Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: An apparatus, a computer program product and a computer-implemented method performed by a computerized device, comprising: receiving a description of a workflow, the workflow comprising a plurality of blocks, wherein each block comprises one or more instructions, the plurality of blocks comprising at least a first block and a second block, wherein the first block is adapted to output information, and the second block is adapted to receive the information wherein at least one of the plurality of blocks is associated with a ratio between a number of records input into the block and a number of records output by the block; and validating that the workflow can operate properly, using the ratio, wherein during execution, each of the first block and the second block can keep an internal state and request to receive again data previously received as input.

Abstract: An apparatus, a computer program product and a computer-implemented method performed by a computerized device, comprising: receiving a description of a workflow, the workflow comprising a plurality of blocks, wherein each block comprises one or more instructions, the plurality of blocks comprising at least a first block and a second block, wherein the first block is adapted to output information, and the second block is adapted to receive the information wherein at least one of the plurality of blocks is associated with a ratio between a number of records input into the block and a number of records output by the block; and validating that the workflow can operate properly, using the ratio, wherein during execution, each of the first block and the second block can keep an internal state and request to receive again data previously received as input.