Abstract: Methods and systems of aligning annotation of fields of documents are provided. Training information that includes first measurement information pertaining to features of each of a plurality of fields associated with training clusters for documents of a document type is accessed. A first training cluster is annotated with a first name and the second training cluster is annotated with a second name. An electronic classification model is generated based on the training information. Second measurement information for features of fields associated with new clusters of a new document is accessed. Each of the new clusters is automatically annotated based on the second measurement information using the classification model. For example, a first new cluster that has fields of the first field type is annotated with the first name and a second new cluster that has fields of the second field type is annotated with the second name.

Abstract: A recovery system and method with repeating communication functionality includes a station configured to send a first message and at least a first remote transmitting unit responsive to the central station and configured to transmit a second message to a locating unit. At least a second remote transmitting unit includes a receiver which receives the second message, a controller configured to process the second message, and a transmitter for transmitting a third message to the locating unit.

Abstract: A recovery system and method with repeating communication functionality includes a station configured to send a first message and at least a first remote transmitting unit responsive to the central station and configured to transmit a second message to a locating unit. At least a second remote transmitting unit includes a receiver which receives the second message, a controller configured to process the second message, and a transmitter for transmitting a third message to the locating unit.

Abstract: A system includes a normal behavior characterization module configured to receive values for a first metric of a plurality of metrics and generate a baseline profile indicating normal behavior of the first metric based on the received values. The system also includes an anomaly identification module configured to identify an anomaly in response to present values of the metric deviating outside the baseline profile. The system also includes an anomaly behavior characterization module configured to analyze a plurality of prior anomalies identified by the anomaly identification module and develop a model of the anomalies of the first metric. The system also includes an anomaly scoring module configured to determine a first score for a present anomaly detected by the anomaly identification module for the first metric. The first score is based on characteristics of the present anomaly and the model of the anomalies of the first metric.

Abstract: A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, and the master node may distribute this metadata with query plans to worker nodes. The worker nodes may request additional metadata from the master node when necessary.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for error developer association. Error developer association can include identifying a number of portions of the source code associated with a message, wherein the message is associated with an error. Error developer association can also include associating a developer with a portion of the source code of the number of portions of the source code. Error developer association can also include identifying a developer of the number of developers to resolve the error.

Abstract: A system includes a normal behavior characterization module configured to receive values for a first metric of a plurality of metrics and generate a baseline profile indicating normal behavior of the first metric based on the received values. The system also includes an anomaly identification module configured to identify an anomaly in response to present values of the metric deviating outside the baseline profile. The system also includes an anomaly behavior characterization module configured to analyze a plurality of prior anomalies identified by the anomaly identification module and develop a model of the anomalies of the first metric. The system also includes an anomaly scoring module configured to determine a first score for a present anomaly detected by the anomaly identification module for the first metric. The first score is based on characteristics of the present anomaly and the model of the anomalies of the first metric.

Abstract: A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, and the master node may distribute this metadata with query plans to worker nodes. The worker nodes may request additional metadata from the master node when necessary.

Abstract: In one example of the disclosure, event notices are received, with each notice indicative of degradation of a configuration item. Configuration item past-preference pairings are accessed. Each pairing includes a count of operator-exhibited preferences for event notices associated with a first configuration item relative to event notices associated with a second configuration item. A prioritized ordering of the received event notices is created utilizing the past-preference pairings.

Abstract: A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, where the master node is the root node in a tree. The master node may distribute query plans and query metadata to other nodes in the cluster. These additional nodes may request additional metadata from each other or the master nodes as necessary.

Abstract: A method is described that involves creating a private key and a public key cryptographic key pair, generating a unique and random identifier for a voter's vote and accepting an election vote from said voter. The vote and identifier are electronically signed with the private key to create a digital signature. The vote and identifier are provided in a human readable format to the voter.

Abstract: In one example of the disclosure, data indicative of a word or phrase communicated during a meeting including a plurality of participants is obtained. For each participant, records electronically accessible to the participant are identified, and each record is associated with a tier from a hierarchy of record-relevancy tiers. A set of explanations for the communication and associated scores is identified, including for each participant, beginning with a most relevant tier, searching the records accessible to the participant tier by tier until an explanation is identified, and assigning a score to the explanation according to the tier associated with the record in which the explanation is found. A preferred explanation for the communication is determined based upon the scores, and a display of the preferred explanation is caused.

Abstract: Examples disclosed herein relate to generating a fingerprint representing a response of an application to a simulation of a fault of an external service. Examples include causing simulation of a fault of an external service in a simulation of the external service, and generating a testing application fingerprint representing a response of an application to the simulation of the fault of the external service during the testing of the application.

Abstract: A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, where the master node is the root node in a tree. The master node may distribute query plans and query metadata to other nodes in the cluster. These additional nodes may request additional metadata from each other or the master nodes as necessary.

Abstract: A computing device includes at least one processor and a satisfaction prediction module. The satisfaction prediction module is to generate a pruned decision tree using historical ticket data for a plurality of customer tickets, where the historical ticket data for each customer ticket includes a satisfaction metric and attribute values of the customer ticket. The satisfaction prediction module is also to generate a plurality of business rules based on the pruned decision tree, obtain at least one attribute value of an active customer ticket, and determine, based on the plurality of business rules and the at least one attribute value, a projected satisfaction metric for the active customer ticket.

Abstract: Method and systems for analyzing event log elements are provided. In one example, a method includes receiving an event log element in a computer. A similarity index is calculated between the event log element and a text element. A threshold of similarity is calculated. The similarity index is compared to the threshold. If the similarity index is greater than the threshold, the event log element is grouped into a cluster with the text element to create a file of cluster assignments.

Abstract: End user data partitioning can include receiving a number of data queries for a data source from a user, developing a dimension relation graph based on attributes of the number of data queries, and partitioning the data source based on the dimension relation graph.

Abstract: A first and a second catalog access statement are obtained. For the first catalog access statement, a first lock schedule which includes a catalog resource being locked and a lock strength is obtained. For the second catalog access statement, a second lock schedule which includes a catalog resource being locked and a lock strength are obtained. A potential deadlock between the first and the second catalog access statement is identified, including by comparing the first lock schedule and the second lock schedule.

Abstract: Determining suspected root causes of anomalous network behavior includes identifying anomalous components in a network exhibiting anomalous behavior from a plurality of network components, assigning a likelihood score to network components based on a scoring policy that considers recent change events affecting the anomalous components, and identifying a subset of the network components that are suspected to be root causes based on the likelihood score.

Abstract: End user data partitioning can include receiving a number of data queries for a data source from a user, developing a dimension relation graph based on attributes of the number of data queries, and partitioning the data source based on the dimension relation graph.