Abstract:
A method for scheduling MapReduce tasks includes receiving a set of task statistics corresponding to task execution within a MapReduce job, estimating a completion time for a set of tasks to be executed to provide an estimated completion time, calculating a soft decision point based on a convergence of a workload distribution corresponding to a set of executed tasks, calculating a hard decision point based on the estimated completion time for the set of tasks to be executed, determining a selected decision point based on the soft decision point and the hard decision point, and scheduling upcoming tasks for execution based on the selected decision point. The method may also include estimating a map task completion time and estimating a shuffle operation completion time. A computer program product and computer system corresponding to the method are also disclosed.
Abstract:
A method and apparatus for providing a test case for a modified program. The method includes the steps of: obtaining a modification item that makes modification on a previous version of the program; locating the modification item after a first instrument and before a second instrument of a plurality of instruments inserted into the program; obtaining an execution path of the modified program that is between the first instrument and the second instrument and associated with the modification as well as a constraint set corresponding to the execution path; obtaining an execution result, outputted by the first instrument, of executing the previous version of the program using an original test case; and determining a test case applicable for the execution path based on the execution result and the constraint set. The apparatus corresponds to the method.
Abstract:
A method for scheduling MapReduce tasks includes receiving a set of task statistics corresponding to task execution within a MapReduce job, estimating a completion time for a set of tasks to be executed to provide an estimated completion time, calculating a soft decision point based on a convergence of a workload distribution corresponding to a set of executed tasks, calculating a hard decision point based on the estimated completion time for the set of tasks to be executed, determining a selected decision point based on the soft decision point and the hard decision point, and scheduling upcoming tasks for execution based on the selected decision point. The method may also include estimating a map task completion time and estimating a shuffle operation completion time. A computer program product and computer system corresponding to the method are also disclosed.
Abstract:
A computer-implemented method, computer program product and computing system for management of a pressure pipe network is provided. A processor retrieves a topology model of a pipe network. The processor retrieves one or more measurement expressions of the pressure pipe network. The processor determines a parameter list for a first measurement expression, wherein a first parameter of the parameter list represents a cutting point measurement device. The processor generates a first subsystem of the pipe network based, at least in part on, the first parameter.
Abstract:
Apparatus and method for the network transmission and displaying of the computer graphics. The method and apparatus for processing three-dimensional model data includes: obtaining the mesh data for an original mesh model; constructing a derivative mesh model from the vertex data for the original mesh model by using a pre-defined mesh model construction algorithm; comparing the mesh data for the original mesh model with the mesh data for the derivative mesh model to obtain the error data for the derivative mesh model; transmitting the vertex data related to the original mesh model; and transmitting the error data for the derivative mesh model.
Abstract:
The present invention provides a method, apparatus, and non-transitory article of manufacture embodying computer readable instructions for scheduling discrete event simulation. One embodiment of the present invention is a method for scheduling discrete event simulation. The method includes: extracting two or more event types in the discrete event simulation in response to having loaded the discrete event simulation; constructing a correlation graph used for the discrete event simulation based on the two or more event types; and scheduling events that are classified into the two or more event types according to the correlation graph wherein each node in the correlation graph describes one of the two or more event types, and an edge in the correlation graph describes the creation or dependency relationship between the two or more event types. Other embodiments of the present invention provide an apparatus and computer program product for scheduling discrete event simulation.
Abstract:
A method, a system and a computer program product may evaluate reduction of disease risk. Patient data of a patient may be received. A selection of a disease outcome may be received. A risk score that the patient will experience the selected disease outcome may be determined. The determining may use the patient data. Intervention options may be generated based on the patient data and by accessing a medical record data structure. An intervention effect for each of the intervention options may be determined. The intervention effect may change the risk score. The intervention effects may be compared. A recommendation of at least one of the intervention options may be provided based on the comparing of the intervention effects.
Abstract:
Methods and systems for treating a disease include navigating a symptom-centric decision tree which accounts for probabilities for symptoms and diseases and emergency values for the symptoms and diseases, based on information provided by a user, to determine a disease. A treatment is provided to the user based on the determined disease.
Abstract:
A method and system for generating a three-dimensional (3D) virtual scene are disclosed. The method includes: identifying a two-dimensional (2D) object in a 2D picture and the position of the 2D object in the 2D picture; obtaining the three-dimensional model of the 3D object corresponding to the 2D object; calculating the corresponding position of the 3D object corresponding to the 2D object in the horizontal plane of the 3D scene according to the position of the 2D object in the picture; and simulating the falling of the model of the 3D object onto the 3D scene from a predetermined height above the 3D scene, wherein the position of the landing point the model of the 3D object in the horizontal plane is the corresponding position of the 3D object in the horizontal plane of the 3D scene.
Abstract:
A machine-guided inquiry recommendation for medical diagnosis. Generate a knowledge graph data structure using one or more electronic medical guideline documents. Evaluate a likelihood value for one or more diseases based on the knowledge graph data structure. Generate a best next inquiry question for use in a medical diagnosis process.