Patent Publication Number: US-2017371726-A1

Title: Rapid predictive analysis of very large data sets using an actor-driven distributed computational graph

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 14/925,974 titled “RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH”, filed on Oct. 28, 2015, the entire specification of which is incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention is in the field of analysis of very large data sets using distributed computational graph tools which allow for transformation of data through both linear and non-linear transformation pipelines. 
     Discussion of the State of the Art 
     Data pipelines, which are a progression of functions which each perform some action or transformation on a data stream, offer a mechanism to process quantities of data in the volume discussed directly above. To date however, data pipelines have either been extremely limited in what they do, for example “move data from a web based merchant site to a distributed data store; extract all purchases and classify by product type and region; store the result logs” or have been rigidly programmed and possibly required the uses of highly specific remote protocol calls to perform needed tasks. Even with these additions their capabilities are very limited and they have all been linear in configuration which prevents their use for analysis and conclusion or action discovery in a majority of complex situations where branching or even recurrent modification is needed. 
     What is needed is a system that intelligently handles data pipelines using event-driven actor-based flows, to enable high-throughput event message handling and more robust operation in a fully decoupled architecture. 
     SUMMARY OF THE INVENTION 
     The inventor has developed a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, that intelligently combines processing of a current data stream with the ability to retrieve relevant stored data in such a way that conclusions or actions could be drawn in a predictive manner. 
     An actor-driven distributed computational graph (DCG) may use a pipeline orchestrator to create and manage individual data pipelines while providing data caching to enable interactions between specific activity actors within pipelines. Each pipeline may then comprise a pipeline manager that creates and manages individual activity actors and directs operations within the pipeline while reporting back to the pipeline orchestrator. According to an aspect, information may be exchanged between peers without any involvement of centralized service beyond coordination by a pipeline manager. 
     According to one aspect, a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, comprising: a pipeline orchestrator configured to: create a plurality of transformation pipelines each comprising at least a pipeline manager; cache a plurality of data contexts provided by a pipeline manager; a pipeline manager configured to: create a plurality of activity actors; provide reporting data to the pipeline orchestrator; receive at least a data context from an activity actor; provide the data context to the pipeline orchestrator; an activity actor configured to: receive at least a set of data as a transformation input; perform an individual transformation upon a set of data; produce a data context based at least in part on the individual transformation; provide the transformed set of data as a transformation output; and provide the data context as a context output, is disclosed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way. 
         FIG. 1  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 2  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 3  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 4  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 5  is a diagram of an exemplary architecture for a system where streams of input data from one or more of a plurality of sources are analyzed to predict outcome using both batch analysis of acquired data and transformation pipeline manipulation of current streaming data according to one aspect. 
         FIG. 6  is a diagram of an exemplary architecture for a linear transformation pipeline system which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect. 
         FIG. 7  is a diagram of an exemplary architecture for a transformation pipeline system where one of the transformations receives input from more than one source which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect. 
         FIG. 8  is a diagram of an exemplary architecture for a transformation pipeline system where the output of one data transformation servers as the input of more than one downstream transformations which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect. 
         FIG. 9  is a diagram of an exemplary architecture for a transformation pipeline system where a set of three data transformations act to form a cyclical pipeline which also introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect. 
         FIG. 10  is a process flow diagram of a method for the receipt, processing and predictive analysis of streaming data according to one aspect. 
         FIG. 11  is a process flow diagram of a method for representing the operation of the transformation pipeline as a directed graph function according to one aspect. 
         FIG. 12  is a process flow diagram of a method for a linear data transformation pipeline according to one aspect. 
         FIG. 13  is a process flow diagram of a method for the disposition of input from two antecedent data transformations into a single data transformation of transformation pipeline according to one aspect. 
         FIG. 14  is a process flow diagram of a method for the disposition of output of one data transformation that then serves as input to two postliminary data transformations according to one aspect. 
         FIG. 15  is a process flow diagram of a method for processing a set of three or more data transformations within a data transformation pipeline where output of the last member transformation of the set serves as input of the first member transformation thereby creating a cyclical relationship according to one aspect. 
         FIG. 16  is a process flow diagram of a method for the receipt and use of streaming data into batch storage and analysis of changes over time, repetition of specific data sequences or the presence of critical data points according to one aspect. 
         FIG. 17  is a process flow diagram for an exemplary method for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 18  is a process flow diagram for an exemplary method for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 19  is a process flow diagram for an exemplary method for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. 
         FIG. 20  is a block diagram illustrating an exemplary hardware architecture of a computing device. 
         FIG. 21  is a block diagram illustrating an exemplary logical architecture for a client device. 
         FIG. 22  is a block diagram showing an exemplary architectural arrangement of clients, servers, and external services. 
         FIG. 23  is another block diagram illustrating an exemplary hardware architecture of a computing device. 
     
    
    
     DETAILED DESCRIPTION 
     The inventor has conceived, and reduced to practice, various systems and methods for predictive analysis of very large data sets using an actor-driven distributed computational graph. 
     One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements. 
     Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical. 
     A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence. 
     When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. 
     The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself. 
     Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art. 
     Definitions 
     As used herein, “graph” is a representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph. The concept of “node” as used herein can be quite general; nodes are elements of a workflow that produce data output (or other side effects to include internal data changes), and nodes may be for example (but not limited to) data stores that are queried or transformations that return the result of arbitrary operations over input data. Nodes can be further qualified by the connection of one or more descriptors or “properties” to that node. For example, given the node “James R,” name information for a person, qualifying properties might be “183 cm tall”, “DOB 08/13/1965” and “speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a “label”. Thus, given a second node “Thomas G,” an edge between “James R” and “Thomas G” that indicates that the two people know each other might be labeled “knows.” When graph theory notation (Graph=(Vertices, Edges)) is applied this situation, the set of nodes are used as one parameter of the ordered pair, V and the set of 2 element edge endpoints are used as the second parameter of the ordered pair, E. When the order of the edge endpoints within the pairs of E is not significant, for example, the edge James R, Thomas G is equivalent to Thomas G, James R, the graph is designated as “undirected.” Under circumstances when a relationship flows from one node to another in one direction, for example James R is “taller” than Thomas G, the order of the endpoints is significant. Graphs with such edges are designated as “directed.” In the distributed computational graph system, transformations within transformation pipeline are represented as directed graph with each transformation comprising a node and the output messages between transformations comprising edges. Distributed computational graph stipulates the potential use of non-linear transformation pipelines which are programmatically linearized. Such linearization can result in exponential growth of resource consumption. The most sensible approach to overcome possibility is to introduce new transformation pipelines just as they are needed, creating only those that are ready to compute. Such method results in transformation graphs which are highly variable in size and node, edge composition as the system processes data streams. Those familiar with the art will realize that transformation graph may assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention. 
     As used herein, “transformation” is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as an example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system. Historically, transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration. Other pipeline configurations are possible. The invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical. 
     A “database” or “data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typically being via some sort of querying interface or language. “Database” may be used to refer to relational database management systems known in the art, but should not be considered to be limited to such systems. Many alternative database or data storage system technologies have been, and indeed are being, introduced in the art, including but not limited to distributed non-relational data storage systems such as Hadoop, column-oriented databases, in-memory databases, and the like. While various aspects may preferentially employ one or another of the various data storage subsystems available in the art (or available in the future), the invention should not be construed to be so limited, as any data storage architecture may be used according to the aspects. Similarly, while in some cases one or more particular data storage needs are described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture. For instance, any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines as is known in the art. Similarly, any single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems known in the art, or in a master/slave arrangement common in the art. These examples should make clear that no particular architectural approaches to database management is preferred according to the invention, and choice of data storage technology is at the discretion of each implementer, without departing from the scope of the invention as claimed. 
     A “data context”, as used herein, refers to a set of arguments identifying the location of data. This could be a Rabbit queue, a .csv file in cloud-based storage, or any other such location reference except a single event or record. Activities may pass either events or data contexts to each other for processing. The nature of a pipeline allows for direct information passing between activities, and data locations or files do not need to be predetermined at pipeline start. 
     A “pipeline”, as used herein and interchangeably referred to as a “data pipeline” or a “processing pipeline”, refers to a set of data streaming activities and batch activities. Streaming and batch activities can be connected indiscriminately within a pipeline. Events will flow through the streaming activity actors in a reactive way. At the junction of a streaming activity to batch activity, there will exist a StreamBatchProtocol data object. This object is responsible for determining when and if the batch process is run. One or more of three possibilities can be used for processing triggers: regular timing interval, every N events, or optionally an external trigger. The events are held in a queue or similar until processing. Each batch activity may contain a “source” data context (this may be a streaming context if the upstream activities are streaming), and a “destination” data context (which is passed to the next activity). Streaming activities may have an optional “destination” streaming data context (optional meaning: caching/persistence of events vs. ephemeral), though this should not be part of the initial implementation. 
     Conceptual Architecture 
       FIG. 1  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  100 , according to one aspect. According to the aspect, a directed computational graph (DCG)  100  may comprise a pipeline orchestrator  101  that may be used to perform the functions of a transformation pipeline software module  561  as described below, with reference to  FIG. 5 . Pipeline orchestrator  101  may spawn a plurality of child pipeline clusters  110   a - b , which may be used as dedicated workers for streamlining parallel processing. In some arrangements, an entire data processing pipeline may be passed to a child cluster  110   a  for handling, rather than individual processing tasks, enabling each child cluster  110   a - b  to handle an entire data pipeline in a dedicated fashion to maintain isolated processing of different pipelines using different cluster nodes  110   a - b . Pipeline orchestrator  101  may provide a software API for starting, stopping, submitting, or saving pipelines. When a pipeline is started, pipeline orchestrator  101  may send the pipeline information to an available worker node  110   a - b , for example using AKKA™ clustering. For each pipeline initialized by pipeline orchestrator  101 , a reporting object with status information may be maintained. Streaming activities may report the last time an event was processed, and the number of events processed. Batch activities may report status messages as they occur. Pipeline orchestrator  101  may perform batch caching using, for example, an IGFS™ caching filesystem. This allows activities  112   a - d  within a pipeline  110   a - b  to pass data contexts to one another, with any necessary parameter configurations. 
     A pipeline manager  111   a - b  may be spawned for every new running pipeline, and may be used to send activity, status, lifecycle, and event count information to the pipeline orchestrator  101 . Within a particular pipeline, a plurality of activity actors  112   a - d  may be created by a pipeline manager  111   a - b  to handle individual tasks, and provide output to data services  120   a - d , optionally using a client API  130  for integration with external services or products. Data models used in a given pipeline may be determined by the specific pipeline and activities, as directed by a pipeline manager  111   a - b . Each pipeline manager  111   a - b  controls and directs the operation of any activity actors  112   a - d  spawned by it. A service-specific client API  130  is separated from any particular activity actor  112   a - d  and may be handled by a dedicated service actor in a separate cluster. A pipeline process may need to coordinate streaming data between tasks. For this, a pipeline manager  111   a - b  may spawn service connectors to dynamically create TCP connections between activity instances  112   a - d . Data contexts may be maintained for each individual activity  112   a - d , and may be cached for provision to other activities  112   a - d  as needed. A data context defines how an activity accesses information, and an activity  112   a - d  may process data or simply forward it to a next step. Forwarding data between pipeline steps may route data through a streaming context or batch context. 
     A client service cluster  130  may operate a plurality of service actors  221   a - d  to serve the requests of activity actors  112   a - d , ideally maintaining enough service actors  221   a - d  to support each activity per the service type. These may also be arranged within service clusters  220   a - d , in an alternate arrangement described below in  FIG. 2 . 
       FIG. 2  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  100 , according to one aspect. According to the aspect, a DCG  100  may be used with a messaging system  210  that enables communication with any number of various services and protocols, relaying messages and translating them as needed into protocol-specific API system calls for interoperability with external systems (rather than requiring a particular protocol or service to be integrated into a DCG  100 ). Service actors  221   a - d  may be logically grouped into service clusters  220   a - d , in a manner similar to the logical organization of activity actors  112   a - d  within clusters  110   a - b  in a data pipeline. A logging service  230  may be used to log and sample DCG requests and messages during operation while notification service  240  may be used to receive alerts and other notifications during operation (for example to alert on errors, which may then be diagnosed by reviewing records from logging service  230 ), and by being connected externally to messaging system  210 , logging and notification services can be added, removed, or modified during operation without impacting DCG  100 . A plurality of DCG protocols  250   a - b  may be used to provide structured messaging between a DCG  100  and messaging system  210 , or to enable messaging system  210  to distribute DCG messages across service clusters  220   a - d  as shown. A service protocol  260  may be used to define service interactions so that a DCG  100  may be modified without impacting service implementations. In this manner, it can be appreciated that the overall structure of a system using an actor-driven DCG  100  operates in a modular fashion, enabling modification and substitution of various components without impacting other operations or requiring additional reconfiguration. 
       FIG. 3  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  100 , according to one aspect. According to the aspect, a variant messaging arrangement may utilize messaging system  210  as a messaging broker using a streaming protocol  310 , transmitting and receiving messages immediately using messaging system  210  as a message broker to bridge communication between service actors  221   a - b  as needed. Alternately, individual services  120   a - b  may communicate directly in a batch context  320 , using a data context service  330  as a broker to batch-process and relay messages between services  120   a - b.    
       FIG. 4  is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph  100 , according to one aspect. According to the aspect, a variant messaging arrangement may utilize a service connector  410  as a central message broker between a plurality of service actors  221   a - b , bridging messages in a streaming context  310  while a data context service  330  continues to provide direct peer-to-peer messaging between individual services  120   a - b  in a batch context  320 . 
     It should be appreciated that various combinations and arrangements of the system variants described above (referring to  FIGS. 1-4 ) may be possible, for example using one particular messaging arrangement for one data pipeline directed by a pipeline manager  111   a - b , while another pipeline may utilize a different messaging arrangement (or may not utilize messaging at all). In this manner, a single DCG  100  and pipeline orchestrator  101  may operate individual pipelines in the manner that is most suited to their particular needs, with dynamic arrangements being made possible through design modularity as described above in  FIG. 2 . 
       FIG. 5  is a block diagram of an exemplary architecture for a system  500  for predictive analysis of very large data sets using a distributed computational graph. According to the aspect, streaming input feeds  510  may be a variety of data sources which may include but are not limited to the internet  511 , arrays of physical sensors  512 , database servers  513 , electronic monitoring equipment  514  and direct human interaction  515  ranging from a relatively few number of participants to a large crowd sourcing campaign. Streaming data from any combinations of listed sources and those not listed may also be expected to occur as part of the operation of the invention as the number of streaming input sources is not limited by the design. All incoming streaming data may be passed through a data filter software module  520  to remove information that has been damaged in transit, is misconfigured, or is malformed in some way that precludes use. Many of the filter parameters may be expected to be preset prior to operation, however, design of the invention makes provision for the behavior of the filter software module  520  to be changed as progression of analysis requires through the automation of the system sanity and retrain software module  563  which may serve to optimize system operation and analysis function. The data stream may also be split into two identical substreams at the data filter software module  520  with one substream being fed into a streaming analysis pathway that includes the transformation pipeline software module  561  of the distributed computational graph  560 . The other substream may be fed to data formalization software module  530  as part of the batch analysis pathway. The data formalization module  530  formats the data stream entering the batch analysis pathway of the invention into data records to be stored by the input event data store  540 . The input event data store  540  can be a database of any architectural type known to those knowledgeable in the art, but based upon the quantity of the data the data store module would be expected to store and retrieve, options using highly distributed storage and map reduce query protocols, of which Hadoop is one, but not the only example, may be generally preferable to relational database schema. 
     Analysis of data from the input event data store may be performed by the batch event analysis software module  550 . This module may be used to analyze the data in the input event data store for temporal information such as trends, previous occurrences of the progression of a set of events, with outcome, the occurrence of a single specific event with all events recorded before and after whether deemed relevant at the time or not, and presence of a particular event with all documented possible causative and remedial elements, including best guess probability information. Those knowledgeable in the art will recognize that while examples here focus on having stores of information pertaining to time, the use of the invention is not limited to such contexts as there are other fields where having a store of existing data would be critical to predictive analysis of streaming data  561 . The search parameters used by the batch event analysis software module  550  are preset by those conducting the analysis at the beginning of the process, however, as the search matures and results are gleaned from the streaming data during transformation pipeline software module  561  operation, providing the system more timely event progress details, the system sanity and retrain software module  563  may automatically update the batch analysis parameters  550 . Alternately, findings outside the system may precipitate the authors of the analysis to tune the batch analysis parameters administratively from outside the system  570 ,  562 ,  563 . The real-time data analysis core  560  of the invention should be considered made up of a transformation pipeline software module  561 , messaging module  562  and system sanity and retrain software module  563 . The messaging module  562  has connections from both the batch and the streaming data analysis pathways and serves as a conduit for operational as well as result information between those two parts of the invention. The message module also receives messages from those administering analyses  580 . Messages aggregated by the messaging module  562  may then be sent to system sanity and retrain software module  563  as appropriate. Several of the functions of the system sanity and retrain software module have already been disclosed. Briefly, this is software that may be used to monitor the progress of streaming data analysis optimizing coordination between streaming and batch analysis pathways by modifying or “retraining” the operation of the data filter software module  520 , data formalization software module  530  and batch event analysis software module  540  and the transformation pipeline module  550  of the streaming pathway when the specifics of the search may change due to results produced during streaming analysis. System sanity and retrain module  563  may also monitor for data searches or transformations that are processing slowly or may have hung and for results that are outside established data stability boundaries so that actions can be implemented to resolve the issue. While the system sanity and retrain software module  563  may be designed to act autonomously and employs computer learning algorithms, according to some arrangements status updates may be made by administrators or potentially direct changes to operational parameters by such, according to the aspect. 
     Streaming data entering from the outside data feeds  510  through the data filter software module  520  may be analyzed in real time within the transformation pipeline software module  561 . Within a transformation pipeline, a set of functions tailored to the analysis being run are applied to the input data stream. According to the aspect, functions may be applied in a linear, directed path or in more complex configurations. Functions may be modified over time during an analysis by the system sanity and retrain software module  563  and the results of the transformation pipeline, impacted by the results of batch analysis are then output in the format stipulated by the authors of the analysis which may be human readable printout, an alarm, machine readable information destined for another system or any of a plurality of other forms known to those in the art. 
       FIG. 6  is a block diagram of a preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph  600 . According to the aspect, streaming input from the data filter software module  520 ,  615  serves as input to the first transformation node  620  of the transformation pipeline. Transformation node&#39;s function is performed on input data stream and transformed output message  625  is sent to transformation node  2   630 . The progression of transformation nodes  620 ,  630 ,  640 ,  650 ,  660  and associated output messages from each node  625 ,  635 ,  645 ,  655 ,  665  is linear in configuration this is the simplest arrangement and, as previously noted, represents the current state of the art. While transformation nodes are described according to various aspects as uniform shape (referring to  FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in a pipeline may be entirely self-contained; certain transformations may involve direct human interaction  630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. Further according to the aspect, individual transformation nodes in one pipeline may represent function of another transformation pipeline. It should be appreciated that the node length of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrary maximum length  640 ,  650 ,  660  as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. It should be further appreciated that there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline  660  may be sent back to messaging software module  562  for predetermined action. 
       FIG. 7  is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph  700 . According to the aspect, streaming input from a data filter software module  520 ,  705  serves as input to the first transformation node  710  of the transformation pipeline. Each transformation node&#39;s function  710 ,  720 ,  730 ,  740 ,  750  is performed on input data stream and transformed output message  715 ,  725 ,  735 ,  745 ,  755 ,  765  is sent to the next step. In this aspect, transformation node  2   720  has a second input stream  760 . The specific source of this input is inconsequential to the operation of the invention and could be another transformation pipeline software module, a data store, human interaction, physical sensors, monitoring equipment for other electronic systems or a stream from the internet as from a crowdsourcing campaign, just to name a few possibilities  760 . Functional integration of a second input stream into one transformation node requires the two input stream events be serialized. The invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring to  FIG. 13 . While transformation nodes are described according to various aspects as uniform shape (referring to  FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in a pipeline may be entirely self-contained; certain transformations may involve direct human interaction  630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. For example, engines may be singletons (composed of a single activity or transformation). Furthermore, leveraging the architecture in this way allows for versioning and functional decomposition (i.e. embedding entire saved workflows as single nodes in other workflows). Further according to the aspect, individual transformation nodes in one pipeline may represent function of another transformation pipeline. It should be appreciated that the node length of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrary maximum length  710 ,  720 ,  730 ,  740 ,  750 , as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. It should be further appreciated that there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline,  750  may be sent back to messaging software module  562  for pre-decided action. 
       FIG. 8  is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph  700 . According to the aspect, streaming input from a data filter software module  520 ,  805  serves as input to the first transformation node  810  of the transformation pipeline. Transformation node&#39;s function is performed on input data stream and transformed output message  815  is sent to transformation node  2   820 . In this aspect, transformation node  2   820  sends its output stream  825 ,  860  to two transformation pipelines  830 ,  840 ,  850 ;  865 ,  875 . This allows the same data stream to undergo two disparate, possibly completely unrelated, analyses  825 ,  835 ,  845 ,  855 ;  860 ,  870 ,  880  without having to duplicate the infrastructure of the initial transform manipulations, greatly increasing the expressivity of the invention over current transform pipelines. Functional integration of a second output stream from one transformation node  820  requires that the two output stream events be serialized. The invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring to  FIG. 14 . While transformation nodes are described according to various aspects as uniform shape (referring to  FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in pipelines, which may be entirely self-contained; certain transformations may involve direct human interaction  630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities. Further according to the aspect, individual transformation nodes in one pipeline may represent function of another transformation pipeline. It should be appreciated that the node number of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrary maximum length  810 ,  820 ,  830 ,  840 ,  850 ;  865 ,  875  as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. Further according to the aspect, there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline  850  may be sent back to messaging software module  562  for contemporary enabled action. 
       FIG. 9  is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph  700 . According to the aspect, streaming input from a data filter software module  520 ,  905  serves as input to the first transformation node  910  of the transformation pipeline. Transformation node&#39;s function may be performed on an input data stream and transformed output message  915  may then be sent to transformation node  2   920 . Likewise, once the data stream is acted upon by transformation node  2   920 , its output is sent to transformation node  3   930  using its output message  925  In this aspect, transformation node  3   930  sends its output stream back  935  to transform node  1   910  forming a cyclical relationship between transformation nodes  1   910 , transformation node  2   920  and transformation node  3   930 . Upon the achievement of some gateway result, the output of cyclical pipeline activity may be sent to downstream transformation nodes within the pipeline  940 ,  945 . The presence of a generalized cyclical pathway construct allows the invention to be used to solve complex iterative problems with large data sets involved, expanding ability to rapidly retrieve conclusions for complicated issues. Functional creation of a cyclical transformation pipeline requires that each cycle be serialized. The invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring to  FIG. 15 . While transformation nodes are described according to various aspects as uniform shape (referring to  FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will appreciate that certain transformations in pipelines, may be entirely self-contained; certain transformations may involve direct human interaction  630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; still other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities. Further according to the aspect, individual transformation nodes in one pipeline may represent the cumulative function of another transformation pipeline. It should be appreciated that the node number of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrary maximum length  910 ,  920 ,  930 ,  940 ,  950 ,  960 ;  965 ,  975  as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. It should be further appreciated that there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline  955  may be sent back to messaging software module  562  for concomitant enabled action. 
     Description of Method Aspects 
       FIG. 17  is a process flow diagram for an exemplary method  1700  for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. In an initial step  1701 , a DCG  100  may define a plurality of data contexts for each of a plurality of actions within a data pipeline. These contexts each in turn define  1702  how their respective activities may interact with data in the pipeline. Any given activity may, based on the defined data context, either process data  1703  (generally by performing any of a number of data transformations as described previously, referring to  FIG. 5 ), or by forwarding at least a portion of the data onward to the next step in the pipeline  1704 , which may in turn be another activity with a defined context determining how it handles the forwarded data. In this manner, operation may continue in a directed fashion wherein each agent has clearly-defined capabilities and data progresses toward the end of the pipeline according to the established definitions. 
       FIG. 18  is a process flow diagram for an exemplary method  1800  for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. In an initial step  1801 , a DCG  100  defines a data context for an activity, determining how the activity handles data that is passed to it. The activity then, according to the context definition, receives data and forwards it  1802  to the next step in the data pipeline. The data is then  1803  passed to a messaging system  210  that acts as a central data broker, receiving the data and passing it on  1804  to the next activity actor in the pipeline, which may then have a context assigned  1801  so that operation continues as shown. This allows brokered, centralized messaging between activity actors within data pipelines, using a messaging system  210  to bridge communication between different actors. 
       FIG. 19  is a process flow diagram for an exemplary method  1900  for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect. In an initial step  1901 , a pipeline orchestrator  101  may spawn a plurality of service connectors  410 , each of which is configured to bridge communication between two or more service actors  221   a - d  for peer-to-peer messaging without using a messaging system  210  as a central broker. When a service actor  221   a - d  forwards data  1902  to another service actor  221   a - d , an appropriate service connector  410  may receive the data and perform any necessary interpretation or modification to bridge service protocols  1903  between the source and destination service actors  221   a - d . The modified data may then be provided  1904  to the destination service actor  221   a - d . Service connectors may be created and destroyed as needed without impacting other operations, producing a scalable and on-the-fly peer-to-peer messaging system that does not rely on any centralized broker to relay messages and permits direct communication between actors. 
       FIG. 10  is a process flow diagram of a method  1000  for predictive analysis of very large data sets using the distributed computational graph. One or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, may be received by system  1001 . The received stream is filtered  1002  to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors. Filtered data may be split into two identical streams at this point (second stream not depicted for simplicity), wherein one substream may be sent for batch processing  1600  while another substream may be formalized  1003  for transformation pipeline analysis  1004 ,  561 ,  600 ,  700 ,  800 ,  900  and retraining  1005 . Data formalization for transformation pipeline analysis acts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples. The formalized data stream may be subjected to one or more transformations. Each transformation acts as a function on the data and may or may not change the data. Within the invention, transformations working on the same data stream where the output of one transformation acts as the input to the next are represented as transformation pipelines. While the great majority of transformations in transformation pipelines receive a single stream of input, modify the data within the stream in some way and then pass the modified data as output to the next transformation in the pipeline, the invention does not require these characteristics. According to the aspect, individual transformations can receive input of expected form from more than one source  1300  or receive no input at all as would a transformation acting as a timestamp. According to the aspect, individual transformations, may not modify the data as would be encountered with a data store acting as a queue for downstream transformations  1303 ,  1305 ,  1405 ,  1407 ,  1505 . According to the aspect, individual transformations may provide output to more than one downstream transformations  1400 . This ability lends itself to simulations where multiple possible choices might be made at a single step of a procedure all of which need to be analyzed. While only a single, simple use case has been offered for each example, in each case, that example was chosen for simplicity of description from a plurality of possibilities, the examples given should not be considered to limit the invention to only simplistic applications. Last, according to the invention, transformations in a transformation pipeline backbone may form a linear, a quasi-linear arrangement or may be cyclical  1500 , where the output of one of the internal transformations serves as the input of one of its antecedents allowing recursive analysis to be run. The result of transformation pipeline analysis may then be modified by results from batch analysis of the data stream  1600  and output  1006  in format predesigned by the authors of the analysis with could be human readable summary printout, human readable instruction printout, human-readable raw printout, data store, or machine encoded information of any format known to the art to be used in further automated analysis or action schema. 
       FIG. 11  is a process flow diagram of a method  1100  for an aspect of modeling the transformation pipeline module  561  of the invention as a directed graph using graph theory. According to the aspect, the individual transformations  1102 ,  1104 ,  1106  of the transformation pipeline t 1  . . . t n  such that each t i  T are represented as graph nodes. Transformations belonging to T are discrete transformations over individual datasets d i , consistent with classical functions. As such, each individual transformation t j , receives a set of inputs and produces a single output. The input of an individual transformation t i , is defined with the function in: t i  d 1  . . . d k  such that in(t i )={d 1  . . . d k ) and describes a transformation with k inputs. Similarly, the output of an individual transformation is defined as the function out: t i  [ld 1 ] to describe transformations that produce a single output (usable by other transformations). A dependency function can now be defined such that dep(t a ,t b ) out(t a )in(t b ) The messages carrying the data stream through the transformation pipeline  1101 ,  1103 ,  1105  make up the graph edges. Using the above definitions, then, a transformation pipeline within the invention can be defined as G=(V,E) where message(t 1 , t 2  . . . t (n-1) , t n )V and all transformations t 1  . . . t n  and all dependencies dep(t i ,t j )E  1107 . 
       FIG. 12  is a process flow diagram of a method  1200  for one aspect of a linear transformation pipeline  1201 . This is the simplest of configurations as the input stream is acted upon by the first transformation node  1202  and the remainder of the transformations within the pipeline are then performed sequentially  1202 ,  1203 ,  1204 ,  1205  for the entire pipeline with no introduction of new data internal to the initial node or splitting output stream prior to last node of the pipeline  1205 , which then sends the results of the pipeline  1206  as output. This configuration is the current state of the art for transformation pipelines and is the most general form of these constructs. Linear transformation pipelines require no special manipulation to simplify the data pathway and are thus referred to as non-decomposable. The example depicted in this diagram was chosen to convey the configuration of a linear transformation pipeline and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention. 
       FIG. 13  is a process flow diagram of a method  1300  for one aspect of a transformation pipeline where one transformation node  1307  in a transformation pipeline receives data streams from two source transformation nodes  1301 . The invention handles this transformation pipeline configuration by decomposing or serializing the input events  1302 - 1303 ,  1304 - 1305  heavily relying on post transformation function continuation. The results of individual transformation nodes  1302 ,  1304  just antecedent to the destination transformation node  1306  and placed into a single specialized data storage transformation node  1303 ,  1305  (shown twice as process occurs twice). The combined results then retrieved from the data store  1306  and serve as the input stream for the transformation node within the transformation pipeline backbone  1307 ,  1308 . The example depicted in this diagram was chosen to convey the configuration of transformation pipelines with individual transformation nodes that receive input from two source nodes  1302 ,  1304  and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention. One knowledgeable in the art will realize the great number of permutations and topologies possible, especially as the invention places no design restrictions on the number of transformation nodes receiving input from greater than one sources or the number sources providing input to a destination node. 
       FIG. 14  is a process flow diagram of a method  1400  for one aspect of a transformation pipeline where one transformation node  1403  in a transformation pipeline receives input data from a transformation node  1402 , and sends output data stream to two destination transformation nodes  1401 ,  1406 ,  1408  in potentially two separate transformation pipelines. The invention handles this transformation pipeline configuration by decomposing or serializing the output events  1404 ,  1405 - 1406 ,  1407 - 1408 . The results of the source transformation node  1403  just antecedent to the destination transformation nodes  1406  and placed into a single specialized data storage transformation node  1404 ,  1405 ,  1407  (shown three times as storage occurs and retrieval occurs twice). The results of the antecedent transformation node may then be retrieved from a data store  1404  and serves as the input stream for the transformation nodes two downstream transformation pipeline  1406 ,  1408 . The example depicted in this diagram was chosen to convey the configuration of transformation pipelines with individual transformation nodes that send output streams to two destination nodes  1406 ,  1408  and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention. One knowledgeable in the art will realize the great number of permutations and topologies possible, especially as the invention places no design restrictions on the number of transformation nodes sending output to greater than one destination or the number destinations receiving input from a source node. 
       FIG. 15  is a process flow diagram of a method  1500  for one aspect of a transformation pipeline where the topology of all or part of the pipeline is cyclical  1501 . In this configuration, the output stream of one transformation node  1504  acts as an input of an antecedent transformation node within the pipeline  1502  serialization or decomposition linearizes this cyclical configuration by completing the transformation of all of the nodes that make up a single cycle  1502 ,  1503 ,  1504  and then storing the result of that cycle in a data store  1505 . That result of a cycle is then reintroduced to the transformation pipeline as input  1506  to the first transformation node of the cycle. As this configuration is by nature recursive, special programming to unfold the recursions was developed for the invention to accommodate it. The example depicted in this diagram was chosen to convey the configuration of transformation pipelines with individual transformation nodes that for a cyclical configuration  1501 ,  1502 ,  1503 ,  1504  and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention. One knowledgeable in the art will realize the great number of permutations and topologies possible, especially as the invention places no design restrictions on the number of transformation nodes participating in a cycle nor the number of cycles in a transformation pipeline. 
       FIG. 16  is a process flow diagram of a method  1600  for one aspect of the batch data stream analysis pathway which forms part of the invention and allows streaming data to be interpreted with historic context. One or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, is received by the system  1601 . The received stream may be filtered  1602  to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors. Data formalization  1603  for batch analysis acts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples. The filtered and formalized stream is then added to a distributed data store  1604  due to the vast amount of information accrued over time. The invention has no dependency for specific data stores or data retrieval model. During transformation pipeline analysis of the streaming pipeline, data stored in the batch pathway store can be used to track changes in specifics of the data important to the ongoing analysis over time, repetitive data sets significant to the analysis or the occurrence of critical points of data  1605 . The functions of individual transformation nodes  620  may be saved and can be edited also all nodes of a transformation pipeline  600  keep a summary or summarized view (analogous to a network routing table) of applicable parts of the overall route of the pipeline along with detailed information pertaining to adjacent two nodes. This framework information enables steps to be taken and notifications to be passed if individual transformation nodes  640  within a transformation pipeline  600  become unresponsive during analysis operations. Combinations of results from the batch pathway, partial and streaming output results from the transformation pipeline, administrative directives from the authors of the analysis as well as operational status messages from components of the distributed computational graph are used to perform system sanity checks and retraining of one or more of the modules of the system  1606 . These corrections are designed to occur without administrative intervention under all but the most extreme of circumstances with deep learning capabilities present as part of the system manager and retrain module  563  responsible for this task. 
     Hardware Architecture 
     Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card. 
     Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments). 
     Referring now to  FIG. 20 , there is shown a block diagram depicting an exemplary computing device  10  suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device  10  may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device  10  may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. 
     In one aspect, computing device  10  includes one or more central processing units (CPU)  12 , one or more interfaces  15 , and one or more busses  14  (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU  12  may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device  10  may be configured or designed to function as a server system utilizing CPU  12 , local memory  11  and/or remote memory  16 , and interface(s)  15 . In at least one aspect, CPU  12  may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. 
     CPU  12  may include one or more processors  13  such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors  13  may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device  10 . In a particular aspect, a local memory  11  (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU  12 . However, there are many different ways in which memory may be coupled to system  10 . Memory  11  may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU  12  may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices. 
     As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit. 
     In one aspect, interfaces  15  are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces  15  may for example support other peripherals used with computing device  10 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces  15  may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity AN hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). 
     Although the system shown in  FIG. 20  illustrates one specific architecture for a computing device  10  for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors  13  may be used, and such processors  13  may be present in a single device or distributed among any number of devices. In one aspect, a single processor  13  handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided. In various aspects, different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below). 
     Regardless of network device configuration, the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block  16  and local memory  11 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory  16  or memories  11 ,  16  may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein. 
     Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language). 
     In some aspects, systems may be implemented on a standalone computing system. Referring now to  FIG. 21 , there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system. Computing device  20  includes processors  21  that may run software that carry out one or more functions or applications of aspects, such as for example a client application  24 . Processors  21  may carry out computing instructions under control of an operating system  22  such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services  23  may be operable in system  20 , and may be useful for providing common services to client applications  24 . Services  23  may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system  21 . Input devices  28  may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices  27  may be of any type suitable for providing output to one or more users, whether remote or local to system  20 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory  25  may be random-access memory having any structure and architecture known in the art, for use by processors  21 , for example to run software. Storage devices  26  may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to  FIG. 20 ). Examples of storage devices  26  include flash memory, magnetic hard drive, CD-ROM, and/or the like. 
     In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to  FIG. 22 , there is shown a block diagram depicting an exemplary architecture  30  for implementing at least a portion of a system according to one aspect on a distributed computing network. According to the aspect, any number of clients  33  may be provided. Each client  33  may run software for implementing client-side portions of a system; clients may comprise a system  20  such as that illustrated in  FIG. 21 . In addition, any number of servers  32  may be provided for handling requests received from one or more clients  33 . Clients  33  and servers  32  may communicate with one another via one or more electronic networks  31 , which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other). Networks  31  may be implemented using any known network protocols, including for example wired and/or wireless protocols. 
     In addition, in some aspects, servers  32  may call external services  37  when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services  37  may take place, for example, via one or more networks  31 . In various aspects, external services  37  may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications  24  are implemented on a smartphone or other electronic device, client applications  24  may obtain information stored in a server system  32  in the cloud or on an external service  37  deployed on one or more of a particular enterprise&#39;s or user&#39;s premises. 
     In some aspects, clients  33  or servers  32  (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks  31 . For example, one or more databases  34  may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases  34  may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various aspects one or more databases  34  may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some aspects, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art. 
     Similarly, some aspects may make use of one or more security systems  36  and configuration systems  35 . Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security  36  or configuration system  35  or approach is specifically required by the description of any specific aspect. 
       FIG. 23  shows an exemplary overview of a computer system  40  as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system  40  without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU)  41  is connected to bus  42 , to which bus is also connected memory  43 , nonvolatile memory  44 , display  47 , input/output (I/O) unit  48 , and network interface card (NIC)  53 . I/O unit  48  may, typically, be connected to keyboard  49 , pointing device  50 , hard disk  52 , and real-time clock  51 . NIC  53  connects to network  54 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part of system  40  is power supply unit  45  connected, in this example, to a main alternating current (AC) supply  46 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices). 
     In various aspects, functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components. 
     The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents.