Abstract: One example method includes creating an ILP model that includes a delay model, an energy model, and a QoS model, and modeling, using the integer linear programming model, a VNF placement problem as an ILP problem, and the modeling includes: using the delay model to identify propagation, transmission, processing, and queuing, delays implied by enabling an instance of the VNF at an edge node to accept a user VNF call; using the energy model to identify energy consumption implied by enabling an instance of the VNF at an edge node to accept a user VNF call; and using the QoS model to identify end-to-end delay, bandwidth consumption, and jitter, implied by enabling an instance of the VNF at an edge node to accept a user virtual network function call. The problem modeled by the ILP model may be resolved by a heuristic method.

Abstract: Techniques are provided for data discovery and data integration in a data lake. One method comprises obtaining data files from a data lake, wherein each data file comprises multiple records having multiple fields; selecting multiple candidate fields from a data file based on a record type; determining a relevance score for each candidate field from the data file based on multiple features extracted from the data file; and clustering the scored candidate fields into clusters of similar domains using a hashing algorithm, wherein a given cluster comprises candidate fields, wherein multiple data files can be integrated based on a domain of the candidate fields in the given cluster. The relevance score for each candidate field is based on multiple features comprising, for example, features that take into account a morphological or semantic similarity between file name, file metadata and/or file records and features that consider statistics of candidate fields in a data file.

Abstract: Techniques are provided for data discovery and data integration in a data lake. One method comprises obtaining data files from a data lake, wherein each data file comprises multiple records having multiple fields; selecting multiple candidate fields from a data file based on a record type; determining a relevance score for each candidate field from the data file based on multiple features extracted from the data file; and clustering the scored candidate fields into clusters of similar domains using a hashing algorithm, wherein a given cluster comprises candidate fields, wherein multiple data files can be integrated based on a domain of the candidate fields in the given cluster. The relevance score for each candidate field is based on multiple features comprising, for example, features that take into account a morphological or semantic similarity between file name, file metadata and/or file records and features that consider statistics of candidate fields in a data file.

Abstract: Techniques are provided for dataflow execution time estimation for distributed processing frameworks. An exemplary method comprises: obtaining an input dataset for a dataflow for execution; determining a substantially minimal data unit for a given operation of the dataflow processed by the given operation; estimating a number of rounds required to execute a number of data units in the input dataset using nodes assigned to execute the given operation; determining an execution time spent by the given operation to process one data unit; estimating the execution time for the given operation based on the execution time spent by the given operation to process one data unit and the number of rounds required to execute the number of data units in the input dataset; and executing the given operation with the input dataset. A persistent cost model is optionally employed to record the execution times of known dataflow operations.

Abstract: Techniques are provided for dataflow execution time estimation for distributed processing frameworks. An exemplary method comprises: obtaining an input dataset for a dataflow for execution; determining a substantially minimal data unit for a given operation of the dataflow processed by the given operation; estimating a number of rounds required to execute a number of data units in the input dataset using nodes assigned to execute the given operation; determining an execution time spent by the given operation to process one data unit; estimating the execution time for the given operation based on the execution time spent by the given operation to process one data unit and the number of rounds required to execute the number of data units in the input dataset; and executing the given operation with the input dataset. A persistent cost model is optionally employed to record the execution times of known dataflow operations.

Abstract: Pattern queries are evaluated in parallel over large N-dimensional datasets to identify features of interest.

Abstract: Techniques are provided for automatic placement of cache operations in a dataflow. An exemplary method obtains a graph representation of a dataflow of operations; determines a number of executions and a computational cost of the operations, and a computational cost of a caching operation to cache a dataset generated by an operation; establishes a dataflow state structure recording values for properties of the dataflow operations for a number of variations of caching various dataflow operations; determines a cache gain factor for dataflow operations as an estimated reduction in the accumulated cost of the dataflow by caching an output dataset of a given operation; determines changes in the dataflow state structure by caching an output dataset of a different operation in the dataflow; and searches the dataflow state structures to determine the output datasets to cache based on a total dataflow execution cost.

Abstract: An architecture, methods and apparatus are provided for managing sensor data. Sensor networks comprised of a plurality of sensors are managed by obtaining measurement data and context data from the plurality of sensors; storing the obtained measurement data and context data using a Massively Parallel Processing Database Management System (MPP DBMS); and managing the sensor network from outside of the sensor network using the MPP DBMS. Context-aware adaptation of sensors is based on context regarding a state of the sensor network and context regarding a state of one or more applications. The sensor nodes are optionally clustered based on semantic similarities among sensor readings from different sensor nodes and a distance among the sensor nodes. A subset of the sensor nodes is optionally selected to be active based on a residual energy of the sensor nodes and a relevance of the sensor nodes to an application. Data prediction models are generated and employed for data sensing and analytics.

Abstract: An architecture, methods and apparatus are provided for managing sensor data. Sensor networks comprised of a plurality of sensors are managed by obtaining measurement data and context data from the plurality of sensors; storing the obtained measurement data and context data using a Massively Parallel Processing Database Management System (MPP DBMS); and managing the sensor network from outside of the sensor network using the MPP DBMS. Context-aware adaptation of sensors is based on context regarding a state of the sensor network and context regarding a state of one or more applications. The sensor nodes are optionally clustered based on semantic similarities among sensor readings from different sensor nodes and a distance among the sensor nodes. A subset of the sensor nodes is optionally selected to be active based on a residual energy of the sensor nodes and a relevance of the sensor nodes to an application. Data prediction models are generated and employed for data sensing and analytics.