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: In the pharmaceutical research and development process, it may be necessary to process large amounts of medical records or clinical literature, to ensure safety of patients consuming a drug. A pharmacovigilance system may assist in this process by efficiently and automatically processing medical records to extract information and relationships contained therein and may also form a preliminary assessment regarding a medical or clinical judgment. The pharmacovigilance system may automatically generate reports based on this information, which may be validated by trained clinicians and medical experts.

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: 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: 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: A device may receive a source document to be processed for contextual information associated with named entities included in the source document. The device may identify a named entity included in the source document, and may identify a context of the source document. The device may identify a reference document associated with the named entity. The reference document may be different from the source document. The device may perform a semantic similarity analysis based on the context of the source document and further based on the reference document. The device may identify contextual information, included in the reference document, based on performing the semantic similarity analysis. The contextual information may relate to the context of the source document. The device may provide the contextual information.

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: 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: 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: 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: 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: 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.

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: In the pharmaceutical research and development process, it may be necessary to process large amounts of medical records or clinical literature, to ensure safety of patients consuming a drug. A pharmacovigilance system may assist in this process by efficiently and automatically processing medical records to extract information and relationships contained therein and may also form a preliminary assessment regarding a medical or clinical judgment. The pharmacovigilance system may automatically generate reports based on this information, which may be validated by trained clinicians and medical experts.

Abstract: A device may receive a source document to be processed for contextual information associated with named entities included in the source document. The device may identify a named entity included in the source document, and may identify a context of the source document. The device may identify a reference document associated with the named entity. The reference document may be different from the source document. The device may perform a semantic similarity analysis based on the context of the source document and further based on the reference document. The device may identify contextual information, included in the reference document, based on performing the semantic similarity analysis. The contextual information may relate to the context of the source document. The device may provide the contextual information.

Abstract: A distribution plan that indicates how to encode and distribute backup data across multiple data centers may be generated. The distribution plan may be generated such that one or more characteristics of the distribution plan, such as costs, are optimized while constraints on the plan, such as protection level, recovery point objective (RPO), and recovery time objective (RTO) are satisfied. The protection level may indicate the number of the data centers that are to remain available such that the backup data is recoverable from encoded fragments of the backup data stored in the data centers that remain available.

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.

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.