Geotab Incorporated

Cristian Frincu

Darren Marc Lohmann Beams

Alexey Sukhov

Methods and systems for simplifying data collected from assets are provided. An example method involves receiving a simplified set of data at a server. The simplified set of data is generated by application of a dataset simplification algorithm on raw data obtained from a data source at an asset upon a determination that a recently obtained point in the raw data differed 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. The method further involves receiving a request for a status of the asset and extrapolating a status of the asset based on the simplified set of data in response to the request.

Methods and systems for fixed extrapolation error data simplification processes for telematics

Systems and storage media containing instructions for asset tracking are provided. An example system includes a server and an asset tracking device onboard an asset. The asset tracking device to receive raw data pertaining to the asset, determine a dynamic group of carry-over data points, derived from the raw data, to be used in the performance of a dataset simplification algorithm on the raw data, perform the dataset simplification algorithm on a set of data that contains the raw data and the dynamic group of carry-over data points to determine one or more data points in the set of data to be recorded, and transmit the recorded data points to the server.

Extrema-retentive data buffering and simplification

Methods and systems for simplifying data collected from assets are provided. An example method involves receiving a set of simplified data at a server. The simplified set of data is generated by application of a dataset simplification algorithm on raw data obtained from a data source at an asset upon satisfaction of a data logging trigger, wherein the dataset simplification algorithm causes interpolation error within the simplified set of data to be limited by an upper bound that is fixed across the simplified set of data. The method further involves receiving a request for a status of the asset and interpolating a status of the asset based on the simplified set of data in response to the request.

Methods and systems for fixed interpolation error data simplification processes for telematics

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.

Methods and devices for fixed extrapolation error data simplification processes for telematics

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 whether a data logging trigger is satisfied, and, when satisfied, performing a dataset simplification algorithm on the raw data to generate a simplified set of data in which interpolation error is limited by an upper bound that is fixed across the simplified set of data.

Methods and devices for fixed interpolation error data simplification processes for telematic

Methods and devices for asset tracking are provided. An example method involves obtaining a stream of raw data, adding data points from the stream of raw data to a data buffer in a first cycle of data, performing a dataset simplification algorithm on the first cycle of data to determine whether one or more data points from the first cycle of data are to be recorded, preparing the data buffer for a second cycle of data, including determining a group of carry-over data points to be included in the second cycle of data, and continuing to add data points from the stream of raw data to the data buffer in the second cycle of data.

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Vancouver, Canada

Cristian Frincu