The present invention relates generally to the field of data analysis, and more particularly to efficiently facilitating human review and computational analysis.
Analytical workflows in decision-making and scientific discovery are often composed of alternate cycles of heavy data processing and subsequent lengthy human interpretation and analysis. The time for human analysis is inherently costly, so attempts to decrease the analyst's downtime are common. Typically, the attempts to speed up workflows entail accelerating the computational stages through High Performance Computing (HPC) infrastructures and parallel processing.
As disclosed herein, a method, executed by a computer, for overlapping computer processing and human analysis includes receiving a set of tasks to be executed on an array of data, receiving a user profile, prioritizing the tasks based on the user profile, partitioning the array of data into a plurality of data blocks based on a current task, prioritizing the plurality of data blocks based on the user profile, executing the current task on the plurality of prioritized data blocks in order of priority, and outputting data results to the user for a data block in response to completing the current task on the data block. The method may include monitoring the user's interactions with the data results and updating the user profile based on these monitored interactions. The method may also include receiving a task profile, and prioritizing the plurality of data blocks based on the user profile and the task profile. The method may also include speculating on additional tasks that need to be executed based on the user profile.
A user 110 may include a person that performs analysis on data outputted by the method disclosed herein. The user 110 may be a single user or a group of users. As depicted, the user provides input to the execution manager 120. The input may include parameters that define how an application, for example, data processing application 130, should be managed.
Execution manager 120 may include software responsible for managing the user application. Managing the user application may include monitoring user activity, partitioning a set of tasks, prioritizing these tasks, receiving user input, and returning results to the user. As depicted, the execution manager 120 returns results to the user after a data processing task is completed.
Data processing application 130 may include software to process a plurality of data blocks and generate a set of results that are analyzed by the user. As depicted, data processing application 130 receives a set of tasks to be executed on a plurality of data blocks from execution manager 120, and then outputs the results of these tasks back to the execution manager 120 upon completion. Data processing application 130 may include software suitable for analyzing a specific type of data, such as earthquake data or surveillance data.
The computing infrastructure 140 may include infrastructure that contains the hardware and software necessary to run data processing applications, and may include components as discussed with reference to
Receiving (210) a set of tasks to be executed on an array of data may include receiving a set of computer processes to be executed on a set of data. The set of tasks may comprise data processing tasks that will be executed to provide data results that a user can analyze. In one embodiment, the set of tasks includes computer processing tasks to be executed on an array of geographic data.
Receiving (220) a user profile may include receiving a profile specific to the user who is interacting with the data results. The user profile may comprise information selected from user experience information, user preference information, and data results interaction information. Receiving a user profile may also include receiving a task profile, wherein the task profile comprises analysis type or analysis activities. The user profile, the task profile, and other profiles disclosed here may be stored on a data storage device associated with the system 100. For example, the computing infrastructure 100 may provide data storage services and/or capacity that is used to store various profiles.
Prioritizing (230) the tasks based on the user profile may include using information in the user profile to identify what order the tasks should be executed in. In some embodiments, prioritizing the tasks based on the user profile includes identifying the past interactions of the user with similar data results, and prioritizing the tasks based on the order in which they were analyzed previously. Prioritizing the tasks based on the user profile may additionally include prioritizing the tasks based on the task profile.
Partitioning (240) the array of data based on a current task may include dividing the array of data into a plurality of data blocks on which the current task will be executed. The partitioning may be done based on the task profile to yield a plurality of data blocks on which the current task can be executed. In some embodiments, partitioning the array of data based on a current task may include dividing the data into small blocks so that the current task can be executed quickly on each small block and results will be available to the user more quickly. In some embodiments, partitioning the array of data based on a current task may include grouping the data into large blocks so that upon completion of the current task on the data blocks, larger scale results will be available to the user for analysis.
Prioritizing (250) the plurality of data blocks based on the user profile may include using information in the user profile to identify in what order the data blocks should be processed. In some embodiments, prioritizing the plurality of data blocks based on the user profile may include identifying the past interactions of the user with similar data results, and prioritizing the data blocks based on the order in which they were analyzed previously. Prioritizing the data blocks based on the user profile may additionally include prioritizing the data blocks based on the task profile.
Executing (260) the current task on the plurality of prioritized data blocks in order of priority may include executing the current task on each prioritized data block, proceeding in order from highest priority to lowest priority. Executing the current task on the plurality of prioritized data blocks in order of priority may also include simultaneously executing the current task on multiple data blocks. Task execution may occur on an elastic execution platform such as a Cloud Computing environment. For example, task execution could occur on a cloud-based implementation of the computing infrastructure 140 shown in
Outputting (270) data results to the user in response to completing the current task on a data block may include providing the user with the results of executing the current task on a data block. Outputting data results may include directly outputting the data results to the appropriate application managed by the execution manager 120 to facilitate user interaction with the results.
Submitting (315) a project and parameters may include inputting the project to be managed and associated software to be executed, for example data processing application 130, and any relevant initial parameters for managing the project and executing the associated software to execution manager 120. The project to be executed may comprise a problem or goal to be investigated via computer processing and human analysis. Initial parameters may include deadlines or budget constraints to be considered when determining how tasks will be executed. Submitting a project and parameters is carried out by the user 310.
Specifying (320) project partitioning may include identifying parts of the project that may need to be carried out by different applications. A project may consist of multiple data processing tasks that each need to be executed by different applications. Consequentially, specifying project partitioning may include specifying which parts of the problem need to be carried out by which applications or computing resources.
Partitioning (325) the project into tasks may include automatically converting project goals into functional tasks to be executed by various applications. Functional tasks may include data processing functions carried out by various software applications. Partitioning the project into tasks may include producing a list of processing tasks to be carried out by the relevant software applications or computing resources.
Scheduling (330) tasks may include determining when each task will be carried out by the computer. Scheduling tasks may also include determining how much of the processing can be done simultaneously and maximizing how many tasks are being executed at any one time. Scheduling the tasks may also include ensuring the tasks are carried out in order of priority.
Executing (335) tasks may include carrying out the data processing functions on their respective platforms. Executing tasks may also include executing the tasks according to the previously determined schedule. In some embodiments, where multiple platforms are available for data processing, executing tasks includes simultaneously executing multiple tasks.
Determining (340) if all tasks have been executed includes checking to see if there are any remaining tasks to be executed. If there are not any remaining tasks to be executed (decision block 340, “yes” branch), the process ends. If there are remaining tasks to be executed (decision block 340, “no” branch), the process continues to outputting (345) partial results.
Outputting (345) partial results may include sending a set of data results for a completed task to the user for analysis. In some embodiments, outputting partial results includes sending the results directly to the appropriate software application for analysis. In some embodiments, outputting partial results includes sending the results directly to the user 110 to be analyzed.
Analyzing (350) a result set may include the user 110 interacting with the data results in an appropriate software application. In some embodiments, analyzing a result set may include the execution manager 120 monitoring the user interaction with the data results within the appropriate software application. Specific user interactions with a result set that the execution manager monitors may include mouse clicks, number of data views, the types of Graphical Users Interface (GUI) commands invoked, eye tracking, user edits, annotations, and other kinds of user interactions. The execution manager may update the user profile with information regarding the user interactions. In certain embodiments, analyzing a result set occurs simultaneously with the execution of tasks on data blocks of lower priority.
Classifying (355) results may include the user 110 identifying results that are of particular importance after having analyzed them and assigning higher priority to similar remaining tasks. For example, if the results of one task prove to be particularly insightful during analysis, the user 110 can identify the remaining tasks of this type and classify them with a higher priority. Conversely, if the results of a certain task prove to be minimally useful, the remaining tasks of this type can be classified with a lower priority or cancelled entirely. In some embodiments, the classification of results is carried out by the execution manager 120 based on the user profile. In these embodiments, the user is still able to manually classify the results as well.
Identifying (360) costs for analyzing the results may include the user 110 determining the costs of each type of analysis and considering these costs in the scope of any relevant budget restrictions. The cost of analysis for each task may be used to estimate the costs of analyzing the results of pending tasks, and determining if these tasks are worth completing and analyzing based on the cost. In some embodiments, identifying costs for analyzing the results is carried out by the execution manager 120. In these embodiments, the user is still able to manually determine whether a task is worth completing and analyzing based on the cost.
Speculating (365) on additional tasks that need to be executed may include automatically identifying any tasks that have not been scheduled for execution that may be important in light of the data analysis. In some embodiments, these tasks are previously run tasks that now require increased granularity. Speculating on additional tasks that need to be executed may occur based on user interaction with the data results or past user behavior on similar projects.
Prioritizing and repartitioning (370) pending tasks may include updating the task schedule to reflect any changes in a task's status. Prioritizing and repartitioning may include ensuring any changes in a tasks' priority are reflected in the schedule. Prioritizing and repartitioning may also include ensuring that any tasks that have been divided for the sake of speed or otherwise are properly repartitioned to produce the desired result. Additionally, prioritizing and repartitioning pending tasks may include inserting any speculated additional tasks into the task schedule for completion.
As depicted, the computer 600 includes communications fabric 602, which provides communications between computer processor(s) 604, memory 606, persistent storage 608, communications unit 612, and input/output (I/O) interface(s) 614. Communications fabric 602 can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric 602 can be implemented with one or more buses.
Memory 606 and persistent storage 608 are computer readable storage media. In the depicted embodiment, memory 606 includes random access memory (RAM) 616 and cache memory 618. In general, memory 606 can include any suitable volatile or non-volatile computer readable storage media.
One or more programs may be stored in persistent storage 608 for execution by one or more of the respective computer processors 604 via one or more memories of memory 606. The persistent storage 608 may be a magnetic hard disk drive, a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.
The media used by persistent storage 608 may also be removable. For example, a removable hard drive may be used for persistent storage 608. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage 608.
Communications unit 612, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit 612 includes one or more network interface cards. Communications unit 612 may provide communications through the use of either or both physical and wireless communications links. I/O interface(s) 614 allows for input and output of data with other devices that may be connected to computer 600. For example, I/O interface 614 may provide a connection to external devices 620 such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices 620 can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards.
Software and data used to practice embodiments of the present invention can be stored on such portable computer readable storage media and can be loaded onto persistent storage 608 via I/O interface(s) 614. I/O interface(s) 614 also connect to a display 622. Display 622 provides a mechanism to display data to a user and may be, for example, a computer monitor.
The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The embodiments disclosed herein include a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out the methods disclosed herein.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.