Abstract: Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

Abstract: Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

Abstract: Described are techniques for determining statistical properties of time series data. The techniques include a method comprising graphing, from time series data, a time series data graph. The method further comprises iteratively segmenting the time series data graph into respective pluralities of subgraphs using respective segmentation schemes until a first plurality of subgraphs generated by a first segmentation scheme exhibits a similarity between respective subgraphs of the first plurality of subgraphs satisfying a similarity threshold. The first segmentation scheme can be selected from: an equidistant segmentation scheme, a local extrema segmentation scheme, and a windowed segmentation scheme. The method further comprises associating a classification to the time series data based on the first segmentation scheme. The classification can be indicative of one selected from: stationarity of the time series data, periodicity of the time series data, and trending of the time series data.

Abstract: Described are techniques for determining statistical properties of time series data. The techniques include a method comprising graphing, from time series data, a time series data graph. The method further comprises iteratively segmenting the time series data graph into respective pluralities of subgraphs using respective segmentation schemes until a first plurality of subgraphs generated by a first segmentation scheme exhibits a similarity between respective subgraphs of the first plurality of subgraphs satisfying a similarity threshold. The first segmentation scheme can be selected from: an equidistant segmentation scheme, a local extrema segmentation scheme, and a windowed segmentation scheme. The method further comprises associating a classification to the time series data based on the first segmentation scheme. The classification can be indicative of one selected from: stationarity of the time series data, periodicity of the time series data, and trending of the time series data.