International Business Machines Corporation

David Alan Rabenhorst

Joseph L Hellerstein

Sheng Ma

Lloyd Alan Treinish

Bernice E Rogowitz

Chang-shing Perng

Luanne M Burns

Methods and systems are described for learning correlation rules used in event management. In one aspect of the invention, a method comprises the steps of: (a) marking one or more event groupings; (b) employing a machine learning program to learn the underlying concept of these groupings; (c) including a rule right-hand side; and (d) putting the new rule in the Rule DB. A system to implement this method may comprise components for: (1) interactive visualization and user interface control; (2) query-based learning; (3) Event DB access; and (4) correlation Rule DB access.

Method and system for machine-aided rule construction for event management

A technique is provided for systematically constructing one or more correlation rules for use by an event management system for managing a network with one or more computing devices. The technique comprises the following steps. First, in association with an event cache, event data representing past or historical events associated with the network of computing devices being managed by the event management system is obtained. Next, a first pattern is found or detected in the obtained event data associated with the event cache. The pattern is then classified. Then, at least one correlation rule is constructed based on the classified pattern. Lastly, in association with the event cache, the one or more events included in the pattern are replaced with a composite or cumulative event such that hierarchical patterns may be subsequently found for use in constructing further correlation rules.

System and method for systematic construction of correlation rules for event management

A system and method for dramatically reducing the number of vertices defining a polygon on a grid, without significantly changing its effective enclosed area is disclosed. A smoothing process is executed on any general purpose computer system to operate on one or more representations of one or more curves. Each of the curves has a set of a plurality of vertices. The smoothing process first selects a first vertex, a third vertex, and a second middle vertex, the first, second, and third vertices being sequential but not necessarily consecutive on the curve. Then the smoothing process determines the area of a triangle formed by the first, second, and third vertices. This triangular area is compared to a threshold area. If the area is less than the threshold, new vertices are selected along the curve and the process is repeated. However if the area of the triangle is greater than or equal to the threshold, the second (middle) vertex is marked as an important vertex before a new set of vertices is selected. The reduced set of only the important vertices needed may effectively substitute for the complete set when processing the vertices and/or when rendering the curve. This is repeated along the curve until a stop criteria is reached.

Curve contour smoothing

Visually analyzing, selecting, manipulating, displaying, and exploring relationships among variables in data bases with a large number of variables is enables by visualization of pictorial presentations, usually scatter plots. Each scatter plot shows the relationship among some subset of variables in the database. The scatter plots are mutually coupled to one another so that any mathematical transformation of an independent variable in one plot causes changes in other plots with dependent variables according to the relationship between the changed independent variable and the dependent variable. The mutual coupling also applies to color selection and performing logical operations with color on the plots. The plots can access other subsidiary presentations by using specified access criteria. Subsidiary presentations also can access other presentations. All presentations are mutually coupled.

Interactive graphical method for analyzing many-dimensional data sets

A rule based system and method interacts with a user to provide the user a series of interim results. The system has a set of one or more operations. Each of these operations in turn has a set of one or more choices, called a choice set, associated with an operation. Rule based intelligence is applied to the operations set and/or one or more operation choice set by a set of rules associated with its respective operation and/or choice set. When one or more rules in a rule set are applied to the operation set, an offered operation set is created which is a subset of the operation set. Only the operations in the offered operation set are offered to the user. When one or more rules in a rule set are applied to a choice set, an offered choice set is created which is a subset of the choice set. Only those operation choices in the offered choice set are offered to the user. The operations (operation choices) that are in the offered operation set (offered choice set) are those that conform to the constraints of the rules that are applied. The user selects operations (operation choices) from the offered operation set (offered choice set). Information about the user selections feeds back to the rules and is used to constrain the next offered operations and operation choices. The user is also permitted to change rules, rule specific metadata, and rule specific metadata algorithms as well as the data in the system. Interacting with the system this way, the user preference, knowledge, and judgment is incorporated in each of a series of interim results and is used to further develop the next interim result in the series of iterations.

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Woodcliff Lake, NJ, United States of America

David Alan Rabenhorst