Patent Publication Number: US-2023143734-A1

Title: Detecting anomalies in visualizations

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a Utility Patent application based on previously filed U.S. Provisional Patent Application No. 63/277,563 filed on Nov. 9, 2021, the benefit of the filing date of which is hereby claimed under 35 U.S.C. § 119(e) and which is further incorporated in entirety by reference. 
    
    
     TECHNICAL FIELD 
     The present invention relates generally to data analysis, and more particularly, but not exclusively to, determining anomalies in visualizations. 
     BACKGROUND 
     The amount of data that organizations manage is rapidly growing. In self-service analytics environments, often analytical content built on this data grows at a correspondingly rapid pace. To keep on top of all the meaningful changes in their data, often users need to frequently visit all their relevant content to understand the historical context of their important data points. However, organizations often have far more data than users can be expected to reasonably analyze. Often users can consume only a portion of the available content created for them, and anomalies that may be hidden outside of this portion of content may be difficult to surface. Thus, it is with respect to these considerations and others that the present invention has been made. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Non-limiting and non-exhaustive embodiments of the present innovations are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified. For a better understanding of the described innovations, reference will be made to the following Detailed Description of Various Embodiments, which is to be read in association with the accompanying drawings, wherein: 
         FIG.  1    illustrates a logical architecture of a system for detecting anomalies in visualizations in accordance with one or more of the various embodiments; 
         FIG.  2    illustrates a portion of a user interface that may be considered a dashboard in accordance with one or more of the various embodiments; 
         FIG.  3    illustrates a logical representation of a portion of a mark visualization for detecting anomalies in visualizations in accordance with one or more of the various embodiments; 
         FIG.  4    illustrates a logical representation of a portion of a visualization in accordance with one or more of the various embodiments; 
         FIG.  5    illustrates an overview flowchart of a process for detecting anomalies in monitored dashboard data in accordance with one or more of the various embodiments; 
         FIG.  6    illustrates a flowchart of a process for detecting anomalies in visualizations in accordance with one or more of the various embodiments; 
         FIG.  7    illustrates a flowchart of a process for detecting anomalies in visualizations in accordance with one or more of the various embodiments; 
         FIG.  8    illustrates a flowchart of a process for detecting anomalies in visualizations in accordance with one or more of the various embodiments; 
         FIG.  9    illustrates a system environment in which various embodiments may be implemented; 
         FIG.  10    illustrates a schematic embodiment of a client computer; and 
         FIG.  11    illustrates a schematic embodiment of a network computer. 
     
    
    
     DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS 
     Various embodiments now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific exemplary embodiments by which the invention may be practiced. The embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the embodiments to those skilled in the art. Among other things, the various embodiments may be methods, systems, media or devices. Accordingly, the various embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense. 
     Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Furthermore, the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments may be readily combined, without departing from the scope or spirit of the invention. 
     In addition, as used herein, the term “or” is an inclusive “or” operator, and is equivalent to the term “and/or,” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.” 
     For example, embodiments, the following terms are also used herein according to the corresponding meaning, unless the context clearly dictates otherwise. 
     As used herein the term, “engine” refers to logic embodied in hardware or software instructions, which can be written in a programming language, such as C, C++, Objective-C, COBOL, Java™, PHP, Perl, JavaScript, Ruby, VBScript, Microsoft .NET™ languages such as C #, or the like. An engine may be compiled into executable programs or written in interpreted programming languages. Software engines may be callable from other engines or from themselves. Engines described herein refer to one or more logical modules that can be merged with other engines or applications, or can be divided into sub-engines. The engines can be stored in non-transitory computer-readable medium or computer storage device and be stored on and executed by one or more general purpose computers, thus creating a special purpose computer configured to provide the engine. 
     As used herein the term “data source” refers to the source of the underlying information that is being modeled or otherwise analyzed. Data sources may include information from or provided by databases (e.g., relational, graph-based, no-sql, or the like), file systems, unstructured data, streams, or the like. Data sources are typically arranged to model, record, or memorialize various operations or activities associated with an organization. In some cases, data sources are arranged to provide or facilitate various data-focused actions, such as, efficient storage, queries, indexing, data exchange, search, updates, or the like. Generally, a data source may be arranged to provide features related to data manipulation or data management rather than providing an easy to understand presentation or visualization of the data. 
     As used herein the term “data model” refers to one or more data structures that provide a representation of an underlying data source. In some cases, data models may provide views of a data source for particular applications. Data models may be considered views or interfaces to the underlying data source. In some cases, data models may map directly to a data source (e.g., practically a logical pass through). Also, in some cases, data models may be provided by a data source. In some circumstances, data models may be considered interfaces to data sources. Data models enable organizations to organize or present information from data sources in ways that may be more convenient, more meaningful (e.g., easier to reason about), safer, or the like. 
     As used herein the term “data object” refers to one or more entities or data structures that comprise data models. In some cases, data objects may be considered portions of the data model. Data objects may represent individual instances of items or classes or kinds of items. 
     As used herein the term “visualization model” refers to one or more data structures that visualization engines may employ to generate visualizations for display on one or more hardware displays. Visualization models may define various features or objects that visualization engines may render into a displayed visualization including styling or user interface features that may be made available to non-authoring users. 
     As used herein, the term “metric” refers to various quantifiable or measurable values derived from a visualization or associated with a visualization. In some cases, the type of metrics that are available may depend on the visualization being analyzed or monitored. 
     As used herein the term “panel” refers to region within a graphical user interface (GUI) that has a defined geometry (e.g., x, y, z-order) within the GUI. Panels may be arranged to display information to users or to host one or more interactive controls. The geometry or styles associated with panels may be defined using configuration information, including dynamic rules. Also, in some cases, users may be enabled to perform actions on one or more panels, such as, moving, showing, hiding, re-sizing, re-ordering, or the like. 
     As used herein the term “configuration information” refers to information that may include rule based policies, pattern matching, scripts (e.g., computer readable instructions), or the like, that may be provided from various sources, including, configuration files, databases, user input, built-in defaults, or the like, or combination thereof. 
     The following briefly describes embodiments of the invention in order to provide a basic understanding of some aspects of the invention. This brief description is not intended as an extensive overview. It is not intended to identify key or critical elements, or to delineate or otherwise narrow the scope. Its purpose is merely to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
     Briefly stated, various embodiments are directed to managing visualizations of data. In one or more of the various embodiments, a plurality of visualizations based on data from one or more data sources may be provided such that each visualization includes one or more marks that may be associated with one or more values from the one or more data sources. 
     In one or more of the various embodiments, one or more monitored visualizations from the plurality of visualizations may be determined based on one or more metrics associated with the one or more monitored visualizations. 
     In one or more of the various embodiments, one or more mark values may be sampled from each monitored visualization such that the one or more sampled mark values may be stored with a timestamp that corresponds to a time of that the one or more mark values are sampled. 
     In one or more of the various embodiments, in response to an amount of the one or more sampled mark values for a monitored visualization being greater than a threshold value further actions may be performed including: training one or more mark models to classify a portion of the one or more sampled mark values associated with the monitored visualization such that the one or more mark models may predict one or more ranges of values for the one or more classified mark values associated with the monitored visualization; and in response to the portion of the one or more classified mark values associated with the monitored visualization having a value that may be outside of the one or more predicted range values, indicating that the monitored visualization may be associated with an anomalous mark value such that the indication may be reported to one or more users. 
     In one or more of the various embodiments, activity associated with the plurality of visualizations may be monitored such that the monitored activity may be one or more of a frequency of access, a number of times a visualization is favorited, a number of users that register to follow a visualization, a number of times a visualization is included in a dashboard, or the like. And, in one or more of the various embodiments, determining one or more values for the one or more metrics based on the monitored activity. 
     In one or more of the various embodiments, a portion of the plurality of visualizations may be excluded from the one or more monitored visualizations based on one or more characteristics of the excluded portion of visualizations such that the one or more characteristics may include one or more of a number of marks in the excluded visualizations, a license associated with the excluded visualizations, a user preference, a data type included in the excluded visualizations, a visualization type of the excluded visualizations, or the like. 
     In one or more of the various embodiments, the one or more mark models may be generated based on Gradient-Boosted Decision Tree models that are set to fit an upper bound and a lower bound separately using quantile regression such that the information corresponding to the one or more sampled mark values may be represented as a feature vector, and such that the feature vectors may include one or more of a mark value, a change from a previous value, date information, or the like. 
     In one or more of the various embodiments, training the one or more mark models to classify the portion of the one or more sampled mark values associated with the monitored visualization may include training one or more different mark models for the monitored visualization such that each different mark model may be directed to classifying one or more different types of anomalies for the monitored visualization. 
     In one or more of the various embodiments, predicting the one or more ranges of values for the one or more classified mark values associated with the monitored visualization may include discarding the one or more trained mark models. 
     In one or more of the various embodiments, sampling the one or more mark values from each monitored visualization may include storing the one or more sampled mark values in another data source that may be separate the from the one or more data sources. 
     Illustrative Logical System Architecture 
       FIG.  1    illustrates a logical architecture of system  100  for detecting anomalies in visualizations in accordance with one or more of the various embodiments. In one or more of the various embodiments, system  100  may be a visualization platform arranged to include various components including, analysis engine  102 , visualization engine  104 , visualizations  106  visualization models  108 , data models  110 , data sources  112 , sampling engine  114 , sample mark values  116 , or the like. 
     In one or more of the various embodiments, data sources  112  represent a source of raw data, records, data items, or the like, that provide the underlying data may be employed for generating visualizations. 
     In one or more of the various embodiments, data models, such as, data models  110  may be data structures, or the like, that provide one or more logical representations of the information stored in one or more data sources, such as, data source  112 . In some embodiments, data models may include data objects that correspond to one or more portions of tables, views, or files in data sources. For example, in some embodiments, if data source  112  is a CSV file or a database, a data model, such as, data model  112  may be comprised of one or more data objects that may correspond to record fields in data source  112 . Likewise, in some embodiments, data models may include fields that correspond to fields or attributes in data sources. For example, in some embodiments, if data source  112  is a Relational Database System (RDBMS), a data model included in data models  110 A may be comprised of one or more data model fields that correspond to one or more columns or one or more tables included in data sources  112 . 
     In some embodiments, a visualization engine, such as, visualization engine  104  may be employed to transform or map some or all of data sources  112  into data models  110 . In some embodiments, visualization engines may be arranged to employ or execute computer readable instructions provided by configuration information to determine some or all of the steps for transforming values in data sources into data models. 
     In one or more of the various embodiments, visualization engines, such as, visualization engine  104  may be arranged to employ visualization models, such as, visualization models  108  to determine the layout, styling, interactivity, or the like, for visualizations, such as, visualizations  106  that may be displayed to users. Also, in some embodiments, visualization engines may be arranged to employ data item values provided via data sources  112  to populate visualizations with values based on a source data model. 
     In one or more of the various embodiments, visualization models may be defined using one or more visualization specifications. In some embodiments, visualization specifications may include computer readable instructions, such as, formal or semi-formal rules that may correspond to visualization models. In some embodiments, visualization specifications may be used to represent or define one or more visualization models. In some embodiments, visualization specifications may be employed to generate visualization models or visualizations. 
     In some embodiments, dashboard user interfaces that include a collection of visualizations may be a common form of visual analytics that may often be employed in business intelligence applications, informatics, or industrial monitoring as well as many other domains or analytical tasks. In some embodiments, these visual displays may take many forms and styles based on the data acquired and the information needs of the viewer or analyst. In some cases, due to this variety, there may be a lack of consistent, agreed-to definitions for what constitutes a quality dashboard visualization. Accordingly, it may be conventional to adopt a broad view of what can be considered a dashboard including infographics, narrative elements, or the like. Herein, for some embodiments, dashboard user interfaces may be user interfaces arranged to at least include a visual display of important information that may be needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance. 
     In some cases, dashboards may be designed to display the current status or KPIs of interest to a user or organization. In some cases, data sources or dashboards may be designed to show historical data, where the history of the changes may be apparent in how the data is stored or visualized. However, it may be often the case that the historical information is not represented anywhere. In this case, the history of the data needs to be captured and stored separately, something that may often require additional IT resources that may make reporting or visualization such information disadvantageous. 
     In some embodiments, sampling engines, such sampling engine  114  may be arranged to automatically determine one or more mark values from monitored visualizations. In some embodiments, metric engines may be arranged to periodically sample values of the one or more marks and store them in a data store, such as, sampled mark values  116 . 
     Further, in some embodiments, analysis engines may be arranged to generate one or more models to identify anomalous mark values in the monitored visualizations. 
     In one or more of the various embodiments, if one or more anomalies may be detected, analysis engines may be arranged to generate one or more notifications, alerts, events, reports, or the like. In some embodiments, notifications, or the like, may be provided to external services or systems that may manage the investigation the reported anomalies. 
     In one or more of the various embodiments, anomalies may be detected based on the individual marks based on snapshots of mark information from monitored visualizations. In some cases, for some embodiments, various criteria may be evaluated to determine the mark information that may be sampled. In some cases, for some embodiments, the criteria may include: marks where time is not already part of the visualization; only on extract data; only on visualizations that have less than a configurable number of marks (E.g., 10,000 or less.) 
     In some embodiments, anomaly detection for a given visualization or dashboard may be delayed until the number of sampled snapshots of a mark is above a configurable threshold value, such as, fifty. 
     In one or more of the various embodiments, sampling engines may be arranged to employ various configurable criteria to determine the visualizations or dashboards that may be sampled. In some cases, this may include the number of times a user has “favorited” a visualization or dashboard, the number of times users have viewed visualizations or dashboards, or the like. 
     In one or more of the various embodiments, sampling engines may be arranged to employ a datastore that may be separate for data sources, or the like, for storing sampled mark information. 
     In one or more of the various embodiments, sampling engines may be arranged to limit data collection to visualized data. Accordingly, in some embodiments, sampled mark information may be based on visualized data that may be curated to show the measures that may be useful in analysis so in effect the analysis may identify which columns to watch rather than watch all columns. Further, sampling from visualizations may provide readily available and proven sources of metadata at the view level that infers what may be relevant to a user, such as, view counts, favorites, subscriptions, or the like. Also, in some embodiments, sampling marks in visualizations may reduce the scale or scope of sampled data as compared to generating snapshots of all the data in data sources which may require monitoring all data in all data sources. 
     In one or more of the various embodiments, sampling engines may be arranged to maintain a list of actively monitored visualizations or dashboards. In some embodiments, sampling engines may be arranged to periodically (e.g., hourly, daily, nightly, or the like) re-evaluate the list. In some embodiments, the criteria used to determine the visualizations or dashboards that may be monitored include a variety of criteria: the list of views may be capped at a maximum size; controlled by the server administrator; such that resource usage may be controlled; if there may be multiple visualization platform instances, particularly in online/web-based embodiments, each may be allocated a particular number of visualizations to be monitored. In some embodiments, the number of monitored visualizations may be proportional to the number of licensed users associated with the instances or instance locations. In one or more of the various embodiments, the determination of monitored dashboards may be optimized such that the most useful visualizations or dashboards may be monitored. In some embodiments, the determination of monitored visualizations may be based on metrics associated with visualizations that have been favorited, subscribed, and general popularity of the visualizations. 
     In some embodiments, if sufficient samples (over time) have been collected, analysis engines may be arranged to attempt anomaly detection on newly sampled mark values. 
     In one or more of the various embodiments, this may include automatically training a model on the sampled mark information data for each mark. In some embodiments, the mark models may be fit to predict a 99% confidence interval, meaning mark models may find an upper bound prediction which may be expected to be higher than 99.5% of the data and a lower bound that may be expected to be lower than 99.5% of the data. In some cases, the confidence interval range may be based on configuration information to account for local requirements or local circumstances. 
     In some embodiments, if the models may be fitted to sampled data for a mark, analysis engines may be arranged to perform an inference step based on comparing the latest mark value to the confidence interval. Accordingly, in some embodiments, mark values determined to be above the upper bound or below the lower bound may be considered an anomaly. 
     In one or more of the various embodiments, analysis engines may be arranged to generate mark models based on Gradient-Boosted Decision Tree models that are set to fit the upper and lower bounds separately using Quantile Regression. In some embodiments, mark data may be represented as a feature vector of the value, the change from the previous value(s), date information (quarter, month, day of week), along with one or more configurable trend- and cycle-fitting features. 
     In one or more of the various embodiments, models may be generated on-the-fly or on-demand and then discarded after evaluating mark information. 
     In one or more of the various embodiments, analysis engines may be arranged to generate or modify one or more user interfaces to represent detected anomalies. In some embodiments, information about anomalies may be displayed alongside the visualizations or dashboards. Also, in some embodiments, analysis engines may be arranged to send notification, reports, email digests, or the like, the include information about one or more detected anomalies. 
       FIG.  2    illustrates a portion of user interface  200  that may be considered a dashboard in accordance with one or more of the various embodiments. In this example, user interface  200  includes several different visualizations that comprise a dashboard, including, visualization  202 , visualization  204 , visualization  206 , visualization  208 , visualization  210 , visualization  212 , or the like. These visualizations may be considered to represent visualizations of various KPIs, statuses, or the like. In some cases, visualizations included in a dashboard, such as, dashboard  200  may include one or more visualizations. 
     In one or more of the various embodiments, user interface  200  may be displayed on one or more hardware displays, such as, client computer displays, mobile device displays, or the like. In some embodiments, user interface  200  may be provided via a native application or as a web application hosted in a web browser or other similar applications. One of ordinary skill in the art will appreciate that for at least clarity or brevity many details common to commercial/production user interfaces have been omitted from user interface  200 . Likewise, in some embodiments, user interfaces may be arranged differently than shown depending on local circumstances or local requirements, such as, display type, display resolution, user preferences, or the like. However, one of ordinary skill in the art will appreciate that the disclosure/description of user interface  200  is at least sufficient for disclosing the innovations included herein. 
       FIG.  3    illustrates a logical representation of a portion of user interface  300  of a visualization that illustrates sampled mark values that may be used for detecting anomalies in visualizations in accordance with one or more of the various embodiments. As described above, in some embodiments, metric engines may be arranged to monitor or record metrics associated with visualizations. Accordingly, in some embodiments, analysis engines may be arranged to generate mark visualizations, such as, mark visualization  302  to display historical information about one or more marks for one or more of the visualizations. 
     In this example, mark visualization  302  may be considered to be based on visualization  202  shown in  FIG.  2   . In this example, for some embodiments, mark visualization  302  may be a line plot that represents the number of cases (from visualization  202  in  FIG.  2   ) over time. In this example, the current view of visualization  202  showing a value of 43 cases is represented by point  304  (‘B’). Accordingly, the visualization  202  in user interface  200  is showing the current number of cases, but it does not provide context or trend information that may be of interest to users. In this example, point  306  (‘A’) represents an earlier time when the number of cases is more than the current value. In this example, if the user viewed the dashboard (user interface  200 ) at point (in time)  308  (‘C’) and then at point (in time)  304  (‘B’), a user may have the impression that the values for visualization  202  have been flat and smooth even the actual value represented by visualization  202  is fluctuating wildly. 
     Also, in one or more of the various embodiments, analysis engines may be arranged to monitor marks in one or more visualizations to identify one or more anomalies based on the mark values. In this example, point  310  (‘D’) represents a low value that may be considered an anomaly. Accordingly, in this example, the analysis engine may generate one or more models that may determine the anomalous mark values even if the user is not viewing the dashboard. As described above, analysis engines may be arranged to generate one or more notifications, events, alerts, or the like, if anomalies may be detected. 
       FIG.  4    illustrates a logical representation of a portion of visualization  400  in accordance with one or more of the various embodiments. As described above, visualization engines may be arranged to employ visualization models and data models to generate visualizations, such as, visualization  400 . In this example, visualization  400  may be considered to represent a bar chart that showing monthly revenue for different products, different business units, or the like. One of ordinary skill in the art will appreciate that visualization models or visualization engines may be arranged to generate many different types of visualizations for various purposes depending on the design goals of visualization authors, users or organizations. Here, visualization  400  is presented as a non-limiting example to help provide clarity to the description of these innovations. One of ordinary skill in the art will appreciate that this example is at least sufficient to disclose the innovations herein and that visualization engines or visualization models may be arranged to generate many different visualizations for many different purposes in many subject matter domains. 
     In this example, visualization  400  may be considered a visualization that may be designated for anomaly monitoring. Accordingly, in some embodiments, analysis engines may be arranged to sample mark information from visualization  400  for detecting anomalies in visualizations. In some embodiments, analysis engines may be arranged to sample some or all marks in visualizations that have been determined for anomaly monitoring. 
     Accordingly, in some embodiments, analysis engines may be arranged to determine visualizations for monitoring based on various criteria, including, popularity of a visualization, the number times a visualization may be bookmarked by users, or the like. Also, in some embodiments, analysis engines may be arranged to provide user interfaces that enable users to select one or more visualizations to monitor. 
     In one or more of the various embodiments, analysis engines may be arranged to periodically sample mark values of monitored visualizations. Accordingly, in some embodiments, analysis engines may be arranged to store the sampled mark values in a data store. In some embodiments, the sampled mark information may be stored in a data store that may be separate from the one or more data sources that supply data for the monitored visualizations. 
     In this example, for some embodiments, table  404  represent a portion of a data store that may be employed for storing sample mark values. In some embodiments, values for each mark may be stored in separate table or data stores. Likewise, in some embodiments, values for more than one mark may be stored in the same table. In this example, mark information for each mark in monitored visualization  400  may be considered to be stored in as columns in table  404 . 
     Accordingly, in this example: column  406  represents timestamp information that may be considered to correspond when the mark value may be sampled; column  408  and column  410  may be considered columns for storing sampled mark values. Further, in some embodiments, analysis engines may be arranged to store additional information associated with the monitored visualizations or the associated marks. For example, in some embodiments, analysis engines may include codes, tags, or flags, that may indicate if a mark is removed or otherwise unavailable. 
     In one or more of the various embodiments, analysis engines may be arranged to generate mark models that are fit to the sampled marks of the monitor visualizations. In some embodiments, mark models may be arranged to predict band of values with high values and low values such that if a sampled mark value falls outside the band, it may be considered to be anomalous. 
     In one or more of the various embodiments, analysis engines may be arranged to generate and train mark models on-the-fly for each monitored visualization. Accordingly, in some embodiments, if enough mark information has been collected for a monitored visualization, analysis engines may be arranged to generate and train a mark model for that visualization. 
     In some embodiments, analysis engines may be arranged to fit mark models to the sampled mark values of monitored visualizations. Accordingly, in some embodiments, analysis engines may be configured to defer mark model generation until a sufficient number of sampled mark values have been collected. In some embodiments, the number of sampled mark values deemed sufficient may vary depending on one or more characteristics of the marks, mark models, or the monitored visualizations. Likewise, in some embodiments, analysis engines may enable users to modify the number of mark samples deemed sufficient for modeling. For example, in some cases, a user may demand increased model accuracy which may require more sampled values that in other cases. 
     In this example, mark model  416  is graphical representation of a mark model with line  418  that predicts an upper bound for marks in a monitored visualization and line  420  that predicts a lower bound for the marks in the monitored visualization. 
     Thus, in some embodiments, if the latest value of a mark, such as, mark  402 , may be outside of the range predicted by the mark model, analysis engines may be arranged to consider the mark value anomalous. 
     In one or more of the various embodiments, analysis engines may be arranged to discard mark models if the monitored visualization has been evaluated. Accordingly, in some embodiments, resources used for the mark models (e.g., memory) may be released. 
     In one or more of the various embodiments, one or more visualizations or marks may be excluded from analysis depending on one or more characteristics, such as, data type of marks, size of visualization (e.g., number of marks), owner/user of visualization (e.g., licensing considerations), type of visualization, or the like. In some embodiments, mark models may be unavailable for particular data types or visualization types. Or, in some embodiments, the mark models available for a particular visualization type or mark type may be limited to providing results that may be unsuitable for a given application. For example, the available mark models may not provide predictions that are precise enough for a particular application. 
     Further, in some embodiments, analysis engines may be configured to balance or distribute analysis resources across multiple organizations, locations, users, or the like. Accordingly, in some embodiments, analysis engines may be enabled to prevent one or more organizations, users, or the like, from starving others of computing resources for analysis. 
     Generalized Operations 
       FIGS.  5 - 8    represents generalized operations for detecting anomalies in visualizations in accordance with one or more of the various embodiments. In one or more of the various embodiments, processes  500 ,  600 ,  700 , and  800  described in conjunction with  FIGS.  5 - 8    may be implemented by or executed by one or more processors on a single network computer, such as network computer  300  of  FIG.  3   . In other embodiments, these processes, or portions thereof, may be implemented by or executed on a plurality of network computers, such as network computer  300  of  FIG.  3   . In yet other embodiments, these processes, or portions thereof, may be implemented by or executed on one or more virtualized computers, such as, those in a cloud-based environment. However, embodiments are not so limited and various combinations of network computers, client computers, or the like may be utilized. Further, in one or more of the various embodiments, the processes described in conjunction with  FIGS.  5 - 8    may be used for detecting anomalies in visualizations in accordance with at least one of the various embodiments or architectures such as those described in conjunction with  FIGS.  1 - 4   . Further, in one or more of the various embodiments, some or all of the actions performed by processes  500 ,  600 ,  700 , and  800  may be executed in part by visualization engine  1122 , analysis engine  1124 , sampling engine  1126 , or the like, running on one or more processors of one or more network computers. 
       FIG.  5    illustrates an overview flowchart of process  500  for detecting anomalies in monitored dashboard data in accordance with one or more of the various embodiments. After a start block, at block  502 , in one or more of the various embodiments, one or more visualizations or dashboards may be determined for monitoring. In one or more of the various embodiments, users may be enabled select dashboards or other visualizations for providing and surfacing metrics. In some embodiments, analysis engines may be arranged to automatically determine one or more visualizations or dashboards based on various criteria, including user affinity, user preferences, the amount of user activity associated with visualizations, or the like. Accordingly, in some embodiments, users or organizations may be enabled to set filters, preferences, or the like, that may determine if a visualizations or dashboards may be processed for detecting and recommending temporal anomalies in monitored dashboard data. 
     At block  504 , in one or more of the various embodiments, sampling engines may be arranged to automatically collect mark information for the one or more monitored visualizations. 
     In some embodiments, the sampling engines may be arranged to sample mark values from the visualization or the visualization model directly. Accordingly, in some embodiments, the analysis engines may ensure that the sampled mark values match the values that may be displayed in the visualization. 
     At decision block  506 , in one or more of the various embodiments, if sufficient mark information has been collected, control may flow to block  508 ; otherwise, control may loop back to block  504 . 
     At block  508 , in one or more of the various embodiments, analysis engines may be arranged to train one or more mark models based on the sample mark information. In one or more of the various embodiments, mark models may be fit such that there may be an upper range and a lower range that represent a predicted range of values for each mark in the visualization. 
     At block  510 , in one or more of the various embodiments, analysis engines may be arranged to classify the most recent sampled mark information, in some embodiments, analysis engines may be arranged to classify the mark values based on predictions provided by the trained models. In some embodiments, mark models may be arranged to predict a range of values for a given mark. Thus, in some embodiments, analysis engines may be arranged to test if the mark values of a monitored visualization fall within the ranges of values predicted by its corresponding mark model. 
     At decision block  512 , in one or more of the various embodiments, if the anomalous mark information may be determined, control may flow to block  514 ; otherwise, control may loop back to block  504 . In some embodiments, if one or more mark values in a monitored visualization may be outside of the range predicted by a corresponding mark model, the mark or the visualization may be considered to be anomalous. 
     At block  514 , in one or more of the various embodiments, analysis engines may be arranged to provide one or more notifications or reports associated with the one or more of the anomalous marks, the associated visualization, the associated dashboards. 
     In one or more of the various embodiments, notifications may include sending text messages, emails, other messages via other messaging applications, audio alerts, or the like. In some embodiments, metric engines may be arranged to register the notification with the visualization engine such that alert information associated with the anomaly may be displayed on a user interface. In some cases, for some embodiments, a visual alert may be displayed on the dashboard user interface that includes the visualization or another visualization generated to represent the metric or the mark value(s) that may be associated with the anomaly. In some embodiments, the notification may include generating a log entry that may be reviewed later. 
     In one or more of the various embodiments, the contents of the notification may vary depending on the type of anomaly, the metric, the visualization, the dashboard, or the like. Accordingly, in some embodiments, metric engines may be arranged to determine notification rules or notification formats based on configuration information to account for local circumstances or local requirements. 
     Next, in one or more of the various embodiments, control may be returned to a calling process. 
       FIG.  6    illustrates a flowchart of process  600  for detecting anomalies in visualizations in accordance with one or more of the various embodiments. After a start block, at block  602 , in one or more of the various embodiments, analysis engines may be arranged to monitored activity associated with one or more visualizations provided by the visualization platform. In one or more of the various embodiments, visualization platforms may include many hundreds or thousands of visualizations, including many collections of visualizations in workbooks or dashboards. 
     In some embodiments, visualization platforms may be arranged to collect one or more metrics for visualizations or visualization collections (e.g., workbooks or dashboards). In some embodiments, analysis engines may be arranged to collect one or more metrics such as frequency of access, number of times a visualization or visualization collection is favorited, number of users that register to follow a visualization or visualization collection, number of times a visualization or visualization collection is included in other collections. 
     At decision block  604 , in one or more of the various embodiments, if the activity associated with one or more visualizations meet one or more declared conditions, control may flow to block  606 ; otherwise, control may loop back to block  602 . In one or more of the various embodiments, analysis engines may be configured declare one or more threshold values for the one or more metrics. Accordingly, in some embodiments, if a metric for a visualization or visualization collection exceeds a relevant metric, those visualizations may be considered eligible for detecting anomalies in visualizations. 
     In one or more of the various embodiments, analysis engines may be arranged to limit the visualization eligible for anomaly detection to reduce the amount of resources required to collect mark information and perform subsequent anomaly analysis. Accordingly, in some embodiments, the specific metrics or threshold values may vary depending on the amount of computing/storage resources that may be made available to analysis engines. In some embodiments, analysis engines may be arranged to enable users or administrators to activate one or more condition or metric threshold for determining if a visualization should be monitored for detecting anomalies. Accordingly, in some embodiments, analysis engines may be arranged to employ rules, instructions, or the like, provided via configuration information to determine the particular metrics, threshold values, other conditions, or the like, for determining if a visualization or visualization collection may be determined eligible for monitoring of anomalies. 
     Further, in some embodiments, there may be other criteria for enabling anomaly detection for a visualization that may be considered, including number of marks in the visualization, type of visualization, data types of marks in the visualization, or the like. For example, in some embodiments, mark models may be unavailable for particular mark data types or visualization types. Accordingly, in some embodiments, such visualizations may be automatically excluded from anomaly detection. Note, in some embodiments, additional mark models may be introduced or developed to enable visualizations once disqualified from anomaly detection to be eligible for anomaly detection. Accordingly, in some embodiments, analysis engines may be arranged to determine visualization eligibility criteria based on configuration information. 
     At block  606 , in one or more of the various embodiments, analysis engines may be arranged to add the one or more visualizations that met the activity condition a monitoring set. In one or more of the various embodiments, analysis engines may be arranged to automatically maintain list of visualizations that should be monitored for anomalies. In some embodiments, analysis engines may be arranged to provide user interfaces that enable one or more users or one or more administrators to remove visualization or visualization collections from the anomaly monitoring list. Likewise, in some embodiments, analysis engines may be arranged to enable one or more users or one or more administrators to manually select one or more visualization or visualization collections to add to the anomaly monitoring list. 
     In one or more of the various embodiments, analysis engines may be arranged to have a limit to the number of visualizations that may be included in the anomaly monitoring list. Accordingly, in some embodiments, analysis engines may be arranged to score visualizations based on the metrics or conditions and place them in the list in rank order based on their scores. Thus, in some cases, for some embodiments, one or more visualization may be omitted or removed from an anomaly monitoring list based on their own metrics and the metrics of the other visualization included in the visualization platform. 
     At block  608 , in one or more of the various embodiments, analysis engines may be arranged to sample mark information from the one or more monitored visualizations. 
     In one or more of the various embodiments, analysis engines may be arranged to periodically collect the current mark values or other mark information for the visualizations that may be included in the anomaly monitoring list. 
     As described above, analysis engines may be arranged to collect mark information for monitored visualization. In some embodiments, in some cases, the mark information may vary depending on the type of visualization or applicable mark models. Accordingly, in some embodiments, analysis engine may be arranged to employ configuration information to determine the particular mark information that may be collected for a particular visualization to account for local requirements or local circumstances. 
     At block  610 , in one or more of the various embodiments, analysis engines may be arranged to store the sampled mark information in a data store. In one or more of the various embodiments, analysis engines may be arranged to employ a dedicate data store for storing sampled mark information. Accordingly, in some embodiments, sampled mark information may be kept separate from the underlying data sources used for the monitored visualizations. 
     Accordingly, in some embodiments, other data stores used for storing visualizations, visualization collections, visualization data, user data, or the like, may be insulated from performance loads associated with sampling, storing, or analyzing sampled mark information. 
     Also, in some embodiments, in some cases, analysis engines may store the sampled mark information in the same data store used by the rest of the visualization platform. Accordingly, in some embodiments, smaller installations may share the same data store for visualizations, visualization collections, visualization data, user data, or the like, as well as information associated with detecting anomalies in visualizations. 
     Next, in one or more of the various embodiments, control may be returned to a calling process. 
       FIG.  7    illustrates a flowchart of process  700  for detecting anomalies in visualizations in accordance with one or more of the various embodiments. After a start block, at decision block  702 , in one or more of the various embodiments, if an analysis engine determines that one or more monitored visualizations may be checked for anomalies, control may flow to block  704 ; otherwise, control may loop back to decision block  702 . As described above, in some embodiments, analysis engines may be arranged to periodically initiate a process to detect anomalies in visualizations, such as, checking every hour, every day, or the like. Accordingly, in some embodiments, analysis engines may be arranged to provide a default period, such as, hourly checks that may be determined from configuration information. Likewise, in some embodiments, analysis engines may be arranged to provide a user interface that enables administrators or other users to set the duration between anomaly checks. In some embodiments, analysis engines may be arranged to enable users or administrator to spontaneously initiate a process to detect anomalies in visualizations. 
     At decision block  704 , in one or more of the various embodiments, if there may be sufficient mark information for the monitored visualizations, control may flow to block  706 ; otherwise, control may loop back decision block  702 . As described above, analysis engines may be arranged to determine if there may be enough time-series mark information collected before attempting to discover anomalies. In some embodiments, different types of mark models may require different amounts of data before the models may be trained or fitted. In some embodiments, the model type may vary depending on the type of visualization or the data types of the mark values. Accordingly, in some embodiments, data requirements may vary depending on the type of mark model. Likewise, in some embodiments, data requirements may depend on user preferences. For example, in some cases, some users may requirement more precise results that other users that may require more data to be collected in some cases as compared to other cases. Accordingly, in some embodiments, analysis engines may be arranged to determine the required amount of sampled mark information based on configuration information to account for local requirements or local circumstances. 
     At block  706 , in one or more of the various embodiments, analysis engines may be arranged to check for one or more anomalies in the one or more monitored visualizations. In some embodiments, analysis engines may be arranged employ one or more mark models to predict non-anomalous values for marks in the monitored visualizations. In some embodiments, more than one mark model may be used to check for different types of anomalies. In some embodiments, one or more mark models may be directed to identifying anomalous values, anomalous trends, absent values, or the like. 
     At decision block  708 , in one or more of the various embodiments, if one or more anomalies may be detected, control may flow to block  710 ; otherwise, control may loop back to decision block  702 . In one or more of the various embodiments, mark models may be trained to predict a range of values for each mark in a monitored visualization. Accordingly, in some embodiments, if an actual mark value for a monitored visualization falls outside of the predicted range of one or more mark models, analysis engine may be arranged to determine that the monitored visualization (or its values) may be anomalous. 
     At block  710 , in one or more of the various embodiments, analysis engines may be arranged to generate one or more notification/reports associated with the detected anomalies or the monitored visualization. In some embodiments, reports/notifications may include associating user interface elements, such as, an icon (e.g., exclamation point, light bulb, bell shape, or the like), color changes, text, highlighting, or the like, with the monitored visualization that includes marks determined to be anomalous. Likewise, in some embodiments, if the monitored visualization may be included in a collection of visualizations (e.g., workbooks, dashboards, or the like), the notification indicators may be associated with the entire collection. 
     Also, in some embodiments, analysis engines may be arranged to generate hourly/daily/weekly/monthly digests that include information about the detected anomalies for the corresponding time period. Accordingly, such digests may be communicated to one or more users. In some embodiments, reports may be automatically provided to one or more users that may be associated with the monitored visualizations. Accordingly, in some embodiments, analysis engines may be arranged to provide user interfaces that enable users set personal or organization level communication rules. For example, some users may opt-out of receiving reports or notifications for one or more monitored visualizations. Likewise, in some embodiments, one or more users may be designated to receive anomaly reports for one or more monitored visualizations. 
     Next, in one or more of the various embodiments, control may be returned to a calling process. 
       FIG.  8    illustrates a flowchart of process  800  for detecting anomalies in visualizations in accordance with one or more of the various embodiments. After a start block, at block  802 , in one or more of the various embodiments, analysis engines may be arranged to provide mark information for a visualization. As described above, analysis engines may be arranged to sample mark values from one or more monitored visualizations. Accordingly, in some embodiments, time-series values of the mark values may be collected and stored in a data store. In some embodiments, if visualizations have been selected for monitoring and if there may be sufficient mark information collected, analysis engines may be configured to periodically initiate anomaly detection. 
     In one or more of the various embodiments, analysis engines may be arranged to extract the sampled mark information from one or more data stores in preparation to search for anomalies. 
     At block  804 , in one or more of the various embodiments, analysis engines may be arranged to train one or more mark models based on the mark information. 
     As described above, analysis engines may be arranged to generate and train mark models on-the-fly as part of the anomaly detection process. In some embodiments, different types of mark models may require different types of training. In some embodiments, one or more mark model types may be associated with the one or more types of visualizations or mark data types. 
     In some embodiments, one or more mark models may be based on two Gradient-Boosted Decision Tree models that may be set to fit the upper and lower bounds of mark values separately using Quantile Regression. Accordingly, in some embodiments, mark data/values may be represented as a feature vector of the values, the change from the previous value(s), date information (quarter, month, day of week), some trend and cycle-fitting features, or the like. However, one of ordinary skill in the art will appreciate that mark models may be adapted or tuned to support additional visualization types or analysis questions. Accordingly, in some embodiments, analysis engines may be arranged to determine the available mark model types and mark model training processes based on configuration information to account for local requirements or local circumstances. Likewise, in some embodiments, one or more different mark model types may be provided or otherwise made available based on other considerations, such as, licensing agreements, hosting environments (e.g., SaaS deployments versus on-premise deployments), computing resource availability, customer requirements (e.g., some model types may lack the required precision), or the like. 
     At block  806 , in one or more of the various embodiments, analysis engines may be arranged to determine the latest mark information for the visualization. In one or more of the various embodiments, detecting anomalies in visualizations may be targeted to identify visualizations have an unexpected mark value as compared to past values. Thus, in some embodiments, analysis engines may be arranged to evaluate if the latest mark values conform to the range of values predicted by the mark models. In some embodiments, the latest mark values may include one or more values that were sampled since the attempt to detect anomalies. 
     At decision block  808 , in one or more of the various embodiments, if the latest mark information has values may be outside of the predicted ranges, control may flow to block  810 ; otherwise, control may flow to block  812 . As described above, mark models may predict a range of values for each mark in visualization, if the latest marks have values that may fall outside of the predicted range, analysis engines may be arranged to consider those mark value as anomalous. 
     At block  810 , in one or more of the various embodiments, analysis engines may be arranged to provide one or more notification associated with the latest marks in the visualizations that may be outside of the ranges predicted by the one or more mark models. 
     At block  812 , in one or more of the various embodiments, optionally, analysis engines may be arranged to discard the trained mark models. In some embodiments, analysis engines may be arranged to discard the trained mark models to reduce the performance costs associated with storing or managing the trained models. 
     In some embodiments, analysis engines may be arranged to retain one or more mark models for various reasons, such as, usage metrics, training difficulty, user preferences, or the like. For example, in some embodiments, analysis engines may be configured cache one or more mark model types depending on how often the same mark model may be required. However, in some embodiments, training and fitting mark models may be generally considered to be performant such that discarding the mark models after use may be advantageous. 
     Accordingly, in some embodiments, analysis engines may be arranged to employ rules, instructions, or the like, provided via configuration information to determine if a mark model may be discarded. 
     Note, in some embodiments, this block may be considered optional if the analysis engine may be configured to preserve the mark models. 
     Next, in one or more of the various embodiments, control may be returned to a calling process. 
     It will be understood that each block in each flowchart illustration, and combinations of blocks in each flowchart illustration, can be implemented by computer program instructions. These program instructions may be provided to a processor to produce a machine, such that the instructions, which execute on the processor, create means for implementing the actions specified in each flowchart block or blocks. The computer program instructions may be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer-implemented process such that the instructions, which execute on the processor, provide steps for implementing the actions specified in each flowchart block or blocks. The computer program instructions may also cause at least some of the operational steps shown in the blocks of each flowchart to be performed in parallel. Moreover, some of the steps may also be performed across more than one processor, such as might arise in a multi-processor computer system. In addition, one or more blocks or combinations of blocks in each flowchart illustration may also be performed concurrently with other blocks or combinations of blocks, or even in a different sequence than illustrated without departing from the scope or spirit of the invention. 
     Accordingly, each block in each flowchart illustration supports combinations of means for performing the specified actions, combinations of steps for performing the specified actions and program instruction means for performing the specified actions. It will also be understood that each block in each flowchart illustration, and combinations of blocks in each flowchart illustration, can be implemented by special purpose hardware-based systems, which perform the specified actions or steps, or combinations of special purpose hardware and computer instructions. The foregoing example should not be construed as limiting or exhaustive, but rather, an illustrative use case to show an implementation of at least one of the various embodiments of the invention. 
     Further, in one or more embodiments (not shown in the figures), the logic in the illustrative flowcharts may be executed using an embedded logic hardware device instead of a CPU, such as, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic (PAL), or the like, or combination thereof. The embedded logic hardware device may directly execute its embedded logic to perform actions. In one or more embodiments, a microcontroller may be arranged to directly execute its own embedded logic to perform actions and access its own internal memory and its own external Input and Output Interfaces (e.g., hardware pins or wireless transceivers) to perform actions, such as System On a Chip (SOC), or the like. 
     Illustrated Operating Environment 
       FIG.  9    shows components of one embodiment of an environment in which embodiments of the invention may be practiced. Not all of the components may be required to practice the invention, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of the invention. As shown, system  900  of  FIG.  9    includes local area networks (LANs)/wide area networks (WANs)—(network)  910 , wireless network  908 , client computers  902 - 905 , visualization server computer  916 , or the like. 
     At least one embodiment of client computers  902 - 905  is described in more detail below in conjunction with  FIG.  2   . In one embodiment, at least some of client computers  902 - 905  may operate over one or more wired or wireless networks, such as networks  908 , or  910 . Generally, client computers  902 - 905  may include virtually any computer capable of communicating over a network to send and receive information, perform various online activities, offline actions, or the like. In one embodiment, one or more of client computers  902 - 905  may be configured to operate within a business or other entity to perform a variety of services for the business or other entity. For example, client computers  902 - 905  may be configured to operate as a web server, firewall, client application, media player, mobile telephone, game console, desktop computer, or the like. However, client computers  902 - 905  are not constrained to these services and may also be employed, for example, as for end-user computing in other embodiments. It should be recognized that more or less client computers (as shown in  FIG.  9   ) may be included within a system such as described herein, and embodiments are therefore not constrained by the number or type of client computers employed. 
     Computers that may operate as client computer  902  may include computers that typically connect using a wired or wireless communications medium such as personal computers, multiprocessor systems, microprocessor-based or programmable electronic devices, network PCs, or the like. In some embodiments, client computers  902 - 905  may include virtually any portable computer capable of connecting to another computer and receiving information such as, laptop computer  903 , mobile computer  904 , tablet computers  905 , or the like. However, portable computers are not so limited and may also include other portable computers such as cellular telephones, display pagers, radio frequency (RF) devices, infrared (IR) devices, Personal Digital Assistants (PDAs), handheld computers, wearable computers, integrated devices combining one or more of the preceding computers, or the like. As such, client computers  902 - 905  typically range widely in terms of capabilities and features. Moreover, client computers  902 - 905  may access various computing applications, including a browser, or other web-based application. 
     A web-enabled client computer may include a browser application that is configured to send requests and receive responses over the web. The browser application may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web-based language. In one embodiment, the browser application is enabled to employ JavaScript, HyperText Markup Language (HTML), eXtensible Markup Language (XML), JavaScript Object Notation (JSON), Cascading Style Sheets (CS S), or the like, or combination thereof, to display and send a message. In one embodiment, a user of the client computer may employ the browser application to perform various activities over a network (online). However, another application may also be used to perform various online activities. 
     Client computers  902 - 905  also may include at least one other client application that is configured to receive or send content between another computer. The client application may include a capability to send or receive content, or the like. The client application may further provide information that identifies itself, including a type, capability, name, and the like. In one embodiment, client computers  902 - 905  may uniquely identify themselves through any of a variety of mechanisms, including an Internet Protocol (IP) address, a phone number, Mobile Identification Number (MIN), an electronic serial number (ESN), a client certificate, or other device identifier. Such information may be provided in one or more network packets, or the like, sent between other client computers, visualization server computer  916 , or other computers. 
     Client computers  902 - 905  may further be configured to include a client application that enables an end-user to log into an end-user account that may be managed by another computer, such as visualization server computer  916 , or the like. Such an end-user account, in one non-limiting example, may be configured to enable the end-user to manage one or more online activities, including in one non-limiting example, project management, software development, system administration, configuration management, search activities, social networking activities, browse various websites, communicate with other users, or the like. Also, client computers may be arranged to enable users to display reports, interactive user-interfaces, or results provided by visualization server computer  916 , or the like. 
     Wireless network  908  is configured to couple client computers  903 - 905  and its components with network  910 . Wireless network  908  may include any of a variety of wireless sub-networks that may further overlay stand-alone ad-hoc networks, and the like, to provide an infrastructure-oriented connection for client computers  903 - 905 . Such sub-networks may include mesh networks, Wireless LAN (WLAN) networks, cellular networks, and the like. In one embodiment, the system may include more than one wireless network. 
     Wireless network  908  may further include an autonomous system of terminals, gateways, routers, and the like connected by wireless radio links, and the like. These connectors may be configured to move freely and randomly and organize themselves arbitrarily, such that the topology of wireless network  908  may change rapidly. 
     Wireless network  908  may further employ a plurality of access technologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generation radio access for cellular systems, WLAN, Wireless Router (WR) mesh, and the like. Access technologies such as 2G, 3G, 4G, 5G, and future access networks may enable wide area coverage for mobile computers, such as client computers  903 - 905  with various degrees of mobility. In one non-limiting example, wireless network  908  may enable a radio connection through a radio network access such as Global System for Mobil communication (GSM), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Wideband Code Division Multiple Access (WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution (LTE), and the like. In essence, wireless network  908  may include virtually any wireless communication mechanism by which information may travel between client computers  903 - 905  and another computer, network, a cloud-based network, a cloud instance, or the like. 
     Network  910  is configured to couple network computers with other computers, including, visualization server computer  916 , client computers  902 , and client computers  903 - 905  through wireless network  908 , or the like. Network  910  is enabled to employ any form of computer readable media for communicating information from one electronic device to another. Also, network  910  can include the Internet in addition to local area networks (LANs), wide area networks (WANs), direct connections, such as through a universal serial bus (USB) port, Ethernet port, other forms of computer-readable media, or any combination thereof. On an interconnected set of LANs, including those based on differing architectures and protocols, a router acts as a link between LANs, enabling messages to be sent from one to another. In addition, communication links within LANs typically include twisted wire pair or coaxial cable, while communication links between networks may utilize analog telephone lines, full or fractional dedicated digital lines including T1, T2, T3, and T4, or other carrier mechanisms including, for example, E-carriers, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, or other communications links known to those skilled in the art. Moreover, communication links may further employ any of a variety of digital signaling technologies, including without limit, for example, DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like. Furthermore, remote computers and other related electronic devices could be remotely connected to either LANs or WANs via a modem and temporary telephone link. In one embodiment, network  910  may be configured to transport information of an Internet Protocol (IP). 
     Additionally, communication media typically embodies computer readable instructions, data structures, program modules, or other transport mechanism and includes any information non-transitory delivery media or transitory delivery media. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic, RF, infrared, and other wireless media. 
     Also, one embodiment of visualization server computer  916  is described in more detail below in conjunction with  FIG.  11   . Although  FIG.  9    illustrates visualization server computer  916  or the like, as a single computer, the innovations or embodiments are not so limited. For example, one or more functions of visualization server computer  916 , or the like, may be distributed across one or more distinct network computers. Moreover, in one or more embodiments, visualization server computer  916  may be implemented using a plurality of network computers. Further, in one or more of the various embodiments, visualization server computer  916 , or the like, may be implemented using one or more cloud instances in one or more cloud networks. Accordingly, these innovations and embodiments are not to be construed as being limited to a single environment, and other configurations, and other architectures are also envisaged. 
     Illustrative Client Computer 
       FIG.  10    shows one embodiment of client computer  1000  that may include many more or less components than those shown. Client computer  1000  may represent, for example, one or more embodiment of mobile computers or client computers shown in  FIG.  9   . 
     Client computer  1000  may include processor  1002  in communication with memory  1004  via bus  1028 . Client computer  1000  may also include power supply  1030 , network interface  1032 , audio interface  1056 , display  1050 , keypad  1052 , illuminator  1054 , video interface  1042 , input/output interface  1038 , haptic interface  1064 , global positioning systems (GPS) receiver  1058 , open air gesture interface  1060 , temperature interface  1062 , camera(s)  1040 , projector  1046 , pointing device interface  1066 , processor-readable stationary storage device  1034 , and processor-readable removable storage device  1036 . Client computer  1000  may optionally communicate with a base station (not shown), or directly with another computer. And in one embodiment, although not shown, a gyroscope may be employed within client computer  1000  to measuring or maintaining an orientation of client computer  1000 . 
     Power supply  1030  may provide power to client computer  1000 . A rechargeable or non-rechargeable battery may be used to provide power. The power may also be provided by an external power source, such as an AC adapter or a powered docking cradle that supplements or recharges the battery. 
     Network interface  1032  includes circuitry for coupling client computer  1000  to one or more networks, and is constructed for use with one or more communication protocols and technologies including, but not limited to, protocols and technologies that implement any portion of the OSI model for mobile communication (GSM), CDMA, time division multiple access (TDMA), UDP, TCP/IP, SMS, MMS, GPRS, WAP, UWB, WiMax, SIP/RTP, GPRS, EDGE, WCDMA, LTE, UMTS, OFDM, CDMA2000, EV-DO, HSDPA, or any of a variety of other wireless communication protocols. Network interface  1032  is sometimes known as a transceiver, transceiving device, or network interface card (MC). 
     Audio interface  1056  may be arranged to produce and receive audio signals such as the sound of a human voice. For example, audio interface  1056  may be coupled to a speaker and microphone (not shown) to enable telecommunication with others or generate an audio acknowledgment for some action. A microphone in audio interface  1056  can also be used for input to or control of client computer  1000 , e.g., using voice recognition, detecting touch based on sound, and the like. 
     Display  1050  may be a liquid crystal display (LCD), gas plasma, electronic ink, light emitting diode (LED), Organic LED (OLED) or any other type of light reflective or light transmissive display that can be used with a computer. Display  1050  may also include a touch interface  1044  arranged to receive input from an object such as a stylus or a digit from a human hand, and may use resistive, capacitive, surface acoustic wave (SAW), infrared, radar, or other technologies to sense touch or gestures. 
     Projector  1046  may be a remote handheld projector or an integrated projector that is capable of projecting an image on a remote wall or any other reflective object such as a remote screen. 
     Video interface  1042  may be arranged to capture video images, such as a still photo, a video segment, an infrared video, or the like. For example, video interface  1042  may be coupled to a digital video camera, a web-camera, or the like. Video interface  1042  may comprise a lens, an image sensor, and other electronics. Image sensors may include a complementary metal-oxide-semiconductor (CMOS) integrated circuit, charge-coupled device (CCD), or any other integrated circuit for sensing light. 
     Keypad  1052  may comprise any input device arranged to receive input from a user. For example, keypad  1052  may include a push button numeric dial, or a keyboard. Keypad  1052  may also include command buttons that are associated with selecting and sending images. 
     Illuminator  1054  may provide a status indication or provide light. Illuminator  1054  may remain active for specific periods of time or in response to event messages. For example, when illuminator  1054  is active, it may back-light the buttons on keypad  1052  and stay on while the client computer is powered. Also, illuminator  1054  may back-light these buttons in various patterns when particular actions are performed, such as dialing another client computer. Illuminator  1054  may also cause light sources positioned within a transparent or translucent case of the client computer to illuminate in response to actions. 
     Further, client computer  1000  may also comprise hardware security module (HSM)  1068  for providing additional tamper resistant safeguards for generating, storing or using security/cryptographic information such as, keys, digital certificates, passwords, passphrases, two-factor authentication information, or the like. In some embodiments, hardware security module may be employed to support one or more standard public key infrastructures (PKI), and may be employed to generate, manage, or store keys pairs, or the like. In some embodiments, HSM  1068  may be a stand-alone computer, in other cases, HSM  1068  may be arranged as a hardware card that may be added to a client computer. 
     Client computer  1000  may also comprise input/output interface  1038  for communicating with external peripheral devices or other computers such as other client computers and network computers. The peripheral devices may include an audio headset, virtual reality headsets, display screen glasses, remote speaker system, remote speaker and microphone system, and the like. Input/output interface  1038  can utilize one or more technologies, such as Universal Serial Bus (USB), Infrared, WiFi, WiMax, Bluetooth™, and the like. 
     Input/output interface  1038  may also include one or more sensors for determining geolocation information (e.g., GPS), monitoring electrical power conditions (e.g., voltage sensors, current sensors, frequency sensors, and so on), monitoring weather (e.g., thermostats, barometers, anemometers, humidity detectors, precipitation scales, or the like), or the like. Sensors may be one or more hardware sensors that collect or measure data that is external to client computer  1000 . 
     Haptic interface  1064  may be arranged to provide tactile feedback to a user of the client computer. For example, the haptic interface  1064  may be employed to vibrate client computer  1000  in a particular way when another user of a computer is calling. Temperature interface  1062  may be used to provide a temperature measurement input or a temperature changing output to a user of client computer  1000 . Open air gesture interface  1060  may sense physical gestures of a user of client computer  1000 , for example, by using single or stereo video cameras, radar, a gyroscopic sensor inside a computer held or worn by the user, or the like. Camera  1040  may be used to track physical eye movements of a user of client computer  1000 . 
     GPS transceiver  1058  can determine the physical coordinates of client computer  1000  on the surface of the Earth, which typically outputs a location as latitude and longitude values. GPS transceiver  1058  can also employ other geo-positioning mechanisms, including, but not limited to, triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference (E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), Enhanced Timing Advance (ETA), Base Station Subsystem (BSS), or the like, to further determine the physical location of client computer  1000  on the surface of the Earth. It is understood that under different conditions, GPS transceiver  1058  can determine a physical location for client computer  1000 . In one or more embodiments, however, client computer  1000  may, through other components, provide other information that may be employed to determine a physical location of the client computer, including for example, a Media Access Control (MAC) address, IP address, and the like. 
     In at least one of the various embodiments, applications, such as, operating system  1006 , visualization client  1022 , other client apps  1024 , web browser  1026 , or the like, may be arranged to employ geo-location information to select one or more localization features, such as, time zones, languages, currencies, calendar formatting, or the like. Localization features may be used in display objects, data models, data objects, user-interfaces, reports, as well as internal processes or databases. In at least one of the various embodiments, geo-location information used for selecting localization information may be provided by GPS  1058 . Also, in some embodiments, geolocation information may include information provided using one or more geolocation protocols over the networks, such as, wireless network  108  or network  111 . 
     Human interface components can be peripheral devices that are physically separate from client computer  1000 , allowing for remote input or output to client computer  1000 . For example, information routed as described here through human interface components such as display  1050  or keyboard  1052  can instead be routed through network interface  1032  to appropriate human interface components located remotely. Examples of human interface peripheral components that may be remote include, but are not limited to, audio devices, pointing devices, keypads, displays, cameras, projectors, and the like. These peripheral components may communicate over a Pico Network such as Bluetooth™, Zigbee™ and the like. One non-limiting example of a client computer with such peripheral human interface components is a wearable computer, which might include a remote pico projector along with one or more cameras that remotely communicate with a separately located client computer to sense a user&#39;s gestures toward portions of an image projected by the pico projector onto a reflected surface such as a wall or the user&#39;s hand. 
     A client computer may include web browser application  1026  that is configured to receive and to send web pages, web-based messages, graphics, text, multimedia, and the like. The client computer&#39;s browser application may employ virtually any programming language, including a wireless application protocol messages (WAP), and the like. In one or more embodiments, the browser application is enabled to employ Handheld Device Markup Language (HDML), Wireless Markup Language (WML), WMLScript, JavaScript, Standard Generalized Markup Language (SGML), HyperText Markup Language (HTML), eXtensible Markup Language (XML), HTML5, and the like. 
     Memory  1004  may include RAM, ROM, or other types of memory. Memory  1004  illustrates an example of computer-readable storage media (devices) for storage of information such as computer-readable instructions, data structures, program modules or other data. Memory  1004  may store BIOS  1008  for controlling low-level operation of client computer  1000 . The memory may also store operating system  1006  for controlling the operation of client computer  1000 . It will be appreciated that this component may include a general-purpose operating system such as a version of UNIX, or LINUX™, or a specialized client computer communication operating system such as Windows Phone™, or the Symbian® operating system. The operating system may include, or interface with a Java virtual machine module that enables control of hardware components or operating system operations via Java application programs. 
     Memory  1004  may further include one or more data storage  1010 , which can be utilized by client computer  1000  to store, among other things, applications  1020  or other data. For example, data storage  1010  may also be employed to store information that describes various capabilities of client computer  1000 . The information may then be provided to another device or computer based on any of a variety of methods, including being sent as part of a header during a communication, sent upon request, or the like. Data storage  1010  may also be employed to store social networking information including address books, buddy lists, aliases, user profile information, or the like. Data storage  1010  may further include program code, data, algorithms, and the like, for use by a processor, such as processor  1002  to execute and perform actions. In one embodiment, at least some of data storage  1010  might also be stored on another component of client computer  1000 , including, but not limited to, non-transitory processor-readable removable storage device  1036 , processor-readable stationary storage device  1034 , or even external to the client computer. 
     Applications  1020  may include computer executable instructions which, when executed by client computer  1000 , transmit, receive, or otherwise process instructions and data. Applications  1020  may include, for example, visualization client  1022 , other client applications  1024 , web browser  1026 , or the like. Client computers may be arranged to exchange communications one or more servers. 
     Other examples of application programs include calendars, search programs, email client applications, IM applications, SMS applications, Voice Over Internet Protocol (VOIP) applications, contact managers, task managers, transcoders, database programs, word processing programs, security applications, spreadsheet programs, games, search programs, visualization applications, and so forth. 
     Additionally, in one or more embodiments (not shown in the figures), client computer  1000  may include an embedded logic hardware device instead of a CPU, such as, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic (PAL), or the like, or combination thereof. The embedded logic hardware device may directly execute its embedded logic to perform actions. Also, in one or more embodiments (not shown in the figures), client computer  1000  may include one or more hardware micro-controllers instead of CPUs. In one or more embodiments, the one or more micro-controllers may directly execute their own embedded logic to perform actions and access its own internal memory and its own external Input and Output Interfaces (e.g., hardware pins or wireless transceivers) to perform actions, such as System On a Chip (SOC), or the like. 
     Illustrative Network Computer 
       FIG.  11    shows one embodiment of network computer  1100  that may be included in a system implementing one or more of the various embodiments. Network computer  1100  may include many more or less components than those shown in  FIG.  11   . However, the components shown are sufficient to disclose an illustrative embodiment for practicing these innovations. Network computer  1100  may represent, for example, one embodiment of at least one of visualization server computer  916 , or the like, of  FIG.  9   . 
     Network computers, such as, network computer  1100  may include a processor  1102  that may be in communication with a memory  1104  via a bus  1128 . In some embodiments, processor  1102  may be comprised of one or more hardware processors, or one or more processor cores. In some cases, one or more of the one or more processors may be specialized processors designed to perform one or more specialized actions, such as, those described herein. Network computer  1100  also includes a power supply  1130 , network interface  1132 , audio interface  1156 , display  1150 , keyboard  1152 , input/output interface  1138 , processor-readable stationary storage device  1134 , and processor-readable removable storage device  1136 . Power supply  1130  provides power to network computer  1100 . 
     Network interface  1132  includes circuitry for coupling network computer  1100  to one or more networks, and is constructed for use with one or more communication protocols and technologies including, but not limited to, protocols and technologies that implement any portion of the Open Systems Interconnection model (OSI model), global system for mobile communication (GSM), code division multiple access (CDMA), time division multiple access (TDMA), user datagram protocol (UDP), transmission control protocol/Internet protocol (TCP/IP), Short Message Service (SMS), Multimedia Messaging Service (MMS), general packet radio service (GPRS), WAP, ultra-wide band (UWB), IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMax), Session Initiation Protocol/Real-time Transport Protocol (SIP/RTP), or any of a variety of other wired and wireless communication protocols. Network interface  1132  is sometimes known as a transceiver, transceiving device, or network interface card (NIC). Network computer  1100  may optionally communicate with a base station (not shown), or directly with another computer. 
     Audio interface  1156  is arranged to produce and receive audio signals such as the sound of a human voice. For example, audio interface  1156  may be coupled to a speaker and microphone (not shown) to enable telecommunication with others or generate an audio acknowledgment for some action. A microphone in audio interface  1156  can also be used for input to or control of network computer  1100 , for example, using voice recognition. 
     Display  1150  may be a liquid crystal display (LCD), gas plasma, electronic ink, light emitting diode (LED), Organic LED (OLED) or any other type of light reflective or light transmissive display that can be used with a computer. In some embodiments, display  1150  may be a handheld projector or pico projector capable of projecting an image on a wall or other object. 
     Network computer  1100  may also comprise input/output interface  1138  for communicating with external devices or computers not shown in  FIG.  11   . Input/output interface  1138  can utilize one or more wired or wireless communication technologies, such as USB™, Firewire™ WiFi, WiMax, Thunderbolt™, Infrared, Bluetooth™, Zigbee™, serial port, parallel port, and the like. 
     Also, input/output interface  1138  may also include one or more sensors for determining geolocation information (e.g., GPS), monitoring electrical power conditions (e.g., voltage sensors, current sensors, frequency sensors, and so on), monitoring weather (e.g., thermostats, barometers, anemometers, humidity detectors, precipitation scales, or the like), or the like. Sensors may be one or more hardware sensors that collect or measure data that is external to network computer  1100 . Human interface components can be physically separate from network computer  1100 , allowing for remote input or output to network computer  1100 . For example, information routed as described here through human interface components such as display  1150  or keyboard  1152  can instead be routed through the network interface  1132  to appropriate human interface components located elsewhere on the network. Human interface components include any component that allows the computer to take input from, or send output to, a human user of a computer. Accordingly, pointing devices such as mice, styluses, track balls, or the like, may communicate through pointing device interface  1158  to receive user input. 
     GPS transceiver  1140  can determine the physical coordinates of network computer  1100  on the surface of the Earth, which typically outputs a location as latitude and longitude values. GPS transceiver  1140  can also employ other geo-positioning mechanisms, including, but not limited to, triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference (E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), Enhanced Timing Advance (ETA), Base Station Subsystem (BSS), or the like, to further determine the physical location of network computer  1100  on the surface of the Earth. It is understood that under different conditions, GPS transceiver  1140  can determine a physical location for network computer  1100 . In one or more embodiments, however, network computer  1100  may, through other components, provide other information that may be employed to determine a physical location of the client computer, including for example, a Media Access Control (MAC) address, IP address, and the like. 
     In at least one of the various embodiments, applications, such as, operating system  1106 , visualization engine  1122 , analysis engine  1124 , sampling engine  1126 , other applications  1129 , or the like, may be arranged to employ geo-location information to select one or more localization features, such as, time zones, languages, currencies, currency formatting, calendar formatting, or the like. Localization features may be used in user interfaces, dashboards, visualizations, reports, as well as internal processes or databases. In at least one of the various embodiments, geo-location information used for selecting localization information may be provided by GPS  1140 . Also, in some embodiments, geolocation information may include information provided using one or more geolocation protocols over the networks, such as, wireless network  108  or network  111 . 
     Memory  1104  may include Random Access Memory (RAM), Read-Only Memory (ROM), or other types of memory. Memory  1104  illustrates an example of computer-readable storage media (devices) for storage of information such as computer-readable instructions, data structures, program modules or other data. Memory  1104  stores a basic input/output system (BIOS)  1108  for controlling low-level operation of network computer  1100 . The memory also stores an operating system  1106  for controlling the operation of network computer  1100 . It will be appreciated that this component may include a general-purpose operating system such as a version of UNIX, or Linux®, or a specialized operating system such as Microsoft Corporation&#39;s Windows® operating system, or the Apple Corporation&#39;s macOS® operating system. The operating system may include, or interface with one or more virtual machine modules, such as, a Java virtual machine module that enables control of hardware components or operating system operations via Java application programs. Likewise, other runtime environments may be included. 
     Memory  1104  may further include one or more data storage  1110 , which can be utilized by network computer  1100  to store, among other things, applications  1120  or other data. For example, data storage  1110  may also be employed to store information that describes various capabilities of network computer  1100 . The information may then be provided to another device or computer based on any of a variety of methods, including being sent as part of a header during a communication, sent upon request, or the like. Data storage  1110  may also be employed to store social networking information including address books, buddy lists, aliases, user profile information, or the like. Data storage  1110  may further include program code, data, algorithms, and the like, for use by a processor, such as processor  1102  to execute and perform actions such as those actions described below. In one embodiment, at least some of data storage  1110  might also be stored on another component of network computer  1100 , including, but not limited to, non-transitory media inside processor-readable removable storage device  1136 , processor-readable stationary storage device  1134 , or any other computer-readable storage device within network computer  1100 , or even external to network computer  1100 . Data storage  1110  may include, for example, data sources  1114 , dashboards/visualizations  1116 , or the like. 
     Applications  1120  may include computer executable instructions which, when executed by network computer  1100 , transmit, receive, or otherwise process messages (e.g., SMS, Multimedia Messaging Service (MMS), Instant Message (IM), email, or other messages), audio, video, and enable telecommunication with another user of another mobile computer. Other examples of application programs include calendars, search programs, email client applications, IM applications, SMS applications, Voice Over Internet Protocol (VOIP) applications, contact managers, task managers, transcoders, database programs, word processing programs, security applications, spreadsheet programs, games, search programs, and so forth. Applications  1120  may include visualization engine  1122 , analysis engine  1124 , sampling engine  1126 , other applications  1129 , or the like, that may be arranged to perform actions for embodiments described below. In one or more of the various embodiments, one or more of the applications may be implemented as modules or components of another application. Further, in one or more of the various embodiments, applications may be implemented as operating system extensions, modules, plugins, or the like. 
     Furthermore, in one or more of the various embodiments, visualization engine  1122 , analysis engine  1124 , sampling engine  1126 , other applications  1129 , or the like, may be operative in a cloud-based computing environment. In one or more of the various embodiments, these applications, and others, that comprise the management platform may be executing within virtual machines or virtual servers that may be managed in a cloud-based based computing environment. In one or more of the various embodiments, in this context the applications may flow from one physical network computer within the cloud-based environment to another depending on performance and scaling considerations automatically managed by the cloud computing environment. Likewise, in one or more of the various embodiments, virtual machines or virtual servers dedicated to visualization engine  1122 , analysis engine  1124 , sampling engine  1126 , other applications  1129 , or the like, may be provisioned and de-commissioned automatically. 
     Also, in one or more of the various embodiments, visualization engine  1122 , analysis engine  1124 , sampling engine  1126 , other applications  1129 , or the like, may be located in virtual servers running in a cloud-based computing environment rather than being tied to one or more specific physical network computers. 
     Further, network computer  1100  may also comprise hardware security module (HSM)  1160  for providing additional tamper resistant safeguards for generating, storing or using security/cryptographic information such as, keys, digital certificates, passwords, passphrases, two-factor authentication information, or the like. In some embodiments, hardware security module may be employed to support one or more standard public key infrastructures (PKI), and may be employed to generate, manage, or store keys pairs, or the like. In some embodiments, HSM  1160  may be a stand-alone network computer, in other cases, HSM  1160  may be arranged as a hardware card that may be installed in a network computer. 
     Additionally, in one or more embodiments (not shown in the figures), network computer  1100  may include an embedded logic hardware device instead of a CPU, such as, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic (PAL), or the like, or combination thereof. The embedded logic hardware device may directly execute its embedded logic to perform actions. Also, in one or more embodiments (not shown in the figures), the network computer may include one or more hardware microcontrollers instead of a CPU. In one or more embodiments, the one or more microcontrollers may directly execute their own embedded logic to perform actions and access their own internal memory and their own external Input and Output Interfaces (e.g., hardware pins or wireless transceivers) to perform actions, such as System On a Chip (SOC), or the like.