Abstract: An example embodiment may involve: obtaining static data from work items of a process and dynamic data from event logs of the process; generating, from the static data and the dynamic data, a causal graph of dependencies between features of the process; providing, to a natural language model, representations of the causal graph and the dependencies; and obtaining, from the natural language model, indications of an inefficiency in the process.

Abstract: A system and non-transitory storage medium for performing object classification using a trained machine learning algorithm (MLA). The MLA includes an embedding layer and a classification layer. A byte representation of an object is received. A set of embedding indices is generated based on the byte representation of the object. The MLA embeds, using the embedding layer, the set of embedding indices to obtain an input vector and predicts an estimated class based on the input vector. In some implementations, the set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: A method and a system for training a machine learning algorithm (MLA) for object classification. The machine learning algorithm includes an embedding layer and a classification layer. A set of embedding indices representing a reference object is received. The set of embedding indices has been generated based on a byte representation of the reference object. A label associated with the reference object indicative of a reference class the objects belongs to is received. The MLA is iteratively trained to classify objects by embedding the set of embedding indices to obtain an input vector and by predicting an estimated class based on the input vector, and updating a parameter of at least one of the embedding layer and the updated embedding layer. The set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: Persistent storage may contain typed data of a plurality of types, directional relationships between pairs of the plurality of types, and a conditional dependency structure for the typed data. One or more processors may be configured to: generate an essential graph from the conditional dependency structure; orient the edges of the essential graph such that they are directed in accordance with the directional relationships; generate typed directed acyclic graphs (DAGs) that can be found in the essential graph; form a t-essential graph from a union of the typed DAGs; identify an event represented as a first vertex in the t-essential graph, wherein the first vertex is of a first type; trace backward from the first vertex and through the t-essential graph to identify a second vertex of a second type; and provide a representation of the second vertex as a cause of the event.

Abstract: A method and a system for training a machine learning algorithm (MLA) for object classification. The machine learning algorithm includes an embedding layer and a classification layer. A set of embedding indices representing a reference object is received. The set of embedding indices has been generated based on a byte representation of the reference object. A label associated with the reference object indicative of a reference class the objects belongs to is received. The MLA is iteratively trained to classify objects by embedding the set of embedding indices to obtain an input vector and by predicting an estimated class based on the input vector, and updating a parameter of at least one of the embedding layer and the updated embedding layer. The set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.