Abstract: Examples of the present disclosure describe systems and methods for electronic communication and file reference association. In an example, a file reference may refer to file content stored by a file service. The file reference may be used to share the file content in an electronic communication among a plurality of users. The file reference may be identified within the electronic communication, and may be used to generate a file node. The file node may store information relating to the file content and/or file reference. A message node associated with the electronic communication may be created, which may be associated with the file node. The file node may later be used to identify electronic communications relating to the file reference. Thus, electronic communications relating to the file reference may be accessible to a user using the file reference or by searching, rather than manually identifying related electronic communications.

Abstract: An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.

Abstract: An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.

Abstract: Examples of the present disclosure describe systems and methods for electronic communication and file reference association. In an example, a file reference may refer to file content stored by a file service. The file reference may be used to share the file content in an electronic communication among a plurality of users. The file reference may be identified within the electronic communication, and may be used to generate a file node. The file node may store information relating to the file content and/or file reference. A message node associated with the electronic communication may be created, which may be associated with the file node. The file node may later be used to identify electronic communications relating to the file reference. Thus, electronic communications relating to the file reference may be accessible to a user using the file reference or by searching, rather than manually identifying related electronic communications.

Abstract: Examples of the present disclosure describe systems and methods for electronic communication and file reference association. In an example, a file reference may refer to file content stored by a file service. The file reference may be used to share the file content in an electronic communication among a plurality of users. The file reference may be identified within the electronic communication, and may be used to generate a file node. The file node may store information relating to the file content and/or file reference. A message node associated with the electronic communication may be created, which may be associated with the file node. The file node may later be used to identify electronic communications relating to the file reference. Thus, electronic communications relating to the file reference may be accessible to a user using the file reference or by searching, rather than manually identifying related electronic communications.

Abstract: Disclosed herein is a method of transmitting data, the method comprising: obtaining a plurality of data blocks; determining a plurality of values of a transmission parameter for a transmitter; determining a plurality of values of a processing parameter of a processor; determining, for each of the obtained data blocks, one of a plurality of compression levels in dependence on at least one of the determined transmission parameter values and/or at least one processing parameter values; compressing each of a plurality data blocks in dependence on the determined compression level each block; and transmitting the data blocks; wherein: the transmitted data blocks comprise data blocks are compressed with different compression levels; and one of the compression levels is a determination to not compress data blocks such that the method does not compress some of the transmitted data blocks.

Abstract: Disclosed herein is a computer-implemented method of including data characterising values of source data in redundant data, wherein there are K source nodes of source data and R redundant nodes of redundant data such that there are a plurality of N nodes, where N=(K+R), wherein each of the N nodes comprises a plurality of sub-blocks of data, wherein a block of data comprises N sub-blocks with each of the N sub-blocks comprised by a different one of the N nodes, such that each block comprises K sub-blocks of source data and R sub-blocks of redundant data, the method comprising: calculating K data characterising values in dependence on sub-blocks comprised by the source nodes, wherein each of the data characterising values is associated with a different one of the K source nodes, each of the K data characterising values is associated with a different block and each of the K data characterising values is calculated in dependence on all of the sub-blocks of the source node that the data characterising value is as

Abstract: Disclosed herein is a computer-implemented method of including signatures of source data in redundant data, wherein there are K source nodes of source data and R redundant nodes of redundant data such that there are a plurality of N nodes, where N=(K+R), wherein each of the N nodes comprises a plurality of sub-blocks of data, wherein a block of data comprises N sub-blocks with each of the N sub-blocks comprised by a different one of the N nodes, such that each block comprises K sub-blocks of source data and R sub-blocks of redundant data, the method comprising: calculating K signatures in dependence on sub-blocks comprised by the source nodes, wherein each of the signatures is associated with a different one of the K source nodes, each of the K signatures is associated with a different block and each of the K signatures is calculated in dependence on all of the sub-blocks of the source node that the signature is associated with except the one sub-block of the source node that is also comprised by the block th

Abstract: Disclosed herein is a computer-implemented method of including data characterising values of source data in redundant data, wherein there are K source nodes of source data and R redundant nodes of redundant data such that there are a plurality of N nodes, where N=(K+R), wherein each of the N nodes comprises a plurality of sub-blocks of data, wherein a block of data comprises N sub-blocks with each of the N sub-blocks comprised by a different one of the N nodes, such that each block comprises K sub-blocks of source data and R sub-blocks of redundant data, the method comprising: calculating K data characterising values in dependence on sub-blocks comprised by the source nodes, wherein each of the data characterising values is associated with a different one of the K source nodes, each of the K data characterising values is associated with a different block and each of the K data characterising values is calculated in dependence on all of the sub-blocks of the source node that the data characterising value is as

Abstract: Disclosed herein is a computer-implemented method of including signatures of source data in redundant data, wherein there are K source nodes of source data and R redundant nodes of redundant data such that there are a plurality of N nodes, where N=(K+R), wherein each of the N nodes comprises a plurality of sub-blocks of data, wherein a block of data comprises N sub-blocks with each of the N sub-blocks comprised by a different one of the N nodes, such that each block comprises K sub-blocks of source data and R sub-blocks of redundant data, the method comprising: calculating K signatures in dependence on sub-blocks comprised by the source nodes, wherein each of the signatures is associated with a different one of the K source nodes, each of the K signatures is associated with a different block and each of the K signatures is calculated in dependence on all of the sub-blocks of the source node that the signature is associated with except the one sub-block of the source node that is also comprised by the block th

Abstract: Examples of the present disclosure describe systems and methods for electronic communication and file reference association. In an example, a file reference may refer to file content stored by a file service. The file reference may be used to share the file content in an electronic communication among a plurality of users. The file reference may be identified within the electronic communication, and may be used to generate a file node. The file node may store information relating to the file content and/or file reference. A message node associated with the electronic communication may be created, which may be associated with the file node. The file node may later be used to identify electronic communications relating to the file reference. Thus, electronic communications relating to the file reference may be accessible to a user using the file reference or by searching, rather than manually identifying related electronic communications.

Abstract: Disclosed herein is a method of generating a generator matrix for defining how to systematically code source data, the method comprising: determining source nodes for comprising a plurality of sub-stripes of source data, wherein the number of source nodes is K and the number of sub-stripes of source data comprised by each source node is S; determining redundant nodes for comprising a plurality of sub-stripes of coded data, wherein the number of redundant nodes is R and the number of sub-stripes of coded data comprised by each redundant node is S; determining values of a first generator matrix according to a systematic coding technique such that K of the rows of the generator matrix to define how to generate all of the K source nodes as comprising source data and R of the rows of the first generator matrix define how to generate all of the R redundant nodes as comprising combinations of two or more of the source nodes; generating a second generator matrix, with a first dimension (K×S) and a second dimension ((

Abstract: A personalized campaign engine is provided. The personalized campaign engine selects a user as a candidate receiver of a campaign message. A personalized campaign message is generated for the user that comprises content items that are relevant to the user based on identified relationships between the user and content items and relationships between the user and other individuals. Relationships between the user and content items and relationships between the user and other individuals are identified by interrogating disparate repositories of information for organizational relationship data and activity data associated with the user. The personalized campaign engine customizes the campaign message for the user by using known information about the user to select a certain template, medium, and delivery strategy.

Abstract: An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.