Patent Application
20230072913
The present disclosure relates to classification, and more specifically, to methods, systems and computer program products for generating a predictive model based on an imbalanced dataset.
Nowadays, classification techniques are widely used in various working environments. For example, a sample including multiples parameters of a computing system may be collected for predicting whether a failure occurs in the computing system. A predictive model may be generated in advance based on an imbalanced dataset including a plurality of samples of the computing system that are collected at previous time points.
Current classification techniques suffer from lacking, in traditional models, to determine the number of positive cases of failure because the results are too small to determine a positive rate especially when the rate is smaller than, for example, 0.01%. Additionally, current solutions suffer from unsatisfied higher false positive rate and negative cases are predicted incorrectly due to incorrect patterns learned by the predictive model.
According to embodiments of the present disclosure, there is provided a computer-implemented method that may be implemented by one or more processors. In the method, one or more processors generate a plurality of predictive models based on a plurality of positive samples included in an imbalanced dataset, respectively, an amount of the plurality of positive samples being below an amount of a plurality of negative samples that are included in the imbalance dataset, and each sample in the plurality of positive and negative samples including a plurality of parameters. One or more processors identify from the plurality of parameters a plurality of influential parameter groups for the plurality of positive samples, respectively. One or more processors determine a final predictive model based on the plurality of predictive models and the plurality of influential parameter groups, the final predictive model being used for classifying a sample into one of a positive type and a negative type. With these embodiments, the positive model may be generated based on the imbalanced dataset in an accurate and effective way.
According to another embodiment of the present disclosure, there is provided a computer-implemented system. The computer-implemented system comprises a computer processor coupled to a computer-readable memory unit, where the memory unit comprises instructions that when executed by the computer processor implements the above method.
According to another embodiment of the present disclosure, there is provided a computer program product. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by an electronic device to cause the electronic device to perform actions of the above method.
Through the more detailed description of some embodiments of the present disclosure in the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become more apparent, wherein the same reference generally refers to the same components in the embodiments of the present disclosure.
Some embodiments will be described in more detail with reference to the accompanying drawings, in which the embodiments of the present disclosure have been illustrated. However, the present disclosure can be implemented in various manners, and thus should not be construed to be limited to the embodiments disclosed herein.
It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present disclosure are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Referring now to
In cloud computing node 10 there is a computer system/server 12 or a portable electronic device such as a communication device, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and so on.
Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
As shown in
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.
System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, database system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.
Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the disclosure as described herein.
Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival database systems, etc.
Referring now to
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and classification processing 96.
It should be noted that the classification processing 96 according to embodiments of the present disclosure could be implemented by computer system/server 12 of
Reference will be made to
There have proposed some solutions for imbalance classification. In one solution, over-sampling and under-sampling techniques are adopted for generating a balanced dataset from the imbalanced dataset. In another solution, Synthetic Minority Oversampling Technique (SMOTE) may be used for simulate positive samples, so as to generate a balanced dataset. In turns, the predictive model may be trained with the balanced dataset. However, the above solutions involve complex procedures and high workload, moreover, the accuracy of the generated predictive model is low.
In view of the above drawbacks, embodiments of the present disclosure may provide solutions for generating a predictive model. Reference will be made to
Here, the positive sample may include parameters when a failure occurs in the computing system (where the “failure” parameter is set to “True”), and the negative sample may include parameters when the computing system is in good condition (where the “failure” parameter is set to “False”). As shown in
Hereinafter, reference will be made to
The training samples in the training dataset 610 may be obtained in various ways. In some embodiments, the negative training sample may be selected directly from the plurality of negative sample 414 in the imbalanced dataset 410. Alternatively and/or in addition to, the positive sample 412 may be modified to obtain a negative training sample. For example, one or more parameters in the positive sample 412 may be adjusted to simulate a negative training sample. Supposing the CPU usage rate is a key parameter for detecting a failure in the computing system, the higher the CPU usage rate is, the higher the possibility of a failure occurs in the computing system. Therefore, the CPU usage rate in the positive sample 412 may be adjusted to a lower value within a normal range for simulating a negative training sample.
In some embodiments, a threshold (such as 1000 or another value) may be defined for the total number of samples in the balanced training dataset 610. At this point, the amount of the negative training samples may be determined as 1000/2=500. Some of the negative training samples may be selected from negative samples in the imbalanced database 410, and some of the negative training samples may be generated by modifying the positive sample 412. With these embodiments, the negative training samples may be obtained in an easy and effective way.
In some embodiments of the present disclosure, the positive sample 412 may be directly obtained from the imbalanced dataset 410 and work as the positive training sample 620. As the training dataset 610 should be a balanced dataset, the amount of the positive training samples should be similar as the amount of the negative training samples. Considering a shortage of the positive training sample, the positive training sample may be assigned with a weight based on a ratio of the number of the plurality of negative training samples and the number of the at least one positive training sample. Continuing the above example, as the number of the negative training samples 630 is 500 (a half of the total number “1000”), there is only one positive training sample 620, the weight may be set to 500. With these embodiments, it does not require the complex procedure for simulating a large amount of positive training samples, instead the positive training sample may be directly obtained in an easy and effective way.
In some embodiments, the training dataset 610 may include more than one positive training sample, and thus more positive training samples may be generated based on the positive sample 412. For example, the CPU usage rate in the positive sample 412 may be slightly increased to simulate another positive training sample. Further, the obtained positive training sample 620 and the negative training sample 630 may be added into the training dataset 610 for the training procedure. Therefore, embodiments of the present disclosure do not require the complex procedures and the workload for generating the training dataset may be decreased. In some embodiments, the balanced training dataset 610 may be used for training an initial predictive model. After the training procedure, the predictive model 520 may be generated automatically.
The above paragraphs only provide an example procedure for generating the predictive model 520 for the positive sample 412. Other positive samples may be subjected to the similar processing so as to obtain the plurality of predictive models 520, . . . , and 530. Once the predictive model 520 is generated, the influential predictor analysis may be performed to the predictive model, so as to identify an influential parameter group for the positive sample 412. The influential predictor analysis is for analyzing a relationship between a sample and a predictive model that is generated based on the sample. Specifically, a group of influential parameters that causes the sample to be classified as a positive type may be identified from the plurality of parameters. For example, a group of parameters of (CPU usage rate, Memory usage rate, . . . ) may be identified from the above Table 1 as the influential parameter group 522 for the positive sample 412.
In some embodiments, an influential parameter group may be identified for each of the positive samples, here the influential parameter group includes a group of parameters in the plurality of parameters that cause the positive sample to be classified as a positive type. Specifically, the Local Interpretable Model Agnostic Explanations (LIME) method may be used for identifying the influential parameter group, and details of the LIME method will be omitted hereinafter. By now, the LIME method is a mature technology and various LIME tools are provided for determining the influential parameter group. In some embodiments, a positive sample and a predictive model may be inputted into a LIME tool, and then the LIME tool may output an influential parameter group associated with the positive sample and the predictive model. Here, the influential parameter group includes a group of parameters in the plurality of parameters that cause the positive sample to be classified as a positive type. Therefore, all the predictive models may be processed based on the LIME tool, such that the influential parameter group 522 may be identified for the positive sample 412 based on the predictive model 520, and the influential parameter group 532 may be identified for the positive sample 510 based on the predictive model 530.
Further, the final predictive model 540 may be generated based on the plurality of predictive models 520, . . . , 530 and the plurality of influential parameter groups 522, . . . , 532. Here the final predictive model may be used for classifying a sample into one of a positive type and a negative type. With these embodiments, the multiple predictive models that are generated based on the multiple positive samples may provide more knowledge for the prediction. As the multiple predictive models are built based on different training datasets, the multiple predictive models may reflect various aspects for the prediction. In other words, the final predictive model may include rich knowledge that fully reflects all the factors for determining a failure. Here, the multiple predictive models may work together for classifying a further sample, and thus the accuracy level of the final predictive model may be greatly increased.
In some embodiments, the final predictive model 540 may be updated as the time goes. For example, during operations of the computing system, more positive samples may be collected by monitoring the multiple parameters. Once a further positive sample is added into the imbalanced dataset 410, a further predictive model may be generated based on the further positive sample, and a further influential parameter group may be identified for the further predictive model. Next, the final predictive model may be updated based on the further predictive model and the further influential parameter group. With these embodiments, the final predictive model 540 may be updated based on more positive samples, such that the accuracy level of the final predictive model 540 may be gradually increased.
In some embodiments, the final predictive model 540 may be used for classifying an incoming sample. Hereinafter, reference will be made to
Further, a model set 740 may be created based on the results from the multiple predictive model 520, . . . , and 530. Here, if it is determined that a predictive type indicates a positive type, a predictive model corresponding to the positive type may be added into the model set 740. In other words, the predictive model(s) that output positive predictive type(s) may be added into the model set 740. In
In some embodiments of the present disclosure, if the model set 740 is empty, it indicates that all the predictive models 520, . . . , 530 classify the target sample 710 into the negative type. At this point, the target sample 710 may be classified as the negative type. Compared with existing solutions where only one predictive model is used, in the present embodiment, all the predictive models 520, . . . , 530 output the negative type, therefore the confidence level of the final classification type is higher.
In some embodiments, if the model set 740 is not empty (i.e., including one or more predictive models that output the positive type), the predictive model(s) in the model set may be subjected to further processing. Referring to
Here, the influential parameter group 532 includes parameters that cause the positive sample 510 to be classified into the positive type. Here, the influential parameter group is an indicator for measuring whether the predictive model 530 is suitable for classifying the incoming sample. If the influential parameter group for the incoming sample is similar to the influential parameter group 532, it means that the predictive model 530 is suitable; otherwise the predictive model 530 is not suitable. Accordingly, a difference 820 may be determined between the target influential parameter group 810 and the influential parameter group 532 for updating the model set 740. In some embodiments, if the difference 820 is above a predefined threshold, it indicates that the positive type 730 is outputted based on different parameters other than parameters included in the influential parameter group 532, therefore the output of the predictive model 530 is unreliable and thus the predictive model should be dropped. Otherwise, if the difference 820 is below the predefined threshold, it indicates that the positive type 730 is outputted based on similar parameters as those included in the influential parameter group 532, therefore the output of the predictive model 530 is reliable and thus the predictive model should be adopted.
In some embodiments, the threshold may be determined based on an overlap of the target influential parameter group 810 and the influential parameter group 532. If the target influential parameter group 810 and the influential parameter group 532 overlap completely, the output of the predictive model 530 is adopted. An overlap rate (for example, 90% or another value) may be defined as the threshold, if the overlap rate between the two groups is above 90%, the output is reliable; otherwise the output is unreliable and may be dropped. Further, continuing the above example of determining a failure in the computing system, if the influential parameter group 532 includes 10 parameters while the target influential parameter group 810 includes 8 parameters (among which only 5 parameters are included in the influential parameter group 532), the difference 820 is below the threshold and thus the predictive model 530 may be removed from the model set 740. In another example, if the difference 820 is above the threshold, the predictive model 530 may be maintained in the model set 740.
Although the above paragraphs have described the processing to only one predictive model 530, other predictive models in the model set 740 may be processed in a similar manner, such that the model set 740 may be updated. After all the predictive models are processed according to
Although the above paragraphs have described the embodiments in an environment of the computing system. Alternatively and/or in addition to, embodiments of the present disclosure may be implemented a medical environment, an image processing environment, and so on. In the medical environment, the imbalanced dataset may include positive samples related to persons with cancer, and negative samples related to healthy persons, and thus a predictive model may be generated based on the imbalanced dataset. In the image processing environment, the imbalanced dataset may include positive samples related to pictures of unqualified products, and negative samples related to pictures of qualified products, and thus a predictive model may be generated based on the imbalanced dataset. With these embodiments, an accurate and effective predictive model may be generated from the imbalanced dataset in various working environments.
The present disclosure may be a system, a method, and/or a computer program product at any possible technical detail level of integration. 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 aspects of the present disclosure.
Reference will be made to
In some embodiments, a predictive model in the plurality of predictive models is generated based on a positive sample in the plurality of positive samples. Specifically, a training dataset is generated based on the positive sample, the training dataset including at least one positive training sample and a plurality of negative training samples. The predictive model is generated by training an initial predictive model with the training dataset.
In some embodiments, the plurality of negative training samples are obtained by any of: selecting a negative sample from the plurality of negative samples, and modifying the positive sample into a negative sample; and adding the plurality of negative training samples into the training dataset.
In some embodiments, the at least one positive training sample are obtained based on the positive sample; and then a weight is assigned to the at least one positive training sample based on a ratio of an amount of the plurality of negative training samples and an amount of the at least one positive training sample.
In some embodiments, each positive sample in the plurality of positive samples is processed for wherein identifying one group in the plurality of influential parameter groups. Specifically, an influential parameter group is identified for the positive sample, the influential parameter group including a group of parameters in the plurality of parameters that cause the positive sample to be classified as a positive type.
In some embodiments, the final predictive model is updated in response to a determination that a further positive sample is added into the imbalanced dataset. Specifically, a further predictive model is generated based on the further positive sample; a further influential parameter group is identified for the further predictive model from the plurality of parameters; and the final predictive model is updated based on the further predictive model and the further influential parameter group.
In some embodiments, if a target sample is received, a plurality of predictive types is determined for the target sample based on the plurality of predictive models included in the final predictive model, respectively. In response to a determination that all of the plurality of predictive types indicate a negative type, the target sample is classified into the negative type.
In some embodiments, in response to a determination that at least one predictive type in the plurality of predictive types indicates a positive type, a model set is generated and the model set includes at least one predictive model corresponding to the at least one predictive type. An influential parameter group associated with a predictive model in the model set is determined. Next, a target influential parameter group is identified from the plurality of parameters for the target sample. Further, the model set is updated based on a difference between the influential parameter group and the target influential parameter group, and then the target sample is classified based on the updated model set.
In some embodiments, in response to a determination that the difference being above a predefined threshold, the predictive model is removed from the model set; and in response to a determination that the difference being below the predefined threshold, the predictive model is maintained in the model set.
In some embodiments, in response to a determination that the updated model set is empty, the target sample is classified into a negative type; and in response to a determination that the updated model set is not empty, the target sample is classified into a positive type.
Reference will be made to
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 disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, 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 so on, and 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 packet, 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 disclosure.
Aspects of the present disclosure 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 disclosure. 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 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 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 disclosure. 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 blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, 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 disclosure 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 described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, 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.
