Abstract: A computer system for security of components includes at least one processor. For a new version of a component, the processor determines, based on a dataset of release events over time, a historical behavioral analysis of (i) a project that is released with prior versions of the component, and/or (ii) historical committer behavior of a committer that committed the new version of the component, and/or (iii) historical behavior of a publisher of the project. The dataset of release events includes event data collected over time regarding open source project, committers, and repository. The processor determines whether the new version of the component presents an unusual risk profile, based on the historical behavioral analysis. The processor facilitates delayed consumption of the new version of the component in response to determining that the new version of the component presents the unusual risk profile.

Abstract: A computer system for security of components includes at least one processor. For a new version of a component, the processor determines, based on a dataset of release events over time, a historical behavioral analysis of (i) a project that is released with prior versions of the component, and/or (ii) historical committer behavior of a committer that committed the new version of the component, and/or (iii) historical behavior of a publisher of the project. The dataset of release events includes event data collected over time regarding open source project, committers, and repository. The processor determines whether the new version of the component presents an unusual risk profile, based on the historical behavioral analysis. The processor facilitates delayed consumption of the new version of the component in response to determining that the new version of the component presents the unusual risk profile.

Abstract: A computer system, method, or non-transitory computer-readable medium provides an authoritative name source for files within an ecosystem. Files in the ecosystem which have identical contends and similar contents to each other are merged into the same supercluster, to capture possibly incremental changes to the files over time in one of the superclusters. For each supercluster which has files with identical and similar contents, the supercluster is broken down into package clusters, based on packages to which the files belong, each of the package clusters has the files from a same package. The package cluster which has most change frequency across versions, is identified as the authoritative package. The authoritative name for the files is resolved, based on the authoritative packages that are determined, across the plurality of superclusters which have files with identical and similar contents, and the authoritative name is generated. Any authoritative name collision is resolved.

Abstract: A computer system, method, or non-transitory computer-readable medium provides an authoritative name source for files within an ecosystem. Files in the ecosystem which have identical contends and similar contents to each other are merged into the same supercluster, to capture possibly incremental changes to the files over time in one of the superclusters. For each supercluster which has files with identical and similar contents, the supercluster is broken down into package clusters, based on packages to which the files belong, each of the package clusters has the files from a same package. The package cluster which has most change frequency across versions, is identified as the authoritative package. The authoritative name for the files is resolved, based on the authoritative packages that are determined, across the plurality of superclusters which have files with identical and similar contents, and the authoritative name is generated. Any authoritative name collision is resolved.

Abstract: Knowledge automation techniques may include selecting a knowledge element from a knowledge corpus of an enterprise for extraction of n-grams, and deriving a term vector comprising terms in the knowledge element. Based at least on a frequency of occurrence of each term in the knowledge element, key terms are identified in the term vector. Thereafter, the identified key terms are used to extract one or more n-grams from the knowledge element. Each of the extracted n-grams is scored as a function of at least a frequency of occurrence of each of the n-grams across the knowledge corpus of the enterprise, and based on the scoring, one or more of the n-grams is added to a collective term and phrase index.

Abstract: Methods and systems for obtaining optimized search results are provided. A method can include receiving, by a data processing system, a search query including a search term from a user. In response to receiving the search query, the data processing system can identify knowledge elements based on a frequency of occurrence of the search term in each knowledge element. User interest patterns of the user are identified and are stored in a user profile associated with the user. The data processing system ranks the plurality of knowledge elements based on the user interest patterns stored in the user profile associated with the user and the knowledge elements are displayed as the search result according to the ranking.

Abstract: Knowledge automation techniques may include receiving a description of a knowledge campaign, and selecting knowledge elements from a data store based on the description of the knowledge campaign. The selected knowledge elements can be compiled into the knowledge campaign, and the knowledge campaign can be provided to target users. The consumption progress of the knowledge campaign by the target users can be monitored, and can be displayed on a graphical user interface.

Abstract: Knowledge automation techniques may include receiving a request for determining a representative image for a knowledge unit and determining a set of one or more images associated with the knowledge unit. The techniques may include providing the set of one or more images to a user on a client device and receiving user input indicative of a selection of a first image from the set of one or more images. Based on the first image, a thumbnail image for the knowledge unit can be generated. The techniques may further include associating the thumbnail image with the knowledge unit and displaying the thumbnail image to the user via the client device. In some embodiments, the techniques include generating a thumbnail image for a knowledge pack, wherein the knowledge pack comprises one or more knowledge units.

Abstract: Knowledge automation techniques may include selecting a knowledge element from a knowledge corpus of an enterprise for extraction of n-grams, and deriving a term vector comprising terms in the knowledge element. Based at least on a frequency of occurrence of each term in the knowledge element, key terms are identified in the term vector. Thereafter, the identified key terms are used to extract one or more n-grams from the knowledge element. Each of the extracted n-grams is scored as a function of at least a frequency of occurrence of each of the n-grams across the knowledge corpus of the enterprise, and based on the scoring, one or more of the n-grams is added to a collective term and phrase index.

Abstract: Knowledge automation techniques may include receiving a description of a knowledge campaign, and selecting knowledge elements from a data store based on the description of the knowledge campaign. The selected knowledge elements can be compiled into the knowledge campaign, and the knowledge campaign can be provided to target users. The consumption progress of the knowledge campaign by the target users can be monitored.

Abstract: Knowledge automation techniques may include displaying, by a data processing system, a graphical user interface including a first area, a second area, and a third area. The first area may display a plurality of knowledge unit icons, each knowledge unit icon in the first plurality of knowledge unit icons corresponding to a knowledge unit. The techniques may include detecting selection of a first knowledge unit icon displayed in the first area and placement of the selected first knowledge icon in the second area to add a first knowledge unit corresponding to the first knowledge icon to a target knowledge pack for one or more target knowledge consumers, and in response to detecting the placement of the first knowledge unit icon in the second area, displaying, in the third area, a list of one or more suggested knowledge consumers for the target knowledge pack.

Abstract: Knowledge automation techniques may include receiving a selection of a knowledge unit from a plurality of knowledge units for addition into a target knowledge pack, and computing, for each remaining knowledge unit in the plurality of knowledge units, a knowledge unit distance metric between the selected knowledge unit and the remaining knowledge unit. Based on the knowledge unit distance metric, a set of one or more relevant knowledge units can be determined. For each relevant knowledge unit, one or more knowledge packs from a set of published knowledge packs that the relevant knowledge unit is part of can be identified. One or more suggested knowledge consumers for the target knowledge pack can be determined from the knowledge consumers of the identified knowledge packs.