Patent Publication Number: US-2022215064-A1

Title: Refining internet search recommendations

Description:
FIELD 
     Embodiments relate, generally, to the field of computing, and more specifically to weighting and refining Internet search recommendations. 
     BACKGROUND 
     The exponential growth of information that is available to the average person on the Internet has developed an environment where the amount of on-line information vastly outstrips any person&#39;s capability to survey it. This has given rise to a market for Internet search engines, where the user may enter a specific query and receive a list of items that match the query, usually ranked by the degree of the match. Over time, the field of machine learning has also rapidly matured and this has led to search engine providers being able to include “recommendation systems” that provide custom, personalized recommendations of the most interesting and useful items in the list to the user by ordering search results according to a weight or score. There are multiple recommendation techniques that are used in the field. One example is “collaborative” techniques that aggregate ratings of items by one or more users, recognize certain commonalities between users based on those ratings and then generate new recommendations based on the relationship between users. Another example is “content-based” techniques, where items of interest are defined by their features, then a user profile of desired features is developed based on past ratings of items by the user. Recommendation systems are frequently implemented as enhancements to search engines to provide users with a more meaningful and helpful way to search the vast information ocean of the Internet. 
     SUMMARY 
     An embodiment is directed to a computer-implemented method for refining Internet search recommendations. The method may include receiving a search input from a user. The method may also include receiving a plurality of sets of search results from respective search engines, each search engine utilizing a respective type of search process. Each of the sets of the search results for a selected one of the search engines may be prioritized according to the type of the search process. In addition, the method may include applying respective weights to the search engines such that the sets of search results have a modified priority based on the weights. The weights may be associated with the user. The method may further include generating modified search results based on the sets of search results, the weights, and the modified priority. 
     The method may also include transmitting the modified search results to the user. The method may further include monitoring user interactions of the user in navigating the modified search results. Lastly, the method may include updating the weights of the search engines based on the user interactions. 
     In addition to a computer-implemented method, additional embodiments are directed to a system and a computer program product for refining Internet search recommendations. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The following detailed description, given by way of example and not intended to limit the exemplary embodiments solely thereto, will best be appreciated in conjunction with the accompanying drawings, in which: 
         FIG. 1  depicts a block diagram of a computing system that facilitates searching the Internet by a user, according to an exemplary embodiment. 
         FIG. 2  depicts a flowchart of a method for refining Internet search engine recommendations, according to an exemplary embodiment. 
         FIG. 3  depicts a block diagram of internal and external components of the computers and servers depicted in  FIG. 1 , according to at least one embodiment. 
         FIG. 4  depicts a cloud computing environment according to an exemplary embodiment. 
         FIG. 5  depicts abstraction model layers according to an exemplary embodiment. 
     
    
    
     The drawings are not necessarily to scale. The drawings are merely schematic representations, not intended to portray specific parameters of the exemplary embodiments. The drawings are intended to depict only typical exemplary embodiments. In the drawings, like numbering represents like elements. 
     DETAILED DESCRIPTION 
     Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. The exemplary embodiments are only illustrative and may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope to be covered by the exemplary embodiments to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments. 
     References in the specification to “one embodiment”, “an embodiment”, “an exemplary embodiment”, etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     In the interest of not obscuring the presentation of the exemplary embodiments, in the following detailed description, some processing steps or operations that are known in the art may have been combined together for presentation and for illustration purposes and in some instances may have not been described in detail. In other instances, some processing steps or operations that are known in the art may not be described at all. It should be understood that the following description is focused on the distinctive features or elements according to the various exemplary embodiments. 
     The exemplary embodiments are directed to a method of refining recommendations for Internet searches to accurately consider the disparate techniques that may be used to provide recommendations. In this method, multiple sets of recommendations from various search engines may be used as input and the method may filter those recommendations for a specific user and/or search engine. Use of results from only one technique may lead to a skewing of results and the user&#39;s intention may be lost. Therefore, a technique that can collect results from one or more search engines, identify the user and search engine and then re-order the results based on these custom factors, as well as continuously update itself to provide the most accurate results, may be useful. 
     Recommendation systems produce individualized recommendations as output or guide the user in a customized way to interesting or useful search results in a large space of possible options. Such systems have an obvious appeal in an environment where the amount of online information vastly outstrips any individual&#39;s capability to survey it. Recommendation systems are now an integral part of some e-commerce sites and popular Internet search engines. 
     Recommendation systems are not the same as search engines. The purpose of a search engine is to return all those items that match a query ranked by the degree of the match. A search engine may refine a search by attempting to modify the user&#39;s search terms, and crudely provide recommendations to the user, but a recommendation system is functionally separate from a search engine. Recommendation systems have background data, or the information that the system has before the recommendation process begins, input data, or the information that the user must communicate to the system in order to generate a recommendation, and also an algorithm that combines the background and input data to arrive at its suggestions. All of the known recommendation techniques have strengths and weaknesses, and many combinations of techniques have been chosen to achieve peak performance. 
     Collaborative recommendation techniques are probably the most familiar, most widely implemented and most mature of the technologies. Collaborative systems aggregate ratings or recommendations of objects and match the current user with other users that have similar behaviors or interests. Recommendations are then generated based on these inter-user comparisons, e.g., once User A and User B have been matched to each other, any items that are favorably rated by User A but have not yet been rated by User B would be recommended to User B. These systems can be either memory-based, comparing users against each other directly using correlation or other measures, or model-based, in which a model is derived from the historical rating data and used to make predictions via machine learning. This has been called “people-to-people correlation”. 
     Content-based recommendation is an outgrowth and continuation of information filtering research. In a content-based system, the objects of interest are defined by their associated features. A content-based system learns a profile of the user&#39;s interests based on the features present in objects the user has rated, which leads to the technique being called “item-to-item correlation.” 
     Demographic techniques aim to categorize the user based on personal attributes and make recommendations based on demographic classes. The benefit of a demographic approach is that it may not require a history of user ratings of the type needed by collaborative and content-based techniques. 
     Utility-based and knowledge-based systems do not attempt to build long-term generalizations about their users, but rather base their advice on an evaluation of the match between a user&#39;s need and the set of options available. Utility-based systems make suggestions based on a computation of the utility of each object for the user. The benefit of a utility-based recommendation is that it can factor non-product attributes, such as vendor reliability and product availability, into the utility computation, making it possible for example to trade off price against delivery schedule for a user who has an immediate need. Knowledge-based systems suggest objects based on inferences about a user&#39;s needs and preferences. Knowledge-based approaches have knowledge about how a particular item meets a particular user&#39;s need and can therefore reason about the relationship between a need and a possible recommendation. 
     Referring now to  FIG. 1 , a block diagram of a computing system that may be used to search the Internet by a user is depicted, according to at least one embodiment. The networked computer environment  100  may include a client computing device  102 , a recommendation server  110  and one or more search servers  120 , interconnected via a communication network  140 . According to at least one implementation, the networked computer environment  100  may include a plurality of client computing devices  102  of which only one is shown for illustrative brevity. 
     The communication network  140  may include various types of communication networks, such as a wide area network (WAN), local area network (LAN), a telecommunication network, a wireless network, a public switched network and/or a satellite network. The communication network  140  may include connections, such as wire, wireless communication links, or fiber optic cables. It may be appreciated that  FIG. 1  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 environments may be made based on design and implementation requirements. Accordingly, the communication network  140  may represent any communication pathway between the various components of the networked computer environment  100 . 
     Client computing device  102  may include a web browser  106  displaying a search engine website and configured to communicate with at least one of the search servers  120  via the communication network  140 , in accordance with an exemplary embodiment. The web browser  106  may provide a user interface in which a user utilizing the client computing device  102  may enter a search input and receive search results where the search results are generated according to the exemplary embodiments. Client computing device  102  may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing device capable of running a program and accessing a network. As will be discussed with reference to  FIG. 3 , the client computing device  102  may include computing system  300 . 
     The recommendation server  110  may be a laptop computer, netbook computer, personal computer (PC), a desktop computer, or any programmable electronic device or any network of programmable electronic devices capable of hosting and running a weight program  112  and a database  114 . The recommendation server  110  is configured to receive search results as input from the one or more search servers  120  and generate a modified list of search results based on applying the output of the weight program  112  to the search servers  120 . The weight program  112  may calculate weights to apply to the search servers  120  based on stored information about the user, e.g., user profile, and also stored information about the search engine  122 , e.g., information about its recommendation technique. By applying a corresponding weight that is specific to the user to the search servers  120 , the search results from the search servers  120  may be prioritized when generating the modified search results that is provided to the client computing device  102 . The database  114  may be used to store user profile information, any information about the search engine  122 , including any potential custom or default weights to apply in the stages of the method below, as well as the medium for updating the weights applied by the weight program  112  in the feedback stage described in the method below. For example, the database  114  may store user profiles corresponding to each user utilizing the features of the exemplary embodiments where each user profile may include historical information used to determine weights that are to be applied to the search servers  120 . 
     The search server  120  may be a laptop computer, netbook computer, personal computer (PC), a desktop computer, or any programmable electronic device or any network of programmable electronic devices capable of hosting and running the search engine  122 . The search engine  122  is configured to receive a search input from a user via the web browser  106  on the client computing device  102 . The search server  120  is configured to process the search terms that are received from the user and return an ordered list of search results. The order of the list is determined by the search process that the search engine  122  and search server  120  are configured to utilize. It is noted that, without the exemplary embodiments, the search server  120  may transmit the determined search results to the user via the search engine  122  and web browser  106 . However, according to the exemplary embodiments, the search server  120  may provide its search results to the recommendation server  110  for subsequent processing. 
     The recommendation server  110  and search server  120  may communicate with the client computing device  102  via the communication network  140 , in accordance with embodiments of the invention. As will be discussed with reference to  FIG. 3 , the recommendation server  110  and search server  120  may each include computing system  300 . As will be discussed with reference to  FIGS. 4 and 5 , the recommendation server  110  and search server  120  may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). The recommendation server  110  and search server  120  may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud. 
     Referring to  FIG. 2 , an operational flowchart illustrating an Internet search recommendation refinement process  200  is depicted according to at least one embodiment. At  202 , the recommendation server  110  receives a search input from a user via the web browser  106  on the client computing device  102 . For example, a user wishes to conduct a search of the Internet and enters explicit search terms into the web browser  106  using an appropriate input device, e.g., mouse, keyboard, microphone, etc. As a result of providing a search input, a plurality of the search engines  122  may receive the search input to perform a search of the Internet and generate a respective list of weighted or scored results of the search. Each of the search engines  122  may generate its own set of results with the items scored and weighted by the internal processes of the search engine  122 . It should be noted that these internal processes of the search engine  122  need not reside on the search server  120  and may include a separate recommendation program and algorithm from the search engine  122  itself. The search results from the search engines  122  may be gathered as sets of search results for input to the weight program  112 , where each set corresponds to a particular one of the search engines  122 . 
     At  204 , the weight program  112  may interface with web servers to find an online history of the user, or user profile. Examples of available online historical data are purchase history, e.g., the user has bought a particular product or type of products more frequently, item category/attributes, e.g., the user only considers buying electronic devices that have fingerprint support, social network posts or profiles, e.g., the user blogged or shared interest in a product over his/her social network, IoT data, e.g., an IoT server in the cloud may have information on what the user is interested in, based on the data from his/her smart home and office, etc. This online historical data for the user may be analyzed and combined to generate, or add to, a user profile containing, for instance, a history of items purchased or viewed by the user, and interests of the user determined from social media. The profile data of the user may be analyzed to determine product category or attribute requirements of the user. Any analysis results may be stored in the database  114  for use in determining how to weight search results for the user. In addition, the user profile may indicate a preferred one of the search engines  122  for the user such that it may weight results from that search engine  122  accordingly or other search engines  122  that use a similar recommendation technique. 
     At  206 , the weight program  112  may determine the various search engines  122  for which the modified search results may be based. For example, a user may enter the search terms “best computer printer”, expecting a list of results that includes product recommendations. A search history of the user may indicate the search engines  122  that have been used. In another example, the recommendation server  110  may be associated with a plurality of the search engines  122  that are to be used in generating the modified search results. By identifying the search engines  122 , the weight program  112  may determine an appropriate weight for search results that come from the specific search engine in addition to the weight information that comes from the user profile of the user who submitted the search input. The weight program  112  may determine each search engine  122  that is in use separately. There are also many types of recommendation techniques that may be used by different search engines  122  and there may be multiple search engines  122  using the same type. It should also be noted that it is not necessary to identify the exact search engine  122  by name but rather the type of recommendation technique that the search engine  122  uses. 
     At  208 , the weight program  112  may determine from the user profile that may be created or accessed at  204  and the identification of the search engine  122  at  206  whether or not its database  114  contains specific weights for the users and search engines  122  that may be applied to the search results gathered at  202 . If the weight program  112  identifies a set of specific weights that it may use, those weights may be assigned to the search results at step  210 . If there are no specific weights already available for the user, the weight program  112  may assign a set of predetermined default weights to the search results at step  212 . At either stage  210  or  212 , because each search engine  122  may output its own recommendation score for each search result, it may be necessary to normalize the recommendation scores of the search engines  122 , e.g., into the range of 0 to 1, prior to assignment by the weight program  112 . The initial preference weight that is assigned, whether default or retrieved from a user profile, may also be calculated for each recommendation engine based on the number of search engines  122  that are used in the initial search. For example, if there are K search engines  122  that were used with the search terms, the initial weight for each search engine may be adjusted by a factor of 1/K. 
     At  214 , the weight program  112  may apply the assigned weight to each search server  120  and compute a final recommendation weight or score that may be the product of the weight assigned by the search engine  122  and the assigned weight from the weight program  112 . All results from the search engines  122  may be scored in this way through weighting the search server  120  and a mixed recommendation list may be displayed to the user without regard to the search engine  122  that produced the original result. The mixed recommendation list may be presented to the user in weighted or scored order, such that the highest recommendation weight or score is at the top of the list. 
     At  216 , the weight program  112  may log user interactions with the mixed recommendation list, such as a number of clicks on a search result or the time spent on specific search results or any other action that a user takes in browsing the mixed recommendation list that is being displayed. The number of interactions may be accumulated as feedback for each search engine  122  separately during a configured time window. For example, if there are two (2) search engines  122  being used, and for the first search engine there are m interactions logged, and n interactions logged for the second search engine during the last business week, a new user focusing weight for each search engine may be calculated based on the accumulated user feedback. This user focusing weight, wf, may be normalized to the range of 0 to 1 and in the referenced example, wf for the first search engine would be m/(m+n), and wf for the second search engine would be n/(m+n). It should be noted that the feedback may be used to adjust the weights that are assigned in steps  210  or  212  and applied in step  214  and this is just one sample calculation to make that update. 
     At  218 , the weight program  112  may update its database  114  such that the weight assigned to a specific search engine may be adjusted by the user focusing weight wf that was calculated in  216 . As an example, a preference weight wp may already be associated with the search engine  122  within the database  114  from the initial identification of the search engine  122  or from a previous profile update. At step  218 , this preference weight wp for search engine  122  may be updated as wp+α*(wf−wp) where α is an update factor to control how fast the feedback will be applied into hybrid recommendation results. In this way, the weight program  112  is consistently updating its database  114  to provide appropriate weights for ordering mixed recommendation lists that may be received. 
     At  220 , the weight program  112  may also use the information gathered at the feedback stage  216  to update the specific weights in the user profile that may be used in subsequent runs of the weight program  112  at step  210 . For example, if the user has expressed through interacting with the search results that they prefer certain recommendations, the weight program  112  may update the weight that is calculated for that set of search engine  122  and user to weight those results more heavily. When the weight program  112  assigns weights to search results again for that user, the results from the preferred search engine  122  will be higher in the resulting list and increase the user&#39;s satisfaction with the results. 
     Referring to  FIG. 3 , a block diagram is depicted illustrating a computer system  300  which may be embedded in the client computing device  102 , the recommendation server  110  and search server  120  depicted in  FIG. 1  in accordance with an embodiment. It should be appreciated that  FIG. 3  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 environments may be made based on design and implementation requirements. 
     As shown, a computer system  300  includes a processor unit  302 , a memory unit  304 , a persistent storage  306 , a communications unit  312 , an input/output unit  314 , a display  316 , and a system bus  310 . Computer programs such as the weight program  112 , web browser  106  or search engine  122  are typically stored in the persistent storage  306  until they are needed for execution, at which time the programs are brought into the memory unit  304  so that they can be directly accessed by the processor unit  302 . The processor unit  302  selects a part of memory unit  304  to read and/or write by using an address that the processor  302  gives to memory  304  along with a request to read and/or write. Usually, the reading and interpretation of an encoded instruction at an address causes the processor  302  to fetch a subsequent instruction, either at a subsequent address or some other address. The processor unit  302 , memory unit  304 , persistent storage  306 , communications unit  312 , input/output unit  314 , and display  316  interface with each other through the system bus  310 . 
     Examples of computing systems, environments, and/or configurations that may be represented by the data processing system  300  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, and distributed cloud computing environments that include any of the above systems or devices. 
     Each computing system  300  also includes a communications unit  312  such as TCP/IP adapter cards, wireless Wi-Fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The web browser  106  in the client computing device  102 , the weight program  112  in the recommendation server  110  and the search engine  122  in the search server  120  may communicate with external computers via a network (for example, the Internet, a local area network or other wide area network) and respective network adapters or interfaces  312 . From the network adapters or interfaces  312 , the web browser  106  in the client computing device  102 , the weight program  112  in the recommendation server  110  and the search engine  122  in the search server  120  are loaded into the respective persistent storage  306 . The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. 
     It is to be understood 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 e-mail). 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 that includes a network of interconnected nodes. 
     Referring now to  FIG. 4 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  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  50  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  54 A-N shown in  FIG. 4  are intended to be illustrative only and that computing nodes  610  and cloud computing environment  50  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. 5 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 4 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 5  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  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  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 include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  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  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and Internet search recommendation refining  96 . 
     Embodiments of the present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. 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, configuration data for integrated circuitry, 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 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 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 blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, 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.