Abstract: A method for publishing a data message to shared storage includes receiving an action targeting a user associated with a distributed network having a plurality of service providers. Each service provider includes a private storage system and has access to a shared storage system. The method also includes parsing data elements of the action into a first set of data elements and a second set of data elements. The method further includes classifying the first set of data elements into categories of a data taxonomy. The method additionally includes generating a message for the action where the message includes the first set of data elements. The method also includes publishing the message to the shared storage system using a first identifier indicating the user and a second identifier that indicates that a respective service provider associated with the action has authority to publish the message to the shared storage system.

Abstract: A system and method builds functions into a web page sourced by a primary server and that would otherwise report to a set of one or more other servers actions performed by users of the web page when they interact with its elements so that the interactions instead report to the user of the web page. The system and method then causes the browser to simulate such interactions with all such web page elements, thereby causing the functions to report the information that would have been sent to the other server, and an identifier of the other server.

Abstract: Systems, methods, and software can be used to cluster software codes in a scalable manner. In some aspects, a computer-implemented method comprises: obtaining a plurality of software samples; computing one or more first hash results for each of the plurality of software samples; computing one or more second hash results for each of the plurality of software samples based on the one or more first hash results, wherein an amount of the one or more second hash results is less than an amount of the one or more first hash results; determining a similarity output based on the one or more second hash results of two of the plurality of software samples; and clustering the plurality of software samples based on the similarity output to generate one or more software sample clusters.

Abstract: Techniques are discussed for preparing and executing scanning plans for particular types of information, including personally identifiable information. A user indicates one or more datastores to be scanned for the particular type of information. A scanner determines scan objectives for the scanning plan and classifiers for use in scans conducted according to the scanning plan. The scanner estimates scan performance metrics and scan quality metrics. The scanner presents estimated results for the scanning plan based on the selected classifiers, scan objectives, estimate scan performance metrics, and estimated scan quality metrics. The user can modify the set of scan objectives or select between alternative sets of scan objectives. The scanning plan may be performed iteratively and the results of previous scan may be used to adjust classifiers or scan objectives to be used in subsequent scans.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity.

Abstract: Systems and techniques are generally described for multi-stage text-based clustering. In various examples, a first data set comprising a first set of elements may be received. Each element of the first set of elements may include text data. A clustering algorithm may be used to determine a first set of clusters of the first set of elements based at least in part on similarities in the text data. A second set of clusters, for each cluster of the first set of clusters, may be determined using the clustering algorithm. A number of resources for labeling the first set of elements may be determined based at least in part on the first set of clusters. The number of resources may be assigned to label clusters of the second set of clusters based at least in part on a number of elements in each cluster of the second set of clusters.

Abstract: A method and apparatus that recommends a feature from a data set. The method includes performing a first conversion on the data set including one target variable and a plurality of feature variables by converting a missing value for each of the feature variables into a preset constant, executing a first algorithm to determine a level of redundancy of the plurality of feature variables, using the data set converted by the first conversion and producing first recommendation information that includes a predetermined number of feature variables selected based on a result of the executing the first algorithm.

Abstract: Protecting consumer data is a key responsibility of an organization. The method of protection need to compare the data with policy documents. Ontology and a threshold to select an optimal match plays a key role in such comparison. The conventional automatic threshold computation methods are complex and not based on semantic similarity. The present disclosure generates an Entity Relationship (ER) model from an input document and is converted into a first ontology. The first ontology and a second ontology obtained from a relational database are compared by an ontology matching algorithm. Further, the plurality of many to many correspondences are optimized to one to one correspondence by an optimization method. Further, a plurality of optimal one to one correspondence is generated based on a threshold. The threshold is computed based on symmetric and transitive property. Further, semantically similar entities are selected based on the optimal one to one correspondence.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically detecting hot areas in heat map visualizations. One example method includes identifying a two-dimensional heat map. The identified two-dimensional heat map is converted to a one-dimensional heat map. Cells of the one-dimensional heat map are clustered using a density-based clustering algorithm to generate at least one dense region of cells. A mean value of cells in each dense region is calculated and the dense regions are sorted by mean value in descending order. An approach for identifying hot areas is selected and the selected approach is used to identify at least one dense region as a hot area of the one-dimensional heat map.