Abstract: A computer-implemented method, a processing pipeline and a system create a hierarchical semantic map of a document and extracted information. The method includes apportioning the document into major sections by accessing the document, recognizing a hierarchical structure of the document, and dividing the document into the major sections by using a data profiler and a machine learning module, classifying the major sections, and mapping the major sections to key elements in one of the multiple levels, searching one major section, and identifying sub-sections from the one major section to achieve a maximum confidence score indicates that the sub-sections associate with the key element, extracting the information from the identified sub-sections by using sequence modelers and linguistic characteristics provided by the data profiler, generating the hierarchical semantic map of the document by using the extracted information, and displaying in a user interface drop down selections of the key elements.

Abstract: A method may include receiving, by a device, an input sample of textual content. The method may include identifying, by the device, a comparison sample that is semantically similar to the input sample. The comparison sample may be identified based on a similarity score, of the comparison sample and the input sample, satisfying a semantic similarity threshold. The method may include identifying, by the device, a plurality of output samples of textual content based on acceptance information corresponding to the plurality of output samples and the comparison sample. The acceptance information may be determined based on a user input regarding similarity or relevance of the plurality of output samples and the comparison sample, and the user input may be received before the input sample is received.

Abstract: A device may obtain text to be categorized, and may determine rules for categorizing portions of the text. A rule may specify text patterns to be used to categorize the portions of the text into a category, and may specify a relationship, between at least two text patterns, to be used to categorize the portions of the text into the category. The device may determine that the text patterns and the relationship between the at least two text patterns exist in a text portion. The device may categorize the text portion into the category based on determining that the text patterns and the relationship between the at least two text patterns exist in the text portion. The device may provide an indication of an association between the text portion and the category based on categorizing the text portion into the category.

Abstract: A rapid application developer system is disclosed that provides a development platform configured to generate an application manifest document for developing a new target application within a certain industry. The application manifest is generated based on components identified from previously developed applications within the same industry as the target application.

Abstract: A method for component discovery from source code may include receiving source code, and determining business classes by excluding packages and classes in the source code identified as belonging to a presentation layer, as belonging to a data access layer, as models and/or as utilities. The method may further include extracting multi-dimensional features from the business classes, estimating similarity for business class pairs based on the extracted multi-dimensional features, clustering the business classes based on the similarity and mapping functional concepts to the clusters. The clusters generated by the clustering may represent components of the source code. The method may also include determining interfaces for the components based on the clustering.

Abstract: A device may obtain text to be processed to identify a trend associated with a topic included in the text. The text may include a plurality of text sections, associated with the topic, that may be associated with a plurality of temporal intervals. The device may determine a respective context for the topic in each of the plurality of text sections. The device may calculate a first specificity score based on the respective context for the topic for one or more text sections associated with the first temporal interval. The device may calculate a second specificity score based on the respective context for the topic for one or more text sections associated with the second temporal interval. The device may identify a trend associated with the topic based on the first specificity score and the second specificity score, and may provide information that identifies the trend.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating structured data using data received from unstructured textual data sources. One of the methods includes receiving unstructured textual data, identifying one or more keywords in the unstructured textual data, determining one or more patterns included in the unstructured textual data using the identified keywords, identifying one or more intelligence types that correspond with the unstructured textual data using the determined patterns, and associating, for each of the identified intelligence types, a data subset from the unstructured textual data with the respective intelligence type.

Abstract: This disclosure relates to assessing database migrations to cloud computing systems. On example method includes determining, by a migration server including one or more hardware processors, a set of possible transactions associated with a database application based at least in part on a set of application attributes associated with the database application; generating, by the migration server, a set of application requirements associated with the set of possible transactions; and creating, by the migration server, a set of migration recommendations associated with the database application based on the set of application requirements, the set of migration recommendations configured to allow the database application to be migrated to a cloud computing system and to allow the database application to comply with the set of application requirements when executed in the cloud computing system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for natural language processing of unstructured text are disclosed. In one aspect, a method includes the actions of receiving one or more unstructured data entries that each include one or more sentences, are each associated with an entity, and are each from a user. The actions further include parsing the one or more sentences. The actions further include determining one or more classifications of each unstructured data entry. The actions further include determining a sentiment. The actions further include accessing structured data. The actions further include defining one or more groups of users based on the structured data, wherein each of the one or more groups shares a common characteristic in the structured data. The actions further include determining sentiments to associate with the group.

Abstract: A device may receive a first command, included in a set of commands, to set a configuration parameter associated with performing feature extraction. The device may receive a second command, included in the set of commands, to set a corresponding value for the configuration parameter. The configuration parameter and the corresponding value may correspond to a particular feature metric that is to be extracted. The device may configure, based on the configuration parameter and the corresponding value, feature extraction for a corpus of documents. The device may perform, based on configuring feature extraction for the corpus, feature extraction on the corpus to determine the particular feature metric. The device may generate a feature vector based on performing the feature extraction. The feature vector may include the particular feature metric. The feature vector may include a feature identifier identifying the particular feature metric. The device may provide the feature vector.

Abstract: A device may receive a first textual document and a set of second textual documents. The first textual document may identify parameters. The device may process the first textual document, based on a natural language processing algorithm, to identify first values corresponding to the parameters. The device may process the plurality of second textual documents, based on the natural language processing algorithm, to identify second values corresponding to the parameters. Each of the plurality of second textual documents may be associated with one or more respective second values of the second values. The device may determine scores for the plurality of second textual documents. A score may be determined based on comparing one or more of the first values to one or more of the second values associated with a second textual document. The device may provide information identifying the plurality of second textual documents and the scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating structured data using data received from unstructured textual data sources. One of the methods includes receiving unstructured textual data, identifying one or more keywords in the unstructured textual data, determining one or more patterns included in the unstructured textual data using the identified keywords, identifying one or more intelligence types that correspond with the unstructured textual data using the determined patterns, and associating, for each of the identified intelligence types, a data subset from the unstructured textual data with the respective intelligence type.

Abstract: Various embodiments provide an assessment tool that enables an automated functional assessment of applications for migration to target cloud computing platforms, such as a Platform as a Service (PaaS). The technical capabilities of various types of applications in a traditional non-platform deployment are studied and support for these technical capabilities is evaluated relative to the target platform.

Abstract: The REsilient LOosely Coupled (ReLoC) system and method implement a session-state management architecture for cloud computing that uses loosely-coupled services and platform agnostic scalable messaging technology to propagate and save session states. Maintaining the state of applications and user sessions is difficult in large scale web-based software systems, particularly in the context of cloud computing (e.g., platform as a service (PaaS) vendors, do not explicitly support state management infrastructure, such as clustering). In a PaaS environment, a user has little or no access and control over the server platform and session management layer. The platform tiers are generally loosely coupled and service-oriented, which make traditional session-state management techniques non-usable. The ReLoC system and method provides a high level of tolerance to failures of the platform tiers without corresponding disruptions in user sessions.

Abstract: According to an example, source code flow analysis may include receiving source code for an application, and identifying virtual flow documents for the application from the source code. The virtual flow documents may represent ordered sequences of method calls for the application. The source code flow analysis may further include extracting features of the virtual flow documents, determining similarity between the virtual flow documents by estimating similarities for the extracted features to determine a flow-to-flow similarity, and clustering the virtual flow documents based on the flow-to-flow similarity. The flow-to-flow similarity may be further used, for example, to generate highest priority virtual flow documents and methods for the source code.

Abstract: Various embodiments provide an assessment tool that enables an automated functional assessment of applications for migration to target cloud computing platforms, such as a Platform as a Service (PaaS). The technical capabilities of various types of applications in a traditional non-platform deployment are studied and support for these technical capabilities is evaluated relative to the target platform.

Abstract: According to an example, source code flow analysis may include receiving source code for an application, and identifying virtual flow documents for the application from the source code. The virtual flow documents may represent ordered sequences of method calls for the application. The source code flow analysis may further include extracting features of the virtual flow documents, determining similarity between the virtual flow documents by estimating similarities for the extracted features to determine a flow-to-flow similarity, and clustering the virtual flow documents based on the flow-to-flow similarity. The flow-to-flow similarity may be further used, for example, to generate highest priority virtual flow documents and methods for the source code.

Abstract: A device may receive a text, from a text source, in association with a request to generate an ontology for the text. The device may generate a set of word vectors from a list of terms determined from the text. The device may determine a quantity of term clusters to be generated to form the ontology based on the set of word vectors. The device may generate term clusters based on the quantity of term clusters, attributes, and/or non-hierarchical relationships. The term clusters may be associated with concepts of the ontology. The device may provide the term clusters for display via a user interface associated with a device.

Abstract: A computer-implemented method, a processing pipeline and a system create a hierarchical semantic map of a document and extracted information. The method includes apportioning the document into major sections by accessing the document, recognizing a hierarchical structure of the document, and dividing the document into the major sections by using a data profiler and a machine learning module, classifying the major sections, and mapping the major sections to key elements in one of the multiple levels, searching one major section, and identifying sub-sections from the one major section to achieve a maximum confidence score indicates that the sub-sections associate with the key element, extracting the information from the identified sub-sections by using sequence modelers and linguistic characteristics provided by the data profiler, generating the hierarchical semantic map of the document by using the extracted information, and displaying in a user interface drop down selections of the key elements.

Abstract: The data vaporizer provides secure online distributed data storage services that securely store and retrieve data in a public distributed storage substrate such as public cloud. The data vaporizer vaporizes (e.g., fragmented into tiny chunks of configurable sizes) data and distributes the fragments to multiple storage nodes so that the data is not vulnerable to local disk failures, secures data so that even if some of the storage nodes are compromised, the data is undecipherable to the attacker, stores data across multiple cloud storage providers and/or parties using keys (e.g., tokens) provided by multiple parties (including the owners of the data) and maintains data confidentiality and integrity even where one or more data storage provider is compromised. The data vaporizer is configurable for different domain requirements including data privacy and anonymization requirements, encryption mechanisms, regulatory compliance of storage locations, and backup and recovery constraints.