Abstract: Aspects of the disclosure provide a computerized method and system that utilizes reference expense reports to build and train one or more neural network learning models that intelligently determine the riskiness of to-be-determined expense reports submitted for reimbursement. In examples, a determined riskiness may inform a reimbursement entity manager when determining whether to approve, reject, and/or flag for further review a to-be-determined expense report. In instances, computerized expense report resolution systems and methods may be further automated in order to omit user interactions with to-be-determined expense reports, such that an intelligent computer determines whether to approve, reject, and/or flag a to-be-determined expense report based on the intelligently determined riskiness of the to-be-determined expense report.

Abstract: This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.

Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.

Abstract: Systems and methods for predictive reliability mining are provided that enable predicting of unexpected emerging failures in future without waiting for actual failures to start occurring in significant numbers. Sets of discriminative Diagnostic Trouble Codes (DTCs) from connected machines in a population are identified before failure of the associated parts. A temporal conditional dependence model based on the temporal dependence between the failure of the parts from past failure data and the identified sets of discriminative DTCs is generated. Future failures are predicted based on the generated temporal conditional dependence and root cause analysis of the predicted future failures is performed for predictive reliability mining. The probability of failure is computed based on both occurrence and non-occurrence of DTCs. The root cause analysis enables identifying a subset of the population when an early warning is generated and also when an early warning is not generated.

Abstract: This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.

Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.

Abstract: Systems and methods for predictive reliability mining are provided that enable predicting of unexpected emerging failures in future without waiting for actual failures to start occurring in significant numbers. Sets of discriminative Diagnostic Trouble Codes (DTCs) from connected machines in a population are identified before failure of the associated parts. A temporal conditional dependence model based on the temporal dependence between the failure of the parts from past failure data and the identified sets of discriminative DTCs is generated. Future failures are predicted based on the generated temporal conditional dependence and root cause analysis of the predicted future failures is performed for predictive reliability mining. The probability of failure is computed based on both occurrence and non-occurrence of DTCs. The root cause analysis enables identifying a subset of the population when an early warning is generated and also when an early warning is not generated.

Abstract: The present invention relates to novel compounds that inhibit LRRK2 kinase activity, processes for their preparation, to compositions containing them and to their use in the treatment of diseases characterized by LRRK2 kinase activity, for example Parkinson's disease or Alzheimer's disease.

Abstract: Estimating warranty cost of products having multiple parts is described. In an implementation, part-failure data indicative of number of cycles at which each part fails in and after a first predefined time period is determined Sensor data and service records data are obtained to determine DTC occurrence data and DTC observance data. The DTC occurrence data and the DTC observance data are indicative of number of cycles at which each DTC associated with each part occurs and is observed for first time in the first predefined time period, respectively. Dependency parameters between the part-failure data, the DTC occurrence data and the DTC observance data are identified based on Bayesian Network that represents probabilistic relationships between the part-failure data, the DTC occurrence data and the DTC observance data. Number of failures of products in a second predefined time period is computed based on the dependency parameters for estimating the warranty cost.

Abstract: The present invention relates to novel compounds that inhibit LRRK2 kinase activity, processes for their preparation, to compositions containing them and to their use in the treatment of diseases characterized by LRRK2 kinase activity, particularly Parkinson's disease and Alzheimer's disease.

Abstract: The present invention relates to novel compounds that inhibit LRRK2 kinase activity, processes for their preparation, to compositions containing them and to their use in the treatment of diseases characterised by LRRK2 kinase activity, for example Parkinson's disease or Alzheimer's disease.

Abstract: The present invention relates to novel compounds that inhibit LRRK2 kinase activity, processes for their preparation, to compositions containing them and to their use in the treatment of diseases characterised by LRRK2 kinase activity, particularly Parkinson's disease and Alzheimer's disease.

Abstract: Cloud database is a single integrated universal database for enterprises, which provides a single database for online analytical processing (OLAP) and online transaction processing (OLTP) capabilities on the same database which stores real data. Cloud Database (cDB) can use any column oriented distributed database hosted on cluster of distributed file system to store the data into files. cDB uses divide and conquer principle to process the analytical queries. cDB hosts databases for various application which uses different database of different vendors. cDB adapter is interface between the enterprise application and cDB and handles query processing for the application.

Abstract: The present invention relates to novel pyridine derivatives such as compounds of the formula (I): and the use of such compounds or pharmaceutical compositions thereof in the treatment of diseases, particularly pain, which are mediated by the activity of the cannabinoid 2 receptor.

Abstract: Combination of one or more CB2 modulators and one or more PDE4 inhibitors, and method of treating conditions which are mediated by the activity of CB2 receptors or conditions which are mediated by PDE4.

Abstract: The present invention is concerned with pharmaceutical compositions containing certain adenosine derivatives having an acetylene group in the 4′ position, which are adenosine A1 agonists, and to their use in therapy.