Data Reference A-G

Objects

AccelerationBehaviorAnnotation

Kind: Object

Implements: ITransportBehaviorAnnotation

Property

Type

Description

type

TransportBehaviorAnnotationType

'AccelerationBehaviorAnnotation'

start

String

end

String

duration

Int

acceleration

AccelerationEnum

path

BehaviorAnnotationPathWaypoint

magnitude

Float

The longitudinal g-force you experience during accelerations and brakes, measured in (0.2*m)/s². Doing a fast acceleration will result in a higher value compared to a slower acceleration.

AccelerationEnum

Kind: ENUM

  • accelerate

  • brake

  • coast

AccessToken

Kind: Object

Property

Type

Description

type

String

'AccessToken'

token

String

The access (bearer) token

expires_at

String

When this token will expire

Account

Kind: Object

Property

Type

Description

type

String

'Account'

id

String

display_name

String

created_at

String

applications

ApplicationsConnection

The applications that belong to this account.

Address

Kind: Object

An Address describes a reverse geocoded location.

Property

Type

Description

type

String

'Address'

country

String

city

String

city_type

String

AggregatedDistanceAnomaly

Kind: Object

Implements: IAnomaly, IDistanceAnomaly, IAggregatedAnomaly, IStationaryAnomaly, ITransportAnomaly, IMomentAnomaly

Property

Type

Description

type

AnomalyType

'AggregatedDistanceAnomaly'

start

String

end

String

analysis_type

AnalysisType

anomaly

Anomaly

sigma

Float

The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.

probability

Float

The larger the probability, the more anomaly. Value is between 0.0 and 1.0.

observed_distance

Float

Observed distance in meter.

expected_distance

Float

Expected distance in meter.

period

AnomalyTimePeriod

Aggregation period over which the data is calculated.

day_part

DayPart

Optional additional aggregation over which the data is calculated.

place_category

String

location_significance

LocationSignificance

transport_mode

TransportMode

transport_mode_category

TransportModeCategory

moment_definition_id

String

AggregatedDurationAnomaly

Kind: Object

Implements: IAnomaly, IDurationAnomaly, IAggregatedAnomaly, IStationaryAnomaly, ITransportAnomaly, IMomentAnomaly

Property

Type

Description

type

AnomalyType

'AggregatedDurationAnomaly'

start

String

end

String

analysis_type

AnalysisType

anomaly

Anomaly

sigma

Float

The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.

probability

Float

The larger the probability, the more anomaly. Value is between 0.0 and 1.0.

observed_duration

Float

Observed duration in seconds.

expected_duration

Float

Expected duration in seconds.

period

AnomalyTimePeriod

Aggregation period over which the data is calculated.

day_part

DayPart

Optional additional aggregation over which the data is calculated.

place_category

String

location_significance

LocationSignificance

transport_mode

TransportMode

transport_mode_category

TransportModeCategory

moment_definition_id

String

AnalysisType

Kind: ENUM

  • processed: The most detailed processing with the highest latency. This processing also takes into account motion sensor data, does advanced timelining corrections.

  • indepth: When data is analyzed with potentially more information available compared to the initial preliminary analysis. This type of processing also takes into account motion sensor data.

  • preliminary: The initial realtime analysis of data with the lowest latency.

    The platform analyzes data in multiple stages, and updates values over time, how well the data is analyzed, can be identified by the analysis type.

Anomaly

Kind: ENUM

  • transport_distance

  • transport_duration

  • transport_mode_distance

  • transport_mode_duration

  • transport_mode_category_distance

  • transport_mode_category_duration

  • transport_day_part_distance

  • transport_day_part_duration

  • transport_mode_category_occurrence_count

  • stationary_location_significance_duration

  • stationary_location_significance_day_part_duration

  • stationary_place_category_day_count

  • stationary_location_significance_day_part_day_count

  • stationary_place_category_duration

  • stationary_place_category_occurrence_count

  • moment_duration

  • commute_distance

  • commute_duration: This is an aggregation over the commute_from_home and commute_from_work moments, it includes small stationaries during commutes (such as stopping at the gas station).

  • commute_transport_mode_category_duration

  • working_duration

  • dogwalk_occurrence_count

AnomalyBehaviorAnnotation

Kind: Object

Implements: ITransportBehaviorAnnotation

Property

Type

Description

type

TransportBehaviorAnnotationType

'AnomalyBehaviorAnnotation'

start

String

end

String

anomaly

BehaviorAnnotationAnomalyEnum

duration

Int

path

BehaviorAnnotationPathWaypoint

AnomalyTimePeriod

Kind: ENUM

  • day: Anomaly calculated on a daily basis.

  • week: Anomaly calculated on a weekly basis.

  • month: Anomaly calculated on a montly basis.

  • semantic_day: Anomaly calculated on the semantic day of the user.

  • weekend_day: Anomaly calculated on a daily basis, ex: each day of the weekend you are at home on average N hours, then if you are not home both days = 2 anomalies (one for saturday, one for sunday) will be available.

AnomalyType

Kind: ENUM

  • DistanceAnomaly

  • AggregatedDistanceAnomaly

  • DurationAnomaly

  • AggregatedDurationAnomaly

  • DayCountAnomaly

  • OccurrenceCountAnomaly

Application

Kind: Object

An Application refers to an integration of the mobile SDK and pools together the users from this mobile app.

Property

Type

Description

type

String

'Application'

id

String

The unique identifier for this application.

secret

String

The secret required when integrating the mobile SDK with this application ID.

name

String

The name of this application. For mobile apps it's the name how it's available in the AppStore/PlayStore.

description

String

A short description of this application.

logging

String

An optional override for the default debug logging behavior of the mobile SDKs.

flavor

String

An optional override for the default flavor configuration of the mobile SDKs.

project_code

String

An optional project code used to assign users from the demo applications to this application.

created_at

String

The time when this user was created, ISO8601. Example: 2015-05-28T14:37:14.839+00:00.

account_id

String

The developer account this application belongs to.

users

UsersConnection

The users that belong to this application.

active_users

UsersConnection

The users that belong to this application and have been active in the last 7 days.

trips

TripConnection

Trips across all users recorded against this application.

ApplicationsConnection

Kind: Object

Access the application nodes

Property

Type

Description

type

String

'ApplicationsConnection'

slice

Application

The individual application nodes. Provide the right paging parameters to slice your response data. By default a slice holds up to 100 applications.

paging

Paging

BehaviorAnnotationAnomalyEnum

Kind: ENUM

  • phone_handling

BehaviorAnnotationPathWaypoint

Kind: Object

Property

Type

Description

type

String

'BehaviorAnnotationPathWaypoint'

latitude

Float

longitude

Float

BigInt

Kind: SCALAR

The BigInt scalar type represents non-fractional signed whole numeric values. BigInt can represent values between -(2^53) + 1 and 2^53 - 1.

BoundaryBehaviorAnnotation

Kind: Object

Implements: ITransportBehaviorAnnotation

Property

Type

Description

type

TransportBehaviorAnnotationType

'BoundaryBehaviorAnnotation'

start

String

end

String

quality

BoundaryBehaviorAnnotationQuality

BoundaryBehaviorAnnotationQuality

Kind: ENUM

  • valid: Section of the transport where the event detection was successful.

Branch

Kind: Object

A possible series of events predicted for the user.

Property

Type

Description

id

String

Id of the branch unique among all the branches returned for the user at the given time.

probability

Float

Percentage probability of the events of this beam taking place.

events

IBranchEvent

The list of events predicted to occur.

CarBehaviorFeatures

Kind: Object

Property

Type

Description

type

String

'CarBehaviorFeatures'

phone_handling

Int

Total time in milliseconds we detected phone handling by the user during this transport. Value will be -1 when the transport data was not sufficient.

distance_during_annotations

Int

Distance in meter during which the system had good quality sensor data available to observe transport behavior. Value will be -1 when the transport data was not sufficient.

CarBehaviorScores

Kind: Object

Property

Type

Description

type

String

'CarBehaviorScores'

overall

Float

An aggregation of all scores where we had sufficient data. A low score in one of the scores will result in a lower overall score.

smooth

Float

The smooth driving score measures how calm you drive. High accelerations and heavy braking result in a lower score, the use of coasting results in a higher score. The higher your score, the calmer you drive! When we did not have sufficient data value will be -1.

legal

Float

The legal driving score measures how well you adhere to speed limits. The higher your score, the more you respect the speed limits! When we did not have sufficient data value will be -1.

anticipative

Float

The anticipative driving score measures how well you anticipate traffic. A fast sequence of braking and accelerations in general traffic situations results in a lower score, the use of coasting results in a higher score. The higher your score, the more anticipative you drive! When we did not have sufficient data value will be -1.

focus

Float

The proportion of time (percentage) the user is focused while driving, being focused means: not using the phone, which is detected through phone handling.

mounted

Float

The proportion of time (percentage) the phone is mounted while driving.

hard_accel

Float

Measures how often you accelerate hard. Every hard acceleration will be penalised by subtracting a percentage of your score. When we do not have sufficient data this value will be -1.

hard_brake

Float

Measures how often you need to brake hard. Every hard brake will be penalised by subtracting a percentage of your score. When we do not have sufficient data this value will be -1.

hard_events

Float

This is a combination of hard_accel and hard_brake score. The hard brakes and accelerations are also normalized by the total number of events. When we do not have sufficient data this value will be -1.

legal_v2

Float

The legal driving score measures how well you adhere to speed limits. The higher your score, the more you respect the speed limits! When we did not have sufficient data value will be -1. The difference with legal score is that this score has better speed estimation.

hard_turn

Float

Measures how often you turn hard. Every hard turn will be penalised by subtracting a percentage of your score. When we do not have sufficient data this value will be -1.

smooth_v2

Float

The smooth driving score measures how smooth you drive. High accelerations, heavy braking and heavy turning result in a lower score. The use of coasting results in a higher score. Scores are normalized with respect to a wide population. The higher your score, the smoother you drive! When we did not have sufficient sensor data characterizing your drive, the value will be -1. Beside an updated normalization, the difference with smooth score v1 is that turns are also taken in account.

anticipative_v2

Float

The anticipative driving score measures how well you anticipate turns. Hard accelerations before or hard braking during a turn result in a lower score. The use of coasting results in a higher score. The higher your score, the more anticipative you drive! When we did not have sufficient sensor data characterizing your drive, the value will be -1. Beside an updated normalization, this new version has a more accurate detection of brakes in turns.

handheld_calling

Float

A score based on how much time you spent using your phone and calling (1 means good behavior).

handsfree_calling

Float

A score based on how much time you spent calling without holding your phone (1 means good behavior).

handling_without_calling

Float

A score based on how much time you spent holding your phone without calling (assume typing, texting etc.)

attention

Float

A combined score of handheld_calling, handsfree_calling and handling_without_calling.

CityMoment

Kind: Object

Implements: IMoment An occurrence of a City moment that we have detected for a user.

Property

Type

Description

type

MomentType

'CityMoment'

start

String

The time this moment started, ISO8601. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00

end

String

The time this moment ended, ISO8601. Value can be null. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00

start_ts

BigInt

end_ts

BigInt

analysis_type

AnalysisType

How well this moment is analyzed by the platform, this value will update over time. Possible values: preliminary, indepth, processed.

moment_definition_id

String

The ID of the MomentDefinition this moment relates to.

moment_definition

MomentDefinition

The MomentDefinition this moment relates to.

city_name

String

The name of the city this moment applies to.

CommuteTimeAggregate

Kind: Object

Implements: ITimeAggregateAttribute, IUserAttribute

Property

Type

Description

type

UserAttributeType

'CommuteTimeAggregate'

period

TimePeriod

transport_duration

Float

mode_category

TransportModeCategory

ControlUser

Kind: Object

Implements: IUser A user that can authenticate using either password or token strategies, has an email address, might have access to dashboards, might have multiple roles, might manage multiple accounts and applications.

Property

Type

Description

type

UserType

'ControlUser'

email

String

The email address that is optionally linked to provide access to the https://developers.sentiance.com and others.

account_roles

UserAccountRole

The accounts this user has elevated permissions to.

id

String

The unique identifier for this user.

can_login

Boolean

created_at

String

The time when this user was created, ISO8601. Example: 2015-05-28T14:37:14.839+00:00

sdk

UserSdkSettings

application_id

String

The ID of the Application this user relates to.

application

Application

The Application this user relates to.

custom_event_history

CustomEvent

Custom Event History

event_history

IEvent

An unordered list of events we have detected for this user.

car_behavior

UserCarBehavior

The user car behavior aggregated over the last 9 weeks.

aggregated_driving_scores

UserTimeAggregatedScores

transport_heatmaps

TransportHeatmaps

The aggregated transport heatmaps calculated over time.

metadata

JSON

All custom set metadata properties on this user. This is a JSON object with key->value pairs.

device

DeviceInfo

The last known active tracking device metadata

active_moments

IMoment

An unordered list of moments that are ongoing from the point of view of the platform.

moment_history

IMoment

An unordered list of moments we have detected for this user.

semantic_time

UserSemanticTime

The user's semantic time averaged over time.

anomaly_history

IAnomaly

segments

ISegment

An unordered list of segments that are detected for this user.

location_clusters

LocationCluster

Locations this user has been stationary at and the features we have learned about those locations (significance, point of interest, ...)

location

Waypoint

The last known location we have for this user.

health

UserHealth

The historical health attributes.

attributes

IUserAttribute

predictions

IPrediction

Event/Moment predictions for this user

prediction_tree

PredictionTree

Multiple possible predictions of events that are about to take place next. They are ordered by the highest probability of each sequence of events taking place.

feedback_history

IFeedback

Feedback on this user

CountryMoment

Kind: Object

Implements: IMoment An occurrence of a Country moment that we have detected for a user.

Property

Type

Description

type

MomentType

'CountryMoment'

start

String

The time this moment started, ISO8601. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00

end

String

The time this moment ended, ISO8601. Value can be null. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00

start_ts

BigInt

end_ts

BigInt

analysis_type

AnalysisType

How well this moment is analyzed by the platform, this value will update over time. Possible values: preliminary, indepth, processed.

moment_definition_id

String

The ID of the MomentDefinition this moment relates to.

moment_definition

MomentDefinition

The MomentDefinition this moment relates to.

country_name

String

The name of the country this moment applies to.

CustomEvent

Kind: Object

Custom Events.

Property

Type

Description

id

String

The ID of the event in the Sentiance system. This is unique across all custom events

created_at

String

The time this event was created ISO8601.

created_at_ts

BigInt

type

String

'CustomEvent'

start

String

The time this event started, ISO8601.

end

String

The time this event ended, ISO8601. Value can be null when it is a one time event.

start_ts

BigInt

end_ts

BigInt

source

CustomEventSources

Where the event originates.

event_id

String

latitude

Float

Latitude value of the event.

longitude

Float

Longitude value of the event.

values

JSON

JSON string of key,value pairs submitted during event creation.

CustomEventSources

Kind: ENUM

  • SDK: Event was generated in the SDK.

  • ENCLOSING_APP: The enclosing application was generating the event through the SDK.

  • CUSTOMER: The event was sent by the Customer.

    Where the custome event originates at.

DayCountAnomaly

Kind: Object

Implements: IAnomaly, IDayCountAnomaly, IAggregatedAnomaly, IStationaryAnomaly, ITransportAnomaly, IMomentAnomaly

Property

Type

Description

type

AnomalyType

'DayCountAnomaly'

start

String

end

String

analysis_type

AnalysisType

anomaly

Anomaly

sigma

Float

The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.

probability

Float

The larger the probability, the more anomaly. Value is between 0.0 and 1.0.

period

AnomalyTimePeriod

Aggregation period over which the data is calculated.

day_part

DayPart

Optional additional aggregation over which the data is calculated.

observed_days

Float

Observed amount of days.

expected_days

Float

Expected amount of days.

place_category

String

location_significance

LocationSignificance

transport_mode

TransportMode

transport_mode_category

TransportModeCategory

moment_definition_id

String

DayPart

Kind: ENUM

  • morning: Local time between 06:00-10:00.

  • noon: Local time between 10:00-14:00.

  • afternoon: Local time between 14:00-17:00.

  • evening: Local time between 17:00-24:00.

  • night: Local time between 00:00-06:00.

  • business: Business hours, local time between 08:00-18:00.

  • non_business: Non-business hours, local time excluding 08:00 - 18:00.

    Grouping of local time.

DeviceInfo

Kind: Object

Tracking device metadata.

Property

Type

Description

type

String

'DeviceInfo'

os

OperatingSystem

The operating system this device is running.

os_version

String

The version of the operating system this device is running.

DistanceAnomaly

Kind: Object

Implements: IAnomaly, IDistanceAnomaly, IStationaryAnomaly, ITransportAnomaly, IMomentAnomaly

Property

Type

Description

type

AnomalyType

'DistanceAnomaly'

start

String

end

String

analysis_type

AnalysisType

anomaly

Anomaly

sigma

Float

The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.

probability

Float

The larger the probability, the more anomaly. Value is between 0.0 and 1.0.

observed_distance

Float

Observed distance in meter.

expected_distance

Float

Expected distance in meter.

place_category

String

location_significance

LocationSignificance

transport_mode

TransportMode

transport_mode_category

TransportModeCategory

moment_definition_id

String

DurationAnomaly

Kind: Object

Implements: IAnomaly, IDurationAnomaly, IStationaryAnomaly, ITransportAnomaly, IMomentAnomaly

Property

Type

Description

type

AnomalyType

'DurationAnomaly'

start

String

end

String

analysis_type

AnalysisType

anomaly

Anomaly

sigma

Float

The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.

probability

Float

The larger the probability, the more anomaly. Value is between 0.0 and 1.0.

observed_duration

Float

Observed duration in seconds.

expected_duration

Float

Expected duration in seconds.

place_category

String

location_significance

LocationSignificance

transport_mode

TransportMode

transport_mode_category

TransportModeCategory

moment_definition_id

String

EventFeedback

Kind: Object

Property

Type

Description

type_assessment

FeedbackAssessment

If the user thinks the detected type is correct.

place_assessment

FeedbackAssessment

If the user thinks the detected place is correct.

place_feedback

LocationPlaceCandidate

The place candidate that was selected by the user as a better match, if any.

significance_assessment

FeedbackAssessment

If the user thinks the detected location significance is correct.

significance_feedback

LocationSignificance

The location significance that was selected by the user as a better match, if any.

mode_assessment

FeedbackAssessment

What the user thinks about the transport mode.

mode_feedback

TransportMode

The transport mode that was selected by the user as a better match, if any.

occupant_role_feedback

TransportOccupantRole

The occupant role that was selected by the user as a better match, if any.

EventType

Kind: ENUM

  • Transport

  • Stationary

FeedbackAssessment

Kind: ENUM

  • correct: When the user confirms the detection is correct.

  • incorrect: When the user finds the detection not correct.

FeedbackType

Kind: ENUM

  • StationaryFeedback: Feedback on a detected Stationary event.

  • TransportFeedback: Feedback on a detected Transport event.

  • MomentFeedback: Feedback on a detected moment.

FloatAttribute

Kind: Object

A float attribute

Property

Type

Description

type

String

'FloatAttribute'

value

Float

GenericMoment

Kind: Object

Implements: IMoment An occurrence of a moment that we have detected for a user.

Property

Type

Description

type

MomentType

'GenericMoment'

start

String

The time this moment started, ISO8601. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00

end

String

The time this moment ended, ISO8601. Value can be null. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00

start_ts

BigInt

end_ts

BigInt

analysis_type

AnalysisType

How well this moment is analyzed by the platform, this value will update over time. Possible values: preliminary, indepth, processed.

moment_definition_id

String

The ID of the MomentDefinition this moment relates to.

moment_definition

MomentDefinition

The MomentDefinition this moment relates to.

GenericSegment

Kind: Object

Implements: ISegment An occurrence of a SegmentDefinition that we have detected for this user.

Property

Type

Description

type

SegmentType

'GenericSegment'

segment_definition_id

String

The ID of the SegmentDefinition this segment relates to.

explanation

String

Reasoning why this segment was assigned to this user from a third person point of view.

explanation_you

String

Reasoning why this segment was assigned to this from a second person point of view.

segment_definition

SegmentDefinition

The SegmentDefinition this segment relates to.