Patent Publication Number: US-10783592-B2

Title: Collecting social media users in a specific customer segment

Description:
BACKGROUND 
     Technical Field 
     The present invention relates generally to social media and, in particular, to collecting social media users in a specific customer segment. 
     Description of the Related Art 
     Profiling techniques for social media users is important for at least the following two reasons. First, profiling techniques are essential to deliver a personalized service which is one of the efficient methodologies to improve user satisfaction and service conversion. One example is recommendation of users, tweets and advertisements in which a user seems to be interested. Second, current social media is too huge and diverse to manually analyze. 
     Accordingly, seeking users who have a specific user profile takes much time because too many users exist on social media. Hence, there is a need a way to harvest social media users in a specific customer segment. 
     SUMMARY 
     According to an aspect of the present principles, a method is provided for collecting social media users who have a specific profile. The method includes retrieving over one or more networks, by a hardware network interface, a set of lists connected by at least one criterion to a particular list. The particular list is included in a set of reliable lists whose users have already been reliably deemed to have a specific profile. The method further includes calculating, by a processor-based confidence value calculator, a list name based confidence value and a list member based confidence value for each list in the retrieved set of lists. The method also includes updating, by a list manager, the set of reliable lists by adding all of the lists in the retrieved set of lists that have the list name based confidence value above a first threshold value and the list member based confidence value above a second threshold value. The method additionally includes outputting, by at least one of a display device and the hardware interface, a listing of users belonging to set of reliable lists as the social media users who have the specific profile. 
     According to another aspect of the present principles, a computer program product is provided for collecting social media users who have a specific profile. The computer program product includes a non-transitory computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform a method. The method includes retrieving over one or more networks, by a hardware network interface, a set of lists connected by at least one criterion to a particular list. The particular list is included in a set of reliable lists whose users have already been reliably deemed to have a specific profile. The method further includes calculating, by a processor-based confidence value calculator, a list name based confidence value and a list member based confidence value for each list in the retrieved set of lists. The method also includes updating, by a list manager, the set of reliable lists by adding all of the lists in the retrieved set of lists that have the list name based confidence value above a first threshold value and the list member based confidence value above a second threshold value. The method additionally includes outputting, by at least one of a display device and the hardware interface, a listing of users belonging to set of reliable lists as the social media users who have the specific profile. 
     According to yet another aspect of the present principles, a system is provided for collecting social media users who have a specific profile. The system includes a hardware network interface for retrieving over one or more networks a set of lists connected by at least one criterion to a particular list. The particular list is included in a set of reliable lists whose users have already been reliably deemed to have a specific profile. The system further includes a processor-based confidence value calculator for calculating a list name based confidence value and a list member based confidence value for each list in the retrieved set of lists. The system also includes a list manager for updating the set of reliable lists by adding all of the lists in the retrieved set of lists that have the list name based confidence value above a first threshold value and the list member based confidence value above a second threshold value. At least one of a display device and the hardware interface outputs a listing of users belonging to set of reliable lists as the social media users who have the specific profile. 
     These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein: 
         FIG. 1  shows an exemplary processing system  100  to which the present principles may be applied, in accordance with an embodiment of the present principles; 
         FIG. 2  shows an exemplary system  200  for collecting social media users in a specific customer segment, in accordance with an embodiment of the present principles; 
         FIG. 3  shows an exemplary method  300  for collecting social media users in a specific customer segment, in accordance with an embodiment of the present principles; 
         FIG. 4  shows exemplary social media groups  400  to which the present principles can be applied, in accordance with an embodiment of the present principles; 
         FIG. 5  shows an exemplary cloud computing node  510 , in accordance with an embodiment of the present principles; 
         FIG. 6  shows an exemplary cloud computing environment  650 , in accordance with an embodiment of the present principles; and 
         FIG. 7  shows exemplary abstraction model layers, in accordance with an embodiment of the present principles. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     The present principles are directed to collecting social media users in a specific customer segment. 
       FIG. 1  shows an exemplary processing system  100  to which the present principles may be applied, in accordance with an embodiment of the present principles. The processing system  100  includes at least one processor (CPU)  104  operatively coupled to other components via a system bus  102 . A cache  106 , a Read Only Memory (ROM)  108 , a Random Access Memory (RAM)  110 , an input/output (I/O) adapter  120 , a sound adapter  130 , a network adapter  140 , a user interface adapter  150 , and a display adapter  160 , are operatively coupled to the system bus  102 . 
     A first storage device  122  and a second storage device  124  are operatively coupled to system bus  102  by the I/O adapter  120 . The storage devices  122  and  124  can be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid state magnetic device, and so forth. The storage devices  122  and  124  can be the same type of storage device or different types of storage devices. 
     A speaker  132  is operatively coupled to system bus  102  by the sound adapter  130 . A transceiver  142  is operatively coupled to system bus  102  by network adapter  140 . A display device  162  is operatively coupled to system bus  102  by display adapter  160 . 
     A first user input device  152 , a second user input device  154 , and a third user input device  156  are operatively coupled to system bus  102  by user interface adapter  150 . The user input devices  152 ,  154 , and  156  can be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Of course, other types of input devices can also be used, while maintaining the spirit of the present principles. The user input devices  152 ,  154 , and  156  can be the same type of user input device or different types of user input devices. The user input devices  152 ,  154 , and  156  are used to input and output information to and from system  100 . 
     Of course, the processing system  100  may also include other elements (not shown), as readily contemplated by one of skill in the art, as well as omit certain elements. For example, various other input devices and/or output devices can be included in processing system  100 , depending upon the particular implementation of the same, as readily understood by one of ordinary skill in the art. For example, various types of wireless and/or wired input and/or output devices can be used. Moreover, additional processors, controllers, memories, and so forth, in various configurations can also be utilized as readily appreciated by one of ordinary skill in the art. These and other variations of the processing system  100  are readily contemplated by one of ordinary skill in the art given the teachings of the present principles provided herein. 
     Moreover, it is to be appreciated that system  200  described below with respect to  FIG. 2  is a system for implementing respective embodiments of the present principles. Part or all of processing system  100  may be implemented in one or more of the elements of system  200 . 
     Further, it is to be appreciated that processing system  100  may perform at least part of the method described herein including, for example, at least part of method  300  of  FIG. 3 . Similarly, part or all of system  200  may be used to perform at least part of method  300  of  FIG. 3 . 
       FIG. 2  shows an exemplary system  200  for collecting social media users who have a specific profile, in accordance with an embodiment of the present principles. 
     The system  200  includes a hardware network interface  210 , a confidence value calculator  220 , a list manager  230 , a display device  240 , and an output manager  250 . 
     The hardware network interface  210  interfaces system  200  with one or more networks (e.g., the Internet) to retrieve, over the one or more networks, a set of lists connected by at least one criterion to a particular list. The particular list is included in a set of reliable lists (e.g., G reliable , as described in further detail herein below) whose users have already been reliably deemed to have a specific profile. The hardware network interface  210  can include a wire-based hardware network interface  210 A and a wireless-based hardware network interface  210 B. 
     The confidence value calculator  220  calculates confidence values for determining which groups have a specific profile (e.g., specific customer segment). The confidence value calculator  220  includes a list name based confidence value calculator  220 A for calculating a list name based confidence value for each list in the retrieved set of lists. The confidence value calculator  220  also includes a list member based confidence value calculator  220 B for calculating a list member based confidence value for each list in the retrieved set of lists. 
     The list manager  230  updates the set of reliable lists by adding all of the lists in the retrieved set of lists that have the list name based confidence value above a first threshold value and the list member based confidence value above a second threshold value. The list manager  230  includes a confidence value evaluator  230 A for comparing, for each list in the retrieved set of lists, the list name based confidence value to the first threshold value. The confidence value evaluator  230 A also compares, for each list in the retrieved set of lists, the list member based confidence value to the second threshold value. 
     The display device  240  and/or the hardware network interface  210  output a listing of users belonging to set of reliable lists as the social media users who have the specific profile. 
     The output manager  250  control the outputting of the listing of users belonging to set of reliable lists as the social media users who have the specific profile. The output manager  250  can direct the listing to either or both of the display device  240  and the hardware network interface  210 . The output manager  250  can also perform sorting or other operations on the listing for the purposes of outputting the listing in a certain order as further described herein. 
     In the embodiment shown in  FIG. 2 , the elements thereof are interconnected by a bus(es)/network(s)  201 . However, in other embodiments, other types of connections can also be used. Moreover, in an embodiment, at least one of the elements of system  200  is processor-based. Further, while one or more elements (e.g., the confidence value calculator  220  and the list manager  230 ) may be shown as separate elements, in other embodiments, these elements can be combined as one element. The converse is also applicable, where while one or more elements (e.g., the list name based confidence value calculator  220 A and the list member based confidence value calculator  220 B) may be part of another element, in other embodiments, the one or more elements may be implemented as standalone elements. These and other variations of the elements of system  200  are readily determined by one of ordinary skill in the art, given the teachings of the present principles provided herein, while maintaining the spirit of the present principles. 
       FIG. 3  shows an exemplary method  300  for collecting social media users in a specific customer segment, in accordance with an embodiment of the present principles. 
     At step  310 , retrieve a set of lists connected to a list L∈G reliable , where list L is included in (∈) G reliable , and where G reliable  includes a set of lists (e.g., at least list L) whose users have already been reliably deemed to have a specific profile. In an embodiment, G reliable  includes at least list L as a starting point. 
     As noted above, the lists in the retrieved set of lists are connected to list L. The “connection” can be based on group label (same or similar label), group member composition (same or similar composition), a textual similarity between group member&#39;s posts, and so forth. Regarding group member composition, the same can be determined from the member list without having to review each user&#39;s profile. The connection can even be based on being on the same social media (e.g., Twitter®, Facebook®, etc.), although using this criterion alone (being on the same social media) will increase processing time, versus pruning the processed groups using the aforementioned, more specific criteria. The preceding criteria and merely illustrative and, thus, other criteria can also be used for the basis of connection while maintaining the spirit of the present principles. 
     The first list in G reliable , presumably list L, is determined (for inclusion in G reliable ) based on, for example, a user pre-selection, textual similarity to a subject, and so forth. Of course, other criteria can also be used while maintaining the spirit of the present principles. 
     At step  320 , for each list in the retrieved set of lists, calculate two types of confidence values, namely a list name based confidence value and a list member based confidence value. In an embodiment, the function c name  described below is used for the list name based confidence value, and the function c user  described below is used for the list member based confidence value. Of course, given the teachings of the present principles provided herein, various modifications to these functions, as well as similar functions, can be readily implemented by one of ordinary skill in the art, while maintaining the spirit of the present principles. 
     At step  330 , for each list in the retrieved set of lists, determine whether or not the list name based confidence value calculated there for is above a list name based threshold value. If so, then the method proceeds to step  340 . Otherwise, the method is terminated. In an embodiment, the list name based threshold value is determined based on experiment, historical data, and so forth. Of course, other basis for the list name based threshold can also be used, while maintaining the spirit of the present principles. 
     At step  340 , for each list in the retrieved set of lists, determine whether or not the list member based confidence value calculated there for is greater than a list member based confidence value. If so, then the method proceeds to step  350 . Otherwise, the method is terminated. In an embodiment, the list member based confidence value is determined based on any known distance metric for two sets including, but not limited to, a dice coefficient, a Hamming distance, a Euclidean distance, and so forth. Of course, other basis for the list member based threshold can also be used, while maintaining the spirit of the present principles. 
     At step  350 , update the reliable set of lists in G reliable  by adding all of the lists in the retrieved set of lists whose confidence values (both the list name based confidence value and the list member based confidence value) are greater than respective thresholds against which the confidence values are compared. 
     At step  360 , output a listing of the users belonging to the reliable set of lists in G reliable  as users who have a specific profile. In an embodiment, step  360  can involve displaying the users belonging to the reliable set of lists in G reliable . In an embodiment, the users can be output in an order (i.e., sorted) based on one or more criterion. For example, in an embodiment, users from groups with the highest margin over both thresholds can be listed descending order (or ascending order). In another embodiments, users from groups with the highest margin over a particular one of the two thresholds can be listed in a particular (e.g., descending or ascending). These and other orderings can be applied to the outputted users, while maintaining the spirit of the present principles. In an embodiment, the specific profile corresponds to a specific customer segment. 
     At step  370 , perform an operation with respect to at least some of the users belonging to the reliable set of lists in G reliable . The operation can be, but is not limited to, marketing, demographics, and so forth. The operation can be, but is not limited to, sending a targeted message, sending a targeted advertisement, sending a target invitation to another group or social media forum or website, forwarding a list of the users belonging to the reliable set of lists in G reliable  to one or more remote devices (e.g., servers, cell phones, etc.), and so forth. The preceding examples of operations are merely illustrative and, thus, other operations can also be performed, while maintaining the spirit of the present principles. 
     It is to be appreciated that method  300  can be repeatedly performed based on some criteria. For example, the criteria can include, but is not limited to, as needed, according to one or more predetermined frequencies, randomly, and so forth. 
       FIG. 4  shows exemplary social media groups  400  to which the present principles can be applied, in accordance with an embodiment of the present principles. 
     The exemplary social media groups  400  include four groups, namely a first group labeled “IBM”®, a second group also labeled “IBM”®, a third group labelled “colleagues”, and a fourth group labeled “university”. 
     In this example, we start with group  2 . Hence, group  2  can be considered to be list L from reliable list G reliable . We then look at group  1 , whose label is the same as group  2  (namely “IBM”). Thus, group  2  will be evaluated by method  300 . 
     We then look at group  3 , whose member composition is similar to group  2 . Thus, group  2  will be evaluated by method  300 . 
     We then look at group  4 , whose label and group membership differ from group  2 . Thus, group  4  will not be evaluated by method  300 . 
     A description will now be given regarding a list name based confidence value, in accordance with an embodiment of the present principles. 
     A function is defined which returns a confidence value for a list based on the list name. A list name (i.e., label) consists of one or more words, and can include hyphenated words. In the case of hyphenated words, each word can be considered separately. For example, “it-developers” consists of two words, namely “it” and “developers”, and each of these words can be considered (processed) in accordance with the present principles. 
     Let W 1  be a set of words of a list g. Thus, in this case, list g would correspond to one of the retrieved lists from step  310 . Let G w  be a set of lists whose name includes word w. Let d be a minimum path length from a list to another list which as w∈W 1 . In an embodiment, d is determined using Dijkstra&#39;s algorithm, which can find the shortest path between nodes in a graph. Of course, other approaches can also be used, while maintaining the spirit of the present principles. Let θ∈[0,1] be a constant number. 
     Let c name  be a function which receives a list and returns a confidence value based on the list name. 
                   c   name     ⁡     (   g   )       =       1          W   g            ⁢       ∑     w   ∈   W               ⁢       g   f     ⁢     word   ⁡     (   w   )               ,         
where F word (w)=log(|G w |+1)×θ d ,
 
and wherein G w  denotes a set of groups whose name includes word w, w g  denotes a set of words of a group g.
 
     The parameter θ decays f word (w) and it is important not to capture common words in the Twitter (or other social media) list. The meanings of common words depend on their context. For example, a list with a name “colleagues” means “colleagues from IBM” in a specific context, but in another context it has a different meaning. The context in this case means the shortest path length of nodes with the same name. For example, the shortest path length between groups names “colleagues in IBM”® is shorter than that of groups named “colleagues in Microsoft”®. 
     A description will now be given regarding a list member based confidence value, in accordance with an embodiment of the present principles. 
     A confidence value of a list is also calculated based on users belonging to the list. We calculate a dice coefficient between a given list g and another list g′∈G reliable  and use a maximum value as the confidence value of the list g. Thus, in this case, list g would correspond to one of the retrieved lists from step  310 . 
     Let f user  be a function which maps a list g to a set of users who belong to g. 
     Let c user  be a function which receives a list and returns a confidence value based on list name, as follows: 
     
       
         
           
             
               
                 
                   
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     A description will now be given regarding various considerations and factors (hereinafter “factors”) on which one or more embodiment of the present principles are premised. 
     One factor is to presume that a list name expresses and/or otherwise represents a profile of its members. For example, a list name of “IBM”® will express and/or otherwise represent users that somehow relate to IBM® (e.g., IBM® employees, IBM® clients, etc.). 
     Another factor is that list names are collected which have the same meaning as a list to which a set of users belong. For example, when collecting list names for correspondence to the list “IBM”®, a list name of “colleagues” can have the same meaning as IBM® in a specific context and will thus be collected. It is to be appreciated that prior art approaches cannot understand “IBM”® and “colleagues” have the same meaning in a specific context, in contrast to the advantageous capabilities of the present principles. 
     Yet another factor is that the functions which return a confidence value use (1) inputted group information as well as (2) context information. For example, the function for a confidence value that is based on a list member utilizes information about G reliable . Thus, in this way, we can avoid collecting other organization&#39;s “colleagues” (e.g., other than IBM®, with respect to the preceding example). 
     Definitions of some of the terms used here will now be provided, in accordance with an embodiment of the present principles. 
     The term “user” refers to a social media user. 
     The term “group” refers to two elements, namely (1) a user of users and (2) a label. 
     The term “label” refers to a short description formed from one or more words. In an embodiment, more than one list can have the same label. It is to be appreciated that the terms “label” and “list name” are used interchangeably herein. 
     Let G ALL  be all groups in social media. 
     Let G reliable ⊂G ALL  be a given set of lists whose members have a specific profile at a high probability. 
     It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
     Referring now to  FIG. 5 , a schematic of an example of a cloud computing node  510  is shown. Cloud computing node  510  is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node  510  is capable of being implemented and/or performing any of the functionality set forth hereinabove. 
     In cloud computing node  510  there is a computer system/server  512 , which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server  512  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like. 
     Computer system/server  512  may be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server  512  may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     As shown in  FIG. 5 , computer system/server  512  in cloud computing node  510  is shown in the form of a general-purpose computing device. The components of computer system/server  512  may include, but are not limited to, one or more processors or processing units  516 , a system memory  528 , and a bus  518  that couples various system components including system memory  528  to processor  516 . 
     Bus  518  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. 
     Computer system/server  512  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  512 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  528  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  530  and/or cache memory  532 . Computer system/server  512  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  534  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  518  by one or more data media interfaces. As will be further depicted and described below, memory  528  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 
     Program/utility  540 , having a set (at least one) of program modules  542 , may be stored in memory  528  by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules  542  generally carry out the functions and/or methodologies of embodiments of the invention as described herein. 
     Computer system/server  512  may also communicate with one or more external devices  514  such as a keyboard, a pointing device, a display  524 , etc.; one or more devices that enable a user to interact with computer system/server  512 ; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server  512  to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces  522 . Still yet, computer system/server  512  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  520 . As depicted, network adapter  520  communicates with the other components of computer system/server  512  via bus  518 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  512 . Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     Referring now to  FIG. 6 , illustrative cloud computing environment  650  is depicted. As shown, cloud computing environment  650  comprises one or more cloud computing nodes  610  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  654 A, desktop computer  654 B, laptop computer  654 C, and/or automobile computer system  654 N may communicate. Nodes  610  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  650  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  654 A-N shown in  FIG. 6  are intended to be illustrative only and that computing nodes  610  and cloud computing environment  650  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 7 , a set of functional abstraction layers provided by cloud computing environment  650  ( FIG. 6 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 7  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  760  includes hardware and software components. Examples of hardware components include mainframes, in one example IBM® zSeries® systems; RISC (Reduced Instruction Set Computer) architecture based servers, in one example IBM pSeries® systems; IBM xSeries® systems; IBM BladeCenter® systems; storage devices; networks and networking components. Examples of software components include network application server software, in one example IBM WebSphere® application server software; and database software, in one example IBM DB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter, WebSphere, and DB2 are trademarks of International Business Machines Corporation registered in many jurisdictions worldwide). 
     Virtualization layer  762  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients. 
     In one example, management layer  764  may provide the functions described below. Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal provides access to the cloud computing environment for consumers and system administrators. Service level management provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  766  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; transaction processing; and collecting social media users in a specific customer segment. 
     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 Java, 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. 
     Reference in the specification to “one embodiment” or “an embodiment” of the present principles, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment of the present principles. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment”, as well any other variations, appearing in various places throughout the specification are not necessarily all referring to the same embodiment. 
     It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as readily apparent by one of ordinary skill in this and related arts, for as many items listed. 
     Having described preferred embodiments of a system and method (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.