Methods, systems, and devices for identifying the habitual places of a user, the habitual places of a user being the places where the user spends most of his/her time. In some embodiments, historical location information of a user is accessed, and a plurality of visit points are identified based on the historical location information. The plurality of visit points is clustered into a plurality of clusters. Each one of the clusters is then associated with a habitual place of the user. When the server receives, from a client device associated with the user, current location information of the user, the server identifies a current location of the user based on the current location information, and, if the current location of the user is in a neighborhood of one of the clusters, determine that the user is at the habitual place associated with the cluster.

In one aspect, a device may include at least one processor and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to categorize a first image into one of a work category and a non-work category. Based on the first image being categorized into the work category, the instructions may be executable to present the first image on a display while the device is disposed at a first location associated with the work category. Based on the first image being categorized into the non-work category, the instructions may be executable to present the first image on the display while the device is disposed at a second location associated with the non-work category.

A controller, responsive to detecting geospatial and temporal information associated with a mention of an entity in a document, for converting the geospatial information into a specified geospatial format and the temporal information into a specified temporal format. The controller for computing prefix-based geospatial values for the converted geospatial information and prefix-based temporal values for the converted temporal information. The controller for encoding an entry in a knowledge graph for the mention of the entity from the document with the prefix-based geospatial values and the prefix-based temporal values, wherein each digit of the prefix-based geospatial values and the prefix-based temporal values in the knowledge graph that matches another one or more prefix-based geospatial values and another one or more prefix-based temporal values in another entry for another entity mention in the knowledge graph reflects a degree of granularity of geotemporal proximity of the entity and the another entity.

A controller, responsive to detecting geospatial and temporal information associated with a mention of an entity in a document, for converting the geospatial information into a specified geospatial format and the temporal information into a specified temporal format. The controller for computing prefix-based geospatial values for the converted geospatial information and prefix-based temporal values for the converted temporal information. The controller for encoding an entry in a knowledge graph for the mention of the entity from the document with the prefix-based geospatial values and the prefix-based temporal values, wherein each digit of the prefix-based geospatial values and the prefix-based temporal values in the knowledge graph that matches another one or more prefix-based geospatial values and another one or more prefix-based temporal values in another entry for another entity mention in the knowledge graph reflects a degree of granularity of geotemporal proximity of the entity and the another entity.

A data storage system includes storage and a global enumerator. The storage stores data chunks, object level metadata associated with portions of the data chunks, and chunk level metadata associated with respective data chunks. The global enumerator obtains an update request including a metadata characteristic and update data; in response to obtaining the update request: matches the metadata characteristic to at least one selected from a group consisting of a portion of the object level metadata and a portion of the chunk level metadata to identify an implicated metadata portion; and modifies, based on the update data, the implicated metadata portion.

A data storage system includes storage and a global enumerator. The storage stores data chunks, object level metadata associated with portions of the data chunks, and chunk level metadata associated with respective data chunks. The global enumerator obtains an update request including a metadata characteristic and update data; in response to obtaining the update request: matches the metadata characteristic to at least one selected from a group consisting of a portion of the object level metadata and a portion of the chunk level metadata to identify an implicated metadata portion; and modifies, based on the update data, the implicated metadata portion.

A system includes at least one processor to perform natural language processing on text from at least one document and assign the at least one document to at least one subjective wellbeing dimension by comparing the text from the at least one document with a subjective wellbeing dimension filter for each subjective wellbeing dimension, insert the at least one document into at least one bin, each bin associated with a particular subjective wellbeing dimension, and analyze each document in each bin associated with the particular subjective wellbeing dimension to determine a score for each subjective wellbeing dimension and an overall score that is based on each score for each subjective wellbeing dimension.

A system includes at least one processor to perform natural language processing on text from at least one document and assign the at least one document to at least one subjective wellbeing dimension by comparing the text from the at least one document with a subjective wellbeing dimension filter for each subjective wellbeing dimension, insert the at least one document into at least one bin, each bin associated with a particular subjective wellbeing dimension, and analyze each document in each bin associated with the particular subjective wellbeing dimension to determine a score for each subjective wellbeing dimension and an overall score that is based on each score for each subjective wellbeing dimension.

An example method of determining geolocations of objects based on information retrieved from heterogeneous data sources comprises: receiving, from a first data source associated with an object by an ontology-defined relationship, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the object; receiving, from a second data source associated with an object by an ontology-defined relationship, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the object and a corresponding time identifier.

An example method of determining geolocations of objects based on information retrieved from heterogeneous data sources comprises: receiving, from a first data source associated with an object by an ontology-defined relationship, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the object; receiving, from a second data source associated with an object by an ontology-defined relationship, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the object and a corresponding time identifier.