Patent Application
20220198264
The present application is related to the Application entitled “TIME SERIES ANOMALY DETECTION” by Songtao Guo, Patrick Ryan Driscoll, Michael Mario Jennings, Robert Perrin Reeves and Bo Yang, filed concurrently with the present application on the same day, hereby incorporated-by-reference in its entirety.
The present disclosure generally relates to technical problems encountered in machine learning. More specifically, the present disclosure relates to time series anomaly ranking.
The rise of the Internet has occasioned two disparate yet related phenomena: the increase in the presence of online networks, with their corresponding user profiles visible to large numbers of people, and the increase in the use of these online networks to provide content. Online networks are able to gather and track large amounts of data regarding various entities, including organizations and companies. For example, online networks are able to track users who transition from one company to another company and thus, in aggregate, these online networks are able to determine, for example, how many users have left a particular company in a particular time period. Additional details may be known and/or added to these types of metrics, such as which companies the users left the company for, and how many users have joined the particular company during the same time period. Additionally, there are many other metrics that online networks could determine about these companies that may be of interest to users.
An issue arises, however, in determining what to do with this information. There are so many potential metrics and values for the metrics that it can be difficult to determine which metric/value may be more important to convey to users.
An additional technical issue arises in the context of large online networks. Specifically, when dealing with large online networks, the amount of data to be analyzed is enormous. As such, any potential solution would need to be scalable to operate in large online networks.
Some embodiments of the technology are illustrated, by way of example and not limitation, in the figures of the accompanying drawings.
The present disclosure describes, among other things, methods, systems, and computer program products that individually provide various functionality. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments of the present disclosure. It will be evident, however, to one skilled in the art, that the present disclosure may be practiced without all of the specific details.
In an example embodiment, a machine-learned model is trained to rank anomaly points in time series data. The model is capable of being applied in parallel to many different time series simultaneously, allowing for a scalable solution for large scale online networks. The model outputs a ranking score for an input anomaly and allows for ranking of anomalies not just in the same time series but anomalies across multiple time series as well. This ranking can then be used to determine how best to present the ranked anomalies to users in a GUI. In prior art software solutions, anomaly ranking needed to be handled serially, and thus anomaly ranking in time series data on the scale of millions or even billions of data points could not be performed in a reasonable amount of time. In an example embodiment, the anomaly ranking is able to be performed on each time series in parallel, allowing anomaly ranking in time series data on the scale of millions or billions of data points to be performed in a reasonable amount of time.
The disclosed embodiments provide a method, apparatus, and system for training a machine-learned model using a machine learning algorithm to rank anomalous data points in discrete time series. A discrete time series comprises data points separated by time intervals. These time intervals may be regular (e.g., once a month) or irregular (e.g., each time a user logs in). While this disclosure will provide specific examples where the time intervals are regular, one of ordinary skill in the art will recognize that there may be circumstances where the techniques described in the present disclosure can be applied to discrete time series with irregular time intervals.
An application program interface (API) server 114 and a web server 116 are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers 118. The application server(s) 118 host one or more applications 120. The application server(s) 118 are, in turn, shown to be coupled to one or more database servers 124 that facilitate access to one or more databases 126. While the application(s) 120 are shown in
Further, while the client-server system 100 shown in
The web client 106 accesses the various applications 120 via the web interface supported by the web server 116. Similarly, the programmatic client 108 accesses the various services and functions provided by the application(s) 120 via the programmatic interface provided by the API server 114.
In some embodiments, any website referred to herein may comprise online content that may be rendered on a variety of devices including, but not limited to, a desktop personal computer (PC), a laptop, and a mobile device (e.g., a tablet computer, smartphone, etc.). In this respect, any of these devices may be employed by a user to use the features of the present disclosure. In some embodiments, a user can use a mobile app on a mobile device (any of the machines 110, 112 and the third-party server 130 may be a mobile device) to access and browse online content, such as any of the online content disclosed herein. A mobile server (e.g., API server 114) may communicate with the mobile app and the application server(s) 118 in order to make the features of the present disclosure available on the mobile device.
In some embodiments, the networked system 102 may comprise functional components of an online network.
As shown in
An application logic layer may include one or more various application server modules 214, which, in conjunction with the user interface module(s) 212, generate various user interfaces (e.g., web pages) with data retrieved from various data sources in a data layer. In some embodiments, individual application server modules 214 are used to implement the functionality associated with various applications 120 and/or services provided by the online network.
As shown in
Once registered, a user may invite other users, or be invited by other users, to connect via the online network. A “connection” may constitute a bilateral agreement by the users, such that both users acknowledge the establishment of the connection. Similarly, in some embodiments, a user may elect to “follow” another user. In contrast to establishing a connection, the concept of “following” another user typically is a unilateral operation and, at least in some embodiments, does not require acknowledgement or approval by the user that is being followed. When one user follows another, the user who is following may receive status updates (e.g., in an activity or content stream) or other messages published by the user being followed, relating to various activities undertaken by the user being followed. Similarly, when a user follows an organization, the user becomes eligible to receive messages or status updates published on behalf of the organization. For instance, messages or status updates published on behalf of an organization that a user is following will appear in the user's personalized data feed, commonly referred to as an activity stream or content stream. In any case, the various associations and relationships that the users establish with other users, or with other entities and objects, are stored and maintained within a social graph in a social graph database 220.
As users interact with the various applications 120, services, and content made available via the online network, the users' interactions and behavior (e.g., content viewed, links or buttons selected, messages responded to, etc.) may be tracked, and information concerning the users' activities and behavior may be logged or stored, for example, as indicated in
In some embodiments, the databases 218, 220, and 222 may be incorporated into the database(s) 126 in
Although not shown, in some embodiments, a social networking system 210 provides an API module via which applications 120 and services can access various data and services provided or maintained by the online network. For example, using an API, an application may be able to request and/or receive one or more recommendations. Such applications 120 may be browser-based applications 120 or may be operating system-specific. In particular, some applications 120 may reside and execute (at least partially) on one or more mobile devices (e.g., phone or tablet computing devices) with a mobile operating system. Furthermore, while in many cases the applications 120 or services that leverage the API may be applications 120 and services that are developed and maintained by the entity operating the online network, nothing other than data privacy concerns prevents the API from being provided to the public or to certain third parties under special arrangements, thereby making the navigation recommendations available to third-party applications 128 and services.
Although features of the present disclosure are referred to herein as being used or presented in the context of a web page, it is contemplated that any user interface view (e.g., a user interface on a mobile device or on desktop software) is within the scope of the present disclosure.
In an example embodiment, when user profiles are indexed, forward search indexes are created and stored. The search engine 216 facilitates the indexing and searching for content within the online network, such as the indexing and searching for data or information contained in the data layer, such as profile data (stored, e.g., in the profile database 218), social graph data (stored, e.g., in the social graph database 220), and user activity and behavior data (stored, e.g., in the user activity and behavior database 222). The search engine 216 may collect, parse, and/or store data in an index or other similar structure to facilitate the identification and retrieval of information in response to received queries for information. This may include, but is not limited to, forward search indexes, inverted indexes, N-gram indexes, and so on.
An insights engine 300 may generate one or more insights regarding data obtained from one or more databases. These databases may include, for example, profile database 218, social graph database 220, and/or user activity and behavior database 222, among others. In an example embodiment, the insights engine 300 may include an anomaly detector 302 and an anomaly ranker 304. The anomaly detector 302 acts to identify one or more anomalies in one or more time series generated from data obtained from the databases 218, 220, 222. The anomaly ranker 304 then ranks these identified anomalies. The present disclosure focuses on the anomaly ranker 304 component.
In an example embodiment, the anomaly ranker 304 includes an anomaly strength machine learned model 306 that is trained to generate an anomaly strength score for an input anomaly (such as those detected by the anomaly detector 302) and normalize the anomaly strength score for cross-time series comparisons. In other words, the anomaly strength score is indicative of both the magnitude of the anomaly's variation from the expected value in the time series where the anomaly lies as well, normalized based on the relative importance of this variation with respect to variations of anomalies in other time series. This model respects both the anomaly's deviation from other neighbor points as well as the gap between expectation and observation.
Definition 1. (Univariate time series) A univariate time series X={xt}t∈T is an ordered set of real-valued observations, where each observation is recorded at a specific time t∈T⊆Z+. Then, xt is the point or observation collected at time t and X[p,p+n−1]=xp, xp+1, . . . , xp+n−1 is the subsequence of length n≤|T| starting at position p of the time series X, for p, t∈T and p≤|T|−n+1. It is assumed that each observation xt is a realized value of a certain random variable Xt. In an example embodiment, all the values in a time series are non-negative integers.
Definition 2. (Anomaly) given a univariate time series, a point at time t can be declared an anomaly if the distance to its expected value is higher than a predefined threshold τ:
|xt−{circumflex over (x)}t|>τ
where xt is the observed data point, and {circumflex over (x)}t is the corresponding expected value.
There are various ways to compute {circumflex over (x)}t and τ, but they are all based on fitting a model. In an example embodiment, prediction model-based methods are used where {circumflex over (x)}t is estimated based on previous observations to xt (past data).
Prior art techniques for ranking anomalies only do so with respect to anomalies in a single time series. They are not applicable when comparing two anomalies from different time series. In the present disclosure, there is a need to not only detect anomalies from a univariate time series, but also to compare anomalies across different time series so that the ones with the highest anomaly strength can be recommended to users.
A technical problem exists in determining how to measure the strength of anomalies with ranking function ƒ(x) detected from different time series while still satisfying desired properties such as:
x
t
≥x
t
′,x
t
>z
+& xt′>z+→ƒ(xt)≥ƒ(xt′)
x
t
≥x
t
′,x
t
<z
−& xt′<z−→ƒ(xt)≤ƒ(xt′)
In an example embodiment, a specialized anomaly strength score is computed for anomalies in an anomaly detection window in time series data. The length of the anomaly detection window may be fixed or may be dynamically determined. The dynamic determination may be performed using its own machine learning algorithm. Indeed, this length may be personalized for different contexts. For example, certain types of time series may have longer anomaly detection window lengths than others, or it may be customized based on the company the data applies to, or to the viewer. In an example embodiment, a mapping between contexts and lengths may be maintained such that the process involves determining a current context, retrieving a corresponding length from the mapping, and using that length for the anomaly detection window. In another example embodiment, another machine learned model can be trained to output a length for an input context/user/company. For example, data about past interactions by user A (or users similar to user A) with a graphical user interface displaying anomalous data points can be used to train a model that predicts the anomaly detection window length that has the highest probability of causing user A to interact with the results of the time series analysis provided in the graphical user interface.
In an example embodiment, the dynamic anomaly detection window length may be determined by first obtaining past interactions in a group of sample data. This group may be determined based on a common characteristic (whether broad or narrow) among the sample data in the group, and the common characteristic can be selected to be any attribute that one would want to “personalize” or customize the length for. In the narrow case, sample data only pertaining to an individual user and users similar to the individual user (as determined by more than a threshold similarity of user profile information, such as employment history, education, location, and skills) can be obtained. In a more broad case, sample data pertaining to all users employed at a particular employer can be obtained. No matter the common characteristic, the sample data may include interactions between users and anomalies presented in a graphical user interface. These interactions may be positive (such as selecting on or hovering over the presented anomaly to view additional information about the presented anomaly), or negative (such as having been presented with the anomaly but not selecting it, or dismissing it if such an option is provided). The positive and negative interactions may be labelled as positive or negative, respectively, and fed to a machine learning algorithm to train a specialized anomaly window length determination machine learned model. The training may include learning weights (coefficients) to be applied to feature data about users. The anomaly window length determination machine learned model may then apply these weights to feature data for a particular user to which the graphical user interface may be currently presented, outputting a specialized anomaly length for the particular user, thus dynamically determining the anomaly window length and potentially affecting how anomalies are ranked for particular users.
The computation of the specialized anomaly strength score utilizes a specialized score, which will be called a modified Z score, to aid in determining anomaly strength across different time series. The algorithm below may be implemented by the anomaly strength machine learned model 306:
This algorithm has the following advantages. First, it reduces the influence of anomalies in the training data to the estimation in the anomaly detection window. Steps 1-3 carefully preserve desired seasonal and trend information while removing undesired anomalies from the remainder (noise) component of a given time series. This is accomplished by first decomposing the time series data into trend, seasonal, and noise components. There is only one trend component and only one noise component for each time series, but there may be one or more seasonal components. MSTL may be used for this process.
MSTL is a function that handles potentially multiple seasonability time series. It operates by iteratively estimating each seasonal component using a seasonal-trend decomposition such as STL. The trend component is computed for the last iteration of STL. STL is a filtering procedure for decomposing a seasonal time series. STL comprises two recursive procedures: an inner loop nested inside an outer loop. In each of the passes through the inner loop, the seasonal and trend components re updated once. Each complete run of the inner loop comprises n(i) such passes. Each pass of the outer loop comprises the inner loop followed by a computation of robustness weights. These weights are used in the next run of the inner loop to reduce the influence of transient, aberrant behavior on the trend and seasonal components. An initial pass of the outer loop is carried out with all robustness weights equal to 1, and then n(o) passes of the outer loop are carried out. In an example embodiment, n(o) and n(i) are preset and static.
Each pass of the inner loop comprises a seasonal smoothing that updates the seasonal component, followed by a trend smoothing that updates the trend component. Specifically, a detrended series is computed. Then each subseries of the detrended series is smoothed by a smoother such as a Loess smoother. Low pass filtering is then applied to the smoothed subseries, and a seasonal component is subtracted from the smoothed and filtered subseries. This is known as detrending the smoothed subseries. A deseasonalized series is then computed. The deseasonalized series is then smoothed (such as by using a Loess smoother).
An outer loop then defines a weight for each time point where the time series does not have missing values. These weights are known as robustness weights and reflect how extreme the remainder is (the time series minus the trend component minus the seasonal component). The robustness weights may be computed using a bisquare weight function. The inner loop is then repeated, but in the smoothings, a neighbourhood weight for a value at a particular time is multiplied by the corresponding robustness weight.
The iterations may continue until a preset number of iterations has occurred.
Referring back to the algorithm, after decomposition, rounding is applied to avoid extreme values in the modified Z score in step 4. Then, normalization is applied on the unbounded modified Z score to make it more comparable across different types of insights in downstream rankers (described later). The score is bounded within the [0, 1] range.
It is expected that the final anomaly strength score is a reflection of the difference between the forecasted value and an observed value. The larger the gap, the higher the score. To achieve this, a discounting function is applied in step 8. For a short time series, such as less than 2 periods, steps 1-3 may be skipped.
Modified Z score may be defined as follows:
For a set X={xi|xi∈, i=1, 2, . . . , n}, let {tilde over (X)}=median(A)
MAD=median(|xi−{tilde over (X)}|)
Mean Absolute Deviation (MeanAD) is defined as:
where the measure of central tendency, m(X) can be mean, median, mode where
and k1 and k2 are weights that can be learned via training or a machine learned model using a machine learning algorithm (and potentially retraining at a later time based on user feedback, which could include subsequent interaction data or alternative means of feedback, such as questionnaire or survey responses). The machine learning algorithm may utilize training data in the form of user profiles and user interaction data and can learn which values of k1 and k2 maximize user selection on ranked anomalies in an anomaly reporting tool of a graphical user interface. Thus, for example, the algorithm may learn which values of k1 and k2 cause a particular user, or users like a particular user, to click most often on displayed ranked anomalies. Values of k1 and k2 can be learned via a process similar to that described above with respect to learning anomaly detection window length. It should be noted that this is a different process than will be described later regarding training the separate user interest machine learned model 308, which is used to independently score the detected anomalies based on user interest (without regard for anomaly strength).
MAD may be used to estimate the population standard deviation and use an anomaly cutoff that is appropriate to the assumed underlying distribution. The cutoff indicates at what value above which a data point is considered an anomaly. In an example embodiment, this anomaly cutoff is learned via machine learning algorithm, such as a logistic regression algorithm or a neural network, much in the same way the machine learning algorithm in the anomaly detection section described above is used to learn weights and values used during that process. This would be a separate machine learning process than those used to train the anomaly strength machine learned model 306 or user interest machine learned model 308.
Referring back to
In supervised machine learning, input data or training examples come with a label, and the goal of the learning is to be able to predict the label for new, unforeseen examples. In this present case, the label is defined as a binary fact of whether or not a user is interested in an insight.
Positive and negative labels can be collected from a variety of different sources. In an example embodiment, five sources for positive and negative labels may be onboarded in parallel. The first is a positive search. This is a case where a user explicitly searches for or selects on a link indicating an interest in a particular report that contains insights. The second is a recruiter search. These include positive labels inferred based on activity within a recruiter tool. The recruiter tool may comprise a GUI that allows users, such as recruiters, to perform searches for other users based on characteristics of the other users, such as skills. Positive labels may be inferred for active users within the recruiter tool, under the assumption that users who are more active in searching for other users are also more likely to be interested in insights from time series data.
The third is search exclusion. Negative labels may be implied from faceted search query filters where the user explicitly excluded some entities for a facet type. The fourth is negative search impression. When a user builds a search query, smart suggestion and type-ahead functionality may be triggered. Both services recommend standardized candidate query terms to facilitate query building. The user may look through the suggested entity list and select one. Those impressed, but unselected entities may be considered as negative candidates. The fifth may be recruiter search exclusion, which is a similar search exclusion in the recruiter tool.
The features may be organized into a record, with each record representing a unique label data point, showing a user's like or dislike of a topic (subject) in a time window, called a label time window. A label time window is a window with a start date and an end date. The length of the label time window determines the prediction horizon (prediction window), specifically how far in the future the predicted event will occur. The theory is that labels obtained from data within a particular month are more likely to be reflective of a user's interest that month than for different months. This also mean that the same event in the training data can be translated into different label points associated with different time windows. For example, a sample data point in April, 2020 may be assigned one label for interaction data occurring within the month of April, 2020 but another label for interaction data occurring within the month of July 2020.
The quality of the training labels derived from the user's explicit or implicit feedback greatly affects the performance of the machine learned models. Besides collecting labels, label preparation provides a unique technical challenge, especially when there is ambiguity of user interest in terms of title, function, and occupation of talent professionals. There are two types of ambiguities: direct conflicts and indirect conflicts. Direct conflicts are ones where a user expresses a positive interest in a subject at one time but expresses negative interest in the same subject at another time. Indirect conflicts are ones where a user expresses positive interest in one subject at one time and a negative interest in a related or parent subject at another time, such as expressing an interest in the “Software Engineer” title at one time and then a disinterest in the “Engineering” function at another time.
In an example embodiment, a taxonomy of subjects may be used to resolve the ambiguities by reducing the noise introduced from the conflicted labels. Subjects are terms or phrases with particular meaning in the system. The taxonomy may indicate relationships between the subjects, such as a relationship between Software Engineering and Software Development (and likewise a lack of relationship between Software Engineering and Cooking). The subjects may be comprised of functions, occupations, and titles. Specifically, two rules may be used to resolve the ambiguities.
Once the user interest machine learned model 308 has been trained, it may be used to generate user interest scores for each potential insight. In an example embodiment, a recommendation model 310 may then combine the user interest score and the anomaly strength score for each anomaly to arrive at a ranking score for the anomaly. In an example embodiment, the recommendation model 310 may generate this based on a weighted sum function, with the user interest score having a first weight and the anomaly strength score having a second weight. In some example embodiments, the recommendation model 310 itself may be learned via training by a machine learning algorithm using some of the techniques described earlier, where the weights are learned through this process. The result is a ranking score that is then passed to a ranker 312 that ranks the anomalies.
The ranking of the anomalies may be passed to an insight GUI generator 314. The insight GUI generator 314 may then generate a GUI to display one or more of the ranked anomalies graphically, based on the ranking. The GUI may take many forms, including a graph in which the top ranked anomalies are highlighted. It should be noted that “top” in this context could be based on a particular set number of top anomalies to highlight (e.g., top 10 ranked anomalies) or may be based on the ranking score itself, where only anomalies with ranking scores transgressing a predetermined threshold are highlighted.
Additionally, in an example embodiment, the threshold may be dynamically adjusted as opposed to predetermined and may be personalized based on a number of factors. For example, in one example embodiment, each company's data could potentially have its own threshold, set independently of other companies' thresholds. In another example embodiment, the threshold may be determined based on the viewing user, and possibly could be output from a machine learned algorithm trained to generate a value representing a “best” threshold for a user with the same attributes as the viewing user. For example, certain users may be more likely to be interested in small variations in the underlying data than other users, and thus these certain users (or users like these certain users) may be dynamically assigned a lower threshold than other users.
At operation 706, a user interest machine learned model is trained to generate a user interest score for an insight (e.g., an anomaly) based on a prediction of a user's interest in the insight. Specifically, this score is indicative of how likely it is that a user would be interested in the insight. This training may or may not use the same sample user profiles, sample time series data, and sample interaction data as obtained in operation 702. Additionally, while depicted in this figure as being performed after operation 704, this training may be performed prior to, or simultaneously with the training in operation 704.
At operation 708, time series data is obtained. The time series data includes a value for a first metric at each of a plurality of time points separated by time intervals. At operation 710, an indication of one or more anomalies in the time series data is received. One method for performing operation 708 is disclosed in the co-pending application entitled “TIME SERIES ANOMALY DETECTION,” filed on the same day as the present application and incorporated by reference as described above.
A loop is then begun for each of the one or more detected anomalies. At operation 712, a modified Z score is calculated for the anomaly using the trained first machine learned model. The modified Z score is a value of the first metric for the anomaly minus a median of values for the first metric in the time series data, divided by a median absolute deviation between the value of the first metric for the anomaly and values of the first metric in the time series data, when the median absolute deviation is non-zero.
At operation 714, the modified Z score is normalized. At operation 716, a discounted anomaly strength is calculated for the anomaly, based on the normalized modified Z score for the anomaly and based on parameters to control slope and shift of a sigmoid function applied to the modified Z score.
Optionally, at operation 718, a user interest score is calculated for the anomaly, using the user interest machine learned model trained in operation 706. At operation 720, a recommendation score is calculated for the anomaly, based on a combination of the discounted anomaly strength score for the anomaly and the user interest score for the anomaly (if used).
At operation 722, it is determined if there are any more detected anomalies in the anomaly detection window. If so, then the method loops back to 708 for the next detected anomaly. If not, then at operation 724 at least one of the one or more detected anomalies is ranked against at least one anomaly from different time series data, based on a comparison of the recommendation score calculated for the at least one of the one or more anomalies and a recommendation score calculated for the at least one anomaly from different time series data.
It should be noted that the training and use of the user interest score machine learned model is optional, and a similar process to that in
In various implementations, the operating system 804 manages hardware resources and provides common services. The operating system 804 includes, for example, a kernel 820, services 822, and drivers 824. The kernel 820 acts as an abstraction layer between the hardware and the other software layers, consistent with some embodiments. For example, the kernel 820 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 822 can provide other common services for the other software layers. The drivers 824 are responsible for controlling or interfacing with the underlying hardware, according to some embodiments. For instance, the drivers 824 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth.
In some embodiments, the libraries 806 provide a low-level common infrastructure utilized by the applications 810. The libraries 806 can include system libraries 830 (e.g., C standard library) that can provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 806 can include API libraries 832 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic context on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 806 can also include a wide variety of other libraries 834 to provide many other APIs to the applications 810.
The frameworks 808 provide a high-level common infrastructure that can be utilized by the applications 810, according to some embodiments. For example, the frameworks 808 provide various GUI functions, high-level resource management, high-level location services, and so forth. The frameworks 808 can provide a broad spectrum of other APIs that can be utilized by the applications 810, some of which may be specific to a particular operating system 804 or platform.
In an example embodiment, the applications 810 include a home application 850, a contacts application 852, a browser application 854, a book reader application 856, a location application 858, a media application 860, a messaging application 862, a game application 864, and a broad assortment of other applications, such as a third-party application 866. According to some embodiments, the applications 810 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 810, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 866 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 866 can invoke the API calls 812 provided by the operating system 804 to facilitate functionality described herein.
The machine 900 may include processors 910, memory 930, and I/O components 950, which may be configured to communicate with each other such as via a bus 902. In an example embodiment, the processors 910 (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 912 and a processor 914 that may execute the instructions 916. The term “processor” is intended to include multi-core processors 910 that may comprise two or more independent processors 912 (sometimes referred to as “cores”) that may execute instructions 916 contemporaneously. Although
The memory 930 may include a main memory 932, a static memory 934, and a storage unit 936, all accessible to the processors 910 such as via the bus 902. The main memory 932, the static memory 934, and the storage unit 936 store the instructions 916 embodying any one or more of the methodologies or functions described herein. The instructions 916 may also reside, completely or partially, within the main memory 932, within the static memory 934, within the storage unit 936, within at least one of the processors 910 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 900.
The I/O components 950 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 950 that are included in a particular machine 900 will depend on the type of machine 900. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 950 may include many other components that are not shown in
In further example embodiments, the I/O components 950 may include biometric components 956, motion components 958, environmental components 960, or position components 962, among a wide array of other components. For example, the biometric components 956 may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 958 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 960 may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 962 may include location sensor components (e.g., a Global Positioning System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
Communication may be implemented using a wide variety of technologies. The I/O components 950 may include communication components 964 operable to couple the machine 900 to a network 980 or devices 970 via a coupling 982 and a coupling 972, respectively. For example, the communication components 964 may include a network interface component or another suitable device to interface with the network 980. In further examples, the communication components 964 may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 970 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).
Moreover, the communication components 964 may detect identifiers or include components operable to detect identifiers. For example, the communication components 964 may include radio frequency identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 964, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
The various memories (i.e., 930, 932, 934, and/or memory of the processor(s) 910) and/or the storage unit 936 may store one or more sets of instructions 916 and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 916), when executed by the processor(s) 910, cause various operations to implement the disclosed embodiments.
As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions 916 and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to the processors 910. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory including, by way of example, semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate array (FPGA), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.
In various example embodiments, one or more portions of the network 980 may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network 980 or a portion of the network 980 may include a wireless or cellular network, and the coupling 982 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling 982 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long-Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data-transfer technology.
The instructions 916 may be transmitted or received over the network 980 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 964) and utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Similarly, the instructions 916 may be transmitted or received using a transmission medium via the coupling 972 (e.g., a peer-to-peer coupling) to the devices 970. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 916 for execution by the machine 900, and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.
