Patent Publication Number: US-11036937-B2

Title: Contraction aware parsing system for domain-specific languages

Description:
STATEMENT ON PRIOR DISCLOSURES BY AN INVENTOR 
     Various aspects of the present invention have been disclosed in the products Decision Server Advanced Restricted V8.6, made publicly available on Jun. 13, 2014. This disclosure is submitted under 35 U.S.C. 102(b)(1)(A). The following documentation is provided in support of the disclosure available on Jun. 13, 2014: 
     (i) IBM Support Portal, Decision Server Advanced Restricted V8.6 Technotes; and 
     (ii) IBM Decision Server Insights V8.6 Installation Guide. 
     BACKGROUND OF THE INVENTION 
     The present invention relates generally to the field of domain-specific languages, and more particularly to parsing domain-specific languages. 
     A domain-specific language (DSL) is a computer language specialized to a particular application domain. This is in contrast to a general-purpose language (GPL), which is broadly applicable across domains and lacks specialized features for a particular domain. DSLs provide a convenient way to instrument applications and engines. There are a wide variety of DSLs, ranging from widely used languages for common domains (e.g., HyperText Markup Language (HTML) for web pages), down to languages used by only a single piece of software. DSLs can be further subdivided by the kind of language and include domain-specific markup languages, domain-specific modeling languages (more generally, specification languages), and domain-specific programming languages. DSLs are capable of expressing complex condition-action rules and event-processing rules. The DSLs can support a rich domain vocabulary allowing the formulation of complex, nested expressions. 
     In computer science, an abstract syntax tree (AST), or syntax tree, is a tree representation of the abstract syntactic structure of source code written in a programming language. Each node of the tree denotes a construct occurring in the source code. The syntax is “abstract” in not representing every detail appearing in the real syntax. ASTs can also be used in program analysis and program transformation systems. 
     SUMMARY 
     Aspects of the present invention disclose a method, computer program product, and system for parsing a domain-specific language (DSL) statement. The method includes one or more processors accessing a DSL statement that includes contracted phrases. The method further includes one or more processors identifying one or more contracted phrases in the DSL statement utilizing an annotated domain vocabulary for a DSL associated with the DSL statement and grammar rules for the DSL. The method further includes one or more processors determining expanded phrases corresponding to the identified one or more contracted phrases based on the annotated domain vocabulary and the grammar rules. The method further includes one or more processors creating an expanded abstract syntax tree (AST) that is representative of the DSL statement with the determined expanded phrases replacing the identified one or more contracted phrases. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of a data processing environment, in accordance with an embodiment of the present invention. 
         FIG. 2  is a flowchart depicting operational steps of a program for determining an expanded AST based on a domain-specific language (DSL) statement that includes contracted statements, in accordance with an embodiment of the present invention. 
         FIG. 3A  is an example depiction of an object-role-modeling diagram for a vocabulary of a DSL, in accordance with an embodiment of the present invention. 
         FIG. 3B  is an example depiction of an object-role-modeling diagram for an extended vocabulary of a DSL, in accordance with an embodiment of the present invention. 
         FIGS. 4A and 4B  are example depictions of an abstract syntax tree (AST), in accordance with an embodiment of the present invention. 
         FIG. 5  depicts a block diagram of components of a computing system representative of the client device and server of  FIG. 1 , in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention allow for a method for parsing domain-specific language (DSL) statements that is capable of recognizing certain kinds of shortened and contracted expressions and to expand the contracted into a form as prescribed by the DSL. A contracted DSL statement is identified and utilized to create an abstract syntax tree (AST) that contains nodes that correspond to the contracted expressions in the DSL statement. Then, the contracted AST is expanded into an AST that does not use contracted expressions and can be processed by an application or engine. 
     Some embodiments of the present invention recognize that expressiveness in DSL statements can lead to lengthy descriptions of rules, which can be difficult to read and edit. Other embodiments recognize that an AST that is based off of a contracted DSL statement may not be compatible with the applications and engines that are associated with the DSL. A contracted DSL statement is a DSL statement that includes shortened or contracted expressions. For example, a comparison DSL statement of “the price of the limousine is smaller than the price of the sports car” can correspond to contracted DSL statement of “the limousine is cheaper than the sports car.” 
     The present invention will now be described in detail with reference to the Figures.  FIG. 1  is a functional block diagram illustrating data processing environment  100 , in accordance with one embodiment of the present invention. 
     An embodiment of data processing environment  100  includes computing device  120  and server  130 , interconnected over network  110 . In an example embodiment, computing device  120  can communicate with server  130 , via network  110 , to send DSL statements to server  130  and access information that is stored on server  130 . In one embodiment, computing device  120  and server  130  communicate through network  110 . Network  110  can be, for example, a local area network (LAN), a telecommunications network, a wide area network (WAN) such as the Internet, or any combination of the three, and include wired, wireless, or fiber optic connections. In general, network  110  can be any combination of connections and protocols that will support communications between computing device  120  and server  130 , in accordance with embodiments of the present invention. 
     In various embodiments of the present invention, computing device  120  may be a workstation, personal computer, personal digital assistant, mobile phone, or any other device capable of executing computer readable program instructions, in accordance with embodiments of the present invention. In general, computing device  120  is representative of any electronic device or combination of electronic devices capable of executing computer readable program instructions. Computing device  120  may include components as depicted and described in further detail with respect to  FIG. 5 , in accordance with embodiments of the present invention. 
     Computing device  120  includes application  122  and user interface  124 . Application  122  is a software application that an individual utilizing computing device  120  can utilize (e.g., via user interface  124 ) to interface with server  130 . In one embodiment, computing device  120  sends DSL statements to server  130  utilizing application  122 . 
     User interface  124  is a program that provides an interface between a user of computing device  120  and a plurality of applications that reside on computing device  120 . A user interface, such as user interface  124 , refers to the information (such as graphic, text, and sound) that a program presents to a user and the control sequences the user employs to control the program. A variety of types of user interfaces exist. In one embodiment, user interface  124  is a graphical user interface. A graphical user interface (GUI) is a type of user interface that allows users to interact with electronic devices, such as a computer keyboard and mouse, through graphical icons and visual indicators, such as secondary notation, as opposed to text-based interfaces, typed command labels, or text navigation. The actions in GUIs are often performed through direct manipulation of the graphical elements. 
     In example embodiments, server  130  can be a desktop computer, a computer server, or any other computer systems known in the art. In certain embodiments, server  130  represents computer systems utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed by elements of data processing environment  100  (e.g., computing device  120 ). In general, server  130  is representative of any electronic device or combination of electronic devices capable of executing computer readable program instructions. Server  130  may include components as depicted and described in further detail with respect to  FIG. 5 , in accordance with embodiments of the present invention. 
     Server  130  includes vocabulary extender  132 , DSL extender  134 , DSL parser  136 , storage device  140 , and parsing program  200 . Storage device  140  includes vocabulary  142 , grammar  144 , and DSL statements  146 . Storage device  140  can be implemented with any type of storage device, for example, persistent storage  508 , which is capable of storing data that may be accessed and utilized by server  130 , such as a database server, a hard disk drive, or a flash memory. In other embodiments, storage device  140  can represent multiple storage devices within server  130 . 
     Vocabulary extender  132  utilizes the vocabulary (e.g., vocabulary  142 ) of a DSL and annotations in the vocabulary to create an extended vocabulary. In one embodiment, vocabulary extender  132  identifies annotations in vocabulary  142  that are associated with operators in the DSL, which vocabulary extender  132  utilizes to create an extended vocabulary. For example, vocabulary extender  132  identifies an annotation of “cheaper” associated with the type “car” attribute. Then, vocabulary extender  132  adds a derived operator of “cheaper” in the creation of the extended vocabulary. 
     Vocabulary  142 , stored in storage device  140 , is associated with a DSL and includes words, phrases, their associated meanings, and other data that the DSL utilizes. A domain vocabulary, such as vocabulary  142 , is a set of terms to describe objects of a domain (e.g., associated with a DSL). Objects are instances of concepts such as ‘Car,’ ‘Color,’ ‘City,’ ‘Country.’ Each concept may have one or more attributes of given types, which are concepts or primitive types such as number or string. For example the concept ‘Car’ may have two attributes of type number, namely ‘price’ and ‘speed,’ as well as an attribute ‘color’ of type ‘Color’. Each instance of a concept has a value for each attribute of the concept. For example, the instance of concept ‘Car,’ named ‘the sports car,’ has a value of ‘Green’ for the attribute ‘color.’ Additionally, a concept may have operators that take instances of the concept as arguments and that result into values of a given primitive type such as number or Boolean (i.e. a type for the truth values true and false). An example is a containment operator that returns true if the city described by a first argument of type ‘City’ is in the country described by a second argument of type ‘Country,’ and that returns false otherwise. A DSL may be based on a given vocabulary (e.g., vocabulary  142 ) and contain expressions for the different vocabulary elements. The expressions can describe instances of concepts, attribute values of the instances, and the results of applying operators from the vocabulary. 
     A vocabulary can be defined utilizing a graphical editor, a DSL for defining vocabularies, an extensible markup language (XML) format, etc. Each of the annotations may be a text of a predefined form, which depends on the type of the contraction and which is associated to a concept or attribute. For example, the attribute ‘price’ of concept ‘Car’ may have the following annotation for defining the cheaper-than operator: “defines order ‘is cheaper than’ using ‘is less than’.” Similarly, the attribute ‘speed’ of concept ‘Car’ may have the annotation for defining the faster-than operator: “defines order ‘is faster than’ using ‘is more than’.” Additional forms of contractions can be specified by other forms of texts. 
     In an example embodiment,  FIG. 3A  depicts original vocabulary  310  for a DSL in the form of an object-role-modeling diagram. Original vocabulary  310  depicts types of “car,” “number,” and “Boolean,” a binary relationship “price” between “car” and “number,” and a ternary relationship “is less than” between “number,” and “Boolean.” The price relationship has an annotation indicating that the relationship represents a “cheaper” relationship over cars. Vocabulary extender  132  utilizes the annotation and adds a ternary relationship between “car” and “Boolean,” creating extended vocabulary  350  (depicted in  FIG. 3B ). 
     In another example embodiment,  FIG. 3B  depicts extended vocabulary  350  for a DSL in the form of an object-role-modeling diagram. Extended vocabulary  350  depicts types of “car,” “number,” and “Boolean,” a binary relationship “price” between “car” and “number,” and a ternary relationship “is less than” between “number,” and “Boolean” (similarly to original vocabulary  310 ). Extended vocabulary  350  includes a ternary relationship of “is cheaper than” between “car” and “Boolean” that is based on the “cheaper” annotation in original vocabulary  310 . 
     DSL extender  134  creates grammar rules for a vocabulary of a DSL, which are added to the grammar of the DSL (e.g., grammar  144 ). Grammar  144 , stored in storage device  140 , is associated with a DSL and includes grammar rules for statements in the DSL. The grammar of a DSL with a vocabulary (e.g., grammar  144 ) may have a non-terminal symbol for each concept representing an expression that evaluates to an instance of the concept. Grammar  144  may have grammar rules for formulating expressions that represent the attribute values or result of operations. In various embodiments, the grammar rules for terms of vocabulary  142  can directly be derived from vocabulary  142  in a generic way and need not be specified explicitly. The grammar rules in grammar  144  that are derived from a vocabulary extend the grammar (e.g., via DSL extender  134 ) of an ordinary DSL without vocabulary. 
     In one embodiment, DSL extender  134  utilizes the extended vocabulary of a DSL (created by vocabulary extender  132 ) and the grammar of a DSL (e.g., grammar  144 ) to determine and create grammar rules to add to the grammar of the DSL. DSL extender  134  adds grammar rules to the grammar of the DSL (e.g., grammar  144 ) based on which parts of the vocabulary (e.g., vocabulary  142 ) are extended. In an example embodiment, a user defines a vocabulary (e.g., vocabulary  142 ) utilizing a graphical editor that has commands for declaring concepts, attributes, and operators. By defining such a vocabulary, the user is able to extend, (e.g., via DSL extender  134 ) a given rule language to a custom domain without needing to manually write new grammar rules. DSL extender  134  creates grammar rules to add to grammar  144  based on extended vocabulary  350 . Extended vocabulary  350  includes additional relationships (compared to original vocabulary  310 ), and DSL extender  134  adds grammar rules to grammar  144  corresponding to the additional relationships in extended vocabulary  350 . 
     Given a vocabulary (e.g., vocabulary  142 ) and an ordinary DSL that does not include a vocabulary, DSL extender  134  extends the grammar of the DSL by a grammar rule for each vocabulary element. For each concept of the vocabulary, DSL extender  134  generates a non-terminal symbol for expressions that evaluate to instances of this concept. For example, DSL extender  134  generates the symbols &lt;car&gt;, &lt;color&gt;, &lt;city&gt;, &lt;country&gt; for the expressions related to the concepts ‘Car,’ ‘Color,’ ‘City,’ ‘Country.’ These symbols complement existing non-terminal symbols representing primitive values such as numbers, Boolean truth values, strings, etc. In addition, DSL extender  134  generates grammar rules for accessing attribute values. The left-hand side of such a rule is the non-terminal system for the attribute type. The right-hand side consists of an article, the attribute name as defined in the vocabulary, the partitive article “of,” and the non-terminal system for the concept that declares the attribute. For example, ‘&lt;number&gt;::=the price of &lt;car&gt;,’‘&lt;number&gt;::=the speed of &lt;car&gt;,’ and ‘&lt;color&gt;::=the color of &lt;car&gt;.’ In additional embodiments, DSL extender  134  generates grammar rules for applying operators from the vocabulary. The left-hand side of such a rule is the non-terminal system for the result type of the operator. The right-hand side may use an infix notation and consist of the expression for the first argument, the operator symbol, and the expression for the second argument. 
     DSL parser  136  is a standard parsing program or application that is capable of parsing DSL statements. DSL parser  136  utilizes the extended grammar of the DSL (e.g., from DSL extender  134 ), the extended DSL vocabulary (e.g., from vocabulary extender  132 ), and DSL statements, which can include contracted statements. 
     Parsing program  200  determines an expanded AST based on a DSL statement that includes contracted statements, in accordance with embodiments of the present invention. In various embodiments, parsing program  200  utilizes DSL parser  136  to parse DSL statements, in accordance with various embodiments of the present invention. In one embodiment, parsing program  200  utilizes DSL parser  136  to parse a DSL statement (e.g., a statement in DSL statements  146 ) that includes contractions, and parsing program  200  annotates the resulting AST nodes corresponding to the contractions. DSL statements  146 , stored in storage device  140 , includes the DSL statements that server  130  has received (e.g., from computing device  120 ). 
       FIG. 2  is a flowchart depicting operational steps of parsing program  200 , a program for determining an expanded AST based on a DSL statement that includes contracted statements, in accordance with embodiments of the present invention. 
     In step  202 , parsing program  200  identifies a DSL statement. In one embodiment, parsing program  200  accesses and identifies a DSL statement from DSL statements  146 , stored in storage device  140 . In another embodiment, parsing program  200  identifies a DSL statement received from computing device  120 . 
     In decision step  204 , parsing program  200  determines whether the identified DSL statement is a contracted statement. In one embodiment, parsing program  200  determines whether a DSL statement (identified in step  202 ) includes contractions and/or shortened phrases (e.g., utilizing semantic annotations). In another embodiment, parsing program  200  receives an indication that the DSL statement is a contracted statement. In response to determining that the identified DSL statement is not a contracted statement (decision step  204 , “no” branch), parsing program  200  returns to step  202 . In an example, parsing program  200  identifies a DSL statement of “the limousine is cheaper than the sports car” (in step  202 ). Then, parsing program  200  analyzes the identified DSL statement utilizing semantic annotations of vocabulary  142  and determines that the identified DSL statement is a contracted statement (decision step  204 , “yes” branch). In this example, parsing program  200  identifies “is cheaper than” as a contracted phrase in the identified DSL statement. 
     In step  206 , parsing program  200  creates an AST that represents the contracted DSL statement. More specifically, in response to determining that the identified DSL statement is a contracted statement (decision step  204 , “yes” branch), parsing program  200  creates an AST that represents the contracted DSL statement (identified in step  202 ). In one embodiment, parsing program  200  utilizes DSL extender  134  and grammar  144  (including grammar extensions) to parse the identified DSL statement (identified in step  202  and determined to be a contracted statement in decision step  204 ) and create an AST that represents the DSL statement. In an example embodiment,  FIG. 4A  depicts contracted AST  400 . Parsing program  200  creates contracted AST (in step  206 ) based on the contracted identified DSL statement of “the limousine is cheaper than the sports car.” Parsing program  200  utilizes annotations and extensions in vocabulary  142  to include an annotation that indicates the contraction of the node in the AST corresponding to “is cheaper than” with the annotation of “compares ‘the price of . . . ’” in contracted AST  400 . The annotation provides an indication (from vocabulary  142 ) that the contraction in contracted AST  400  is associated with a price operator. 
     In step  208 , parsing program  200  identifies contractions in the nodes of the created AST. In one embodiment, parsing program  200  identifies one or more contractions in the created AST (created in step  206 ). For example, parsing program  200  utilizes semantic annotations (e.g., provided in vocabulary  142 ) to identify contracted phrases (e.g., shortened forms of phrases in a DSL statement). In an example, parsing program  200  analyzes contracted AST  400  (depicted in  FIG. 4A ) and utilizes the sematic annotation of “compares ‘the price of . . . ’” to identify that the corresponding AST node of “is cheaper than” is a contracted phrase. In various embodiments, parsing program  200  can identify a plurality of contractions in an AST in a plurality of locations within the AST. 
     In step  210 , parsing program  200  creates an updated AST. In one embodiment, parsing program  200  creates an updated AST that resolves and expands the identified contractions (from step  208 ). In an example embodiment, when resolving contractions, parsing program  200  applies the semantic annotations that correspond to the nodes in the AST that include a contracted phrase. The annotations facilitate expansion of the AST into an expanded AST, in which the identified contractions (from step  208 ) are expanded. Parsing program  200  replaces the identified contractions in the AST by expanding the contractions according to vocabulary  142 , which results in an updated, expanded AST. For example, parsing program  200  utilizes the semantic annotation of “compares ‘the price of . . . ’” on the node in contracted AST  400  of “is cheaper than,” vocabulary  142 , and grammar  144  to create an expanded AST.  FIG. 4B  depicts expanded AST  450 , which is a contraction-free AST. In an example embodiment, parsing program  200  processes contracted AST  400 , expands the contractions in contracted AST  400 , and utilizes the expanded contractions to create expanded AST  450 . In this example embodiment, expanded AST  450  is representative of a DST statement of “the price of the limousine is less than the price of the sports car.” In various embodiments, parsing program  200  creates an AST (e.g., expanded AST  450 ) that is capable of being utilized by the applications and/or engines associated with the DSL. 
     In decision step  212 , parsing program  200  determines whether additional contractions are present in the updated AST. In one embodiment, parsing program  200  analyzes (e.g., utilizing semantic annotations) the updated AST (created in step  210 ) to determine whether the updated AST includes any additional contracted phrases. In various embodiments, if the original AST (e.g., created in step  206 ) included contractions within contractions (e.g., composed contractions or nested contractions), then parsing program  200  can determine that the updated AST does still include one or more contractions (decision step  212 , “yes” branch). In response to determining that the updated AST does include one or more additional contractions, parsing program  200  returns to step  208 . 
     In step  214 , parsing program  200  stores the updates AST. In one embodiment, in response to determining that no additional contractions are present in the updates AST (decision step  212 , “no” branch), parsing program  200  stores the updated AST (e.g., in storage device  140 ). The stored AST is capable of being accessed and utilized by desired applications of systems. In another embodiment, parsing program  200  sends the updates AST a corresponding application for processing. 
     In a first example embodiment, a concept such as a “car” may have an attribute “price” of a type integer. Grammar  144  can include a construction for comparing two expressions of type integers (e.g., &lt;expr&gt; is less than &lt;expr&gt;). Given two cars called “limousine” and “sports car” in vocabulary  142 , the corresponding DSL is capable of accepting a DSL statement of “the price of the limousine is smaller than the price of the sports car.” Vocabulary  142  and grammar  144  include information that permits a semantic annotation of the attribute “price” to indicate that the price attribute represents an order called “cheaper” on cars, which permits a contracted phrase of “the limousine is cheaper than the sports car.” Parsing program  200  uses the semantic annotation to recognize the contraction and produce an AST (e.g., expanded AST  450 ) corresponding to the expanded DSL statement (i.e., “the price of the limousine is smaller than the price of the sports car”). 
     In a second example embodiment, a concept such as “event” may have an attribute “time” representing points in time. Grammar  144  can include a construction for comparing two points in time, and the corresponding DSL is capable of accepting a DSL statement of “the time of the computer purchase event is before the time of the car purchase event.” Vocabulary  142  and grammar  144  include information that permits a semantic annotation of the concept “event” to indicate that the attribute “time” defines a temporal aspect of the concept, which permits an expression of type “event” can be used where a time point is expected. In this embodiment, “the computer purchase event is before the car purchase event” is a potential contracted DSL statement. Parsing program  200  utilizes the semantic annotation to recognize the contraction and produce an AST corresponding to the expanded DSL statement (i.e., “the time of the computer purchase event is before the time of the car purchase event”). 
     In a third example embodiment, vocabulary  142  can provide attributes for direct relationships (e.g., ownership relationships, transitive possessive relationships, etc.). Indirect relationships can be expressed by paths and statements that can be verbose, for example, “the city of the address of the shop of the purchase event is in the United Kingdom.” If vocabulary  142  and grammar  144  include a unique path from the source concept “purchase event” to the target concept “address,” then the DSL statement can be contracted. In this example, the DSL statement can be contracted to “the city of the purchase event is in the United Kingdom.” Parsing program  200  can recognize the contraction utilizing grammar rules for attribute accesses in grammar  144 . In additional embodiments, different forms of contractions (e.g., temporal relationships, direct relationships, etc.) can be combined together. 
       FIG. 5  depicts a block diagram of components of computer  500 , which is representative of client device  120  and server  130 , in accordance with an illustrative embodiment of the present invention. It should be appreciated that  FIG. 5  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
     Computer  500  includes communications fabric  502 , which provides communications between computer processor(s)  504 , memory  506 , persistent storage  508 , communications unit  510 , and input/output (I/O) interface(s)  512 . Communications fabric  502  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  502  can be implemented with one or more buses. 
     Memory  506  and persistent storage  508  are computer readable storage media. In this embodiment, memory  506  includes random access memory (RAM)  514  and cache memory  516 . In general, memory  506  can include any suitable volatile or non-volatile computer readable storage media. Software and data  522  are stored in persistent storage  508  for access and/or execution by processors  504  via one or more memories of memory  506 . With respect to client device  120 , software and data  522  includes application  122 . With respect to server  130 , software and data  522  includes vocabulary  132 , DSL extender  134 , DSL parser  136 , parsing program  200 , vocabulary  142 , grammar  144 , and DSL statements  146 . 
     In this embodiment, persistent storage  508  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  508  can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  508  may also be removable. For example, a removable hard drive may be used for persistent storage  508 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  508 . 
     Communications unit  510 , in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit  510  may include one or more network interface cards. Communications unit  510  may provide communications through the use of either or both physical and wireless communications links. Software and data  522  may be downloaded to persistent storage  508  through communications unit  510 . 
     I/O interface(s)  512  allows for input and output of data with other devices that may be connected to computer  500 . For example, I/O interface  512  may provide a connection to external devices  518  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  518  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data  522  can be stored on such portable computer readable storage media and can be loaded onto persistent storage  508  via I/O interface(s)  512 . I/O interface(s)  512  also can connect to a display  520 . 
     Display  520  provides a mechanism to display data to a user and may be, for example, a computer monitor. Display  520  can also function as a touch screen, such as a display of a tablet computer. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.