Abstract: Examples disclosed herein relate to receiving a software package deployed to a device, identifying, according to the software package, at least one feature to be provided to the device, determining whether the software package comprises a verified cryptographic signature, and in response to determining that the software package comprises the verified cryptographic signature, activating a component of the software package to provide the at least one feature to the device from an external service

Abstract: Semantic consolidation of data is disclosed. One example is a system including a data processor, a semantic relations module, and a consolidation module. The data processor identifies at least two data objects, each data object including a plurality of data elements. The semantic relations module identifies, via a processor, semantically similar data elements of the plurality of data elements. The consolidation module merges the identified semantically similar data elements via the processor, and generates a consolidated data object based on the merged data elements.

Abstract: Examples disclosed herein relate to receiving, via a user interface, a power allocation for a first application, receiving a request to initiate the first application, and determining whether the first application has remaining power under its power allocation from a power supply. In response to determining that the first application has remaining power under its allocation from the power supply, the first application may be initiated.

Abstract: A method for classifying a document using identified patterns includes determining frequent patterns based on a group of resources, where the frequent patterns include sets of words associated with resources that are related to a particular topic; determining frequent anti-patterns based on another group of resources, where the frequent anti-patterns include sets of words associated with resources that are not related to the particular topic, where the second group of resources is different from the first group of resources; determining a probability that the document is related to the particular topic based on the frequent patterns and the frequent anti-patterns; and determining a topic classification of the document based on the determined probability.

Abstract: Semantic consolidation of data is disclosed. One example is a system including a data processor, a semantic relations module, and a consolidation module. The data processor identifies at least two data objects, each data object including a plurality of data elements. The semantic relations module identifies, via a processor, semantically similar data elements of the plurality of data elements. The consolidation module merges the identified semantically similar data elements via the processor, and generates a consolidated data object based on the merged data elements.

Abstract: Classifying a document using identified patterns includes determining frequent patterns related to a topical classification based on identification of a first group of resources; determining frequent anti-patterns that are related to a non-topical classification based on identification of a second group of resources; an classifying the document based on the frequent patterns and the frequent anti-patterns.

Abstract: Systems and methods for classification of documents based on topic to which the documents pertain are described herein. In one implementation, the method comprises computing a probability of a document being topical based on a number of constituent elements that are topical and a total number of constituent elements and computing a probability of the document being anti-topical based on a number of constituent elements that are anti-topical and the total number of constituent elements. The method further comprises determining whether the probability of the document being topical is greater than the probability of the document being anti-topical. Thereafter, the method includes classifying the document as topical on determining the probability of the document being topical to be greater than the probability of the document being anti-topical.

Abstract: Examples of the present disclosure include methods, systems, and machine-readable and executable instructions for linking media access between devices. An example method can include receiving by a stationary media transfer device a media link request from a mobile wireless communication device and linking access to a media file between the stationary media transfer device and the mobile wireless communication device based upon a transfer of metadata stored in a cloud concerning an application state of the media file.

Abstract: A system and method of modelling a communications network using a computer system is disclosed, the method including generating a network representation using computer-readable code that represents structured information; parsing the network representation; generating a network model using the parsed network representation, the network model including a plurality of network objects and relationships between the plurality of network objects; and storing the network model in memory. Any type of network may be modeled. The computer-readable code may be any suitable language or instructions for representing structured information such as, for example, extensible mark-up language (XML). A network inventory adapter receives the network representation from the network. The network inventory adapter is a software component that may be used to connect applications to the network.

Abstract: A system and method of modelling a communications network using a computer system is disclosed, the method including generating a network representation using computer-readable code that represents structured information; parsing the network representation; generating a network model using the parsed network representation, the network model including a plurality of network objects and relationships between the plurality of network objects; and storing the network model in memory. Any type of network may be modeled. The computer-readable code may be any suitable language or instructions for representing structured information such as, for example, extensible mark-up language (XML). A network inventory adapter receives the network representation from the network. The network inventory adapter is a software component that may be used to connect applications to the network.