Abstract: A sandbox database is created. The sandbox database is typically a temporary database. For example, the sandbox database may be a test database for evaluating a new version of software. Creating the sandbox database comprises creating a sandbox cache in the sandbox database and copying metadata from a main database to the sandbox database. The sandbox cache is used to store record(s) that are accessed during the use of the sandbox database. The metadata is used to reference the record(s). This allows for a simpler process for creating a temporary database to be used for testing software.

Abstract: A distributed database system maintains a database including a data shard for which a primary computing node is responsible. The primary computing node identifies a data storage plan for the data shard. The plan identifies a file subset of data storage files of the shard to be merged into a larger data storage file, and a node subset of computing nodes of the system that subscribe to the data shard. The primary node identifies which computing nodes of the node subset each have sufficient computing resources to execute the plan, as candidate computing nodes. The primary node identifies which files of the file subset each candidate computing node locally caches. The primary node selects one candidate computing node to execute the plan, based on the files of the file subset that each candidate computing node locally caches. The primary node causes the selected candidate computing node to execute the plan.

Abstract: A distributed database system maintains a database including a data shard for which a primary computing node is responsible. The primary computing node identifies a data storage plan for the data shard. The plan identifies a file subset of data storage files of the shard to be merged into a larger data storage file, and a node subset of computing nodes of the system that subscribe to the data shard. The primary node identifies which computing nodes of the node subset each have sufficient computing resources to execute the plan, as candidate computing nodes. The primary node identifies which files of the file subset each candidate computing node locally caches. The primary node selects one candidate computing node to execute the plan, based on the files of the file subset that each candidate computing node locally caches. The primary node causes the selected candidate computing node to execute the plan.

Abstract: A distributed database system maintains a database including a data shard for which a primary computing node is responsible. The primary computing node identifies a data storage plan for the data shard. The plan identifies a file subset of data storage files of the shard to be merged into a larger data storage file, and a node subset of computing nodes of the system that subscribe to the data shard. The primary node identifies which computing nodes of the node subset each have sufficient computing resources to execute the plan, as candidate computing nodes. The primary node identifies which files of the file subset each candidate computing node locally caches. The primary node selects one candidate computing node to execute the plan, based on the files of the file subset that each candidate computing node locally caches. The primary node causes the selected candidate computing node to execute the plan.

Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Disclosed are various embodiments of invasive and non-invasive systems for electrophysiological mapping of a patient's heart, where cardiac action potentials (APs) on a myocardial surface of a human heart are reconstructed. In one embodiment, the APs are reconstructed using known electrical potentials on the myocardial surface in combination with known geometries of the heart and/or the torso. A mathematical model of cardiac electrical activity may be employed that takes into account homogeneous and isotropic intracellular, extracellular, and human body electrical conductivities.

Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Disclosed are various embodiments of invasive and non-invasive systems for electrophysiological mapping of a patient's heart, where cardiac action potentials (APs) on a myocardial surface of a human heart are reconstructed. In one embodiment, the APs are reconstructed using known electrical potentials on the myocardial surface in combination with known geometries of the heart and/or the torso. A mathematical model of cardiac electrical activity may be employed that takes into account homogeneous and isotropic intracellular, extracellular, and human body electrical conductivities.