TARGET INSIGHT MEDIA ENGINE

A system, method, and computer program product may identify a preferred geographic area for targeted advertising for a given brand to a targeted consumer. A set of locations of residences of actual consumers of physical brand locations may first be determined. The actual distance traveled by the consumer to the physical brand location is calculated based on the determined residence locations. A sphere of influence for the physical location is identified and a set of geographic areas within the sphere of influence is selected as a target area in which to perform targeted advertising for the particular brand.

BACKGROUND

The present disclosure relates to targeted advertising, and more specifically, to targeted advertisement based on weather and customer reach for a particular brand.

Targeted advertising is a form of advertising where online advertisers can use sophisticated methods to target receptive audiences with certain traits, often based on the product or person the advertiser is promoting. These traits maybe demographic, which may focus on aspects such as income level, location, employment type, or other demographic features, or psychographic, which may focus on the values, personality or attitude of a consumer who has opted-in to allowing their data to be used for such commercial purposes.

Weather-specific technology may empower brands by utilizing customer campaign triggers to anticipate consumer demand and capture market opportunity in anticipation of a weather pattern or the moment weather strikes. Some technologies may use forecast elements such as temperature, wind speed, precipitation, and humidity to launch paid search and display ads based on the real-time weather outside consumers' doors.

SUMMARY

Embodiments of the present disclosure may be directed toward a method for identifying a preferred geographic area for a targeted advertising of a given brand to a target consumer. The method may include determining a set of locations of residences of actual customers. Actual customers may be users who have visited a particular physical location of a given brand. Then an actual distance traveled to the particular physical location of the given brand by the actual customers may be calculated. This calculation may be based on the set of locations of residences of the customers. A sphere of influence (SOI) may be identified based on the calculations of the particular physical location and a set of geographic areas within the SOI may be selected as target areas, where the target areas are areas in which to perform the targeted advertising for the particular brand.

Other aspects of the disclosure may be directed toward a method for identifying a set of demographic profiles for a set of individuals within the target areas. This may include using the SOI and look-alike modeling to more accurately pinpoint geographic areas outside of the identified SOI for targeted advertising. Demographic data and modeling can be used to identify geographic areas like zip codes outside of a current customer base.

Other aspects of the disclosure may be directed toward a method for applying weather data to a machine learning system in order to identify a set of weather-based advertising triggers. Thus, current weather data may be used to generate a set of location and content based triggers. The weather-based advertising triggers are predictive of which set of advertisements are preferred for a current weather pattern in the target areas.

In embodiments, the set of locations of residence of actual consumers may be determined by identifying an average geographic location for a user between a certain time period (for example, early in the morning), and assigning it as the location of residence for a user. The method may then involve determining that a user is an actual customer of an actual brand and assigning as a data point the location of residence of the user as one of an actual customer.

Embodiments of the present disclosure may be directed toward a system for identifying a preferred geographic area for a targeted advertising of a given brand to a target consumer. The system may include engines configured to determine a set of locations of residences of actual customers. Actual customers may be users who have visited a particular physical location of a given brand. Then an actual distance traveled to the particular physical location of the given brand by the actual customers may be calculated. This calculation may be based on the set of locations of residences of the customers. A sphere of influence (SOI) may be identified based on the calculations of the particular physical location and a set of geographic areas within the SOI may be selected as target areas, where the target areas are areas in which to perform the targeted advertising for the particular brand.

Other aspects of the disclosure may be directed toward a system for identifying a set of demographic profiles for a set of individuals within the target areas. This may include using the SOI and look-alike modeling to more accurately pinpoint geographic areas outside of the identified SOI for targeted advertising. Demographic data and modeling can be used to identify geographic areas like zip codes outside of a current customer base.

Other aspects of the disclosure may be directed toward a system for applying weather data to a machine learning system in order to identify a set of weather-based advertising triggers. Thus, current weather data may be used to generate a set of location and content based triggers. The weather-based advertising triggers are predictive of which set of advertisements are preferred for a current weather pattern in the target areas.

In embodiments, the system may be configured to perform a method, wherein the set of locations of residence of actual consumers may be determined by identifying an average geographic location for a user between a certain time period (for example, early in the morning), and assigning it as the location of residence for a user. The method may then involve determining that a user is an actual customer of an actual brand and assigning as a data point the location of residence of the user as one of an actual customer.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method for identifying a preferred geographic area for a targeted advertising of a given brand to a target consumer. The method may include determining a set of locations of residences of actual customers. Actual customers may be users who have visited a particular physical location of a given brand. Then an actual distance traveled to the particular physical location of the given brand by the actual customers may be calculated. This calculation may be based on the set of locations of residences of the customers. A sphere of influence (SOI) may be identified based on the calculations of the particular physical location and a set of geographic areas within the SOI may be selected as target areas, where the target areas are areas in which to perform the targeted advertising for the particular brand.

Other aspects of the disclosure may be directed toward a method for identifying a set of demographic profiles for a set of individuals within the target areas. This may include using the SOI and look-alike modeling to more accurately pinpoint geographic areas outside of the identified SOI for targeted advertising. Demographic data and modeling can be used to identify geographic areas like zip codes outside of a current customer base.

Other aspects of the disclosure may be directed toward a method for applying weather data to a machine learning system in order to identify a set of weather-based advertising triggers. Thus, current weather data may be used to generate a set of location and content based triggers. The weather-based advertising triggers are predictive of which set of advertisements are preferred for a current weather pattern in the target areas.

In embodiments, the set of locations of residence of actual consumers may be determined by identifying an average geographic location for a user between a certain time period (for example, early in the morning), and assigning it as the location of residence for a user. The method may then involve determining that a user is an actual customer of an actual brand and assigning as a data point the location of residence of the user as one of an actual customer.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to targeted advertising, more particular aspects targeted advertising based on location and weather. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

Weather influences our mood, eating habits, choice of clothes, decisions about going out or staying inside, and so many others, often to our disbelief, our purchasing decisions. It has been show to affect how consumers feel, their moods, and thus, to an extent, their intent to make a purchase. Understanding how weather factors into influencing consumer behavior is a first step toward designing weather-based marketing campaigns.

Location and weather triggered advertising may currently lack an ability to dynamically target users. Location based advertisements may be triggered if potential consumers are within a store or within a predetermined geo-fenced area and subject to a host of regulation and privacy laws. Weather based advertisements may trigger on specific weather conditions, but they are limited in their targeting methodologies. Thus, valuable data regarding consumer trends and its distinct ties to weather patterns may be lost.

As discussed herein, while several attempts have been made to harness the power of weather-triggered advertising and location-based advertisements, none have attempted to capture, synthesize, and integrate the exponential increase in value of the two approaches used conjointly. The disclosed herein represents an improvement to a computer system in that it may use models and predetermined configurations (e.g., filters as described herein) to reduce CPU requirements in predicting buyer behavior. It also may use a combination of models (sphere of influence models, look alike models, weather-based models) to more accurately target potential customers for a particular brand in a manner that reduces computing power requirements (CPU usage) as well as time requirements (e.g., fewer delays). The disclosed herein may also improve presentation of data (e.g., weather-based triggers) in a computer system through the use of a display or UI configured according to user settings.

According to embodiments, a method, system, and computer program product may be used to dynamically identify preferred zip codes for targeted advertising based on distances traveled by users from their homes to each physical location of a brand (e.g., each brand point of interest (POI)). Their homes may be equated to their estimated sleep locations, and the physical location of a brand may be, for example, a brick-and-mortar location of a store.

The computer system may also be used to dynamically identify preferred zip codes for targeted advertising by estimating an ideal consumer characteristics based on estimated sleep locations of brand visitors and utilizing look-a-like modeling to target optimal demographic and location-based behavioral profiles for each brand.

The system may identify a preferred set of target geographic areas outside of the identified areas of influence within a Designated Marketing Area (DMA) during particular weather patterns by synthesizing geographic consumer data, location-based ping and journey data, demographic data, and weather data. First, the system may be used to identify a sphere of influence (SOI). The SOI may be a determined reach (e.g., a geographic distance in each direction) that indicates how far consumers are willing to travel to a brand's commercial point of interest (POI) (e.g., a physical location of the brand, as described herein). For example, an SOI may indicate that a consumer in a particular city will travel on average a maximum of 1.5 miles in order to reach the brand's store (i.e. the brand's commercial POI).

Additionally, a more complex analysis may be used to determine the SOI. For example, a specific zip code from which a set of users is traveling may be identified and monitored. In embodiments, a set of consumers may be travelling from their specific zip code in the suburbs into the city (e.g., to the brand POI). The system may identify the time of day, the day of the week, the type of day (e.g., weekday or weekend), the weather, or other appropriate factors that may impact a triggering of a SOI for the particular POI. In other embodiments, a SOI may identified not exclusively as a radial distance (e.g., 3 miles or 5 miles away), but rather as an identified set of zip codes, based on factors including those described herein. For example, a SOI may include a zip code about 5 miles away from the POI, but may exclude a neighboring zip code (e.g., one only 2 miles away from the POI), if the zip code is less accessible (e.g., by public transit, by well-paved roads, etc.), or if for some other reason there is less traffic from the zip code to the POI (e.g., few consumers living in the zip code, residents of the zip code who are less inclined to purchase from the brand, etc.).

Second, the system may identify a set of demographic profiles for a set of individuals within the SOI. Using look-alike modeling and the demographic profiles of consumers and those within the target areas, additional areas outside of the SOI may be identified as additional target areas. For example, a set of users outside of the 1.5 miles may have demographic data substantially similar to the some of the brands highest-value consumers inside the SOI. In some cases, based on the modeling, these consumers, via their locations of residence, may be identified as potential consumers with their locations (e.g., zip codes) identified as additional target areas for advertisement. Third, weather data may be applied, using natural language processing, to determine which advertisements for the particular brand may be best suited for each of these target areas and additional target areas during a particular weather pattern or the current weather in the location.

While this determination may be made according to various embodiments, one example is described below:

In embodiments, an SOI is calculated for all of a brand's POI in a selected portion of a geographic area (for example, a designated marketing area (DMA) may be used). First, visitors from the selected portion of the geographic area may be removed, in order to perform the calculation using primarily residents. Then, a distance traveled by users is calculated using the Haversine formula. Third, the travel distance two standard deviation above the mean is calculated. This may be known as the SOI for that particular brand's POI. Finally, all zip codes that fall within the SOI in the selected portion of the geographic area zip codes that include POI visitors and zip codes with look-a-like demographics may be selected, as described below.

In embodiments, look-alike modeling may be performed using relevant users who visit the POI for brand. In embodiments, and as described herein, a sleep location of all users is calculated utilizing the median latitude and longitude between a predetermined range of time. For example, a sleep location could be determined using the median latitude and longitude between 3 AM and 5 AM for a user. This location is then converted into a census tract (i.e., a Bureau of Census-established grouping, roughly equivalent to a neighborhood), and then to a zip code for each user. Next, the demographics at the zip code level are merged into the user data. In some embodiments, this may create a profile for a user or set of users, on an individual or zip code level of individuation. In embodiments, the demographic may include consumer, household, and neighborhood data. This demographic data may be identified using census data, or other readily available consumer data. Then, visitation rates may be calculated at each zip code level and decision tree models may be generated to find demographic threshold where brand visitation rate is highest. Finally, all zip codes within the DMA and SOI with demographic profiles that suggest higher visitation rates may be identified and selected. Embodiments as described herein may use and access data (e.g., location data) that are user-consent based and fully compliant with all applicable regulations and laws.

Finally, weather triggers are generated using the pre-selected zip codes (based on the above higher visitation based selections) and weather factors that drive higher brand visitation. In embodiments, the triggers may be generated by models trained for each DMA and season. First, the footfall at all brand POI locations in a DMA is counted on a daily basis. The POI locations may be updated on a regular basis, for example, on a monthly basis. Next, weather data may be aggregated at a daily level for historical, current, and forecasted conditions. Then, higher than and lower than median visitation days are identified for prediction. Then, a decision tree may be build utilizing weather variable to identify weather conditions that impact or increase brand visitation rates. In some embodiments, models may be adjusted or modified according to various error testing rates or accuracy thresholds. For example, models with out of sample test scores with an absolute change in 10% relative to the training set are removed. Finally trigger files are generated using rules for each DMA and season to alert in pre-selected zip codes based on the identified weather conditions in real-time when the system is called.

FIG. 1depicts a system diagram of a system100for generating a set of weather triggers in a set of target areas based on current weather patterns, according to embodiments. System100ofFIG. 1is carried out on various computer processing circuits, and may include more, fewer, or different engines than those described herein.

A trigger generation module118may comprise a set of engines including a sphere of influence engine122, a look-alike generator engine120, a blended filter engine124, and a weather data integration engine126. These engines, as well as the other elements of the system100may be physically or communicatively coupled, and may communicate bidirectionally over a network or networks112. The network112can include, but are not limited to, local area networks, point-to-point communications, wide area networks, the global Internet, and combinations thereof.

A client-specific POI locator module102may be used to query and store brand-based data for a particular client. In embodiments, a set of client data may be stored in a client data database104. This data can include brand data including branding logos, company information, and category data related to the type of business or retail in which the particular brand engages. For example, a client data database104could contain the name of a brand (“NAME”) and a category in which the brand operates, (e.g., “outdoor clothing”). This data may be obtained from the client itself, from external sources including the Internet, from internally-generated processes, or in another way as appropriate to the client. In embodiments, the client-specific POI locator module102may also comprise a points of interest database106, which may store data including geographic or location data, type of POI data, and other data related to the particular set of POIs for a brand. For example, the points of interest database106could contain a street address and a latitudinal and longitudinal coordinate set for a flagship store. In embodiments, databases104and106may be updated at regular intervals or based on changes in the POIs (e.g., an addition or loss of a POI), at an administrator request, or upon another setting-based trigger. In embodiments, a POI for a brand may be a brick-and-mortar store location for the brand, a brick-and-mortar retail location that carries the brand, a physical pop-up location for the brand, or another brand-specific physical location.

A consumer characteristic profiles module108may contain a hyper-local demographic database110. The hyper-local demographic data contained in the database may contain a set of demographic data described herein, associated with a particular zip code, may comprise demographic data including consumer, household and neighborhood or zip code-specific data. The consumer characteristic profiles module108may compile and organize profiles for individual users or zip codes.

The consumer characteristics profile module108may also access mobile user data stored in a mobile user databased114, described herein, which may include data regarding a user's location of residence, as determined by a user's median location during a predetermined period of time (e.g., during early morning hours). The mobile user database114may also contain data regarding a set of “pings” at each of a brand's POIs. In embodiments a broad set of “pings” for each location of the user may be filtered out to generate a set of brand-specific pings (e.g., pings at a POI for the particular brand). As used herein, ‘pings’ will refer to the filtered set of brand-specific POI check ins. A ping may be a recording of an instance (i.e., a count) of a user entering a space or a radius around a pre-identified location, in this case a POI for a particular brand. The location of the mobile user may be identified using a smart phone, laptop computer, tablet, personal fitness device, or any other suitable location-detecting device. This data may be merged by the consumer characteristic profiles module108with the hyper-local data from the hyper-local demographic data110to generate a profile or set of profiles for users entering the POIs for the particular brand.

In embodiments, the system may generate a set of profiles or ‘personas’ based on a tracking of behaviors or a set of behaviors of users and brand-specific pings. For example, a profile for a ‘commuter’ persona may be generated based on a historical or anticipated collection of behaviors (e.g., a commuter from the suburbs and his or her purchasing habits and relation to the POI). Another example may include a small business owner persona, wherein a set of behaviors relative to the brand's POI, typical to a small business owner may be generated. Other personas may be generated and stored in profile, to help with the clustering of the look-alike modeling.

The sphere of influence engine122of the trigger generation module118may also access mobile user data, for example a mobile user's pings to a POI for the particular brand, from the mobile user database114over the network112, and based on the data select a distinct set of users (for example, by location, by store, by hour, etc.). For each of these users, the SOI engine122may then calcite average travel distances to each POI, and identify zip codes that fall within the average travel distances, as described herein. These identified zip codes may be referred to as “target areas.”

The look-alike generator engine120may access profile data (e.g., merged hyper-local demographic data mapped to a user's home location) and aggregate it by store, by location, and by time and the zip codes identified by the SOI engine122. This data may be used by the look-alike generator engine120to train the model to find a key set of consumer characteristics. For example, the key set of consumer characteristics may reflect various demographic similarities amongst a set of consumers, for example, a set of highly-influenced or highly-active consumers for the particular brand. Thus, a group of users may be identified who demographically look and may act like those highly-influence or highly-active consumers in the original target areas. In this way, a look-alike model is generated and used to identify an additional set of geographic areas (e.g., zip codes) that most or more similarly resemble the key set of consumer characteristics more than those areas surrounding the additional set of geographic areas. The additional set of geographic areas or zip codes identified by the look-alike generator engine120may be referred to as additional target areas.

The blended filter engine124may then access the target areas identified by the sphere of influence engine122and the additional target areas identified by the look-alike generator engine120, and combine the two into a robust set of brand target areas.

The weather data integration engine126may access location-specific weather data from a location-specific weather database116. The location-specific weather database116may store weather data obtained from the Internet or other sources, for each particular store location. The data may also include time data associated with the weather data. The database116may also comprise a set of city-specific and season-specific data, for example a zip code-to-city mapping, as well as a set of region-specific season definitions. The weather data integration engine126may synthesize this data and model the effect of the weather on visitors to each store. Based on this data, the engine126may generate and output weather triggers. For example, a set of weather triggers may be generated and output to THE WEATHER COMPANY into a WEATHERFX product. The model used by the weather data integration engine126may be rerun at a particular predetermined increment (e.g., monthly), to ingest new data including demographics data, user location ping behavior and weather characteristics, to update the preferred weather variable and conditions to trigger the identified target areas inside each DMA. As discussed in more detail herein, models may be trained for each DMA and each season.

FIG. 2depicts a flow diagram of a method200for identifying a sphere of influence, according to embodiments of the present disclosure. The method200may start by determining a location of residences of brick-and-mortar brand consumers, per202. For example, the system may determine a street address or a latitude and longitude data set for the home of a consumer who shops at the brand's primary physical location a mile from her home. The location of the home may be determined by a users identified average location during a predetermined period of time. The user may be identified as an actual consumer at the brand's brick-and-mortar store by a ping, generated when her personal fitness device entered the store's geographic radius. The ping could also be generated based on a particular threshold of time spent in the location, based on a purchase, or based on another user-based indicator of consumer interest.

At204, the system may calculate an average distance traveled to the brick-and-mortar store for the particular brand. This may be calculated using the haversine formula for users determined to have visited the POI, based on the user's identified location of residence. As noted herein, ‘visitors’, as defined as those determined to reside outside of the DMA, may be removed from the calculation. The travel distance one standard deviation away from the mean may be calculated. Per206, this calculation may then be referred to as the SOI for the brand's POI (e.g., the particular store). Zip codes that fall within the identified SOI may be selected as target areas, per208. The method200may end upon the selection of target areas. In embodiments, method300may start as a step following step208.

FIG. 3depicts a flow diagram of a method300for identifying additional target areas and generating weather-based triggers for the areas, according to embodiments. As discussed herein, method300may be carried out over a series of processors and may begin at the end of method200, using the target areas selected at208of method200.

Method300may start with identifying a target area or areas, per302. In embodiments, the target areas may be the zip codes within the SOI identified for targeted advertising in method200. At304the system may identify demographic profiles for individuals within the target areas. See further description of how the demographic profiles for the particular individuals may be generated. The demographic data can include consumer and household purchasing data, income data, and other relevant demographic data.

Using look-alike modeling, geographic areas outside of the SOI may be identified. In embodiments, this may a calculations of visitation rates for the particular POI for the particular brand for each zip code. For example, a visitation rate could numerically describe the frequency at which residents of the particular target area visit the identified POI. A decision tree may generated to find demographic thresholds in locations where brand visitation rate is the highest, and a set of geographic areas (e.g., zip codes) with demographic profiles that correlate with those higher visitation rates may be identified. Per306, these additional geographic areas may be identified as additional target areas. At308, the additional target areas may be added to the previously identified set of target areas to generate a full set of brand target areas.

At310, weather data may be applied to a machine learning system, to model weather impact on brand visitation rates. In embodiments, models may be trained for each DMA in each season. Models could be trained on various other levels of specificity, as preferred by the client. In embodiments, a daily count of the footfall (e.g., footfall) at all brand POI locations (e.g., all brick-and-mortar retail locations for a particular brand) may be taken and update on a monthly or weekly basis. Next, weather data may be aggregated on a daily level. In embodiments, time-of-day data could be aggregated along with the weather data, allowing for various levels of granularity in weather data analysis. The system may then identify higher than median visitations days for use in prediction. Using this data, a decision tree may be build using weather variables to identify weather conditions that increase brand visitation rates. Finally, rules based on the decision tree may be used to generate trigger files, which may be specific to each POI (e.g., a different trigger file for each store) and for each season (e.g., a different trigger file or set of trigger files for each store in each season), per312. The trigger files may indicate a predicted more effective advertisement or set of advertisements for a brand's POI during a particular identified weather. Thus, a store would be notified of the preferred advertisement for based on or in anticipation of the day's weather for the identified particular user set residing in the set of target areas. The method300may then end. According to embodiments, the system may be updated, with triggers revised, removed, or added on a regular basis, according to system and client preferences.

FIG. 4depicts a flow of map images400to depict the use of a series of filters to identify a robust set of target areas, according to embodiments. In embodiments, the progression of images400may be reflective of steps200-308, ofFIGS. 2 and 3, respectively. At402, an image of a map that represents a particular DMA is depicted. A first set of target areas (e.g., zip codes within each SOI) identified within each POI for the brand are identified by a set of darkened zip codes.

At404, a map image depicts a set of look-alike trigger areas. This application of a look-alike model uses demographic characteristics of those consumers within the SOI, depicted at402, to identify an additional set of target areas. The third map image at406depicts a combined visualization of sphere-of-influenced based target area identification and look-alike model based additional target area identification. The combined set of darkened zip codes indicate a full set of targeted areas for effective trigger-based advertisements.

FIG. 5is a high-level block diagram illustrating an exemplary computer system500that can be used in implementing one or more of the methods, tools, components, and any related functions described herein (e.g., using one or more processor circuits or computer processors of the computer). In some embodiments, the major components of the computer system500comprise one or more processors502, a memory subsystem504, a terminal interface512, a storage interface516, an input/output device interface514, and a network interface518, all of which can be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus503, an input/output bus508, bus interface unit507, and an input/output bus interface unit510.

The computer system500contains one or more general-purpose programmable central processing units (CPUs)502-1,502-2, and502-N, herein collectively referred to as the CPU502. In some embodiments, the computer system500contains multiple processors typical of a relatively large system; however, in other embodiments the computer system500can alternatively be a single CPU system. Each CPU502may execute instructions stored in the memory subsystem510and can include one or more levels of on-board cache.

The memory504can include a random-access semiconductor memory, storage device, or storage medium (either volatile or non-volatile) for storing or encoding data and programs. In some embodiments, the memory504represents the entire virtual memory of the computer system500, and may also include the virtual memory of other computer systems coupled to the computer system500or connected via a network. The memory504is conceptually a single monolithic entity, but in other embodiments the memory504is a more complex arrangement, such as a hierarchy of caches and other memory devices. For example, memory may exist in multiple levels of caches, and these caches may be further divided by function, so that one cache holds instructions while another holds non-instruction data, which is used by the processor or processors. Memory can be further distributed and associated with different CPUs or sets of CPUs, as is known in any of various so-called non-uniform memory access (NUMA) computer architectures.

These components are illustrated as being included within the memory504in the computer system500. However, in other embodiments, some or all of these components may be on different computer systems and may be accessed remotely, e.g., via a network. The computer system500may use virtual addressing mechanisms that allow the programs of the computer system500to behave as if they only have access to a large, single storage entity instead of access to multiple, smaller storage entities. Further, although these components are illustrated as being separate entities, in other embodiments some of these components, portions of some of these components, or all of these components may be packaged together.

In an embodiment, the trigger generation module520includes instructions that execute on the processor502or instructions that are interpreted by instructions that execute on the processor502to carry out the functions as further described in this disclosure. In another embodiment, the trigger generation module520is implemented in hardware via semiconductor devices, chips, logical gates, circuits, circuit cards, and/or other physical hardware devices in lieu of, or in addition to, a processor-based system. In another embodiment, the trigger generation module520includes data in addition to instructions. In embodiments, trigger generation module520may be the trigger generation module118ofFIG. 1.

Although the memory bus503is shown inFIG. 5as a single bus structure providing a direct communication path among the CPUs502, the memory subsystem510, the display system506, the bus interface507, and the input/output bus interface510, the memory bus503can, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the input/output bus interface510and the input/output bus508are shown as single respective units, the computer system500may, in some embodiments, contain multiple input/output bus interface units510, multiple input/output buses508, or both. Further, while multiple input/output interface units are shown, which separate the input/output bus508from various communications paths running to the various input/output devices, in other embodiments some or all of the input/output devices may be connected directly to one or more system input/output buses.

The computer system500may include a bus interface unit507to handle communications among the processor502, the memory504, a display system506, and the input/output bus interface unit510. The input/output bus interface unit510may be coupled with the input/output bus508for transferring data to and from the various input/output units. The input/output bus interface unit510communicates with multiple input/output interface units512,514,516, and518, which are also known as input/output processors (IOPs) or input/output adapters (IOAs), through the input/output bus508. The display system506may include a display controller. The display controller may provide visual, audio, or both types of data to a display device505. The display system506may be coupled with a display device505, such as a standalone display screen, computer monitor, television, or a tablet or handheld device display. In alternate embodiments, one or more of the functions provided by the display system506may be on board a processor502integrated circuit. In addition, one or more of the functions provided by the bus interface unit507may be on board a processor502integrated circuit.

It is noted thatFIG. 5is intended to depict the representative major components of an exemplary computer system500. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG. 5, Components other than or in addition to those shown inFIG. 5may be present, and the number, type, and configuration of such components may vary.

In some embodiments, the data storage and retrieval processes described herein could be implemented in a cloud computing environment, which is described below with respect toFIGS. 5 and 6. 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.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 6, illustrative cloud computing environment600is depicted. As shown, cloud computing environment600includes one or more cloud computing nodes610with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone620-1, desktop computer620-2, laptop computer620-3, and/or automobile computer system620-4may communicate. Nodes610may 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 environment600to 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 devices620-1-620-4shown inFIG. 6are intended to be illustrative only and that computing nodes610and cloud computing environment600can 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 toFIG. 7, a set of functional abstraction layers600provided by cloud computing environment700(FIG. 7) is shown. It should be understood in advance that the components, layers, and functions shown inFIG. 6are 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 layer710includes hardware and software components. Examples of hardware components include: mainframes711; RISC (Reduced Instruction Set Computer) architecture based servers712; servers713; blade servers714; storage devices715; and networks and networking components716. In some embodiments, software components include network application server software717and database software718.

Virtualization layer720provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers721; virtual storage722; virtual networks723, including virtual private networks; virtual applications and operating systems724; and virtual clients725.

Workloads layer740provides examples of functionality for which the cloud computing environment can be utilized. Examples of workloads and functions that can be provided from this layer include: mapping and navigation741; software development and lifecycle management742; virtual classroom education delivery743; data analytics processing744; transaction processing745; and machine learning for reaction rule database correction746.