Abstract: Embodiments of the present disclosure relate to a fault tolerant root cause analysis (RCA) system that is able to handle calculation failures during runtime. Calculations (e.g., evaluation of a diagnostic model for a specific component or device) that are performed during the RCA are integrated using different resources of the system under analysis. In order to make a final diagnosis, the resources exchange messages containing calculation inputs and outputs. Calculation problems due to calculation failures in a particular resource can be resolved efficiently which reduces resource utilization and minimizes failure propagation to other parts of the system. Accordingly, the system is able to recover and output a diagnosis even if some of the resources fail or generate problems.

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: A thing-sourcing project request including requirements for a thing-sourcing task that requires data input by a thing-sourcing device is received from a requestor device. A determination is made if real-time data is needed in order to complete the thing-sourcing task. In response to determining that real-time data is not needed, a determination is made if a similar thing-sourcing task has been previously completed. If not, the method determines if the thing-sourcing task can be completed using pre-existing data. If so, a data archive is searched for relevant pre-existing data that can be used to complete the thing-sourcing task. The thing-sourcing task is completed using the relevant pre-existing data, and a response to the thing-sourcing project request is transmitted to the requestor device.

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: A method includes performing, by a processor: detecting a performance anomaly in a production computer system, generating a snapshot image of software and data that were executed on the production computer system during the performance anomaly, generating diagnostic information for the performance anomaly, communicating the diagnostic information to an experiment computer system, generating an experiment based on the diagnostic information and the snapshot image to create an experimental image, executing the experimental image on the experiment computer system to perform the experiment, and evaluating an effect of the experiment on the performance anomaly.

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: A non-intrusive system which monitors a data center by detecting electromagnetic waves alleviates a resource burden on components and is inexpensive to deploy and scale within a data center. The system detects waves using omnidirectional antennas positioned throughout the data center, thus alleviating the need to physically attach directional antennas to components. The system performs a learning phase wherein representations of detected waves are mapped to occurrence of events within the data center. Once the learning phase is complete, operation of existing network monitoring tools, such as agents and probes, may cease, and the system may begin monitoring for events based on the detected waves. The system may also analyze wave data prior to the occurrence of events to identify event prediction indicators, e.g. distinctive wave values or patterns, which may be used to predict the occurrence of an event.

Abstract: In a computer server, a ticket element generated by an issue tracking system responsive to a client request is received, where the ticket element includes a data field including data indicative of an attribute. A database including state data stored therein is accessed responsive to receipt of the ticket element. The state data relates a plurality of states that are specific to the attribute. One of the plurality of states related by the state data is identified as corresponding to a future state of the attribute based on a current state of the attribute, and a future probability of escalation of the ticket element is computed based on the future state of the attribute responsive to identification of the one of the plurality of states as corresponding thereto. An action indicator is provided in response to the client request.

Abstract: A data processing system, and an article of manufacturing, join rows associated with a source table column with rows associated with a target table column. A source node and a target node contain the source and target tables, respectively. A reduced representation of selected rows associated with the source table column is generated, as is a representation of the target table column. A filtering module filters the generated reduced representation of selected rows associated with the source table column through the generated representation of the target table column, the filtered generated reduced representation of selected rows identifying source table rows that do not have to be joined with the target table. The rows associated with the source table column minus the filtered generated reduced representation of selected rows are joined to the rows associated with the target table column.

Abstract: Embodiments of the present disclosure relate to a fault tolerant root cause analysis (RCA) system that is able to handle calculation failures during runtime. Calculations (e.g., evaluation of a diagnostic model for a specific component or device) that are performed during the RCA are integrated using different resources of the system under analysis. In order to make a final diagnosis, the resources exchange messages containing calculation inputs and outputs. Calculation problems due to calculation failures in a particular resource can be resolved efficiently which reduces resource utilization and minimizes failure propagation to other parts of the system. Accordingly, the system is able to recover and output a diagnosis even if some of the resources fail or generate problems.

Abstract: Systems and methods are provided for aiding participants in meaningfully contributing to event online Q&A with minimal loss of attention to the event. As a user is inputting an event contribution fragment, probable event contribution completions are predicted. Based on the predicted event contribution completions, semantically-similar, prior-received event contributions are presented to the user. The user is permitted to act upon the similar contributions in lieu of completing and/or publishing his or her own contribution. For instance, the user may be provided the ability to vote on, comment on, or amend the semantically-similar, prior-received event contribution in lieu of completing and/or publishing his or her own contribution. Upon receiving an indication of a desired user action with respect to a semantically-similar, prior-received event contribution, the user-input event contribution fragment is discarded and the user's action on the prior-received event contribution is published in lieu thereof.

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, 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: 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, 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, 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: Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Abstract: As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.

Abstract: A first event notification is received. The first event notification is associated with a first event. Criteria is determined based, at least in part, on the first event notification. A first component is identified based, at least in part, on the criteria a component graph. An operational status associated with the first component is determined. It is determined not to generate a second event notification based, at least in part, on the operational status.

Abstract: A method for controlling and visualizing an internationalization process may include detecting a plurality of modules within source code, determining a respective status for each of the plurality of modules, formatting for display at a graphical user interface the respective status for each of the plurality of modules, parsing the plurality of modules to determine whether any particular module has one of a plurality of predetermined internationalization issues, and determining that a particular module has one of the plurality of predetermined internationalization issues. In response to determining that the particular module has one of the plurality of predetermined internationalization issues, the method may include updating the respective status of the particular module to a first status.