Silicon Graphics, Incorporated

Enkata Technologies, Inc.

Daniel A Sommerfield

Ron Kohavi

Joel D Tesler

Barry Glenn Becker

Hinrich H Schuetze

Stan Stukov

A method for loading data into a datamart. The method includes identifying business data in a first format from a real business process and identifying a desired second format. The method includes designing a transformation algorithm that transforms the business data in the first format into the second format and implementing the transformation algorithm in computer executable code. The method includes running the computer executable code on business data in the first format and generating business data in the second format and storing the business data in the second format in a datamart.

System and method for efficient enrichment of business data

A method, system, and computer program product visualizes the structure of an evidence classifier. An evidence inducer generates an evidence classifier based on a training set of labeled records. A mapping module generates visualization data files. An evidence visualization tool uses the visualization data files to display an evidence pane and/or a label probability pane. A first evidence pane display view shows a normalized conditional probability of each label value, for each attribute value. The first evidence pane display view can be a plurality of rows of pie charts. Each pie slice in a pie chart has a size which is a function of the normalized conditional probability of each label value for the respective attribute value. For each pie chart, the mapping module maps a height that is a function of the number of records in the training set associated with the evidence classifier. A second evidence pane display view shows relative conditional probabilities of a selected label value, for each attribute value. The second evidence pane display view can be a plurality of rows of bars. Bar height is a function of a conditional probability of a respective attribute value conditioned on the selected label value. Bar heights can represent Evidence For a selected label value or Evidence Against a selected label. A first label probability pane display view shows a pie chart of prior probabilities of each label value based on the training set. A second label probability pane display view shows a pie chart of posterior probabilities of each label value based on at least one selected attribute value. An importance slider controls filtering of attributes based on the importance of the attributes to a classification of unlabeled records. A count slider filters out attribute values having relatively low record counts. The evidence classifier visualization tool further provides sorting of attributes and/or attribute values. A subtracting minimum evidence capability is provided.

Method system and computer program product for visualizing an evidence classifier

A system and method determines how well various attributes in a record discriminate different values of a chosen label attribute. An attribute is considered a relevant attribute if it discriminates different values of a chosen label attribute either alone or in conjunction with other attributes. According to the present invention, a label attribute is selected by a user from a set of records, with each record having a plurality of attributes. Next, one or more first important attributes considered important by the user are selected. The present invention then generates one or more second important attributes. The second important attributes together with the user chosen first important attributes discriminate well between different values of the label attribute. A measure called 'purity' (a number from 0 to 100) informs how well each attribute discriminates the different label attributes. The purity measure allows the attributes to be ranked based on their importance.

System and method for selection of important attributes

A method, system, and computer program product visualizes the structure of an evidence classifier. An evidence inducer generates an evidence classifier based on a training set of labeled records. A mapping module generates visualization data files. An evidence visualization tool uses the visualization data files to display an evidence pane and/or a label probability pane. A first evidence pane display view shows a normalized conditional probability of each label value, for each attribute value. The first evidence pane display view can be a plurality of rows of pie charts. Each pie slice in a pie chart has a size which is a function of the normalized conditional probability of each label value for the respective attribute value. A second evidence pane display view shows relative conditional probabilities of a selected label value, for each attribute value. The second evidence pane display view can be a plurality of rows of bars. Bar height is a function of a conditional probability of a respective attribute value conditioned on the selected label value. A first label probability pane display view shows a pie chart of prior probabilities of each label value based on the training set. A second label probability pane display view shows a pie chart of posterior probabilities of each label value based on at least one selected attribute value. An importance slider controls filtering of attributes based on the importance of the attributes to a classification of unlabeled records. A count slider filters out attribute values having relatively low record counts. The evidence classifier visualization tool further provides sorting of attributes and/or attribute values. A subtracting minimum evidence capability is provided.

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San Mateo, CA, United States of America

Daniel A Sommerfield