Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes unifying terms of interest in the collection of terms of interest to identify variants of the terms of interest. This includes identifying candidate variants of a term of interest based on semantic similarity between the term of interest and other terms in the database, determined using an unsupervised machine learning algorithm. Linguistic features and contextual features of the term of interest and its candidate variants are extracted, at least the contextual features being extracted using the unsupervised machine learning algorithm. And a supervised machine learning algorithm is used with the linguistic features and contextual features to identify variants of the term of interest from the candidate variants, such as for application to generate features of the documents for data analytics performed thereon.

Abstract: An apparatus comprises an acoustic sensing system and an analyzer module. The acoustic sensing system is positioned relative to an object, wherein the acoustic sensing system detects acoustic emissions and generates acoustic waveform data for the acoustic emissions detected. The analyzer module is implemented in a computer system that receives load data and the acoustic waveform data for the object, generates a plurality of frequency distribution functions using the acoustic waveform data, and generates a frequency distribution function time evolution image containing a plurality of points of each of the plurality of frequency distribution functions.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes text mining the documents for terms of interest, including receiving a set of seed nouns as input to an iterative process an iteration of which includes searching for multiword terms having seed nouns as their head words, at least some of which define a training set of a machine learning algorithm used to identify additional multiword terms at least some of which have nouns outside the set of seed nouns as their head words. The iteration also includes adding the nouns outside the set of seed nouns to the set and thereby identifying a new set of seed nouns for a next iteration. The method includes unifying terms of interest to produce normalized terms of interest for application to generate features of the documents for data analytics performed thereon.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes unifying terms of interest in the collection of terms of interest to identify variants of the terms of interest. This includes identifying candidate variants of a term of interest based on semantic similarity between the term of interest and other terms in the database, determined using an unsupervised machine learning algorithm. Linguistic features and contextual features of the term of interest and its candidate variants are extracted, at least the contextual features being extracted using the unsupervised machine learning algorithm. And a supervised machine learning algorithm is used with the linguistic features and contextual features to identify variants of the term of interest from the candidate variants, such as for application to generate features of the documents for data analytics performed thereon.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes text mining the documents for terms of interest, including receiving a set of seed nouns as input to an iterative process an iteration of which includes searching for multiword terms having seed nouns as their head words, at least some of which define a training set of a machine learning algorithm used to identify additional multiword terms at least some of which have nouns outside the set of seed nouns as their head words. The iteration also includes adding the nouns outside the set of seed nouns to the set and thereby identifying a new set of seed nouns for a next iteration. The method includes unifying terms of interest to produce normalized terms of interest for application to generate features of the documents for data analytics performed thereon.

Abstract: An apparatus comprises an acoustic sensing system and an analyzer module. The acoustic sensing system is positioned relative to an object, wherein the acoustic sensing system detects acoustic emissions and generates acoustic waveform data for the acoustic emissions detected. The analyzer module is implemented in a computer system that receives load data and the acoustic waveform data for the object, generates a plurality of frequency distribution functions using the acoustic waveform data, and generates a frequency distribution function time evolution image containing a plurality of points of each of the plurality of frequency distribution functions.

Abstract: A method, apparatus and computer program product are provided for identifying a part name within a data record. A maintenance expression is initially identified within a data record and a candidate part name string is then identified by identifying a head noun within a window that is positioned within the data record based upon the expression. In addition to identifying the head noun, a modifier may also be identified adjacent to or near any occurrence of the head noun in the course of identifying the candidate part name string. The candidate part name string may then be separately matched to respective ones of a plurality of standard names with each of a plurality of string matching techniques. The resulting potential matches are then analyzed to determine a best match.

Abstract: A method and apparatus for analyzing an object using acoustic emissions. Load data is received for the object. Acoustic waveform data is received for the object from an acoustic sensing system. The acoustic waveform data represents acoustic emissions emanating from the object and is detected using the acoustic sensing system. A plurality of bins is created for the load data. A plurality of frequency distribution functions is generated for the plurality of bins using the acoustic waveform data. A set of learning algorithms is applied to the plurality of frequency distribution functions and the acoustic waveform data to generate an output that allows an operator to more easily and quickly assess a structural integrity of the object.

Abstract: A method and apparatus for analyzing an object using acoustic emissions. Load data is received for the object. Acoustic waveform data is received for the object from an acoustic sensing system. The acoustic waveform data represents acoustic emissions emanating from the object and is detected using the acoustic sensing system. A plurality of bins is created for the load data. A plurality of frequency distribution functions is generated for the plurality of bins using the acoustic waveform data. A set of learning algorithms is applied to the plurality of frequency distribution functions and the acoustic waveform data to generate an output that allows an operator to more easily and quickly assess a structural integrity of the object.

Abstract: A method and apparatus are provided to efficiently generate a fulsome query in order to increase the recall and/or precision provided by the search. A method may construct a query by receiving the one or more initial search terms and then defining a concept for each search term. In order to define a concept, the method may determine if a concept associated with a respective search term has been previously defined. In an instance in which a concept associated with a respective search term has been previously defined, the method at least initially utilizes the previously defined concept. However, in an instance in which a concept associated with a respective search term has not been previously defined, the method constructs the concept based on terms related to the respective search term. The method may then combine the concepts defined for the one or more search terms to generate the query.

Abstract: A method including processing social network data to establish a tensor model of the social network data, the tensor model having at least an order of four. The tensor model is decomposed into a plurality of principal factors. A summary tensor is synthesized from a subset of the plurality of principal factors. The summary tensor represents a plurality of relationships among a plurality of entities in the tensor model. A synthesis of relationships is formed and stored. At least one parameter is identified using one of the summary tensor and a single principal factor in the subset. The at least one parameter is selected from the group consisting of: a correlation among the plurality of entities, a similarity between two of the plurality of entities, and a time-based trend of changes in the synthesis of relationships. The at least one parameter is communicated.

Abstract: Methods and maintenance systems for use in analyzing data related to maintenance of at least one vehicle are disclosed. One example method includes receiving at least one fault message, receiving a maintenance event log for a vehicle. The maintenance event log including at least one maintenance event associated with the at least one fault message. The example method further includes automatically identifying a corrective action within a most recent maintenance event of the at least one maintenance event and storing a diagnostic entry including the at least one fault message and the identified corrective action, such that the diagnostic entry is retrievable from the computing device to evaluate a subsequent like fault message.

Abstract: A method including processing social network data to establish a tensor model of the social network data, the tensor model having at least an order of four. The tensor model is decomposed into a plurality of principal factors. A summary tensor is synthesized from a subset of the plurality of principal factors. The summary tensor represents a plurality of relationships among a plurality of entities in the tensor model. A synthesis of relationships is formed and stored. At least one parameter is identified using one of the summary tensor and a single principal factor in the subset. The at least one parameter is selected from the group consisting of: a correlation among the plurality of entities, a similarity between two of the plurality of entities, and a time-based trend of changes in the synthesis of relationships. The at least one parameter is communicated.

Abstract: Technologies are described herein for providing automated analysis and summarization of free-form comments in survey response data. A number of topic words are identified from the survey response comments, and a numeric weight is calculated for each topic word that reflects the relevance of the topic word to each comment. Each topic word is associated with one or more topics and the comments relevant to each topic is then determined based on the weights of the associated topic words in each comment. A report is generated which summarizes the topics and their relative importance in the survey response comments based upon the number of comments relevant to each.

Abstract: Methods and maintenance systems for use in analyzing data related to maintenance of at least one vehicle are disclosed. One example method includes receiving at least one fault message, receiving a maintenance event log for a vehicle. The maintenance event log including at least one maintenance event associated with the at least one fault message. The example method further includes automatically identifying a corrective action within a most recent maintenance event of the at least one maintenance event and storing a diagnostic entry including the at least one fault message and the identified corrective action, such that the diagnostic entry is retrievable from the computing device to evaluate a subsequent like fault message.

Abstract: A method of mining data to discover activity patterns within the data is described. The method includes receiving data to be mined from at least one data source, determining which of a number of specified interests and constraints are associated with the mining process, selecting corresponding mining agents that combine search algorithms with propagators from the specified constraints, and finding any activity patterns that meet the specified interests and constraints.

Abstract: Technologies are described herein for providing automated analysis and summarization of free-form comments in survey response data. A number of topic words are identified from the survey response comments, and a numeric weight is calculated for each topic word that reflects the relevance of the topic word to each comment. Each topic word is associated with one or more topics and the comments relevant to each topic is then determined based on the weights of the associated topic words in each comment. A report is generated which summarizes the topics and their relative importance in the survey response comments based upon the number of comments relevant to each.

Abstract: A method of mining data to discover activity patterns within the data is described. The method includes receiving data to be mined from at least one data source, determining which of a number of specified interests and constraints are associated with the mining process, selecting corresponding mining agents that combine search algorithms with propagators from the specified constraints, and finding any activity patterns that meet the specified interests and constraints.

Abstract: A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.

Abstract: A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.