Abstract: A network device associated with a database management system receives information associated with a customer support ticket. Based on information in the database management system, a direct relationship between the received customer support ticket and a customer support ticket in the database may be determined. A graph including nodes representing customer support tickets is generated based on information in the database. Edge prediction is performed on the graph to derive relationships among the nodes in the graph. A predictive relationship between customer support tickets is derived. A relationship data set based on the direct relationship between the customer support tickets and based on the predictive relationship between the customer support tickets is generated. The relationship data set associated with the customer support ticket is communicated to the user device.

Abstract: In a datacenter setting, a summary of differences and similarities between two or more states of the same or similar systems are predicted. Initially, a Long Short-Term Memory (LSTM) neural network is trained with to predict a summary describing the state change between at least two states of the datacenter. Given a set of training data (at least two datacenter states that are annotated with a state change description), the LSTM neural network learns which similarities and differences between the datacenter states correspond to the annotations. Accordingly, given a set of test data comprising at least two states of a datacenter represented by context graphs that indicate a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, the LSTM neural network is able to determine a state change description that summarizes the differences and similarities between the at least two states of the datacenter.

Abstract: A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.

Abstract: In a datacenter setting, annotations or descriptions of relevant parts or subgraphs corresponding to components in the datacenter are predicted. Given a set of training data (library of subgraphs seen in the past labeled with a textual description explaining why were they considered relevant enough to be placed in the historical database), the recurrent neural network (RNN) learns how to combine the different textual annotations coming from each relevant region into a single annotation that describes the whole system. Accordingly, given a set of input or test data (datacenter state modeled a context graph that is not annotated), the system determines which regions of the input graph are more relevant, and for each of these regions, the RNN predicts an annotation even in a previously unseen or different datacenter infrastructure.

Abstract: Incomplete state information for nodes of a datacenter is completed utilizing historical state information. A context graph is received having a plurality of nodes that correspond to components of the datacenter, each node including properties that correspond to the represented component. It is determined that at least one of the properties for a node is incomplete. A context hash is derived from the context graph and compared to a plurality of subgraph hashes. Based upon the comparison, one or more subgraphs are identified that are similar to the context hash. Where a single similar subgraph is identified, the property information for the single subgraph is imputed to the context graph to complete the incomplete information. Where more than one similar subgraph is identified, the property information for the identified similar subgraphs is aggregated and the aggregated property information is imputed to the context graph to complete the incomplete information.

Abstract: Incomplete state information for nodes of a datacenter is completed utilizing historical state information. A context graph is received having a plurality of nodes that correspond to components of the datacenter, each node including properties that correspond to the represented component. It is determined that at least one of the properties for a node is incomplete. A context hash is derived from the context graph and compared to a plurality of subgraph hashes. Based upon the comparison, one or more subgraphs are identified that are similar to the context hash. Where a single similar subgraph is identified, the property information for the single subgraph is imputed to the context graph to complete the incomplete information. Where more than one similar subgraph is identified, the property information for the identified similar subgraphs is aggregated and the aggregated property information is imputed to the context graph to complete the incomplete information.

Abstract: In a datacenter setting, future states of nodes of a datacenter (each node representing a component of the datacenter in a context graph) are predicted. Initially, historical metrics collected from the datacenter nodes, as well as historical metrics of neighboring nodes. The metrics are aggregated into historical metric summary vector representations of the nodes which are utilized to train a future state predictor to predict future datacenter states. Once trained, metrics may be input into the future state predictor and the future state predictor may be utilized to predict a future state of one or more of the nodes of the datacenter.

Abstract: In a datacenter setting, annotations or descriptions of relevant parts or subgraphs corresponding to components in the datacenter are predicted. Given a set of training data (library of subgraphs seen in the past labeled with a textual description explaining why were they considered relevant enough to be placed in the historical database), the recurrent neural network (RNN) learns how to combine the different textual annotations coming from each relevant region into a single annotation that describes the whole system. Accordingly, given a set of input or test data (datacenter state modeled a context graph that is not annotated), the system determines which regions of the input graph are more relevant, and for each of these regions, the RNN predicts an annotation even in a previously unseen or different datacenter infrastructure.

Abstract: In a datacenter setting, a summary of differences and similarities between two or more states of the same or similar systems are predicted. Initially, a Long Short-Term Memory (LSTM) neural network is trained with to predict a summary describing the state change between at least two states of the datacenter. Given a set of training data (at least two datacenter states that are annotated with a state change description), the LSTM neural network learns which similarities and differences between the datacenter states correspond to the annotations. Accordingly, given a set of test data comprising at least two states of a datacenter represented by context graphs that indicate a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, the LSTM neural network is able to determine a state change description that summarizes the differences and similarities between the at least two states of the datacenter.

Abstract: In a datacenter setting, root causes of anomalies corresponding to components in the datacenter are predicted. Initially, a convolutional neural network (CNN) is utilized to consider the evolution sequence of the datacenter infrastructure. Given a set of training data (sequences of datacenter states that are labeled with root causes of the anomalies present in the sequences), the CNN learns which sequences of datacenter states correspond to the labels of root causes. Accordingly, given a set of input or test data (sequences of datacenter states that are not labeled with root causes of the anomalies present in the sequences), the CNN is able to predict a root cause for the anomaly even in a previously unseen or different datacenter infrastructure.

Abstract: A method includes performing, by a processor: receiving a first document, the first document comprising a first plurality of sub-documents that are related to one another in a first time sequence; converting the first plurality of sub-documents to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; detecting a plurality of topics within the vectorized document, the plurality of topics being related to one another in the first time sequence; applying a process discovery algorithm to the plurality of topics to generate a model that is representative of relationships between the plurality of topics; receiving a second document containing subject matter related to a course of action, the second document comprising a second plurality of sub-documents that are related to one another in a second time sequence; using the model to generate a classification for the second document; and adjusting the course of acti

Abstract: A network device associated with a database management system receives information associated with a customer support ticket. Based on information in the database management system, a direct relationship between the received customer support ticket and a customer support ticket in the database may be determined. A graph including nodes representing customer support tickets is generated based on information in the database. Edge prediction is performed on the graph to derive relationships among the nodes in the graph. A predictive relationship between customer support tickets is derived. A relationship data set based on the direct relationship between the customer support tickets and based on the predictive relationship between the customer support tickets is generated. The relationship data set associated with the customer support ticket is communicated to the user device.

Abstract: A method includes performing by a processor of a server: receiving a requirement description of a service for a software application, generating a migration graph that comprises vertices representing candidate service providers for the service, respectively, identifying migration capability information between the vertices of the migration graph by connecting a portion of the vertices with edges, generating a centrality metric for each of the vertices of the migration graph based on the migration capability information and a number of edges terminating at the respective vertex, grading the candidate service providers based on one of the migration capability information and the centrality metric, and deploying the service using one of the candidate service providers responsive to grading the candidate service providers receiving an architecture description for a software application that identifies a plurality of generic services, receiving a requirement description for the software application that comprises

Abstract: A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.