Abstract: A method of generating analytics to provide an analysis of data from distinct data domains includes collecting sensor data from at least two distinct data domains, deriving parameters from the collected data, wherein at least one of the parameters is a first domain parameter derived from one of the data domains and at least another one of the parameters is a second domain parameter derived from the other data domain, providing a data model that enables a user to specify at least one of the first parameters and at least one of the second domain parameters and generate at least one rule based on the selected parameters, and generating analytics that analyze the collected data against the rules to determinate whether the rules have been satisfied and provide results of the analysis to a user of the analytics.

Abstract: A method of estimating a transit demand graph includes collecting conditional information that includes at least one condition that when satisfied converts at least one non-rider into a rider, generating a non-rider transit demand graph by satisfying one of the conditions, and generating a normalized transit demand graph from the non-rider transit demand graph and a rider transit demand graph. The riders use public transit and the non-riders do not use public transit. The non-rider transit demand graph shows the demand of the non-riders for a public transit route. The rider transit demand graph shows the demand of riders for the same public transit route.

Abstract: A method of estimating a transit demand graph includes collecting conditional information that includes at least one condition that when satisfied converts at least one non-rider into a rider, generating a non-rider transit demand graph by satisfying one of the conditions, and generating a normalized transit demand graph from the non-rider transit demand graph and a rider transit demand graph. The riders use public transit and the non-riders do not use public transit. The non-rider transit demand graph shows the demand of the non-riders for a public transit route. The rider transit demand graph shows the demand of riders for the same public transit route.

Abstract: A method of estimating a transit demand graph includes collecting conditional information that includes at least one condition that when satisfied converts at least one non-rider into a rider, generating a non-rider transit demand graph by satisfying one of the conditions, and generating a normalized transit demand graph from the non-rider transit demand graph and a rider transit demand graph. The riders use public transit and the non-riders do not use public transit. The non-rider transit demand graph shows the demand of the non-riders for a public transit route. The rider transit demand graph shows the demand of riders for the same public transit route.

Abstract: A method of generating analytics to provide an analysis of data from distinct data domains includes collecting sensor data from at least two distinct data domains, deriving parameters from the collected data, wherein at least one of the parameters is a first domain parameter derived from one of the data domains and at least another one of the parameters is a second domain parameter derived from the other data domain, providing a data model that enables a user to specify at least one of the first parameters and at least one of the second domain parameters and generate at least one rule based on the selected parameters, and generating analytics that analyze the collected data against the rules to determinate whether the rules have been satisfied and provide results of the analysis to a user of the analytics.

Abstract: Exemplary embodiments of this invention provide a method that includes estimating an empirical distribution of a metric for a company. The method includes estimating a distribution of the metric for a plurality of companies. The method further includes determining whether the company is an outlier, based on the empirical distribution of the metric for the company and the distribution of the metric for the plurality of companies, and generating at least one recommendation based determining whether the company is an outlier.

Abstract: The present invention is directed to a method and apparatus for assisting in rating and filtering multimedia content, such as images, videos and sound recordings. One embodiment comprises a computer implemented method for rating the objectionability of specified digital content that comprises one or more discrete content items, wherein the method includes the step of moving the specified content to one or more filtering stages in a succession of filtering stages. After the specified content is moved to a given one of the filtering stages, a rating procedure is carried out to determine whether a rating can be applied to one or more of the content items, and if so, a selected rating is applied to each of the one or more content items. The method further comprises moving content items of the specified content to the next stage in the succession after the given stage, when at least one content item of the specified content remains without rating, after the rating procedure at the given stage.

Abstract: Exemplary embodiments of this invention provide a method that includes estimating an empirical distribution of a metric for a company. The method includes estimating a distribution of the metric for a plurality of companies. The method further includes determining whether the company is an outlier, based on the empirical distribution of the metric for the company and the distribution of the metric for the plurality of companies, and generating at least one recommendation based determining whether the company is an outlier.

Abstract: The present invention is directed to a method and apparatus for assisting in rating and filtering multimedia content, such as images, videos and sound recordings. One embodiment comprises a computer implemented method for rating the objectionability of specified digital content that comprises one or more discrete content items, wherein the method includes the step of moving the specified content to one or more filtering stages in a succession of filtering stages. After the specified content is moved to a given one of the filtering stages, a rating procedure is carried out to determine whether a rating can be applied to one or more of the content items, and if so, a selected rating is applied to each of the one or more content items. The method further comprises moving content items of the specified content to the next stage in the succession after the given stage, when at least one content item of the specified content remains without rating, after the rating procedure at the given stage.

Abstract: A method, system and program product for evaluating annotations to content are described. Under aspects of the present invention, annotations made to content are received and evaluated for accuracy. Each annotation typically includes at least one element (e.g., terms) describing the content. The evaluation includes a syntactic level evaluation and at least one of a semantic level evaluation, a source level evaluation, a content level evaluation, or an annotator level evaluation. Based on the evaluations, feedback can be provided to an annotator making the annotations.

Abstract: A cross descriptor learning system, method and program product therefor. The system extracts descriptors from unlabeled exemplars. For each unlabeled exemplar, a cross predictor uses each descriptor to generate labels for other descriptor. An automatic label generator also generates labels for the same unlabeled exemplars or, optionally, for labeled exemplars. A label predictor results for each descriptor by combining labels from the cross predictor with labels from the automatic label generator.

Abstract: A system, method, and program product for tracking content are described. Aspects of invention allow bodies of content, whether from a common channel or from different channels, to be compared for relatedness. Comparison of different bodies of content involves analyzing both the actual content, characteristics of the source(s) of the content, and optionally, elapsed time between their respective broadcasts/communications. To this extent, a content similarity value, a source characteristic value and an optional temporal value for the portions of content are determined, and then used to compute a relatedness value of the (bodies of) content.

Abstract: A method, system and program product developing an annotation lexicon are described. Under aspects of the present invention, annotation(s) to piece(s) of content are received and analyzed using one or more computational analyses. Based on the analyses, feedback will be generated to improve the annotation lexicon and/or the ontology thereof. Such improvement can lead to, among other things: the re-arrangement of interrelationships of terms in the annotation lexicon; the addition, modification or deletion of terms from the annotation lexicon; the re-arrangement or clustering of terms within the annotation lexicon; etc.

Abstract: A method and apparatus for extracting a model vector representation from multimedia documents. A model vector provides a multidimensional representation of the confidence with which multimedia documents belong to a set of categories or with which a set of semantic concepts relate to the documents. A model vector can be associated with multimedia documents to provide an index of its content or categorization and can be used for comparing, searching, classifying, or clustering multimedia documents. A model vector can be used for purposes of information discovery, personalizing multimedia content, and querying a multimedia information repository.

Abstract: An autonomous classification device which enables the creation of autonomous classifiers that are easy to deploy, adapt and optimize in the environment in which they are used. The classifier is autonomous in that it can perform three functions that define autonomic systems: automatically configure itself in an environment, optimize its performance using the environment and mechanisms for performance, and continually adapting to improve performance and heal itself in a changing environment.

Abstract: A system and method for detecting a concept from digital content are provided. A plurality of representations is generated for same data content for concept detection from the plurality of representations. A plurality of concepts is simultaneously detected from the plurality of representations of the same data content wherein at least one detector provides selection information for selecting the representations generated or a combination of the generated representations. This results in multiple instances of a representation being considered for concept detection.

Abstract: Interoperability is enabled between participants in a network by determining values associated with a value metric defined for at least a portion of the network. Information flow is directed between two or more of the participants based at least in part on semantic models corresponding to the participants and on the values associated with the value metric. The semantic models may define interactions between the participants and define at least a portion of information produced or consumed by the participants. The determination of the values and the direction of the information flow may be performed multiple times in order to modify the one or more value metrics. The direction of information flow may allow participants to be deleted from the network, may allow participants to be added to the network, or may allow behavior of the participants to be modified.

Abstract: A method, system and recording medium in which descriptors at a first granularity level are propagated, mapped, and/or classified to generate an output content having descriptors at a second granularity level that is finer than the first granularity level.

Abstract: A method and apparatus for extracting a model vector representation from multimedia documents. A model vector provides a multidimensional representation of the confidence with which multimedia documents belong to a set of categories or with which a set of semantic concepts relate to the documents. A model vector can be associated with multimedia documents to provide an index of its content or categorization and can be used for comparing, searching, classifying, or clustering multimedia documents. A model vector can be used for purposes of information discovery, personalizing multimedia content, and querying a multimedia information repository.