Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering user interface event data for analysis and retrieval are disclosed. In one aspect, a system includes a data store and computer(s) that interact with the data store and execute instructions that cause the computer(s) to receive, for a user interface event, event data specifying a structure of a user interface presented during the user session. The event is assigned to a respective cluster based on a comparison of the structure of the user interface specified by the event data to a user interface structure that represents the respective cluster. For each cluster, a user interface attribute indicative of a user interface state of user interfaces specified by the event data in the cluster is determined. User interface state groups are generated based on the user interface attribute for each cluster.

Cognitive and heuristics-based emergent financial management is provided. A method includes obtaining data related to an individual, an organization, a process, or combinations thereof. The data is obtained from internal sources, external sources, or combinations thereof. The method also includes creating data sets from the data based on determined classifications of the data. Further, the method includes establishing relationships between the data sets and determining a conclusion based on the relationships. The conclusion is based on a hypothesis that has undergone a test process.

Cognitive and heuristics-based emergent financial management is provided. A method includes obtaining data related to an individual, an organization, a process, or combinations thereof. The data is obtained from internal sources, external sources, or combinations thereof. The method also includes creating data sets from the data based on determined classifications of the data. Further, the method includes establishing relationships between the data sets and determining a conclusion based on the relationships. The conclusion is based on a hypothesis that has undergone a test process.

An aircraft noise monitoring system uses a set of geographically distributed noise sensors to receive data corresponding to events captured by the noise sensors. Each event corresponds to noise that exceeds a threshold level. For each event, the system will receive a classification of the event as an aircraft noise event or a non-aircraft noise event. It will then use the data corresponding to the events and the received classifications to train a convolutional neural network (CNN) in a classification process. After training, when the system receives a new noise event, it will use the CNN to classify the new noise event as an aircraft noise event or a non-aircraft noise event, and it will generate an output indicating whether the new noise event is an aircraft noise event or a non-aircraft noise event.

An aircraft noise monitoring system uses a set of geographically distributed noise sensors to receive data corresponding to events captured by the noise sensors. Each event corresponds to noise that exceeds a threshold level. For each event, the system will receive a classification of the event as an aircraft noise event or a non-aircraft noise event. It will then use the data corresponding to the events and the received classifications to train a convolutional neural network (CNN) in a classification process. After training, when the system receives a new noise event, it will use the CNN to classify the new noise event as an aircraft noise event or a non-aircraft noise event, and it will generate an output indicating whether the new noise event is an aircraft noise event or a non-aircraft noise event.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform functions comprising: tracking interaction data for one or more webpages of a website; determining a content score for the one or more webpages of the website; determining a link equity score for the one or more webpages of the website; classifying the one or more webpages of the website into one or more classifications using the interaction data, the content score, and the link equity score; and removing the one or more webpages from a sitemap of the website based on the one or more classifications. Other embodiments are disclosed herein.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform functions comprising: tracking interaction data for one or more webpages of a website; determining a content score for the one or more webpages of the website; determining a link equity score for the one or more webpages of the website; classifying the one or more webpages of the website into one or more classifications using the interaction data, the content score, and the link equity score; and removing the one or more webpages from a sitemap of the website based on the one or more classifications. Other embodiments are disclosed herein.

Systems and methods for managing content provenance are provided. A network system accesses a plurality of documents. The plurality of documents is then hashed to identify one or more content features within each of the documents. In one embodiment, the hash is a MinHash. The network system compares the content features of each of the plurality of documents to determine a similarity score between each of the plurality of documents. In one embodiment, the similarly score is a Jaccard score. The network system then clusters the plurality of documents into one or more clusters based on the similarity score of each of the plurality of documents. In one embodiment, the clustering is performed using DBSCAN. DBSCAN can be iteratively performed with decreasing epsilon values to derive clusters of related but relatively dissimilar documents. The clustering information associated with the clusters are stored for use during runtime.

Systems and methods for managing content provenance are provided. A network system accesses a plurality of documents. The plurality of documents is then hashed to identify one or more content features within each of the documents. In one embodiment, the hash is a MinHash. The network system compares the content features of each of the plurality of documents to determine a similarity score between each of the plurality of documents. In one embodiment, the similarly score is a Jaccard score. The network system then clusters the plurality of documents into one or more clusters based on the similarity score of each of the plurality of documents. In one embodiment, the clustering is performed using DBSCAN. DBSCAN can be iteratively performed with decreasing epsilon values to derive clusters of related but relatively dissimilar documents. The clustering information associated with the clusters are stored for use during runtime.

Disclosed herein are systems and methods for classifying organizational structure for implementing data protection policies. In one exemplary aspect, a method may comprise retrieving a plurality of data files of an organization, wherein the plurality of data files are stored in a data storage; retrieving structural information of the organization, the structural information comprising details of user accounts, organizational roles, and file metadata within the organization; classifying the structural information into an organization type of a plurality of organization types; classifying each respective data file of the plurality of data files into a respective topic of a plurality of topics, wherein the plurality of topics are associated with the organization type; generating a data protection policy for the organization based on each respective topic of the plurality of data files and the organization type; and executing the data protection policy on the data storage.