Abstract: Historical device positioning data captured from one or more devices over a period of time is received. The historical device positioning data includes historical latitude, longitude, and elevation data of the one or more devices. Building boundaries for a give building are identified based upon the historical latitude and longitude data. The historical device positioning data corresponding to locations within the building boundaries of the building is clustered using a machine learning-based clustering algorithm, resulting in clusters with corresponding cluster centroids. The cluster centroids are associated with respective floors within the building. A current floor of the building on which a specific device is located is determined by mapping current device positioning data of the specific device to the closest cluster centroid.

Abstract: An approach is described of query processing via a lambda application including a batch processing layer and a speed processing layer. The approach may improve speed and efficiency with respect to computation of query results associated with the batch processing layer. A method pertaining to such approach may include receiving a query including an initial timeframe and computing partial query results within the initial timeframe. The partial query results within the initial timeframe may include a result for data associated with the speed processing layer and results for a first set of data segments associated with the batch processing layer. The method further may include combining the partial query results within the initial timeframe into a final query result. The method further may include computing partial query results outside of the initial timeframe including results for a second set of data segments associated with the batch processing layer.

Abstract: Historical device positioning data captured from one or more devices over a period of time is received. The historical device positioning data includes historical latitude, longitude, and elevation data of the one or more devices. Building boundaries for a give building are identified based upon the historical latitude and longitude data. The historical device positioning data corresponding to locations within the building boundaries of the building is clustered using a machine learning-based clustering algorithm, resulting in clusters with corresponding cluster centroids. The cluster centroids are associated with respective floors within the building. A current floor of the building on which a specific device is located is determined by mapping current device positioning data of the specific device to the closest cluster centroid.

Abstract: An approach is described of query processing via a lambda application including a batch processing layer and a speed processing layer. The approach may improve speed and efficiency with respect to computation of query results associated with the batch processing layer. A method pertaining to such approach may include receiving a query including an initial timeframe and computing partial query results within the initial timeframe. The partial query results within the initial timeframe may include a result for data associated with the speed processing layer and results for a first set of data segments associated with the batch processing layer. The method further may include combining the partial query results within the initial timeframe into a final query result. The method further may include computing partial query results outside of the initial timeframe including results for a second set of data segments associated with the batch processing layer.

Abstract: An approach is described of query processing via a lambda application including a batch processing layer and a speed processing layer. The approach may improve speed and efficiency with respect to computation of query results associated with the batch processing layer. A method pertaining to such approach may include receiving a query including an initial timeframe and computing partial query results within the initial timeframe. The partial query results within the initial timeframe may include a result for data associated with the speed processing layer and results for a first set of data segments associated with the batch processing layer. The method further may include combining the partial query results within the initial timeframe into a final query result. The method further may include computing partial query results outside of the initial timeframe including results for a second set of data segments associated with the batch processing layer.

Abstract: An approach is described of query processing via a lambda application including a batch processing layer and a speed processing layer. The approach may improve speed and efficiency with respect to computation of query results associated with the batch processing layer. A method pertaining to such approach may include receiving a query including an initial timeframe and computing partial query results within the initial timeframe. The partial query results within the initial timeframe may include a result for data associated with the speed processing layer and results for a first set of data segments associated with the batch processing layer. The method further may include combining the partial query results within the initial timeframe into a final query result. The method further may include computing partial query results outside of the initial timeframe including results for a second set of data segments associated with the batch processing layer.