A first stack running on a processor receives the transaction data, reference data, and context data. The reference data is independent of the transaction and of a user. The context data is associated with the user but is independent of the transaction. The first stack strips the transaction of derivable data to obtain stripped data. The derivable data includes data that can be derived from the stripped data, the context data, and the reference data. The derivable data can stream the stripped data to a global database available and redundant across multiple geographical regions. After the first stack fails, a second stack can resume the transaction by retrieving the stripped data from the global database, and retrieving the context data, and the reference data. The second stack can recreate the transaction data based on the stripped data, the context data, and the reference data, and can resume the transaction.

Implementations of various methods and systems for creating and calculating a transportation capacity unit index and trading derivative products based thereon to transact and trade transportation seats or freight or transportation capacity units and resulting financial swap, futures, forwards and option structures in airline transport, subway transport, train transport, automobile transport, autonomous vehicle transport, taxi transport, space transport, package freight transport, tractor trailer freight transport, cargo freight transport, container freight transport, virtual transport, underground transport, ship or sea transport, public transport, private transport or drone transport on a computer, mobile computer device, audio computer device, virtual reality computer device or mixed reality computing device.

Implementations of various methods and systems for creating and calculating a transportation capacity unit index and trading derivative products based thereon to transact and trade transportation seats or freight or transportation capacity units and resulting financial swap, futures, forwards and option structures in airline transport, subway transport, train transport, automobile transport, autonomous vehicle transport, taxi transport, space transport, package freight transport, tractor trailer freight transport, cargo freight transport, container freight transport, virtual transport, underground transport, ship or sea transport, public transport, private transport or drone transport on a computer, mobile computer device, audio computer device, virtual reality computer device or mixed reality computing device.

Embodiments provide systems and methods for three-dimensional building generation from machine learning and topological models. The method uses topology models that are converted into vertices and edges. A BGAN (Building generative adversarial network) is used to create fake vertices/edges. The BGAN is then used to generate random samples from seen sample of different structures of building based on relationship of vertices and edges. The embeddings are then fed into a machine trained network to create a digital structure from the image.

Systems and methods are provided for improved security services. In one aspect, a method is provided for controlling an autonomous data machine situated near a monitored environment. The method comprises: obtaining security data from a plurality of data sources; analyzing the security data to generate an analysis result; determining, based on the analysis result, an action to be performed by the autonomous data machine; and transmitting a command to the autonomous data machine causing it to perform the action.

Systems and methods are provided for improved security services. In one aspect, a method is provided for controlling an autonomous data machine situated near a monitored environment. The method comprises: obtaining security data from a plurality of data sources; analyzing the security data to generate an analysis result; determining, based on the analysis result, an action to be performed by the autonomous data machine; and transmitting a command to the autonomous data machine causing it to perform the action.

Embodiments are disclosed for a feature-based simultaneous localization and mapping (SLAM) system and method that generates radio maps for environments that are not accessible for surveying. More accurate radio maps are generated for an unsurveyed environment by determining a best estimate of a mobile device state from harvested traced data that maximizes a posterior probability of the mobile device state given measurements, landmarks and loop constraints.

Embodiments are disclosed for a feature-based simultaneous localization and mapping (SLAM) system and method that generates radio maps for environments that are not accessible for surveying. More accurate radio maps are generated for an unsurveyed environment by determining a best estimate of a mobile device state from harvested traced data that maximizes a posterior probability of the mobile device state given measurements, landmarks and loop constraints.

A system and associated methodology manages localization data. According to one embodiment of the present invention a primary set of data associated with one or more specific locations is imported and matched to a predefined format. Thereafter external, secondary, data associated with each of the localities listed in the primary data set is collected from a plurality of third party location service providers. With the collected secondary data matched to the same predefined format a comparison is made between the plurality of secondary data sets and the primary data set. Differences between the data sets are identified and the primary data set modified as necessary. Thereafter, normalized data from the modified primary data set is exported to the third party location service providers to enhance consistency and reliable of locational data.

A system and associated methodology manages localization data. According to one embodiment of the present invention a primary set of data associated with one or more specific locations is imported and matched to a predefined format. Thereafter external, secondary, data associated with each of the localities listed in the primary data set is collected from a plurality of third party location service providers. With the collected secondary data matched to the same predefined format a comparison is made between the plurality of secondary data sets and the primary data set. Differences between the data sets are identified and the primary data set modified as necessary. Thereafter, normalized data from the modified primary data set is exported to the third party location service providers to enhance consistency and reliable of locational data.