the data attributes of the matching events of the reference sequence, and identifying the candidate combination with the best overall similarity score as reference sequence detected.

Abstract: A method of visualizing sets of correlated events of a stream of events on a display, each event having a time of occurrence in the stream and at least one attribute indicating its correlation with the other events of a set, including the steps of plotting a glyph for each event on the display at a radial distance and an angular position with respect to an origin; the radial distance and the size of a glyph being proportional to the occurrence time of its event; the angular position of the glyph of a succeeding event in a set being chosen equal to that of the preceding event in the set unless these two glyphs would over-lap in which case the angular position of the glyph of said subsequent event is shifted in a given direction by an amount just sufficient to avoid such overlap; and the angular position of the glyph of the first event in a set being chosen so that overlapping of all glyphs is minimized.