Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

Abstract: Methods and arrangements for efficiently mining a control flow graph from execution logs of a distributed system. Using at least one text clustering technique, two text clusters are generated from the plurality of execution logs. At least one approximate template is generated based on the at least two text clusters. At least one refined template is created via refining the at least one approximate template using multimodal sequencing. The control flow graph is created based on the at least one refined template. An anomaly is detected in the control flow graph.

Abstract: Methods and arrangements for managing map-reduce jobs. There are identified intermediate data produced, in a current map-reduce cycle, by a plurality of nodes in the distributed network, the nodes being selected from the group consisting of: a plurality of map nodes, and a plurality of reducer nodes. There are identified a plurality of classes of data, for classifying the intermediate data. Discrete portions of the intermediate data are classified into respective ones of the classes of data, wherein at least one of the classes of data comprises intermediate data which are processed in a map-reduce cycle subsequent to the current map-reduce cycle. Other variants and embodiments are broadly contemplated herein.

Abstract: Techniques, systems, and articles of manufacture for collaborative analytics with edge devices. A method includes identifying multiple items of data pertaining to a user from one or more user actions implemented across multiple user devices, identifying one or more rules associated with one or more user preferences, exchanging the multiple items of data across the multiple user devices, and applying the one or more rules to the multiple items of data across the multiple user devices to generate an output via at least one of the multiple user devices.

Abstract: Methods, systems, and computer program products for processing multi-way theta join queries involving arithmetic operators on MapReduce are provided herein. A method includes converting (i) a multi-way theta join query into a multi-way interval join query, and (ii) one or more items of data associated with the multi-way theta join query to one or more items of interval data; executing the multi-way interval join query on the one or more items of interval data via MapReduce to generate an output, wherein the output comprises a set of multiple responses to the multi-way interval join query; and processing the output to generate a solution to the multi-way theta join query, wherein said processing comprises discarding each response from the set that does not satisfy the multi-way theta join query.

Abstract: Methods and arrangements for measuring similarity with respect to moving object trajectories. First and second moving object trajectories are input, each trajectory being defined by at least two spatial dimensions and a temporal dimension. At least one segment is defined with respect to each trajectory, each segment being defined between two defining points. At least one segment from the first trajectory is matched with at least one segment from the second trajectory. A spatial distance and temporal distance are each computed between the at least one segment from the first trajectory and the at least one segment from the second trajectory. The spatial distance and temporal distance are combined to provide a measure of a spatio-temporal distance between the at least one segment from the first trajectory and the at least one segment from the second trajectory. Other variants and embodiments are broadly contemplated herein.

Abstract: Search results are provided for a search query. A search query including an entity and entity type is received. The entity is parsed into semantic components. Variants are generated based on input from auxiliary information and user configuration information for each of the semantic components. The entity is recomposed in different morphological forms from different variants of the semantic components. At least one morphological form for the entity is presented as a search result.

Abstract: Methods and arrangements for segmenting log records. A log file is received. Candidates for a sequential pattern within the log file are automatically discerned, and, for each candidate, a likelihood is estimated that it represents a boundary within the log file. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and arrangements for efficiently mining a control flow graph from execution logs of a distributed system. Using at least one text clustering technique, two text clusters are generated from the plurality of execution logs. At least one approximate template is generated based on the at least two text clusters. At least one refined template is created via refining the at least one approximate template using multimodal sequencing. The control flow graph is created based on the at least one refined template. An anomaly is detected in the control flow graph.

Abstract: An embodiment provides intermediate data derived in the form of column stores which are in turn based on hierarchical data stores. This intermediate data represents a reduced subset of data matched appropriately to a query (or modified query) such that the amount of data handled in a query processing task on large data is greatly reduced. An embodiment may appropriately choose column data stores and/or modify queries in order leverage parallelization techniques such as map-reduce in order to query large data. The result is the ability to query large data stores in parallel while reducing the amount of data that must be handled.

Abstract: Methods and arrangements for managing map-reduce jobs. There are identified intermediate data produced, in a current map-reduce cycle, by a plurality of nodes in the distributed network, the nodes being selected from the group consisting of: a plurality of map nodes, and a plurality of reducer nodes. There are identified a plurality of classes of data, for classifying the intermediate data. Discrete portions of the intermediate data are classified into respective ones of the classes of data, wherein at least one of the classes of data comprises intermediate data which are processed in a map-reduce cycle subsequent to the current map-reduce cycle. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and arrangements for evaluating a regular expression. Text strings are received. A regular expression is also received, the regular expression comprising a pattern for specifying and recognizing at least one text string from among the received text strings. There is generated, with respect to the received text strings, a data structure containing grams with positional information. The data structure is employed to evaluate the regular expression via identifying a subset of the text strings comprising at least one match for the given regular expression. Other variants and embodiments are broadly contemplated herein.

Abstract: Embodiments of the present invention provide an efficient and scalable scheme for role-based access control to resources. The resources are assigned a protection class. Resources in the same protection class share the same access control policy. Permissions granted to various roles are then defined based on privilege sets and protection classes. Accordingly, the permissions of a role can be dynamically determined at runtime. Furthermore, as new resources are added, they can be assigned to a pre-existing protection class. The new resource may thus automatically inherit the various permissions and roles attached to the protection class.

Abstract: Methods and arrangements for conducting a search using query logs. A query log is consulted and query rewrite rules are learned automatically based on data in the query log. The learning includes obtaining click-through data present in the query log.