Domain specific languages and complex event handling for mobile health machine intelligence systems

Systems and methods can support complex event handling. A complex event handler can receive a current event. The current event may be stored to an event log. The current event may be matched against rule conditions within a rule implementation system. Prior events may be identified, within the rule implementation system, upon which the matched rule conditions also depend. The event log may be searched for the identified prior events. Prior event parameters, corresponding to the identified prior events, may be retrieved from the event log. The rule conditions may be evaluated in view of the current event and the retrieved prior event parameters. Actions may be executed that correspond to the rule conditions triggered in response to the evaluating. Domain specific language expression may be received, processed, and incorporated as rules and facts into the rule implementation system.

BACKGROUND

Complex events, such as those encountered within healthcare enterprises, may be defined as sequences of events in time that are dynamically coupled together through explicit or implied relationships and interdependencies. The implied interdependencies may be subtle and may exist through multiple layers of indirection. Evaluating automated rules upon such complex event conditions is generally extremely challenging. When the combined events and time sequences making up the complex events are subtly related or when their relationships depend upon multiple layers of indirection, the challenges increase significantly, effectively becoming intractable to traditional approaches.

Data associated with complex events may be weakly structured or even unstructured. Such datasets often attempt to aggregate frequently changing information from many diverse sources having different structures and formats. Operating rules associated with such data may also be complex and highly dynamic.

There are numerous challenges to implementing intelligent rule systems to handle complex events that may be made up of loosely interrelated time sequences of events that each may involve complex datasets of information and their associated production rules.

There is a need in the art for complex event handling technology that can safely and efficiently support very large, unstructured datasets of interrelated sequences of occurrences, each of which may involve numerous parameters, the values and relationships of which may frequently change in real time. Such solutions would be particularly applicable in large, information-driven enterprises such as healthcare facilities or systems thereof.

SUMMARY

In certain example embodiments described herein, methods and systems can support complex event handling. A complex event handler can receive a current event. The current event may be stored to an event log. The current event may be matched against rule conditions within a rule implementation system. Prior events may be identified, within the rule implementation system, upon which the matched rule conditions also depend. The event log may be searched for the identified prior events. Prior event parameters, corresponding to the identified prior events, may be retrieved from the event log. The rule conditions may be evaluated in view of the current event and the retrieved prior event parameters. Actions may be executed that correspond to the rule conditions triggered in response to the evaluating. Domain specific language expression may be received, processed, and incorporated as rules and facts into the rule implementation system.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

The methods and systems described herein enable efficient complex event handling. The complex event handler can leverage a rule engine or rule implementation system. The rule implementation system can operate directly within, or in conjunction with, a graph database system. Events may be stored to an event log of prior events. Certain prior events may be retrieved from the event log when evaluating newly received events. Rule conditions associated with the complex event handler may be evaluated in view of the newly received events as well as prior events retrieved form the event log. Actions may be executed when the rule conditions are appropriately triggered. Domain specific language expression may be received, processed, and incorporated as rules and facts into the rule implementation system. The events, actions, and domain specific language expressions may be associated with various communication occurrences within a healthcare enterprise. Examples of such communication occurrences may include messaging, alarms, voice calls, staff responsibility changes, and so forth.

The functionality of the various example embodiments will be explained in more detail in the following description, read in conjunction with the figures illustrating the program flow. Turning now to the drawings, in which like numerals indicate like (but not necessarily identical) elements throughout the figures, example embodiments are described in detail.

Example System Architectures

FIG. 1is a block diagram depicting a complex event handler110associated with a rule implementation system120and an event log130in accordance with one or more embodiments presented herein. The complex event handler110can execute in association with a complex event handler module140. The complex event handler110can receive incoming events from one or more event generators150. The complex event handler110can store incoming events to the event log130. The complex event handler110can match incoming events against rules within the rule implementation system120. Triggering of the rules within the rule implementation system120may also be dependent upon prior events stored in the event log130. Triggering of rules can generate resultant events and actions180. One or more domain modules160may provide rules for use within the rule implementation system120. A domain specific language (DSL)170may be used in conjunction with each of the domain modules160.

The complex event handler110and associated complex event handler module140can receive incoming events from one or more event generators150. The complex event handler110can support making decisions based one or more incoming current in combination with one or more prior events. The decisions may be determined by the rule implementation system120based upon incoming events along with prior events stored within the event log130. In addition to the rule implementation system120triggering rules based upon incoming events, rules associated with the complex event handler110may further depend upon prior events retrieved form the event log130. The condition of such a rule may be a function of both current incoming events as well as prior events.

According to one or more examples related to an enterprise messaging system, a current event may be generated when user Adam sends a first message to user Beth, both within the same organization. The complex event handler110may store, within the event log130, an event indicating that the first message was sent. Subsequently, user Beth's role within the organization may change. For example, Beth may have been in charge of deliveries on the day when the first message was sent, but is now in charge of customer service. The change in Beth's role may generate an event that may be stored by the complex event handler110into the event log130. At a future time, user Adam may send a second message to user Beth. Sending the message may generate an event, which is received by the complex event handler110. The complex event handler110may trigger a rule within the rule implementation system120. The complex rule may be a function of both the incoming event (a second message was sent) and two prior events (a first message was sent and the role associated with the receiver has changed). The two prior events may be retrieved from the event log130by the rule implementation system120while testing the rule. The rule may indicate that if user Adam sends a first message to user Beth, user Beth's role changes, and then user Adam sends a second message to user Beth, one or more actions should alert one or both of the users Adam and Beth to these details. For example, the rule implementation system120may generate an action to notify user Adam that user Beth has changed roles within the organization since their last interaction. The rule implementation system120may also generate an action to notify user Beth that user Adam sent a message and has been notified of Beth's new role. User Beth may also be provided the opportunity to redirect the second message to the new person in her prior role, if appropriate. Furthermore, user Beth may be provided with an option to ignore the message or to simply continue the conversation with user Adam.

It should be appreciated that rules associated with the complex event handler110can act on multiple incoming current events from multiple sources as well as depending upon multiple prior events retrieved form the event log130. A rule may be a function of any number of incoming current events and any number of prior events.

The rule implementation system120may be an example of a rule engine system, production system, or production rule system. These are often used in artificial intelligence, automated planning, expert systems, action selection systems, or other such machine-based knowledge or decision systems.

The rule implementation system120may implement a plurality of rules. Each rule may contain a condition that when matched triggers a resultant action. The rule implementation system120may receive events that can be pattern-matched against the rule conditions. When a rule condition is properly matched to incoming events and/or stored prior events, the rule action associated with the condition may be triggered. The rule condition may be specifically formatted to match the formatting of the event data associated with incoming events and/or prior events stored within the event log130. According to various embodiments, the rule implementation system120may be based upon a graph database.

The event log130can store events received by the complex event handler110. The event log130can store events within a computerized database, a computer memory, a computer data storage system, a cloud-based data store, any other data storage mechanisms, or any combinations thereof. Each events may be time-stamped and/or date-stamped in order to establish when the complex event handler110received each events. According to various embodiments, stored prior events may be removed from the event log130when they reach a specified expiration date. Similarly, a rule may trigger indicating removal of an event entry from the event log130. A given rule may also indicate a specified time window of prior events to be considered when evaluating the condition of the rule.

The resultant events and actions180may be generated as a result of triggering rules within the rule implementation system120in association with the complex event handler110. When incoming and/or stored events match the patterns of a rule condition within the rule implementation system120, that rule may be triggered generating an action. The action may include the generation of, or cause the generation of, a new event. The complex event handler110may again process that new event, which may include storing the event in the event log130and/or trigger rules within the rule implementation system120that match the new event. The actions may also impact or instruct operations of one or more other systems.

The event generator150may generally be any source of events that may be processed by the complex event handler110. One example event generator150according to an example enterprise messaging system may be the transmission of a message from one user to another user. Another example event generator150according to an example healthcare enterprise system may the generation of a notification to a member of the nursing staff when an alarm is generated by a piece of patient instrumentation.

A domain specific language170may be specified for use within a particular knowledge domain. Examples of knowledge domains in a healthcare context may include pharmacy, nursing, radiology, cardiology, and so forth. The domain specific language170may be used in conjunction with one or more of the domain modules160to support specifying rules for use within the rule implementation system120. Domain users and/or domain experts may provide rules expressed using their respective domain specific language170. The rules may be provided in plain text, speech-to-text, or some other format. The rules may also be provided through a graphical user interface, wherein the rules may be constructed using elements of the DSL170.

The domain module160can support inputs using the domain specific language170for defining rules within the rule implementation system120. Each given domain may be associated with its own domain specific language170. Because the domain specific language170may be oriented toward users within a particular domain, and may be generally quite specific to that domain, the domain experts generally do not need to be familiar with operational details of the complex event handler110or the rule implementation system120to provide inputs using the domain specific language170. A condition generator functionality associated with the domain module160may process inputs expressed in the domain specific language170to generate a rule condition. Such inputs may be processed into a rule formatted in a fashion that simplifies insertion as a rule into the rule implementation system120. Similarly, an action generator functionality associated with the domain module160may process inputs expressed in the domain specific language170to extract resultant actions associated with the rule being triggered within the rule implementation system120.

The complex event handler110, the rule implementation system120, systems associated with the event generators150, systems associated with the domain module160, systems associated with the resultant events and actions180, or any other systems associated with the technology presented herein may be any type of computing machine such as, but not limited to, those discussed in more detail with respect toFIG. 5. Furthermore, any modules (such as the complex event handler module140, or the domain module160) associated with any of these computing machines or any other modules (scripts, web content, software, firmware, or hardware) associated with the technology presented herein may by any of the modules discussed in more detail with respect toFIG. 5. The computing machines discussed herein may communicate with one another as well as other computer machines or communication systems over one or more networks such as the network technology discussed with respect toFIG. 5.

FIG. 2is a block diagram depicting a rule implementation system120associated with a graph database240in accordance with one or more embodiments presented herein. This example rule implementation system120can operate within the complex event handler110to match incoming and prior events to trigger resultant events and actions180. The rule implementation system120can operate on rules250and facts260to respond to questions265or to establish additional rules250, additional facts260, or trigger actions270. It should be appreciated that in this context, the facts260may be incoming and prior events associated with the complex event handler110. The rule implementation system120can include various modules such as an interface module210, a controller module220, and a database translation module230. The rule implementation system120can operate directly within, or in conjunction with, a graph database240. Rules250, facts260, questions265or actions270associated with the rule implementation system120may be communicated directly to or from the rule implementation system120. These communications may also occur in conjunction with one or more networks280.

A rule250associated with the rule implementation system120generally consists of two components: a condition and a result. The condition and result of a rule250may be said to have an “if, then” relationship. If the condition obtains, then the rule250is triggered causing the result to be fired. The condition generally obtains by the existence, or truth, of one or more facts260. For example, a condition could be, “if it is Wednesday,” which would trigger when presented with the fact that the day of the week is Wednesday. The result of firing a rule250generally involves one or more other facts260or the generation of an action270. Firing of the rule250can assert or retract one or more other facts260. Firing of the rule250can also generate one or more actions270as output of the rule implementation system120. Asserting or retracting one or more facts260can alter the state of the rule implementation system120, which may then affect the conditions of other rules250causing them to fire. Altering the state of the rule implementation system120may also affect the responses provided to questions265processed by the rule implementation system120.

The graph database240can provide the working memory of the rule implementation system120. This working memory can store information comprising the current state or knowledge of the rule implementation system120. The graph database240can store information in a graph structure where nodes are interconnected by edges. The nodes generally represent entities or things such as individuals, departments, or equipment. Edges generally connect nodes representing the relationship between them. Each node may be associated with one or more properties, which may contain information pertinent to that respective node.

The interface module210of the rule implementation system120can provide an application programming interface (API), scripting interface, domain-specific language (DSL)170, or other mechanism for interfacing to the rule implementation system120. The interface module210may support transactions with other modules, systems, or entities associated with the rule implementation system120. These transactions may involve providing rules250or facts260to the rule implementation system120, receiving and reacting to questions265, retrieving rules250or facts260from the rule implementation system120, or receiving actions270or information associated with actions270from the rule implementation system120.

The controller module220of the rule implementation system120can process control operations of the rule implementation system120. Examples of the operations may include executing queries, starting/stopping rule evaluation, and so forth.

The database translation module230of the rule implementation system120can provide low-level interactions with the graph database240. These interactions may include performing queries, handling fact node assertion or retraction, database administrative tasks, and so forth.

It should be appreciated that in addition to the interface module210, controller module220, database translation module230, and graph database240, the rule implementation system120may include or interface with other modules. It should also be appreciated that any two or more of these modules may be combined into the same module or modules. Furthermore, any one or more of these modules may split functionally, or load share, between two or more modules or execute on two or more computing machines. Any such modules may operate in a parallel, distributed, or networked fashion without departing from the spirit or scope of the technology presented herein.

Within the rule implementation system120, a rule-fact graph may be a graph stored in the graph database240. The rule-fact graph can include various nodes connected by edges. The rule-fact graph within the graph database240can serve as the working memory of the rule implementation system120. This working memory can store information comprising the current state or knowledge of the rule implementation system120. This information can include various facts260, which may be stored as nodes connected by edges representing relationships between the nodes such that the nodes and edges together can encode the rules250.

Within the rule implementation system120, rule interpretation may be provided by executing queries on the rule-fact graph within the graph database240. The queries may be associated with questions265posed to the rule implementation system120. The queries and questions265may relate to incoming and prior events. The queries can pattern-match facts260against the encoded rules250determining which of the rules250to apply. The condition portion of each rule250may be tested against the current state of the working memory by pattern matching against the rule-fact graph. The consequent results can update the knowledge represented by the rule-fact graph by asserting or retracting information. The consequent results can also update the event knowledge by generating an event that will in turn be stored to the event log130. Rule interpretation can execute forward chaining when updated information affects other rules250implied within the rule-fact graph. The results consequent to the condition can also trigger actions270which may include generating new events. Queries of the rule-fact graph within the graph database240can leverage a schema-free storage structure supporting index-free adjacency where any node may be directly linked (by one or more edges) to its adjacent nodes such that index lookups are unnecessary.

Within the rule implementation system120, representing rules250within the graph database240provides the ability to establish adjacencies between any nodes (and thus relationships between facts260) without having to rebuild schemas or introduce associating tags or indices. Accordingly, rules250may be changed or introduced anew within the rule-fact graph very efficiently and with reduced effort or overhead. Changing this rule representation in a traditional database for a huge number of entries can be extremely time consuming and nearly impossible, if such rules changed as frequently as they might in certain complex enterprise environments such as health care information systems.

Some example benefits to the rule implementation system120leveraging the graph database240relate to it being more suitable for managing ad hoc and changing data with evolving schemas. For example, in a healthcare enterprise such as a hospital department, the number of nurses may vary from shift to shift, as might the number of patients. Furthermore, the roles of the nurses may change and the assignments relating the nurses to specific patients may also change. When additional facts260and rules250associated with the hospital department come into play, the rule-fact graph can quickly become very large even while being dynamic (rapidly changing). For example, the additional events, facts260, and rules250may relate to procedures, medications, food service, radiology, tests, specialist referrals, admit/discharges, code emergencies, monitoring alarms, and so forth. Other example additional events, facts260, and rules250may relate to routing messages, alarms, notifications, voice calls, text messages, or other communication modalities to one or more nurses (or wireless mobile devices associated therewith) within a healthcare enterprise. This type of information is also well addressed by the schema-less structure support of the rule implementation system120and its associated graph database240.

Another example benefit to the rule implementation system120leveraging the graph database240stems from the native pattern matching capabilities of the graph database240. Such native pattern matching support can provide for significant increases in efficiencies related to rule interpretation and associated queries.

Yet another example benefit to the rule implementation system120leveraging the graph database240relates to the disk-backed performance of the rule implementation system120and its associated graph database240. Disk-backed operation can provide persistence of state by maintain information within the graph database240. Disk-backed operation can also overcome working memory limitations encountered in operating on a rule-fact graph of ever increasing size and complexity. It should be appreciated that this “working knowledge” associated with the rule-fact graph maintains rules and is in contrast to the prior event knowledge stored in the event log130.

The rule implementation system120, systems associated with the rules250, facts260, or actions270, systems associated with the graph database240, or any other systems associated with the technology presented herein may be any type of computing machine such as, but not limited to, those discussed in more detail with respect toFIG. 5. Furthermore, any modules (such as the interface module210, controller module220, or database translation module230) associated with any of these computing machines or any other modules (scripts, web content, software, firmware, or hardware) associated with the technology presented herein may by any of the modules discussed in more detail with respect toFIG. 5. The computing machines discussed herein may communicate with one another as well as other computer machines or communication systems over one or more networks such as network280. The network280may include any type of data or communications network including any of the network technology discussed with respect toFIG. 5.

Example Processes

According to methods and blocks described in the embodiments presented herein, and, in alternative embodiments, certain blocks can be performed in a different order, in parallel with one another, omitted entirely, and/or combined between different example methods, and/or certain additional blocks can be performed, without departing from the scope and spirit of the invention. Accordingly, such alternative embodiments are included in the invention described herein.

FIG. 3is a block flow diagram depicting a method300for providing domain specific rules for complex event handling in accordance with one or more embodiments presented herein.

In block310, a domain module160can support applying knowledge from domain experts into complex event handler110. Leveraging a DSL170associated with a domain module160, rules250may be implemented within the rule implementation system120by domain experts. The domain experts may use jargon or terminology specific to their specific domain of expertise. Certain example domains, within a healthcare context, may include pharmacy, radiology, skilled nursing, cardiology, and so forth.

In block320, the domain module160can receive an expression provided by a domain user in a domain specific language170. The domain user may provide the input as a text expression or an assemblage of textual elements selected according to a graphical user interface. The domain user may also provide the input as an audible voice instruction that may be converted to a textual input.

In block330, the domain module160can process the received expression to extract a rule condition. The rule condition may be a function of any number of present events, any number of prior events, or a combination thereof. The received expression may be processed according to structures, definitions, and syntax of the domain specific language170. For example, a domain specific language170associated with the domain of pharmacy may specify language structures and syntaxes for times and dosages of pharmaceutical prescriptions. Applying these structures to the provided expression can support the appropriate extraction of the rule condition intended by the domain user providing the input.

In block340, the domain module160can process the received expression to extract a rule action270. The rule action270may be associated with a rule condition. The rule action270may be executed when the associated rule condition is triggered. The received expression may be processed according to structures, definitions, and syntax of the domain specific language170. For example, a domain specific language170associated with the domain of pharmacy may specify language structures and syntaxes for times and dosages of pharmaceutical prescriptions. Applying these structures to the provided expression can support the appropriate extraction of the rule action270intended by the domain user providing the input.

In block350, the rule condition and the rule action270can be incorporated into the rule implementation system120as a new rule250. The rule condition of the new rule250may be a function of incoming and/or stored events. When the rule implementation system120matches incoming and/or stored events to the rule condition, the rule action270of the new rule250may be triggered.

In block360, the complex event handler110can support leveraging a rule implementation system120associated with a graph database240. The rule implementation system120can convert a received rule250to a format suitable for insertion into the graph database240. For example, the rule250may be converted to one or more nodes and one or more edges. The database translation module230of the rule implementation system120can insert the converted rule250into the graph database240. Rules250may be inserted by adding one or more nodes to the graph database240and then forming one or more edges between newly inserted nodes or existing nodes. These edges can establish relationships between the nodes, which may represent facts260. For example, the rule “all ravens are black,” might be represented in the graph database240by creating nodes for “ravens” and “black” followed by connecting those nodes with an edge having the property or implication of “is” or “are.” Were this same example rule received while nodes for “ravens” and “black” already in existence, then the rule may be entered by merely forming the relationship edge between those two nodes.

FIG. 4is a block flow diagram depicting a method400for complex event handling in accordance with one or more embodiments presented herein. In block410, the complex event handler110can receive an event. The event may be a current incoming event arriving from an event generator150.

In block420, the complex event handler110can store the received event to the event log130. The event log130can maintain stored events to be queried for future rule testing. A time/date stamp and other identifiers may be applied to the events that are entered into the event log130.

In block430, the received event may be matched to rule conditions associated with the rule implementation system120. A rule250with a condition matching the received event may be triggered by the rule implementation system120. A rule250with a condition matching the received event but also depending upon one or more stored events may cause the event log130to be searched for those stored events. Accordingly the rule condition may be tested against the incoming events and the relevant stored events.

In block440, the event log130may be searched for prior events in response to a matched rule condition being a function of prior events. A rule250with a condition matching the received event but also depending upon one or more stored events may cause the event log130to be searched for those stored events. Accordingly the rule condition may be tested against the incoming events and the relevant stored events.

In block450, event parameters may be retrieved from the event log130. Stored events that were identified by the search discussed with respect to block440may be retrieved from the event log130. Parameters associated with the stored events being retrieved may also be retrieved form the event log130. For example, if the identified stored event relates to a patient alarm, parameters may include a patient identifier, what system generated the alarm, which clinicians were notified of the alarm, when the alarm occurred, and so forth. The rule250having the condition that is dependent upon the identified stored event may be further evaluated in light of the retrieved event and its associated parameters as necessary.

In block460, the rule condition that was matched by the received event may be evaluated in view of the presently received events, the prior event(s) retrieved from the event log130, and the parameters associated with the prior event(s). The rule implementation system120can evaluate the received event(s), the prior event(s), and any associated parameters against the rule250as though they were facts260or questions265to match against the rule condition.

In block470, rule implementation system120can generate resultant events and actions180in response to triggered rules. When, the rule implementation system120matches a rule250with the received event(s), necessary prior event(s), and any associated parameters, the rule250may be said to have triggered. Triggering a rule250may generate resultant events and actions180.

In block480, generated events can be fed back into the complex event handler110. These events that have been fed back into the complex event handler110may be stored and processed just as any other received event.

Example Systems

FIG. 5depicts a computing machine2000and a module2050in accordance with one or more embodiments presented herein. The computing machine2000may correspond to any of the various computers, servers, mobile devices, embedded systems, or computing systems presented herein. The module2050may comprise one or more hardware or software elements configured to facilitate the computing machine2000in performing the various methods and processing functions presented herein. The computing machine2000may include various internal or attached components such as a processor2010, system bus2020, system memory2030, storage media2040, input/output interface2060, and a network interface2070for communicating with a network2080.

The computing machine2000may be implemented as a conventional computer system, an embedded controller, a laptop, a server, a mobile device, a smartphone, a set-top box, a kiosk, a vehicular information system, one more processors associated with a television, a customized machine, any other hardware platform, or any combination or multiplicity thereof. The computing machine2000may be a distributed system configured to function using multiple computing machines interconnected via a data network or bus system.

The processor2010may be configured to execute code or instructions to perform the operations and functionality described herein, manage request flow and address mappings, and to perform calculations and generate commands. The processor2010may be configured to monitor and control the operation of the components in the computing machine2000. The processor2010may be a general purpose processor, a processor core, a multiprocessor, a reconfigurable processor, a microcontroller, a digital signal processor (“DSP”), an application specific integrated circuit (“ASIC”), a graphics processing unit (“GPU”), a field programmable gate array (“FPGA”), a programmable logic device (“PLD”), a controller, a state machine, gated logic, discrete hardware components, any other processing unit, or any combination or multiplicity thereof. The processor2010may be a single processing unit, multiple processing units, a single processing core, multiple processing cores, special purpose processing cores, co-processors, or any combination thereof. According to certain embodiments, the processor2010along with other components of the computing machine2000may be a virtualized computing machine executing within one or more other computing machines.

The system memory2030may include non-volatile memories such as read-only memory (“ROM”), programmable read-only memory (“PROM”), erasable programmable read-only memory (“EPROM”), flash memory, or any other device capable of storing program instructions or data with or without applied power. The system memory2030also may include volatile memories, such as random access memory (“RAM”), static random access memory (“SRAM”), dynamic random access memory (“DRAM”), and synchronous dynamic random access memory (“SDRAM”). Other types of RAM also may be used to implement the system memory2030. The system memory2030may be implemented using a single memory module or multiple memory modules. While the system memory2030is depicted as being part of the computing machine2000, one skilled in the art will recognize that the system memory2030may be separate from the computing machine2000without departing from the scope of the subject technology. It should also be appreciated that the system memory2030may include, or operate in conjunction with, a non-volatile storage device such as the storage media2040.

The storage media2040may include a hard disk, a floppy disk, a compact disc read only memory (“CD-ROM”), a digital versatile disc (“DVD”), a Blu-ray disc, a magnetic tape, a flash memory, other non-volatile memory device, a solid sate drive (“SSD”), any magnetic storage device, any optical storage device, any electrical storage device, any semiconductor storage device, any physical-based storage device, any other data storage device, or any combination or multiplicity thereof. The storage media2040may store one or more operating systems, application programs and program modules such as module2050, data, or any other information. The storage media2040may be part of, or connected to, the computing machine2000. The storage media2040may also be part of one or more other computing machines that are in communication with the computing machine2000such as servers, database servers, cloud storage, network attached storage, and so forth.

The computing machine2000may operate in a networked environment using logical connections through the network interface2070to one or more other systems or computing machines across the network2080. The network2080may include wide area networks (“WAN”), local area networks (“LAN”), intranets, the Internet, wireless access networks, wired networks, mobile networks, telephone networks, optical networks, or combinations thereof. The network2080may be packet switched, circuit switched, of any topology, and may use any communication protocol. Communication links within the network2080may involve various digital or an analog communication media such as fiber optic cables, free-space optics, waveguides, electrical conductors, wireless links, antennas, radio-frequency communications, and so forth.

The processor2010may be connected to the other elements of the computing machine2000or the various peripherals discussed herein through the system bus2020. It should be appreciated that the system bus2020may be within the processor2010, outside the processor2010, or both. According to some embodiments, any of the processor2010, the other elements of the computing machine2000, or the various peripherals discussed herein may be integrated into a single device such as a system on chip (“SOC”), system on package (“SOP”), or ASIC device.

The example embodiments described herein can be used with computer hardware and software that perform the methods and processing functions described previously. The systems, methods, and procedures described herein can be embodied in a programmable computer, computer-executable software, or digital circuitry. The software can be stored on computer-readable media. For example, computer-readable media can include a floppy disk, RAM, ROM, hard disk, removable media, flash memory, memory stick, optical media, magneto-optical media, CD-ROM, etc. Digital circuitry can include integrated circuits, gate arrays, building block logic, field programmable gate arrays (“FPGA”), etc.