Abstract: Example artificial intelligence systems and methods provide parallel storage of data to primary storage and notification to a model server supported by the primary storage. A primary storage system receives operations on a training data set from a model trainer and sends a model instance of a computational model to a model server. When a new data element is received by a data ingester, the model server is initiated to evaluate the new data element using the model instance while the primary storage system stores the new data element in parallel.

Abstract: Example tiered storage systems, storage devices, and methods provide intelligent data access across tiered storage systems. An example system can comprise one or more computing devices, a file system, an object storage system comprising an object storage, and a data tiering application. The data tiering application is executable by one or more computing devices to perform operations comprising determining, using machine learning logic, a cluster of associated files stored in the file system; and archiving the cluster of associated files from the file system to the object storage coupled for electronic communication to the file system via a computer network.

Abstract: Example artificial intelligence systems and methods provide parallel storage of data to primary storage and notification to a model server supported by the primary storage. A primary storage system receives operations on a training data set from a model trainer and sends a model instance of a computational model to a model server. When a new data element is received by a data ingester, the model server is initiated to evaluate the new data element using the model instance while the primary storage system stores the new data element in parallel.

Abstract: Example tiered storage systems, storage devices, and methods provide intelligent data access across tiered storage systems. An example system can comprise one or more computing devices, a file system, an object storage system comprising an object storage, and a data tiering application. The data tiering application is executable by one or more computing devices to perform operations comprising determining, using machine learning logic, a cluster of associated files stored in the file system; and archiving the cluster of associated files from the file system to the object storage coupled for electronic communication to the file system via a computer network.

Abstract: A system deploys visualization tools, business analytics software, and big data software in a multi-instance mode on a large, coherent shared memory many-core computing system. The single machine solution provides or high performance and scalability and may be implemented remotely as a large capacity server (i.e., in the cloud) or locally to a user. Most big data software running in a single instance mode has limitations in scalability when running on a many-core and large coherent shared memory system. A configuration and deployment technique using a multi-instance approach, which also includes visualization tools and business analytics software, maximizes system performance and resource utilization, reduces latency and provides scalability as needed, for end-user applications in the cloud.

Abstract: A system and method for archiving and analyzing data are disclosed. The system receives event data associated with a process; responsive to receiving the event data, determines process data associated with the process; generates process metadata from the event data and the process data; and stores the event data, the process data, and the process metadata in a data repository organized by the process metadata. Since the process data is determined early on in the data pipeline, the system can significantly reduce the amount of computation required for generating data analytics. The system is also capable of providing analytic results computed against a massive amount of archived data in real-time or near real-time as user requests are initiated. Efficiency of process mining and process optimization is also improved due to enhanced information stored for archived processes.

Abstract: A system and method for archiving and analyzing data are disclosed. The system receives event data associated with a process; responsive to receiving the event data, determines process data associated with the process; generates process metadata from the event data and the process data; and stores the event data, the process data, and the process metadata in a data repository organized by the process metadata. Since the process data is determined early on in the data pipeline, the system can significantly reduce the amount of computation required for generating data analytics. The system is also capable of providing analytic results computed against a massive amount of archived data in real-time or near real-time as user requests are initiated. Efficiency of process mining and process optimization is also improved due to enhanced information stored for archived processes.

Abstract: A system deploys visualization tools, business analytics software, and big data software in a multi-instance mode on a large, coherent shared memory many-core computing system. The single machine solution provides or high performance and scalability and may be implemented remotely as a large capacity server (i.e., in the cloud) or locally to a user. Most big data software running in a single instance mode has limitations in scalability when running on a many-core and large coherent shared memory system. A configuration and deployment technique using a multi-instance approach, which also includes visualization tools and business analytics software, maximizes system performance and resource utilization, reduces latency and provides scalability as needed, for end-user applications in the cloud.

Abstract: A system for deploying big data software in a multi-instance node. The optimal CPU memory and core configuration for a single instance database is determined. After determining an optimal core-memory ratio for a single instance execution, the software is deployed in multi-instance mode on single machine by applying the optimal core-memory ratio for each of the instances. The multi-instance database may then be deployed and data may be loaded in parallel for the instances.

Abstract: A system for deploying big data software in a multi-instance node. The optimal CPU memory and core configuration for a single instance database is determined. After determining an optimal core-memory ratio for a single instance execution, the software is deployed in multi-instance mode on single machine by applying the optimal core-memory ratio for each of the instances. The multi-instance database may then be deployed and data may be loaded in parallel for the instances.

Abstract: A system deploys visualization tools, business analytics software, and big data software in a multi-instance mode on a large, coherent shared memory many-core computing system. The single machine solution provides or high performance and scalability and may be implemented remotely as a large capacity server (i.e., in the cloud) or locally to a user. Most big data software running in a single instance mode has limitations in scalability when running on a many-core and large coherent shared memory system. A configuration and deployment technique using a multi-instance approach, which also includes visualization tools and business analytics software, maximizes system performance and resource utilization, reduces latency and provides scalability as needed, for end-user applications in the cloud.

Abstract: A system for deploying big data software in a multi-instance node. The optimal CPU memory and core configuration for a single instance database is determined. After determining an optimal core-memory ratio for a single instance execution, the software is deployed in multi-instance mode on single machine by applying the optimal core-memory ratio for each of the instances. The multi-instance database may then be deployed and data may be loaded in parallel for the instances.

Abstract: A system deploys visualization tools, business analytics software, and big data software in a multi-instance mode on a large, coherent shared memory many-core computing system. The single machine solution provides or high performance and scalability and may be implemented remotely as a large capacity server (i.e., in the cloud) or locally to a user. Most big data software running in a single instance mode has limitations in scalability when running on a many-core and large coherent shared memory system. A configuration and deployment technique using a multi-instance approach, which also includes visualization tools and business analytics software, maximizes system performance and resource utilization, reduces latency and provides scalability as needed, for end-user applications in the cloud.

Abstract: An adapter retrieves graph data from one or more graph databases and adapts the data to be shown through a visualization tool. The adapter may be used to convert multiple formats of graph data into a format which is readable and useable by the visualization tool. The adapter module may make a connection with a graph database and query the database for particular graph data. Once retrieved, the stream of retrieved graph data may be used to populate a template in Java form. From the template, the visualization tool may provide a visualization of the retrieved data.

Abstract: A system for deploying big data software in a multi-instance node. The optimal CPU memory and core configuration for a single instance database is determined. After determining an optimal core-memory ratio for a single instance execution, the software is deployed in multi-instance mode on single machine by applying the optimal core-memory ratio for each of the instances. The multi-instance database may then be deployed and data may be loaded in parallel for the instances.