A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

A computer-implemented position-centric personnel modeling apparatus, system, and method may be provided. A nodal hierarchy may be created to correlate with the personnel structure of a business enterprise, wherein each node may be a cyberspace representation of an individual within the personnel structure of the business enterprise. Attributes correlating to individual personnel in the business enterprise may be mapped to a respective node. Objectives may subsequently be tasked within the model and the nodal hierarchy may be rearranged based on the results. Results from the model may optionally be reflected by a business enterprise.

In an illustrative embodiment, an automated system engineers customized feature vectors from geospatial information system (GIS) metadata. The system may include computing systems and devices for extracting metadata for GIS features located within a predetermined distance of a property from a GIS map file and storing the extracted GIS features within a feature vector. The system can augment each of the extracted GIS features with amplifying data features extracted from external data sources. The system can calculate a distance between the property and each extracted GIS feature, which establishes a relationship between the property and each GIS feature and associated amplifying data features. Amounts of correlation between each of the extracted GIS features and associated amplifying data features within the feature vector and a market assessment of the property location can be identified using a data model trained with a data set customized to characteristics of the property.

Systems and methods are disclosed for processing and executing queries in a data intake and query system. An indexing system of the data intake and query system receives data and stores at least a portion of it in buckets, which are then stored in a shared storage system. The indexing system merges multiple buckets to generate merged buckets and uploads the merged buckets to the shared storage system.

A system for extracting latent information from data includes obtaining or generating components of the data, where the data components include scores indicating how the component relates to the data. Memory is allocated for the components and the components are stored in the allocated memory. The components are then transformed into documents using a suitable transformation function, and the documents are analyzed using natural language processing, to extract latent information contained in the data.

The field of the invention relates to systems and methods for data mining and processing, and more particularly to systems and methods for automating content from performance assessment data. In one embodiment, an automated notes and categorization system may include a primary database comprising performance assessment data. The primary database is operatively coupled to a computer program product having a computer-usable medium having a sequence of instructions which, when executed by a processor, causes said processor to execute a process that analyzes and converts raw performance data into automated content that presents data in readable user-friendly form to facilitate human understanding.

Methods for generating content summaries in a web content management service, wherein in one embodiment a digital page editor and a component browser are launched to enable selection of a first content item. A summary of the first content item is automatically generated according to parameters that may have default values or values set by a user. The parameters may specify a size for the summary as a percentage of the first content item's size, as a particular number of lines, characters or words, as a size for a particular type of device, etc. The automatically generated summary is provided to the digital page editor, which can edit it and add it to the digital page. The summary is stored in a content repository as an independent summary content item with its own metadata.

Systems and methods for processing events are disclosed. Event data comprising passive event data, active event data, or both is received. It is determined whether the received event data is available for a pattern of passive event data and active event data. In response to determining that the received event data is available for the pattern of passive event data and active event data, one or more constraints between the passive event data and the active event data are converted into one or more query terms. The query terms are used to construct at least one query. Remaining passive event data that is related to some, but not all, of the active event data is obtained using the constructed at least one query.

Techniques for detecting and correcting anomalies in computer-based reasoning systems are provided herein. The techniques can include obtaining current context data and determining a contextually-determined action based on the obtained context data and a reasoning model. The reasoning model may have been determined based on one or more sets of training data. The techniques may cause performance of the contextually-determined action and, potentially, receiving an indication that performing the contextually-determined action in the current context resulted in an anomaly. The techniques include determining a portion of the reasoning model that caused the determination of the contextually-determined action based on the obtained context data and causing removal of the portion of the model that caused the determination of the contextually-determined action, to produce a corrected reasoning model. Subsequently, second context data is obtained, a second action is determined based on that data and the corrected reasoning model, and the second contextually-determined action can be performed.