The method includes receiving historical data from a first data source; analyzing the historical data for a desired characteristic; determining a representative value for the desired characteristic of the historical data; determining a first data expectation for the historical data based on the representative value; transmitting the first data expectation to a first data recipient; receiving first incoming data from the first data source; analyzing the desired characteristic of the first incoming data; determining a first incoming data value for the desired characteristic for the first incoming data; comparing the first incoming data value and the representative value; determining a first difference between the first incoming data value and the representative value; and/or comparing the first difference to a difference threshold which indicates whether a difference between an incoming data value and the representative value is significant.

A data server may support segment identification based on a selected user profile. For example, a user may select a user profile as the basis for identifying a segment of additional user profiles. The server may identify attributes associated with the selected user identifier and generate an expression based on the identified subset. The expression may include a normalization function corresponding to at least one attribute. The normalization function may identify correlated attribute values for an attribute associated with the selected user profile. The data server may query a data storage system to identify the additional user profiles based on the expression. The data server may also support user defined Boolean expressions such that the expression is used to identify user identifiers associated with a first attribute and a second attribute.

Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

Methods and devices for simplifying data collected from assets are provided. An example method involves obtaining raw data from a data source at an asset, determining that a data logging trigger is satisfied by determining that a recently obtained point in the raw data differs from a corresponding predicted point predicted by extrapolation based on previously saved points included in one or more previously generated simplified sets of data by an amount of extrapolation error that is limited by an upper bound that is fixed as the raw data is collected over time, and, when the data logging trigger is satisfied, performing a dataset simplification algorithm on the raw data to generate a simplified set of data.

A method is described in which a topic similarity score, a product similarity score and an operating system similarity score between an original post and each one of a plurality of previous posts are determined; an overall similarity score of the each one of the plurality of previous posts based on the topic similarity score, the product similarity score and the operating system similarity score is determined; and a recommendation of a top K number of the plurality of previous posts based on the overall similarity score of the each one of the plurality of previous posts is sent to a display device.

A method is described in which a topic similarity score, a product similarity score and an operating system similarity score between an original post and each one of a plurality of previous posts are determined; an overall similarity score of the each one of the plurality of previous posts based on the topic similarity score, the product similarity score and the operating system similarity score is determined; and a recommendation of a top K number of the plurality of previous posts based on the overall similarity score of the each one of the plurality of previous posts is sent to a display device.

First sensor data may be acquired from a first galvanic skin response sensor monitoring a first user. Second sensor data may be acquired from a second galvanic skin response sensor monitoring a second user. At least one programmable processor may generate a compatibility score between the first user and the second user. The generating may include executing a compatibility algorithm to generate the compatibility score based at least on a comparison of at least one type of response contained in the first sensor data and the second sensor data. A client device may generate an electronic indication of the compatibility score.

First sensor data may be acquired from a first galvanic skin response sensor monitoring a first user. Second sensor data may be acquired from a second galvanic skin response sensor monitoring a second user. At least one programmable processor may generate a compatibility score between the first user and the second user. The generating may include executing a compatibility algorithm to generate the compatibility score based at least on a comparison of at least one type of response contained in the first sensor data and the second sensor data. A client device may generate an electronic indication of the compatibility score.

An information analysis apparatus includes: a weight assigning unit that assigns a weight to each of a plurality of items based on an action taken by a user who has viewed a sales content on which the plurality of items to be recommended are posted; a selection unit that selects a plurality of pairs in which two items are selected among the plurality of items placed in the sales content and associated with each other; and an evaluation unit that evaluates a characteristic based on characteristic information indicating a property of each of the two items selected as a pair by the selection unit and the weight assigned by the weight assigning unit to the two items.

Various systems and methods are provided for performing soft entity resolution. A plurality of data objects are retrieved from a plurality of data stores to create aggregated data objects for one or more entities. One or more retrieved data objects may be associated with the same entity, based at least in part upon one or more attribute types and attribute values of the data objects. In response to a determination that the one or more of the retrieved data objects should be associated with the same entity, metadata is generated that associates the data objects with the entity, the metadata being stored separately from the data objects, such that the underlying data objects remain unchanged. In addition, one or more additional attributes may be determined for the entity, based upon the data objects associated with the entity.