Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: A message transmission method for a roadside equipment includes the following steps. A plurality of external sensor information is received. A road intersection sign phase information and a road map information are inputted. An object position analysis, a speed analysis, and an sign analysis in object moving direction are performed based on the external sensor information, the road intersection sign phase information, and the road map information, and a classification of dangerous objects in different groups is outputted. According to a current transmission bandwidth limitation and the classification of the dangerous objects, a dangerous object message with a higher classification of the dangerous objects is preferentially selected and transmitted within available transmission bandwidth.

Abstract: The system is configured to: 1) receive a client request; 2) determine executor(s) to generate a response to the user request; 3) provide each of the executor(s) with an indication; 4) receive for each indication a response including an output of either a cloud output or an in-line output to generate a group of in-line outputs and a group of cloud outputs; 5) determine whether the group of in-line outputs comprises all outputs; and 6) in response to the group of in-line outputs not comprising all the outputs for the client request: a) convert the group of in-line outputs to a converted group of cloud outputs; b) generate metadata for the converted group of cloud outputs and the group of cloud outputs; and c) provide response to the client request including the metadata for the converted group of cloud outputs and the group of cloud outputs.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: The system is configured to: 1) receive a client request; 2) determine executor(s) to generate a response to the user request; 3) provide each of the executor(s) with an indication; 4) receive for each indication a response including an output of either a cloud output or an in-line output to generate a group of in-line outputs and a group of cloud outputs; 5) determine whether the group of in-line outputs comprises all outputs; and 6) in response to the group of in-line outputs not comprising all the outputs for the client request: a) convert the group of in-line outputs to a converted group of cloud outputs; b) generate metadata for the converted group of cloud outputs and the group of cloud outputs; and c) provide response to the client request including the metadata for the converted group of cloud outputs and the group of cloud outputs.

Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: A system comprises an interface, a processor, and a memory. The interface is configured to receive a query. The processor is configured to: determine a set of nodes for the query; determine whether a node of the set of nodes comprises a first engine node type or a second engine node type, wherein determining whether the node of the set of nodes comprises the first engine node type or the second engine node type is based at least in part on determining whether the node is able to be executed in a second engine; and generate a plan based at least in part on the set of nodes. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: A system for monitoring job execution includes an interface and a processor. The interface is configured to receive an indication to start a cluster processing job. The processor is configured to determine whether processing a data instance associated with the cluster processing job satisfies a watchdog criterion; and in the event that processing the data instance satisfies the watchdog criterion, cause the processing of the data instance to be killed.

Abstract: A tissue cutter includes a tissue cutting stent and a first reefing thread. The tissue cutting stent is expandable in a radial direction. The expanded tissue cutting stent is loudspeaker-shaped gradually opening from a proximal end to a distal end, and a loudspeaker-shaped accommodating space is formed inside the expanded tissue cutting stent. The tissue cutting stent is provided with a plurality of first threading holes at the distal end, and the plurality of first threading holes is arranged along a circumferential direction of the tissue cutting stent. The first reefing thread passes through the plurality of first threading holes. The first reefing thread has opposite first and second ends. The first end is secured to a starting threading hole in the plurality of first threading holes.

Abstract: A system for monitoring job execution includes an interface and a processor. The interface is configured to receive an indication to start a cluster processing job. The processor is configured to determine whether processing a data instance associated with the cluster processing job satisfies a watchdog criterion; and in the event that processing the data instance satisfies the watchdog criterion, cause the processing of the data instance to be killed.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: A system comprises an interface, a processor, and a memory. The interface is configured to receive a query. The processor is configured to: determine a set of nodes for the query; determine whether a node of the set of nodes comprises a first engine node type or a second engine node type, wherein determining whether the node of the set of nodes comprises the first engine node type or the second engine node type is based at least in part on determining whether the node is able to be executed in a second engine; and generate a plan based at least in part on the set of nodes. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: A system comprises an interface, a processor, and a memory. The interface is configured to receive a query. The processor is configured to: determine a set of nodes for the query; determine whether a node of the set of nodes comprises a first engine node type or a second engine node type, wherein determining whether the node of the set of nodes comprises the first engine node type or the second engine node type is based at least in part on determining whether the node is able to be executed in a second engine; and generate a plan based at least in part on the set of nodes. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: A system for monitoring job execution includes an interface and a processor. The interface is configured to receive an indication to start a cluster processing job. The processor is configured to determine whether processing a data instance associated with the cluster processing job satisfies a watchdog criterion; and in the event that processing the data instance satisfies the watchdog criterion, cause the processing of the data instance to be killed.

Abstract: A system for monitoring job execution includes an interface and a processor. The interface is configured to receive an indication to start a cluster processing job. The processor is configured to determine whether processing a data instance associated with the cluster processing job satisfies a watchdog criterion; and in the event that processing the data instance satisfies the watchdog criterion, cause the processing of the data instance to be killed.

Abstract: A method includes receiving, at a data link layer (layer 2) gateway device configured to connect the physical network to the SDN network, routing data to a computing device, the computing device selected to receive a packet transmitted from the physical network to the SDN network; receiving, from a source element in the physical network, an address request for a layer 2 address of a router element in the SDN network, the address request including a networking layer address of the router element; transmitting, to the source element in response to receiving the address request, a layer 2 address of the router element using the routing data; receiving, from the source element, a routing request to route a packet to a destination element in the SDN network using the layer 2 address; routing, using the routing data, the received packet to the computing device to route to the destination element.

Abstract: A system for directory level atomic commits includes an interface and a processor. The interface is configured to receive an indication to provide a set of files. The processor is configured to determine whether a file in a directory has been either 1) atomically committed or 2) written by a non-atomic process and not designated as deleted and provide the file as one file of the set of files in the event that the file in the directory has been either 1) atomically committed or 2) written by a non-atomic process and not designated as deleted.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: collecting logging data generated by operation of a distributed computing system that is used by a plurality of tenants; storing the logging data as a plurality of files stored in a multi tier, shared volume storage system, with the storage of the logging data as a plurality of files including: dividing the plurality of files among and between a plurality of shared volume data structures, and organizing each shared volume data structure of the plurality of shared volume data structures according to a plurality of tiers; for each given file of the plurality of files; and mapping, by a domain agent and in a mapping table data structure, an association between the given file and the shared volume data structure in which the given file is stored.