Patent Publication Number: US-2019196672-A1

Title: Visual effects system for &#34;big data&#34; analysis workflow editors, distribution platforms, execution engines, and management systems comprising same

Description:
TECHNICAL FIELD 
     This disclosure relates generally to data processing and, more specifically, to visualization, distribution platforms, and event-driven management of workflows that define data analysis when large datasets are involved (“Big Data”). 
     BACKGROUND 
     The approaches described in this section could be pursued but are not necessarily approaches that have previously been conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. 
     A traditional workflow management system can manage and define a series of tasks within a project to produce a final result workflow management systems can allow defining different workflows for different types of tasks or processes. Furthermore, workflow management systems can assist a user in development of complex applications at a higher level by orchestrating functional components without handling the implementation details. At each stage in the workflow, one or more executable software modules may be responsible for a specific task. Once the task is complete, the workflow software can ensure that the next task is executed by the modules responsible for the next stage of the process. The workflow management system can reflect the dependencies required for the completion of each task. In general, the workflow management system can control automated processes by automating redundant tasks and ensuring that uncompleted tasks are followed up on. 
     The workflow management system can be developed in a specialized form for specific needs. Specifically, a scientific workflow management system can be designed to compose and execute a series of computational and data processing operations for a scientific application. An example of a scientific workflow management system is a bioinformatics workflow management system. Bioinformatics can be defined as an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. However, it should be understood that applications of the technology disclosed here are not necessarily limited to bioinformatics. 
     Since scientific workflows may differ from traditional business process workflows, the scientific workflow management system can enable scientists to perform specific steps. For example, interactive tools can be provided to enable scientists to execute scientific workflows and to view results interactively. Additionally, scientists may be able to track the source of the scientific workflow execution results and the steps used to create the workflow. 
     The need to extract insights from the ever-increasing data set sizes associated with Big Data analysis is leading to more and more complex workflows to manage. Consequently, visualization of such workflows in the workflow management systems becomes correspondingly complex, often obstructing visual perception and editability of the workflow for a workflow developer. As a result, developing and editing workflows by workflow developers can be time and effort consuming. A workflow with a default set of tools can be purchased from a developer of the workflow. However, adding tools developed by other developers or modifying the default tools may not be possible because of the compatibility and other issues. Furthermore, available workflows and workflow engines are restricted to specific types of applications and their adaptation for a range of other specific purposes can be difficult. In addition, available workflow engines are usually configured as directed acyclic graphs. In a directed acyclic graph, each node represents a task to be executed and edges represent either data flow or execution dependencies between different tasks. Thus, sequences of data may only flow in a specific direction and may not allow for parallel execution of computational units. 
     SUMMARY 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
     The present disclosure is related to approaches for visualization of elements of a workflow that defines an analysis of very large data sets using multi-node compute clusters. Specifically, a method for visualization of elements of a workflow comprises displaying the workflow via a graphical user interface (GUI) on a computer terminal. Based on predetermined grouping criteria, one or more collapsible groups of elements are defined within the workflow. Upon receiving a request to collapse the collapsible groups of elements, the collapsible groups of elements are collapsed into collapsed groups of elements. After the collapsing, a layout of the plurality of elements and the collapsed groups of elements can be selectively readjusted. The method further comprises adding further elements to the workflow, removing elements from the workflow, and modifying elements in the workflow. 
     According to another approach of the present disclosure, there is provided a system for visualization of elements of a workflow that defines an analysis of very large data sets using multi-node compute clusters. The system comprises a processor configured to define collapsible groups of elements within the workflow. The defining can be made based on predetermined grouping criteria. Upon receiving from a user a request to collapse the collapsible groups of elements, the processor can collapse the collapsible groups of elements into collapsed groups of elements. The processor is further configured to selectively readjust a layout of the plurality of elements and the collapsed groups of elements. The system further comprises a user interface configured to display the workflow that includes a plurality of elements. 
     The present disclosure is further related to approaches for distribution of a workflow that defines an analysis of very large data sets using multi-node compute clusters. Specifically, a digital workflow distribution platform comprises a user interface configured to allow a user to select a workflow based on one or more parameters associated with the workflow. Based on the selection, a distribution module enables the user to acquire the workflow and import the workflow into a user environment. The digital workflow distribution platform further comprises a management engine for workflow configured to support development of the workflow and workflow tools imported into the user environment. While prior art may include workflow distributions, it does not include subsequently modifiable workflows. 
     According to another approach of the present disclosure, there is provided a computer-implemented method for distribution of a workflow that defines an analysis of very large data sets using multi-node compute clusters. According to the method, a user interface receives a user command to select a workflow based on one or more parameters associated with the workflow. In response to the user command, the user is enabled to acquire the workflow and to import the workflow into a user environment. After the import of the workflow, development of the workflow can be supported by a management engine for workflows. 
     The present disclosure is further related to approaches for computer-implemented event-driven management of workflows that defines an analysis of very large data sets using multi-node compute clusters. Specifically, an event-driven management engine for such workflows may comprise a decision node configured to determine that a condition is true by running a conditional loop. Based on the determination, the decision node may selectively activate a computational module. The event-driven management engine for such workflows may further comprise a fork join queuing cluster. The fork join queuing cluster may allocate the computational module non-sequentially to participant computational nodes and process a data set according to predetermined criteria. The participant computational nodes may be located in a distributed cloud computing environment. A distributed database of the event-driven management engine for workflows that define an analysis of very large data sets using multi-node compute clusters may store the computational modules and conditions associated with the computational modules. A computation module may remain inactivated until the condition is true. 
     According to another approach of the present disclosure, there is provided a computer-implemented event-driven management method for workflows that define an analysis of very large data sets using multi-node compute clusters. According to the method, a database may store computational modules and conditions associated with the computational modules. The method may comprise a decision node running a conditional loop to determine that the condition is true. Based on the determination, the decision node may selectively activate the computational module. The method may further comprise allocating, by a fork-join queuing cluster, the computational module non-sequentially to participant computational nodes in a distributed cloud computing environment. The computational module may be configured to process a data set according to predetermined criteria. 
     In further example embodiments of the present disclosure, the method steps are stored on a machine-readable medium comprising instructions, which when implemented by one or more processors perform the recited steps. In yet further example embodiments, hardware systems or devices can be adapted to perform the recited steps. In yet a further example embodiment, the multi-node compute clusters is a Hadoop-based multi-node compute cluster. Other features, examples, and embodiments are described below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments are illustrated by way of example, and not by limitation, in the figures of the accompanying drawings, in which like references indicate similar elements and in which: 
         FIG. 1  shows an environment within which an event-driven management engine for workflows that define an analysis of very large data sets using multi-node compute clusters and corresponding methods can be implemented, according to an example embodiment. 
         FIG. 2  is a block diagram showing various modules of an event-driven management engine for workflows that define an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 3  is a block diagram illustrating processing of a task by an event-driven management engine for workflows that define an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 4  is a block diagram illustrating processing of a task by a fork join queuing cluster, according to an example embodiment. 
         FIG. 5  is a block diagram illustrating processing of a task by a fork join queuing cluster, according to an example embodiment. 
         FIG. 6  is a process flow diagram showing an event-driven management method for workflows that define an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 7  is a flow chart illustrating a detailed computer-implemented event-driven management method for workflows that define an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 8  is a flow chart illustrating a method for checking a condition, according to an example embodiment. 
         FIG. 9  is flow chart illustrating a conditional loop, according to an example embodiment. 
         FIG. 10  is a flow chart illustrating a conditional loop, according to an example embodiment. 
         FIG. 11  shows an environment within which a platform for digital distribution of a workflow that defines an analysis of very large data sets using multi-node compute clusters and corresponding methods can be implemented, according to an example embodiment. 
         FIG. 12  is a block diagram showing various modules of a distribution platform for a digital workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 13  is a scheme illustrating a method for distribution of a workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 14  is a scheme illustrating a method for distribution of tools for a workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 15  is a process flow diagram showing a computer-implemented method for distribution of a workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 16  shows an environment within which a system for visualization of elements of a workflow that defines an analysis of very large data sets using multi-node compute clusters and associated methods can be implemented, according to example embodiments. 
         FIG. 17  is a process flow diagram showing a method for visualization of elements of a workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 18  is a block diagram showing various modules of a system for visualization of elements of a workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 19  is a block diagram illustrating a collapsed workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 20  is a block diagram illustrating a partially collapsed workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 21  is a scheme illustrating a partially collapsed workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIGS. 22A-C  illustrate an expanded workflow that defines an analysis of very large data sets using multi-node compute clusters, according to an example embodiment. 
         FIG. 23  shows a diagrammatic representation of a computing cluster for a machine in the example electronic form of a computer system, within which a set of instructions for causing the machine to perform any one or more of the methodologies discussed herein can be executed. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show illustrations in accordance with example embodiments. These example embodiments, which are also referred to herein as “examples,” are described in enough detail to enable those skilled in the art to practice the present subject matter. The embodiments can be combined, other embodiments can be utilized, or structural, logical, and electrical changes can be made without departing from the scope of what is claimed. The following detailed description is therefore not to be taken in a limiting sense, and the scope is defined by the appended claims and their equivalents. In this document, the terms “a” and “an” are used, as is common in patent documents, to include one or more than one. In this document, the term “or” is used to refer to a nonexclusive “or,” such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. 
     The techniques of the embodiments disclosed herein are implemented using technologies associated with Big Data analyses. Specifically, the use of multi-node compute clusters is a preferred embodiment. More specifically, the use of the open-source Hadoop software ecosystem for management of large-scale multi-node compute clusters is a preferred embodiment. The present disclosure relates to systems and methods for providing workflow editors with improved visualization and for improving the usability by developers of workflows that define an analysis of very large data sets using multi-node compute clusters. Specifically, embodiments described herein include methods for the visualization of elements of such workflows. An example workflow can include multiple elements. The elements can include loops, conditional statements, markers, algorithms, nested workflows, tools, such as computational tools, and so forth. The elements of the workflow can be represented in a layout of the workflow. The layout can have a specific arrangement, sizing, spacing, and placement of the elements of the workflow shown on a user interface. In case of a complex management system for workflows with a great number of elements, it may be difficult for a user, e.g. a workflow developer, to see the functionality of each element. The method disclosed herein can provide improved editability of the workflow over methods known in the art. The method disclosed herein can enable adding new elements to the layout, connecting elements together, removing elements, hiding elements, reordering the elements automatically, and so forth. The method disclosed here can be implemented by an event-driven management engine which can enable, for example, scientists in the field of biology, bionomics and bioinformatics, to more efficiently than possible with prior art methods to query and analyze large genetic data sets using a number of informatics tools and to save results. 
     According to the present disclosure, elements of the layout can be ordered into groups. These groups can be collapsed into a single element representing the group. Similarly, the collapsed group can be expanded to show the constituent elements. Furthermore, the user can be allowed to connect several elements in the layout of the workflow to define a collapsible group. The group can be increased in size to show which elements are included in the group. When a user needs to access a particular element of the group, the user can expand the combined element and select the specific element. 
     Furthermore, in an example embodiment, the user can use markers for some elements of the workflow. The user can arrange several elements into a block and mark the block with the markers. The marked block can be smaller compared to the initial size of the block. In an example embodiment, the marker can show that the marked element is in a collapsed state and can be expanded, that the block is removed from the layout, and so forth. Furthermore, the user may provide a name for the block or mark the block with a symbol. The name can describe the elements included in the block. When the user needs to use a particular element hidden in a collapsed block, the user can get information on the collapsed block by reading the name, and if needed, expand the block. After expanding the block, all elements of the block can be shown and the user can select specific elements. Furthermore, where several elements of the workflow can be used in a similar way, the user can create algorithms to use these elements as a group. These elements can be connected into a single block and named according to their common functionality. 
     Furthermore, the workflow editor of the present disclosure provides for self-positioning of the elements in the layout to optimize the position of the elements in the layout for better visualization. Therefore, the elements may be positioned in the layout in such a way as to avoid unused portions of the layout between the elements of the workflow. 
     Collapsing and expanding the elements of the workflow can be especially helpful to demonstrate nested workflows. Nested workflows are workflows inside a main workflow. The nested workflows can include elements. A workflow can include any number of nested workflows, and each of the nested workflows, in turn, can include further nested workflows. A nested workflow can be collapsed or expanded within the main workflow. The nested workflow can be marked with a specific marker. 
     The present disclosure relates further to systems and methods for workflow distribution. Specifically, embodiments described herein include a digital workflow distribution platform and a computer-implemented method for workflow distribution. The digital workflow distribution platform described herein provides a virtual marketplace for workflows. The platform can be configured to distribute workflows for data analysis as well as to support various workflow tools. The digital workflow distribution platform enables scientists in the fields of science such as, for example, biology, bionomics, and bioinformatics, to more efficiently than is possible with prior art methods to query and analyze large genetic data sets using a number of informatics tools and to save the results. 
     In some example embodiments, a user can access the digital workflow distribution platform via a user interface and review available workflows. Upon selection of a workflow, the user can send a request for acquisition of the workflow to the digital workflow distribution platform. The digital workflow distribution platform can provide the workflow to the user as software as a service (SaaS). The workflow is hosted in a cloud environment. 
     Upon receiving the user request, the digital workflow distribution platform can import the selected workflow into the user environment. The user is provided with access to the user environment (for example, by registering with the platform). Upon authentication, the user is provided with access to the workflow and is able to perform various workflow operations. For example, the workflow can be used to manage computations of biological data. 
     The digital workflow distribution platform can allow users to develop the workflow imported into the user environment. In particular, the user is able to select and change parameters of the workflow, add, remove, and modify tools associated with the workflow, select conditions and order of execution, and so forth. An operator of the digital workflow distribution platform can be responsible for maintenance and support of the workflow development environment. 
     After importing the workflow into the user environment, the user may desire to acquire or develop additional workflow tools. The digital workflow distribution platform allows for development and operational supports of workflow tools. The platform can facilitate development of the tools using an Application Programming Interface (API) associated with the platform. The platform can also allow distribution of the tools to users as well as running the tools in a cloud computing environment. For example, a user can access the digital workflow distribution platform and select tools to be added to the workflow. The digital workflow distribution platform receives the user request and adds the selected tool to the workflow. After the tools are added, the user can utilize the tool in the workflow. 
     As mentioned above, the digital workflow distribution platform allows users to develop tools imported into the user environment. In particular, the user is able to add, create, or modify the tools of the workflow (e.g., by editing tool parameters). 
     The present disclosure still further relates to systems and methods for generating and implementing automated workflow activities. Specifically, embodiments described herein include an event-driven management engine for workflows and method. Conventional workflow engines create a process for each workflow that can determine a current state of the workflow and a next step to be executed. In other words, such workflow engines may need to permanently trace the current state of the workflow and make decisions as to what action should be taken next. Furthermore, the conventional workflow system may need control points to save the current state of the workflow in order to ensure a successful restart of the workflow in case of a failure. The event-driven management engine enables scientists in the fields of science such as, for example, biology, bionomics, and bioinformatics, to query and analyze large genetic data sets using a number of informatics tools and save the results. 
     As outlined in the summary, the embodiments of the present disclosure are directed to event-driven management for workflows. An event-driven workflow may be determined by events occurring in the workflow, such as a user action, a sensor output, notifications from other programs, and so forth. The disclosed technology may allow defining conditions associated with each event occurring in the workflow and storing the conditions in a database. Furthermore, the database may store steps and associated tasks to be performed upon satisfaction of the condition. Therefore, when the event occurs, the engine may read the database to confirm that the conditions associated with the event are satisfied and run a corresponding process to execute the task associated with the condition. 
     Specifically, a decision node may run a conditional loop that may check whether the condition is satisfied. Once the condition is satisfied, the decision node may activate the computational node responsible for processing the satisfied condition and execute the corresponding part of the workflow. Computational nodes responsible for processing may run only after the conditions are satisfied. Until the conditions are satisfied, the computational nodes may be in a waiting mode (i.e., inactivated). 
     It should be noted that in the case of an unexpected shutdown, the workflow may be easily restored by running conditional loops and determining which conditions are satisfied. After determining which conditions are satisfied, the tasks associated with the satisfied conditions may be restarted. Thus, there is no need to save control points to restart the workflow. 
     Furthermore, the present technology may be used in scientific workflow systems, such as bioinformatics workflow management systems, to manage computations performed on biological data, which are computationally intensive. To improve the efficiency, the present technology may involve data processing in a parallel-distributed software framework. The parallel-distributed software framework may support computationally-intensive distributed tasks by running tasks on a number of computational clusters in parallel. The parallel-distributed software framework may be Hadoop-based. The present technology may utilize fork-queuing nodes to split tasks between multiple computational clusters. Furthermore, the fork-queuing nodes may be configured to divide a task associated with the event into multiple task fragments, each of which can be executed in parallel with other fragments on any node of the cluster. The fork-queuing cluster may select the nodes for execution of these task fragments. The nodes may include cloud-based computational clusters. After execution of the fragments by the nodes, the fork-queuing cluster may join the executed fragments into resulting data. 
     The resulting data may be shown to a user on a user interface. The user may choose the way in which the processed data may be represented. For example, the processed data may be shown as data tables, diagrams, text, graphs, drawings, and so forth. 
     Referring now to the drawings,  FIG. 1  illustrates a large-scale, computer cluster  100  within which an event-driven management engine for workflow that defines an analysis of very large data sets using multi-node compute clusters and method can be implemented. The environment  100  may include a network  110 , a user  120 , an event-driven management engine  200  for workflows, a user interface  130 , one or more client devices  140 , and a database  150 . 
     The network  110  may include the Internet or any other network capable of communicating data between devices. Suitable networks may include or interface with any one or more of, for instance, a local intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a MAN (Metropolitan Area Network), a virtual private network (VPN), a storage area network (SAN), a frame relay connection, an Advanced Intelligent Network (AIN) connection, a synchronous optical network (SONET) connection, a digital T1, T3, E1 or E3 line, Digital Data Service (DDS) connection, DSL (Digital Subscriber Line) connection, an Ethernet connection, an ISDN (Integrated Services Digital Network) line, a dial-up port such as a V.90, V.34 or V.34bis analog modem connection, a cable modem, an ATM (Asynchronous Transfer Mode) connection, or an FDDI (Fiber Distributed Data Interface) or CDDI (Copper Distributed Data Interface) connection. Furthermore, communications may also include links to any of a variety of wireless networks, including WAP (Wireless Application Protocol), GPRS (General Packet Radio Service), GSM (Global System for Mobile Communication), CDMA (Code Division Multiple Access) or TDMA (Time Division Multiple Access), cellular phone networks, GPS (Global Positioning System), CDPD (cellular digital packet data), RIM (Research in Motion, Limited) duplex paging network, Bluetooth radio, or an IEEE 802.11-based radio frequency network. The network  110  can further include or interface with any one or more of an RS-232 serial connection, an IEEE-1394 (Firewire) connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI (Small Computer Systems Interface) connection, a USB (Universal Serial Bus) connection or other wired or wireless, digital or analog interface or connection, mesh or Digi® networking. The network  110  may include a network of data processing nodes that are interconnected for the purpose of data communication. The network  110  may include a Software-defined Networking (SDN). The SDN may include one or more of the above network types. Generally the network  110  may include a number of similar or dissimilar devices connected together by a transport medium enabling communication between the devices by using a predefined protocol. Those skilled in the art will recognize that the present disclosure may be practiced within a variety of network configuration environments and on a variety of computing devices. 
     The client device  140 , in some example embodiments, may include a Graphical User Interface (GUI) for displaying the user interface  130 . In a typical GUI, instead of offering only text menus or requiring typed commands, the engine  200  may present graphical icons, visual indicators, or special graphical elements called widgets. The user interface  130  may be utilized as a visual front-end to allow the user  120  to build and modify complex tasks with little or no programming expertise. 
     The client device  140  may include a mobile telephone, a computer, a lap top, a smart phone, a tablet Personal Computer (PC), and so forth. In some embodiments, the client device  140  may be associated with one or more users  120 . The client device  140  may be configured to utilize icons used in conjunction with text, labels, or text navigation to fully represent the information and actions available to the user  120 . The user  120 , in some example embodiments, may be a person interacting with the user interface  130  via one of the client devices  140 . The user  120  may represent a person that uses the event-driven management engine  200  for workflows for his or her needs. For example, the user  120  may include a scientist using the event-driven management engine  200  for scientific workflows for performing a series of intensive computational or data manipulation steps. As shown on  FIG. 1 , the user  120  may input data to an application running on the client device  140 . The application may utilize the event-driven management engine  200  for workflows. Based on the input of the user  120 , the event-driven management engine  200  for workflows may generate and execute the automated workflow of the application running on the client device  140 . The event-driven management engine  200  for workflows may be connected to one or more databases  150 . The databases  150  may store data associated with tasks that need to be executed in the course of the workflow. 
       FIG. 2  shows a detailed block diagram of the event-driven management engine  200  for workflows that define an analysis of very large data sets using multi-node compute clusters, in accordance with an example embodiment. The engine  200  may include a decision node  202 , a fork join queuing cluster  204 , a database  206 , and, optionally, a user interface  208 . 
     The user may run an application that may utilize the event-driven management engine  200  for workflows. During running of the application, an event may occur. The event may include a user action, a sensor output, a notification from other programs, and so forth. Each event may be associated with one or more conditions that may be stored in the event-driven management engine  200  for workflows. The management engine  200  for workflows may include a decision node  202 , a fork-join queuing cluster  204 , a database  206 , and, optionally, a user interface  208 . 
     In an example embodiment, the decision node  202  may be configured to determine that the at least one condition is true. The determination that the at least one condition is true may be performed by running a conditional loop. The conditional loop may be configured to check whether the at least one condition is true. 
     The decision node  202  may be further configured to selectively activate, based on the determination, at least one computational module. The computational module may include a computational tool. The workflow may support a plurality of biological data formats and translations between the plurality of biological data formats. Therefore, the computational tool may refer to a specific field of science (for example, bioinformatics). In this case, the computational tool may include a bioinformatics tool enabling the user to process specific bioinformatics tasks. 
     After activation of the computational module, the fork-join queuing cluster  204  may allocate at least one computational module non-sequentially to participant computational nodes. The participant computational nodes may be located in a distributed cloud computing environment. By means of the participant computational nodes, the fork join queuing cluster  204  may process a data set according to predetermined criteria. The fork-join queuing cluster  204  enhances the speed and efficiency of the analysis of very large data sets using node-compute clusters. 
     The distributed database  206  may be configured to store at least one computational module. Furthermore, the distributed database  206  may be configured to store at least one condition associated with the at least one computational module. The user interface  208  may allow a user to build computational modules, modify computational modules, specify data sources, specify conditions for execution of the computational modules, etc. 
     The engine  200  is further described in detail with reference to  FIG. 3 .  FIG. 3  shows a graphical representation  300  for managing the workflow using the engine  200 . Each event occurring in the workflow may be associated with one or more conditions. In other words, a condition may be satisfied when the event associated with this condition occurs. A decision node of the event-driven management engine for workflows may run a conditional loop in order to check whether the at least one condition is true. Upon occurring of an event  310 , the decision node may determine that the at least one condition is true. Each true condition  320  associated with the event  310  may run a task. A task may include processing a data set, such as performing computations, sorting data, drawing diagrams, and so forth. In an example embodiment, the data set may be selected by the user (e.g., from a database). Furthermore, the data set may be obtained from testing equipment. The user may use the user interface to specify data sources from which the data set may be obtained. 
     After the determination that there is a true condition  320 , the decision node may selectively activate at least one computational module. The computational modules and the condition associated with the computational modules may be stored in a database  206 . In an example embodiment, the user may use a user interface to build or modify the computational modules, as well as specify conditions for execution of the computational modules. 
     Once there is at least one activated computational module  330 , a fork-join queuing cluster of the event-driven management engine for workflows may allocate at least one computational module non-sequentially to participant computational nodes in a distributed cloud computing environment. The cloud computing environment may include a plurality of computational clusters to increase performance and enable parallel execution of the tasks. Furthermore, the fork join queuing cluster may process a data set according to predetermined criteria. 
     The parallel steps performed by the fork-join queuing cluster are illustrated in detail on a scheme  400  of  FIG. 4 . Conventional workflow execution engines support only predefined splits and cannot handle dynamic splits that depend on specific parameters. Thus the fork-join queuing cluster represents a significant enhancement over the prior art. In the disclosed technology, if a number of parallel tasks are being performed, the fork join queuing cluster can split, at a fork point  450 , incoming task  410  into a number of sub-tasks represented as fragments  420 . In contrast to the prior art workflow execution engines, the splits can be determined when the fork join queuing cluster starts splitting tasks. The fragments  420  can be processed by numerous computational nodes (not shown). After processing of the fragments  420 , the processed fragments  430  may be joined at a join point  460  into a processed data set  440 . It should be noted that splitting steps need to be finalized before the joining step. 
       FIG. 5  shows a flow chart  500  for performing asymmetric splitting and joining by the fork join queuing cluster. Conventional workflow execution engines require pairs of fork and join points. In the disclosed technology, fork points do necessarily have corresponding join points. Thus, the fork join queuing cluster shown in  FIG. 5  has two fork points and three join points. Fork points and join points may be located anywhere in the workflow. In fact, any tool that allows input from multiple sources can serve as a join point. If the join point has an input from several fork points, the join point can join the fragments when results from all fork points are available. This greatly enhances the flexibility and efficiency of the disclosed technology over the conventional workflow execution engines of the prior art. 
     As shown on  FIG. 5 , the fork-join queuing cluster may split, at a fork point  560 , an incoming task  510  into a number of sub-tasks represented as fragments  520 ,  525 . The fragments  520  can be processed by numerous computational nodes (not shown). After processing of the fragments  520 , the processed fragments  530  can be joined at a join point  580 . 
     The fragment  525  can still be too complex for processing by a single computational node. Therefore, the fragment  525  may be split, at a fork point  570 , into a number of fragments  540 . The fragments  540  may be processed by the computational nodes. After processing of the fragments  540 , some of the processed fragments, in particular the processed fragments  550 , can be joined, at a join point  585 , with the processed fragments joined at the join point  580 . Another portion of the processed fragments, in particular the processed fragments  555 , can be joined, at a join point  590 , with the processed fragments joined at the join point  585 . After joining at the join point  590 , a processed data set  595  can be obtained. 
     Referring again to  FIG. 3 , the fork join queuing cluster may include a master node and participant computational nodes. The master node may be configured to receive tasks associated with the computational module, divide the tasks into a plurality of fragments, and distribute fragments to participant computational nodes. The participant computational nodes may be configured to process the fragments and send processed fragments to the master node. 
     Specifically, allocation of the computational module to the participant computational nodes may be performed by dividing tasks associated with the computational module into a plurality of fragments  340 . Each fragment  340  may be processed on a participant computational node  350 . The computational module may be configured to use one or more fork join queuing clusters configured to divide the tasks for service by the participant computational nodes  350 . The participant computational nodes  350  may process the fragments  340  to obtain processed fragments  360 . After processing by the participant computational nodes  350 , the master node may collect the processed fragments  360  from the participant computational nodes  350  and join the processed fragments  360  into a processed data set  370 . The processed data set  370  may be provided to the user by a user interface. 
       FIG. 6  is a process flow diagram showing a computer-implemented event-driven management method  600  for workflows, according to an example embodiment. The method  600  may be performed by processing logic that may comprise hardware (e.g., decision making logic, dedicated logic, programmable logic, and microcode), software (such as software running on a general-purpose computer system or a dedicated machine), or a combination of both. 
     The method  600  may commence with storing, by a distributed database, at least one computational module at operation  610 . At operation  620 , the method may comprise storing, by the distributed database, at least one condition associated with the computational module. The computation module may be not activated until the at least one condition is true. 
     At operation  630 , a decision node may determine that the at least one condition is true by running a conditional loop configured to check whether the at least one condition is true. Based on the determination, the decision node may selectively activate the at least one computational module at operation  640 . 
     After the computational module is activated, at operation  650 , a fork join queuing cluster may allocate the computational module non-sequentially to participant computational nodes in a distributed cloud computing environment. The cloud computing environment may include a plurality of computational clusters to increase performance and enable parallel execution of the tasks. The workflow may support a plurality of biological data formats and translations between the plurality of biological data formats. In view of this, in an example embodiment, the computational module may comprise a bioinformatics tool. 
     The computational module may be configured to process a data set according to predetermined criteria. In an example embodiment, the computational module may be allocated to the participant computational nodes by dividing tasks associated with the computational module into a plurality of fragments. Each fragment may be processed on a participant computational node. The processed fragments may be joined into a processed data set. 
     Specifically, the computational module may use one or more fork-join queuing clusters configured to divide the tasks for processing by the participant computational nodes. The fork-join queuing clusters may join processed fragments after processing by the participant computational nodes. In particular, each of the fork join queuing clusters may include a master node and participant computational nodes. The master node may be configured to receive tasks associated with the computational module, divide the tasks into a plurality of fragments, and distribute fragments to participant computational nodes. The participant computational nodes may be configured to process the fragments and send processed fragments to the master node. The master node may collect the processed fragments from the participant computational nodes and join the processed fragments into a processed data set. 
     In more detail, the method  600  logics are illustrated on  FIG. 7 . FIG 7  shows a flow chart illustrating a detailed computer-implemented event-driven management method  700  for workflows, in accordance with some embodiments. As shown in  FIG. 7 , the method  700  may commence at operation  710  with receiving, by a decision node, a condition associated with an event occurring during in the workflow. 
     At operation  720 , the decision node may run a conditional loop to check whether the received condition is true. If the condition is not true, the decision node may run a further conditional loop at operation  710  to check further conditions. If the condition is true, the condition may process a task associated with the event. For this purpose, the decision node may activate a computational module at operation  730 . The computational module may be configured to process a data set associated with the task according to predetermined criteria. 
     After activation of the computational module, a fork-join queuing cluster may divide the task into a number of fragments at operation  740 . The computational nodes of the fork-join queuing cluster may process the fragments at operation  750 . After processing, the fork-join queuing cluster may join the processed fragments into a processed data set at operation  760 . Optionally, the processed data set may be represented to a user on a user interface. 
       FIG. 8  is a flow chart  800  illustrating method  800  for checking a condition. During a condition check  810 , it is determined which of conditions  820 ,  830 ,  840  are satisfied. If conditions  820 ,  830  are satisfied, the decision node performs corresponding steps  850  or  860 . If none of the conditions  820 ,  830  is satisfied, the decision node selects a default condition  840  and performs step  870 . When the condition check is finalized, step  880  is executed. 
     The conditional loop of step  720  in  FIG. 7  is illustrated in more detail in  FIG. 9  as a conditional loop  900 . A condition check  910  is performed before the conditional loop  900  is executed. If, during the condition check  910 , it is determined that the condition is false, all loop steps are added to the database. After adding the loop steps to the database, the loop steps shown as a first step in the loop  920  and a second step in the loop  930  are executed. If the condition is true, the conditional loop  900  terminates and all steps subsequent to the conditional loop  900  are added to the database. After the steps are added to the database, step  940  is executed. If the condition is true for the first check, none of the loop steps are executed. 
       FIG. 10  illustrates another example conditional loop  1000 . The loop  1000  is executed at least once before the condition check  1030 . During execution of the conditional loop  1000 , several steps can be performed, shown as a first step  1010  in the loop and a second step  1020  in the loop. If the condition is false, the conditional loop  1000  is executed again. The steps  1010  and  1020  of the conditional loop  1000  can be added to the database. 
     If the condition is true, the conditional loop  1000  terminates. All steps after the conditional loop  1000  are added to the database. After adding the steps to database, the first step  1040  is executed. 
       FIG. 11  illustrates an environment  1100  within which a digital workflow distribution platform and a method for workflow distribution can be implemented. The environment  1100  includes a network  1110 , a user  1120 , a digital workflow distribution platform  1200 , a user interface  1130 , one or more user devices  1140 , and a database  1150 . 
     The network  1110  includes the Internet or any other network capable of communicating data between devices. Suitable networks include or interface with any one or more of, for instance, a local intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a MAN (Metropolitan Area Network), a virtual private network (VPN), a storage area network (SAN), a frame relay connection, an Advanced Intelligent Network (AIN) connection, a synchronous optical network (SONET) connection, a digital T1, T3, E1 or E3 line, Digital Data Service (DDS) connection, DSL (Digital Subscriber Line) connection, an Ethernet connection, an ISDN (Integrated Services Digital Network) line, a dial-up port such as a V.90, V.34 or V.34bis analog modem connection, a cable modem, an ATM (Asynchronous Transfer Mode) connection, or an FDDI (Fiber Distributed Data Interface) or CDDI (Copper Distributed Data Interface) connection. Furthermore, communications also include links to any of a variety of wireless networks, including WAP (Wireless Application Protocol), GPRS (General Packet Radio Service), GSM (Global System for Mobile Communication), CDMA (Code Division Multiple Access) or TDMA (Time Division Multiple Access), cellular phone networks, GPS (Global Positioning System), CDPD (cellular digital packet data), RIM (Research in Motion, Limited) duplex paging network, Bluetooth radio, or an IEEE 802.11-based radio frequency network. The network  1110  can further include or interface with any one or more of an RS-232 serial connection, an IEEE-1394 (Firewire) connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI (Small Computer Systems Interface) connection, a USB (Universal Serial Bus) connection or other wired or wireless, digital or analog interface or connection, mesh or Digi® networking. The network  1110  includes a network of data processing nodes that are interconnected for the purpose of data communication. Generally, the network  1110  includes a number of similar or dissimilar devices connected together by a transport medium enabling communication between the devices by using a predefined protocol. Those skilled in the art will recognize that the present disclosure may be practiced within a variety of network configuration environments and on a variety of computing devices. 
     The user device  1140 , in some example embodiments, includes a Graphical User Interface (GUI) for displaying the user interface  1130 . In a typical GUI, instead of offering only text menus or requiring typed commands, the platform  1200  presents graphical icons, visual indicators, or special graphical elements called widgets. The user interface  1130  is utilized as a visual front-end to allow the user  1120  to build and modify complex tasks with little or no programming expertise. 
     The user device  1140  includes a mobile telephone, a computer, a lap top, a smart phone, a phablet, tablet PC, and so forth. In some embodiments, the user device  1140  is associated with one or more users  1120 . The user device  1140  is configured to utilize icons used in conjunction with text, labels, or text navigation to fully represent the information and actions available to the user  1120 . The user  1120 , in some example embodiments, is a person interacting with the user interface  1130  via one of the user devices  1140 . The user  1120  may include a person that uses the platform  1200  for his or her needs. For example, the user  1120  includes a scientist intending to use a workflow provided by the platform  1200  for performing a series of computational or data manipulation steps. The platform  1200  can be connected to one or more databases  1150 . The databases  1150  can store data associated with the workflows, the user  1120 , and other data needed for development of the workflow. 
     As shown on  FIG. 11 , the user  1120  can access the platform  1200  via the user interface  1130  presented on the user device  1140 . The user  1120  can send a request to the platform  1200  via the user interface  1130 . In an example embodiment, the request can be provided by selection of the workflow to be acquired. In response to the request of the user  1120 , the platform  1200  imports the selected workflow into a user environment. 
       FIG. 12  shows a detailed block diagram of the digital workflow distribution platform  1200 , in accordance with an example embodiment. The platform  1200  can include a user interface  1202 , a distribution module  1204 , a management engine  1206  for workflows, and, optionally, a database  1208 . 
     The user can utilize the user interface  1202  to access the platform  1200 . The user interface  1202  can allow the user to select one of the workflows from the workflows available on the platform  1200 . The user can make the selection based on one or more parameters associated with the workflow. Specifically, after accessing the platform  1200 , the user can view one or more of the following parameters: a list of available workflows, specific information associated with each of the workflows, tools available for each of the workflows, price of the workflows, and so forth. In an example embodiment, the user can select the workflow of interest by clicking on the workflow pictogram. 
     In an example embodiment, the user interface  1202  can be configured to provide one or more of the following functionalities: searching for the workflow, viewing information associated with the workflow, purchasing the workflow, importing the workflow into a user environment, enabling a developer to develop a tool and upload the tool to the management engine for workflows, and so forth. 
     The user interface  1202  can be regulated by a platform operator. In an example embodiment, each workflow requires an approval process and compliance with predetermined guidelines. The approval process and checking compliance of the workflow with predetermined guidelines can be performed by the platform operator. 
     The distribution module  1204  of the platform  1200  can be configured to enable the user to acquire the workflow. Furthermore, the distribution module  1204  can be operable to enable importing the workflow into a user environment. For example, the workflow can be implemented as application installed on a user device or as a web-based application. 
     In an example embodiment, the distribution module  1204  can allow assessing fees from a workflow user. For example, a user account associated with the platform  1200  can be charged for the workflow to be imported into the user environment. Thus, for example, before the workflow is imported into the user environment, an amount corresponding to the price of the workflow can be subtracted from the user account and transferred to an account associated with the workflow owner. Moreover, a percentage of the fees or a flat amount can be paid to the platform operator. The workflow can be sold on a subscription basis, for example by paying a monthly fee or an annual fee. In another example embodiment, the workflow can be sold on a per use basis, for a one-time lump sum, and so forth. 
     The workflow is available as SaaS so that the workflow and associated data are centrally hosted in a cloud environment. In such environment, a user can access the workflow via a web browser using a thin client. When the workflow is provided as a SaaS, its use can be easily tracked and the user charged per use. 
     In a multi-tenant SaaS environment, the cost of user provisioning (i.e., creation, maintenance and deactivation of user attributes) is relatively low. Thus, the workflow provider may even offer the user a free workflow service with limited functionality or scope. In this case, the fees can be charged only for enhanced functionality in addition to the basic free workflow service. 
     The management engine  1206  for workflows of the platform  1200  can be configured to support development of the workflow imported into the user environment. The management engine  1206  for workflows can comprise a decision node, a fork join queuing cluster, and a distributed database. The management engine  1206  for workflows can be communicatively coupled to an application running in the user environment and enable the user to manage and define a series of tasks within the application. Various events can occur as the applications runs. These events can include user actions, sensor outputs, notification from other programs, and so forth. Each event can be associated with one or more conditions that are stored in the management engine  1206  for workflows. 
     In an example embodiment, the decision node is configured to determine that at least one condition is true. The determination that the at least one condition is true can be performed by running a conditional loop. The conditional loop can be configured to check whether the at least one condition is true. The decision node can be further configured to selectively activate, based on the determination, the at least one computational module. The computational module can include a computational tool. The workflow can support a plurality of biological data formats as well as translations between the plurality of biological data formats. A computational tool can pertain to a specific field of science (for example, bioinformatics). In one embodiment, the computational tool is a bioinformatics tool enabling the user to process specific bioinformatics tasks. 
     After activation of the computational module, the fork join queuing cluster can allocate at least one computational module non-sequentially to participant computational nodes. The participant computational nodes can be located in a distributed cloud computing environment. Using the participant computational nodes, the fork join queuing cluster can process a data set according to predetermined criteria. In a further example embodiment, the management engine  1206  for workflows of the platform  1200  is the event-driven management engine for workflows  200  in  FIG. 2 . 
     The distributed database  1208  can be configured to store at least one computational module and at least one condition associated with the at least one computational module. Furthermore, the distributed database  1208  can be configured to store data associated with the workflows, the user, and other data needed for development of the workflow by the user. Once the workflow is imported into the user environment, the user can be able to edit the workflow. Furthermore, the user can edit parameters and tools associated with the workflow. 
       FIG. 13  illustrates a method for workflow distribution  1300 , according to another example embodiment. The user  1120  can use a user device  1140  having a user interface to connect to the digital workflow distribution platform  1200 . The user device  1140  can be connected with the digital workflow distribution platform  1200  via the network  1110 . Upon connecting to the digital workflow distribution platform  1200 , the user  1120  can search for workflows that can be acquired from the digital workflow distribution platform  1200 . The user  1120  can view information associated with the available workflows. The user  1120  can select a workflow and send a user request  1310  to the digital workflow distribution platform  1200 . 
     In an example embodiment, the user request  1310  can be related to acquiring the workflow and importing the workflow into the user environment. Upon receiving the user request  1310 , the digital workflow distribution platform  1200  processes the user request  1310 . After processing the user request  1310 , the digital workflow distribution platform  1200  can provide the workflow  1320  to the cloud-based environment  1330  of the user  1120 . In the embodiment shown on  FIG. 13 , the workflow  1320  is a web-based workflow and is configured to be imported into the cloud-based environment  1330 . The user  1120  can access the cloud-based environment  1330  via the user interface on the user device  1140 . Providing the workflow  1320  to the user  1120  can include importing workflow  1320  into the cloud-based environment  1330 . After import of the workflow  1320 , the user  1120  may edit the workflow  1320  according to his or her needs. 
       FIG. 14  illustrates a method for workflow tool distribution  1400 , according to an example embodiment. After import of the workflow into the cloud-based environment shown on  FIG. 13 , the user  1120  can develop the workflow  1410  by acquiring tools associated with the workflow  1410 . The user  1120  can utilize a user device  1140  having a user interface  1130  to connect to the digital workflow distribution platform  1200  via the network  1110 . Upon connecting to the digital workflow distribution platform  1200 , the user  1120  can search for tools available for acquisition in the digital workflow distribution platform  1200 . The tools are then associated with the workflow  1410  installed in the cloud-based environment  1330  (i.e., the tools can be added into the workflow  1410 ). The user  1120  views information associated with the available tools and selects the tools of interest. The user  1120  can send a tool request  1420  to the digital workflow distribution platform  1200 . In an example embodiment, the tool request  1410  includes acquiring the tool to be added into the user environment  1330 . Upon receiving the tool request  1420 , the digital workflow distribution platform  1200  can process the tool request  1420 . After processing the tool request  1420 , the digital workflow distribution platform  1200  can add the tool  1430  to the workflow  1410  in the cloud-based environment  1330 . After adding the tool  1430 , the user  1120  is able to edit the tool  1430  associated with the workflow  1410 . 
       FIG. 15  is a process flow diagram showing a computer-implemented method  1500  for workflow distribution, according to an example embodiment. The method  1500  can be performed by processing logic that comprises hardware (e.g., decision making logic, dedicated logic, programmable logic, and microcode), software (such as software running on a general-purpose computer system or a dedicated machine), or a combination of both. 
     The method  1500  can commence with receiving, by a user interface, a user command to select a workflow at operation  1510 . The user can make a selection based on one or more parameters associated with the workflow. At operation  1520 , the user is able to acquire the workflow. At operation  1530 , the user is able to import the workflow into a user environment. In an example embodiment, the workflow can include an application installed on a user device, such as client software accessing the platform or a web-based workflow. The workflow can be sold on a subscription basis, a per use basis, a one-time lump sum basis, a peer-to-peer basis, or the like. In an example embodiment, the workflow is distributed as SaaS. 
     After import of the workflow, a management engine  1206  for workflows supports development of the workflow imported into the user environment at operation  1540 . In order to support the development of the workflow, the management engine for workflows can include a decision node, a fork-join queuing cluster, and a distributed database. The decision node is configured to determine that at least one condition associated with an event occurring in the workflow is true. The determination that the at least one condition is true is performed by running a conditional loop configured to check whether the at least one condition is true. Furthermore, based on the determination, the decision node can selectively activate at least one computational module. The computational module can processes a task associated with the true condition. The fork join queuing cluster can be configured to allocate the at least one computational module non-sequentially to participant computational nodes in a distributed cloud computing environment. The fork join queuing cluster can process a data set according to predetermined criteria. The distributed database can be configured to store the computational module and the condition associated with the computational module. The computation module is not activated until the at least one condition is true. Development of the workflow includes modifying the workflow and modifying parameters and tools associated with the workflow after the workflow is imported into the user environment. 
     In an example embodiment, the user interface is configured to provide one or more of the following functionalities: searching for the workflow, viewing information associated with the workflow, purchasing the workflow, importing the workflow into a user environment, enabling a developer to develop a tool and upload the tool to the management engine for workflows, and so forth. The user interface can be regulated by a platform operator. Each workflow may require an approval process and compliance with predetermined guidelines. The platform operator can perform the approval process and control compliance of the workflow with the predetermined guidelines. 
       FIG. 16  illustrates an environment  1600  within which a method for visualization of elements of a workflow and a system can be implemented. The environment  1600  may include a network  1610 , a user  1620 , a system  1800  for visualization of elements of a workflow, a user interface  1630 , one or more client devices  1640 , and a database  1650 . 
     The network  1610  may include the Internet or any other network capable of communicating data between devices. Suitable networks may include or interface with any one or more of, for instance, a local intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a MAN (Metropolitan Area Network), a virtual private network (VPN), a storage area network (SAN), a frame relay connection, an Advanced Intelligent Network (AIN) connection, a synchronous optical network (SONET) connection, a digital T1, T3, E1 or E3 line, Digital Data Service (DDS) connection, DSL (Digital Subscriber Line) connection, an Ethernet connection, an ISDN (Integrated Services Digital Network) line, a dial-up port such as a V.90, V.34 or V.34bis analog modem connection, a cable modem, an ATM (Asynchronous Transfer Mode) connection, or an FDDI (Fiber Distributed Data Interface) or CDDI (Copper Distributed Data Interface) connection. Furthermore, communications may also include links to any of a variety of wireless networks, including WAP (Wireless Application Protocol), GPRS (General Packet Radio Service), GSM (Global System for Mobile Communication), CDMA (Code Division Multiple Access) or TDMA (Time Division Multiple Access), cellular phone networks, GPS (Global Positioning System), CDPD (cellular digital packet data), RIM (Research in Motion, Limited) duplex paging network, Bluetooth radio, or an IEEE 802.11-based radio frequency network. The network  1610  can further include or interface with any one or more of an RS-232 serial connection, an IEEE-1394 (Firewire) connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI (Small Computer Systems Interface) connection, a USB (Universal Serial Bus) connection or other wired or wireless, digital or analog interface or connection, mesh or Digi® networking. The network  1610  may include a network of data processing nodes that are interconnected for the purpose of data communication. The network  1610  may include a Software-defined Networking (SDN). The SDN may include one or more of the above network types. Generally the network  1610  may include a number of similar or dissimilar devices connected together by a transport medium enabling communication between the devices by using a predefined protocol. Those skilled in the art will recognize that the present disclosure may be practiced within a variety of network configuration environments and on a variety of computing devices. 
     The client device  1640 , in some example embodiments, may include a Graphical User Interface (GUI) for displaying the user interface  1630 . In a typical GUI, instead of offering only text menus or requiring typed commands, the user interface  1630  may present graphical icons, visual indicators, or special graphical elements called widgets. The user interface  1630  may be utilized as a visual front-end to allow the user  1620  to build and modify workflows with little or no programming expertise. 
     The client device  1640  may include a mobile telephone, a computer, a lap top, a smart phone, a tablet personal computer (PC), and so forth. In some embodiments, the client device  1640  may be associated with one or more users  1620 . The client device  1640  may be configured to utilize icons used in conjunction with text, labels, or text navigation to fully represent the information and actions available to the user  1620 . The user  1620 , in some example embodiments, may be a person interacting with the user interface  1630  via one of the client devices  1640 . The user  1620  may represent a person that uses the system  1800  for visualization of elements of a workflow for his or her needs. For example, the user  1620  may include a scientist using the system  1800  for visualization of the elements of the workflow for performing a series of computational or data manipulation steps. As shown on  FIG. 16 , the user  1620  may input data to an application running on the client device  1640 . The application may utilize the system  1800  for visualization of the elements of the workflow. Based on the input of the user  1620 , the system  1800  for visualization of the elements of the workflow may visualize the workflow of the application running on the client device  1640 . The system  1800  for visualization of the elements of the workflow may be connected to one or more databases  1650 . The databases  1650  may store data associated with tasks that need to be executed in the course of the workflow, rules associated with positioning workflow elements on a layout of the workflow, and so forth. 
       FIG. 17  is a process flow diagram showing a computer-implemented method  1700  for visualization of elements of a workflow, according to an example embodiment. The method  1700  may be performed by processing logic that may comprise hardware (e.g., decision making logic, dedicated logic, programmable logic, and microcode), software (such as software running on a general-purpose computer system or a dedicated machine), or a combination of both. 
     The method  1700  commences with displaying, via a user interface, the workflow at operation  1710 . The workflow includes a plurality of elements, such as a word, an idea, a task, and so forth. The elements may be shown on the user interface in a form of blocks. Connections between elements may be shown as connections between the blocks. At operation  1720 , the method comprises defining one or more collapsible groups of elements within the workflow. The defining is made based on predetermined grouping criteria. The one or more collapsible groups of elements include one or more of a loop, a conditional statement, a computational tool, a marker, an algorithm, a nested workflow, and so forth. 
     At operation  1730 , a request to collapse the one or more collapsible groups of elements is received from a user. After receiving the request, the one or more collapsible groups of elements is collapsed into one or more collapsed groups of elements at operation  1740 . 
     After collapsing of the one or more collapsible groups of elements, at operation  1750 , a layout of the plurality of elements and the one or more collapsed groups of elements is selectively readjusted. A block depicting the collapsed group of elements on the layout may be of a greater size than the blocks of the collapsible group of elements. 
     In an example embodiment, the method  1700  further comprises receiving, from the user, a request to expand the one or more collapsed groups of elements formed at operation  1740 . In response to the request, the collapsed group of elements is expanded into the one or more groups of elements. After expanding the groups of elements, the layout of the workflow is selectively readjusted. 
     In an example embodiment, the method  1700  optionally comprises receiving a request from the user to add a further element to the workflow. In response to the request, the further element is added to the workflow and the layout of the workflow is selectively readjusted. 
     In an example embodiment, the method  1700  optionally comprises receiving a request from the user to remove a further element from the workflow. In response to the request, the further element is removed from the workflow and the layout of the workflow is selectively readjusted. 
     In a further example embodiment, the method  1700  optionally comprises receiving a request from the user to modify a further element of the workflow. In response to the request, the further element of the workflow is modified and the layout of the workflow is selectively readjusted. 
     In some example embodiments, the method  1700  comprises adding a space saving element to the layout of the workflow. The space saving element is configured to reorder the arrangement of the elements of the workflow to optimize the arrangement of the elements on the layout. In some embodiments, the reordering takes place automatically. That is, each element of the workflow is self-positioned in response to receiving user requests to collapse the collapsible groups of elements, to expand the collapsed groups of elements, to add further elements to the workflow, and the like. 
     In an example embodiment, the method  1700  comprises receiving a request to create a visualization of an element or a group of elements of the workflow. The visualization allows the user to edit the element or the group of elements while working on the workflow. In response to the request, the visualization of the element or the group of elements of the workflow is created. 
     In an example embodiment, the visualization comprises an inline editor. The inline editor allows users to dynamically edit elements shown via the user interface. The inline editor enables the user to create markers of the elements of the workflow and depict the markers as an expandable block. The marker may include a description of the element included in the expandable block. After creation of the markers, the markers are depicted on the layout. 
     When the user needs to execute the elements of the block marked by the marker, the user gives a request to expand the expandable block marked by the marker. In response to the request, the expandable block expands and the user selects the needed element of the workflow. The user may select several markers, the elements of which are to be executed in the workflow. In such a case, the elements of the unselected markers are not executed during the workflow. 
     Furthermore, in an example embodiment, the user creates an algorithm for a selected group of elements of the workflow and marks the selected group of elements with a marker describing the algorithm. All elements of the selected group of elements are executed using the algorithm created by the user. 
       FIG. 18  shows a detailed block diagram of a system  1800  for visualization of elements of a workflow, in accordance with an example embodiment. The system  1800  may include a processor  1802 , a user interface  1804 , and, optionally, a database  1806 . 
     In an example embodiment, the processor  1802  is configured to define, based on predetermined grouping criteria, one or more collapsible groups of elements within the workflow. Furthermore, the processor  1802  is configured to receive, from a user, a request to collapse the one or more collapsible groups of elements. In response to the request, the processor  1802  is configured to collapse the one or more collapsible groups of elements into one or more collapsed groups of elements. The one or more collapsible groups of elements include a loop, a conditional statement, a computational tool, a marker, an algorithm, a nested workflow, and so forth. After collapsing the one or more collapsible groups of elements, the processor selectively readjusts a layout of the plurality of elements and the one or more collapsed groups of elements. 
     In an example embodiment, the processor  1802  is further configured to receive a request to add a further element to the workflow. In response to the request, the processor  1802  adds the further element to the workflow and selectively readjusts the layout of the workflow. In a further example embodiment, the processor  1802  is further configured to receive a request to remove a further element from the workflow. In response to the request, the processor  1802  removes the further element from the workflow and selectively readjusts the layout of the workflow. In an example embodiment, the processor  1802  is further configured to receive a request to modify a further element of the workflow. In response to the request, the processor  1802  modifies the further element to the workflow and selectively readjusts the layout of the workflow. 
     In a further example embodiment, the processor  1802  is configured to add a space saving element to the layout of the workflow. The space saving element is configured to reorder the arrangement of the elements of the workflow to optimize the arrangement of the elements on the layout. 
     In a further example embodiment, the processor  1802  is configured to receive a request to create a visualization of an element or a group of elements of the workflow. The visualization allows the user to edit the element or the group of elements while working on the workflow. In response to the request, the processor  1802  creates the visualization of the element or the group of elements of the workflow. In an example embodiment, the visualization comprises an inline editor. In a further example embodiment, the processor  1802  is the digital workflow distribution platform  1200  in  FIG. 12 . 
     The user interface  1804  of the system  1800  is configured to display the workflow. The workflow includes a plurality of elements. The plurality of elements includes a word, an idea, a task, and the like. In an example embodiment, the user interface  1804  depicts the elements of the workflow as blocks. Connections between the elements of the workflow are depicted as connections between the blocks. 
     The databases  1806  stores data associated with the workflow, such as tasks that need to be executed in the course of the workflow, rules associated with positioning workflow elements on a layout of the workflow, and so forth. 
       FIG. 19  shows a scheme  1900  for a workflow in a collapsed form. The workflow comprises tasks  1910 - 1960 . Each task is shown in a separate block. The blocks of the tasks  1910 - 1960  comprise markers  1970 ,  1980 ,  1990 ,  1995 . The markers  1970 ,  1980 ,  1990 ,  1995  show actions available to be done on the tasks  1910 - 1960 . For example, the task  1960  may be removed or hidden from the layout of the workflow by using the marker  1990 . The tasks  1920 ,  1930 ,  1950  may be expanded by using the markers  1995 . The markers  1970 ,  1980  may represent any information relevant to the tasks  1910 - 1960 , such as ability of the task to be expanded, obligatory task of the workflow, optional task of the workflow, and the like. 
       FIG. 20  shows a scheme  2000  for the collapsed workflow of  FIG. 19 , in which the task  1920  is expanded. The task  1920  comprises several steps shown as steps  2010 - 2060 . The expanded task  1920  may be collapsed to the initial form using the marker  1995 . The markers  2070  are used to remove the steps  2010 - 2060  from the task  1920 . The marker  2080  is used to close the task  1920 . 
       FIG. 21  shows a scheme  2100  for the collapsed workflow of  FIG. 19 , in which the task  1930  is expanded. For clear illustration, tasks  1910 ,  1950 ,  1960  are not shown on  FIG. 21 . The task  1930  comprises several steps shows as steps  2110 - 2160 . The expanded task  1930  may be collapsed to the initial form using the marker  1995 . The marker  2080  is used to close the task  1930 . The markers  2070  are used to remove the steps  2110 - 2160  from the task  1930  or to remove the task  1940  from the workflow. 
       FIGS. 22A-22C  show a scheme  2200  for a workflow of  FIG. 19  in an expanded form. In particular, as shown on  FIG. 22A , the task  2210  is non-expandable. Task  2220  is expanded and comprises steps  2221 - 2226 . The markers  2070  are used to remove any of steps  2221 - 2226  from the task  2220 . The expanded task  2220  may be collapsed to the initial form using the marker  1995 . The marker  2080  is used to close the task  2220 . 
       FIG. 22B  shows the task  2230  in an expanded form. Task  2230  is expanded and comprises steps  2231 - 2236 . The marker  1995  may be used to collapse the task  2230 . The markers  2070  are used to remove any of steps  2231 - 2236  from the task  2230 . The marker  2080  is used to close the task  2230 . 
     As shown on  FIG. 22C , the tasks  2240  and  2260  are non-expandable. The task  2250  is expanded and comprises steps  2251 - 2257 . The marker  1995  is used to collapse the task  2250 . The markers  2070  are used to remove any of steps  2251 - 2257  from the task  2250 . The marker  2080  is used to close the task  2250 . 
     The tasks  2220 ,  2230 ,  2250  represent nested workflows comprised in the workflow shown on  FIGS. 22A-22C . Specifically, the tasks  2220 ,  2230 ,  2250  are workflows that are executed during running of the workflow shown on  FIGS. 22A-22C . 
       FIG. 23  shows a diagrammatic representation of a machine in the example electronic form of a computer system  2300 , within which a set of instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In various example embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a PC, a tablet PC, a set-top box (STB), a cellular telephone, a portable music player (e.g., a portable hard drive audio device such as an Moving Picture Experts Group Audio Layer 3 (MP3) player), a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  2300  includes a processor or multiple processors  2302  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory  2304 , and a static memory  2306 , which communicate with each other via a bus  2308 . The computer system  2300  may further include a video display unit  2310  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  2300  may also include an alphanumeric input device  2312  (e.g., a keyboard), a cursor control device  2314  (e.g., a mouse), a disk drive unit  2316 , a signal generation device  2318  (e.g., a speaker), and a network interface device  2320 . 
     The disk drive unit  2316  includes a non-transitory computer-readable medium  2322 , on which is stored one or more sets of instructions and data structures (e.g., instructions  2324 ) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions  2324  may also reside, completely or at least partially, within the main memory  2304  and/or within the processors  2302  during execution thereof by the computer system  2300 . The main memory  2304  and the processors  2302  may also constitute machine-readable media. 
     The instructions  2324  may further be transmitted or received over a network  2326  via the network interface device  2320  utilizing any one of a number of well-known transfer protocols (e.g., Hyper Text Transfer Protocol (HTTP)). 
     While the computer-readable medium  2322  is shown in an example embodiment to be a single medium, the term “computer-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the present application, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term “computer-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. Such media may also include, without limitation, hard disks, floppy disks, flash memory cards, digital video disks, random access memory (RAM), read only memory (ROM), and the like. 
     The example embodiments described herein can be implemented in an operating environment comprising computer-executable instructions (e.g., software) installed on a computer, in hardware, or in a combination of software and hardware. The computer-executable instructions can be written in a computer programming language or can be embodied in firmware logic. If written in a programming language conforming to a recognized standard, such instructions can be executed on a variety of hardware platforms and for interfaces to a variety of operating systems. Although not limited thereto, computer software programs for implementing the present method can be written in any number of suitable programming languages such as, for example, Hypertext Markup Language (HTML), Dynamic HTML, Extensible Markup Language (XML), Extensible Stylesheet Language (XSL), Document Style Semantics and Specification Language (DSSSL), Cascading Style Sheets (CSS), Synchronized Multimedia Integration Language (SMIL), Wireless Markup Language (WML), Java™, Jini™, C, C++, Perl, UNIX Shell, Visual Basic or Visual Basic Script, Virtual Reality Markup Language (VRML), ColdFusion™ or other compilers, assemblers, interpreters or other computer languages or platforms. 
     Thus, methods and systems for visualization of elements of a workflow, for workflow distribution, and for event-driven management for workflows are disclosed. Although embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes can be made to these example embodiments without departing from the broader spirit and scope of the present application. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.