A system and method for determining smart standards for use in data analytics environments.

Abstract

A system and method for providing dynamically generated analytical data metrics or criteria (referred to herein as smart criteria) for use in a data analytics environment. Smart criteria may be scoped to a dataset and associated with metadata that indicates an understanding of the scoped data or changes thereto that are of interest to a particular user. The system may operate according to the smart criteria and associated rules to monitor its associated data and broadcast relevant information, such as anomalies, trends, or other notable changes, to subscribers. Smart criteria may be automatically discovered, defined, or updated by the system, for example, as dynamically generated key performance indicators. For example, conditional formatting may be used when presenting smart criteria as data metrics or visualizations.

Description

Technical Field

[0001] Copyright Notice Part of the disclosure of this patent document contains materials that are subject to copyright protection. Since the patent document or patent disclosure is publicly available in the patent files and records of the United States Patent and Trademark Office, the copyright owner has no objection to its reproduction by anyone, but otherwise retains all copyrights without exception.

[0002] Priority Claim and Cross - Reference to Related Applications This application claims priority under U.S. Patent Application No. 17 / 543,406, filed on December 6, 2021, entitled "SYSTEM AND METHOD FOR DETERMINATION OF SMART MEASURES FOR USE WITH A DATA ANALYTICS ENVIRONMENT," and U.S. Provisional Application No. 63 / 122,592, filed on December 8, 2020, entitled "SYSTEM AND METHOD FOR DETERMINATION OF SMART MEASURES FOR USE WITH AN ANALYTIC APPLICATIONS ENVIRONMENT," and U.S. Patent No. 10,516,980, filed on September 22, 2016, issued on December 24, 2019, entitled "AUTOMATIC REDISPLAY OF A USER INTERFACE INCLUDING A Related applications include U.S. Patent Application No. 15 / 273, 567, entitled "Visualization (Automatic Redisplay of User Interface Including Visualization)", U.S. Patent Application No. 16 / 662, 695, entitled "Techniques for Semantic Searching", filed on 24 October 2019 and published on 16 April 2020 as U.S. Patent Publication No. 2020 / 0117658, and U.S. Patent Application No. 16 / 586, 347, entitled "Techniques for Data-Driven Correlation of Metrics", filed on 27 September 2019 and published on 2 April 2020 as U.S. Patent Publication No. 2020 / 0104775, all of which are incorporated herein by reference.

[0003] Technical field The embodiments described herein generally relate to systems and methods for providing data analytics, and more particularly to systems and methods for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment. [Background technology]

[0004] background Generally speaking, data analytics enables the computer-based examination of data volumes, deriving analytical data, metrics, conclusions, or other types of analytical information from source data, or deriving analytical data, metrics, conclusions, or other types of analytical information that describes source data. Using systems and methods, analytical business intelligence data can be generated in a format convenient for decision-makers, such as a set of data metrics or criteria that function as key performance indicators, describing an organization's business-related data based on analysis.

[0005] Systems for data analytics may be provided within a cloud or other shared computing environment. Because customers or users from different organizations have different requirements regarding how their analytical data is presented, providing suitable analytical data may require manually configuring corresponding dashboards, visualizations, user interfaces, key performance indicators, or other analytical criteria specifically tailored for these customers / users.

[0006] However, manually configuring and maintaining a large number of organization-specific or user-specific key performance indicators and other analytical criteria incurs a significant administrative burden.

[0007] In addition, the usability of key performance indicators or criteria, which are thus manually constructed, is generally limited to their original purpose unless they are manually reconfigured to address the subsequent needs of the organization / users. [Overview of the project]

[0008] overview In accordance with the embodiments described herein, systems and methods for providing dynamically generated analytical data metrics or criteria (hereinafter referred to as smart criteria) for use in a data analytics environment are described herein.

[0009] According to the embodiment, the smart criteria may range a dataset and may be associated with metadata that indicates an understanding of the ranged data or changes thereto that are of interest to a particular user. The system may operate according to the smart criteria and associated rules to monitor the associated data and broadcast relevant information to subscribers, such as anomalies, trends, or other notable changes.

[0010] According to the embodiment, smart criteria may be automatically discovered, defined, or updated by the system as dynamically generated key performance indicators, for example, based on an understanding of the data set and / or information received from the user community. For example, conditional formatting may be used when presenting smart criteria as data metrics or visualizations. [Brief explanation of the drawing]

[0011] [Figure 1] This figure illustrates a system for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment, according to an embodiment of the system. [Figure 2]This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 3] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 4] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 5] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 6] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 7] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 8] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 9] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 10] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 11] This figure further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment. [Figure 12] This figure illustrates various examples of rules that may be used by the system when generating and utilizing dynamically generated analytical data metrics or smart criteria according to the embodiment. [Figure 13] This is a diagram illustrating an exemplary use of the smart standard according to an embodiment. [Figure 14]A diagram further illustrating an exemplary use of smart criteria according to an embodiment. [Figure 15] A diagram illustrating another exemplary use of smart criteria according to an embodiment. [Figure 16] A diagram further illustrating another exemplary use of smart criteria according to an embodiment. [Figure 17] A diagram illustrating an exemplary use of smart criteria in a data analytics environment according to an embodiment. [Figure 18] A diagram showing a process or method for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment according to an embodiment. **Modes for Carrying Out the Invention**

[0012] **Detailed Description** As described above, a system for data analytics may be provided within the context of a cloud or other shared computing environment. However, manually configuring and maintaining a large number of organization-specific or user-specific key performance indicators, and other analysis criteria, requires an administrative burden. In addition to this, the usability of key performance indicators or criteria manually configured in this way is generally limited to their original purpose unless they are manually reconfigured again to address the subsequent needs of the organization / user.

[0013] According to an embodiment, a system and method for providing dynamically generated analytical data metrics or criteria (referred to herein as smart criteria) for use in a data analytics environment will be described herein.

[0014] According to an embodiment, the smart criteria may be specified for a range in a dataset, or may be associated with metadata indicating an understanding of the specified data or changes thereto that are of interest to a particular user. The system may operate according to the smart criteria and associated rules to monitor the associated data and broadcast relevant information, such as anomalies, trends, or other notable changes, to subscribers.

[0015] According to an embodiment, the smart criteria may be automatically discovered, defined, or updated by the system as, for example, dynamically generated key performance indicators based on an understanding of information received from the dataset and / or user community. For example, conditional formatting may be used when presenting the smart criteria as data metrics or visualizations.

[0016] According to various embodiments, the technical advantages of the systems and methods described herein include that the system can automatically generate analysis data metrics or criteria associated with a dataset, or analysis data metrics or criteria that describe the dataset, thereby enabling the efficient generation of data analytics and reducing the amount and storage of manually configured key performance indicators that would otherwise be required to address changing organization-specific or user-specific needs.

[0017] In addition, according to various embodiments, by providing such dynamically generated analysis data metrics or criteria as smart criteria, the system can effectively configure itself to, for example, quickly detect and react to changes within an associated dataset, and utilize information obtained from the community without delay caused by human management.

[0018] Dynamically generated analysis data metrics (smart criteria) According to the embodiments, dynamically generated analytical data metrics (referred to as “smart criteria” in various embodiments herein) can be viewed as analytical data metrics or criteria adapted to function in a dynamically updated and communicative manner, in a “self-aware” or “active criteria” style.

[0019] For example, in contrast to traditional KPIs (Key Performance Indicators) that can be manually tailored and updated to address specific organizational or user requirements regarding how analytical data is presented, according to this embodiment, smart criteria may be automatically discovered, defined, or updated by the data analysis system, for example, as dynamically generated KPIs, based on an understanding of the data set and / or the user community.

[0020] Figure 1 illustrates a system for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment, according to an embodiment.

[0021] As shown in Figure 1 and described in detail later, according to the embodiment, the data analytics environment 30 comprises a data analysis system 100 or may operate in combination with the data analysis system 100, and comprises computer hardware 101. The data analysis system 100 provides access to enterprise data or other data 34 and comprises a data metrics subsystem 40 or components. The data metrics subsystem 40 comprises a smart criteria generation unit 44 and enables the use of conditional formatting 42.

[0022] According to the embodiment, the data analysis system may be provided by a cloud computing system, or by another appropriately programmed computer system comprising one or more computer servers, one or more processing units (processors, CPUs), memory, and data storage. Computer operations may be implemented as appropriate by hardware, computer-executable instructions, firmware, or a combination thereof. Data storage may be implemented using persistent storage, such as one or more memory devices or other non-temporary computer-readable storage media.

[0023] According to one embodiment, a client device 10 having device hardware 12 (e.g., processor / CPU, memory, storage), a data analytics application 14, and a user interface 16 may communicate with a data analytics system, for example, via a mobile / web interface 32 provided by the data analytics environment. The user 50 may use the client device to interact with the data analytics environment 52 and view, or otherwise access, analytical data such as a set of data metrics or criteria that function as KPIs, or other types of metrics.

[0024] According to the embodiment, the further components and processes shown in Figure 1 (which will be further described herein with respect to various other embodiments) may be provided as software or program code that can be executed by a computer system or other type of processing device.

[0025] As will be described later, according to the embodiment, the data metrics subsystem enables smart criteria to be scoped to a dataset and associated with metadata that indicates an understanding of the scoped data or changes thereto that are of interest to a particular user. The data analysis system may operate according to the smart criteria and associated rules to monitor its associated data and broadcast relevant information to subscribers, such as anomalies, trends, or other notable changes.

[0026] According to the embodiment, the data analytics environment may have, or may operate in conjunction with, a data crawl / search function that can receive from a user community information related to the search, access, and / or use of specific types of data or analytical metrics or criteria. This information may then be used by the system to determine user interest in a particular dataset, generate smart criteria, or associate specific smart criteria with actions that are likely to be of interest to a particular user.

[0027] According to the embodiment, the data analytics environment may include, or may operate in combination with, a pattern / trend determination function that enables the system to provide users with relevant information in the form of personalized trend metrics or anomaly metrics, for example, specifically for that particular user.

[0028] According to one embodiment, users may choose to define smart criteria themselves. Alternatively, the data analysis system may employ statistical, ML (machine learning), AI (artificial intelligence), or other computer-automated processes to learn from the user community which aspects of a particular analytical metric or criterion are important, and then gradually update the relevant metadata to use when generating appropriate smart criteria. As the system continues to learn more about each criterion, it will be able to continue to utilize the information provided by the community to provide progressively more relevant information.

[0029] According to the embodiment, such metadata may include, for example, ranges, filters, directions, thresholds / targets, or indications of other changes in the data that are likely to be of interest to one or more users. Smart criteria may be automatically discovered, defined, or updated by the system, for example, as dynamically generated key performance indicators, based on an understanding of information about the dataset and / or analytical data metrics received from the user community.

[0030] According to the embodiment, the user may directly provide metadata to associate with smart criteria. Alternatively, the metadata may be collected indirectly through various data crawling / search environments, such as the Oracle BI Ask environment.

[0031] For example, as shown in Figure 1, according to the embodiment, if the data analysis system observes that a user or a user's community 60 regularly looks at a particular type of data or a particular analytical data metric, or generally takes action in response to changes in such data, the system can apply user / community-based learning 70 to understand the user's (or community's) interest in that data and make inferences from the user's actions.

[0032] According to one embodiment, the data analysis system may record such up-to-date knowledge / understandings as up-to-date metadata / rules associated with smart criteria. If a user or user community provides enough metadata for the system to recognize a particular analytical data metric as a smart criterion, one or more dynamically generated analytical data metrics or criteria 72, 74 may be generated by the data analysis system 80.

[0033] According to the embodiment, once a smart standard is defined and associated with a user or group of users, the smart standard may be used, transmitted, broadcasted, or otherwise provided to that user or other users as appropriate on a context basis, across different / various types of computing environments, such as mobile devices, web browsers, on-premise systems, or third-party applications.

[0034] For example, according to one embodiment, the data analysis system may determine smart criteria for a dataset by collecting metadata indicating when an analysis metric or criterion is, for example, in a trend (upward or downward) and meets a threshold to which the system associates the trend information with smart criteria.

[0035] According to one embodiment, by recognizing which users may have used similar analytical metrics or criteria, the system may associate smart criteria with displays for specific users who might be interested in smart criteria, and record such information in the relevant metadata.

[0036] According to the embodiment, when generated by a data analysis system, smart criteria can exhibit various characteristics in providing, for example, an understanding of which data points in the associated dataset are good or bad (e.g., directional); normal, abnormal, or ideal; important thresholds, goals, and benchmarks; or an understanding of which particular users might be interested in receiving such smart criteria information.

[0037] According to the embodiment, conditional formatting may be used, for example, when presenting smart criteria as display data metrics or visualizations. Conditional formatting allows the data analytics environment to present information in various different ways within the data visualization, for example, to highlight parts of the visualization to reflect differences in values ​​of relevant data.

[0038] For example, according to the embodiment, the smart criterion may use conditional formatting when reporting its status (e.g., as red / yellow / green); or use various conditional formatting or other visualization processes to effectively communicate information about how the data associated with the smart criterion changes over time; notify people who might be interested in the data; suggest appropriate actions for the data; predict when the smart criterion is likely to meet a particular goal; provide trends; provide differences from the goal and gaps to the goal; or provide a smart overview of the data.

[0039] According to the embodiment, the smart criteria may be adapted to look at past and / or present information to determine which data is likely to be most important to a particular user / subscriber, and then use that knowledge to determine which data provided by an analytics application environment, cloud computing, or other type of data analytics environment to monitor and / or report to those users / subscribers.

[0040] According to the embodiment, the data analysis system may include, or operate in combination with, a social / shared information discovery and retrieval environment that can be used to receive information from a user community related to accessing and using data. This information can then be used by the system to understand user interests and generate appropriate smart criteria, or to associate specific smart criteria with actions that are likely to be of interest to the user.

[0041] According to the embodiment, the data analysis system may use input from a user community to train the system with appropriate criteria and actions. As users interact with the system, metadata is collected and associated with appropriate smart criteria. The trained system can then generate or define new smart criteria based on the analysis of newly received data. The metadata is added to specific smart criteria or data points to indicate, for example, that the data associated with those smart criteria or data points is of interest within a particular company or community, or to a particular user.

[0042] For example, according to one embodiment, if used in an environment such as Oracle Day-by-Day, the system may enhance existing metadata used in such an environment for a specific user using that user's smart criteria to indicate that the user is interested in that information, and then use that information to also provide smart criteria for other users in the community.

[0043] According to one embodiment, the system may interpret user input as, for example, text input related to a specific dataset or its modification, and utilize natural language processing techniques when determining appropriate smart criteria.

[0044] For example, according to one embodiment, a data analysis system may leverage metadata obtained by a data crawl / search environment and supplement that metadata with smart criteria metadata. The data crawl / search environment may obtain information about which data a user is interested in, and the user may then adhere to smart criteria, for example, as an Oracle Day-by-Day feed or in a social environment or social platform. In this way, standard metadata may be augmented so that user analytical insights can be associated with further personal information that identifies or suggests the user's interest in specific criteria.

[0045] According to the embodiment, the data analysis system may, after directly or indirectly surveying the user community to evaluate which specific information is more important or relevant to other information or to a particular user type, adapt to augment the criteria-associated metadata using the user-provided information without requiring any administrative configuration.

[0046] For example, according to one embodiment, the data analysis system may be adapted to conduct surveys of a community and provide insights into that data, and the insights provided by the community may be used to further supplement or refine metadata mapped to smart standards using crowdsourced methods or collective wisdom.

[0047] In this way, according to the embodiment, each smart standard can become a personal standard for the user, and then, as other users crowdsource knowledge of the smart standard over time, the smart standard can become a "smarter" standard for that user based on the knowledge of that community / peers, while simultaneously providing them with the benefits by suggesting information that other users in the community may find of interest and relevant to their specific circumstances.

[0048] For example, according to one embodiment, if the system determines that many people are interested in a particular criterion or direction, the data analysis system uses crowdsourced information in conjunction with personalized user context to appropriately guide the process, for example, by evaluating what users are currently doing and then determining relevant information based on what they might be doing at a particular time.

[0049] Data crawl / search function As described above, according to the embodiment, the data analytics environment may have, or may operate in combination with, a data crawl / search function that can receive from a user community information related to the search, access, and / or use of specific types of data or analytical metrics or criteria. This information may then be used by the system to determine user interest in a particular dataset, to generate smart criteria, or to associate specific smart criteria with actions that are likely to be of interest to a particular user.

[0050] Figure 2 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0051] According to one embodiment, the environment shown in Figure 2 is provided for the purpose of illustrating an example of data crawl / search functionality that can be used to support smart standards. According to various embodiments, other types of data crawl / search functionality may be used.

[0052] As shown in Figure 2, according to the embodiment, the data analysis system may communicate with one or more enterprise computer systems or data sources 134, for the purpose of accessing enterprise / other data, for example, one or more enterprise applications 145, an access management system 146, a web server 147, a presentation server 148, and / or a BI (Business Intelligence) server 149.

[0053] According to the embodiment, the processing of user requests or queries to access data may be limited, for example, to datasets that the user can access based on the user's role or identity. Alternatively, the data may be crawled at a level relevant to all users, for example, related to a specific corporate computer system.

[0054] According to this embodiment, the crawl subsystem 152 performs the operation of generating a reference store 140 and a data index 142. Together, the reference store 140 and the data index 142 can provide a logical / index mapping of data, which can be used when processing received data requests / queries. As will be described in more detail later, the crawl subsystem may include a scheduler 154 and a crawl manager 164.

[0055] According to one embodiment, the query subsystem 180 may include, or operate in combination with, a logical mapping of data stored in the semantic data model 110. The logical mapping enables the use of a data index to semantically analyze received data access requests / queries. As will be described in more detail later, the query subsystem may include an input handler 182, an index manager 184, a query generation unit 186, and a visualization manager 188.

[0056] According to the embodiment, the data crawl / search function shown in Figure 2 may be provided, for example, as part of the interactive framework 150 of the Oracle BI Ask environment, or in combination with the Oracle BI Ask environment.

[0057] Figure 3 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0058] As shown in Figure 3, according to the embodiment, the crawl service 162 operates, for example, to manage one or more crawls that query data from one or more data sources according to a schedule, and to call a crawl manager to generate an index of such data. The data index may form part of the logical data mapping of the semantic data model.

[0059] According to the embodiment, the crawl manager can start one or more crawl tasks 166. When executed, the crawl task 166 queries data from the data source based on the language or query defined for the crawl.

[0060] According to the embodiment, the query executor 168 translates the query of the crawl task into a language appropriate for the data source and generates an index. For example, the query of the crawl task may be translated into a query defined by Logical SQL (Logical Structured Query Language).

[0061] According to various embodiments, the query may be sent directly to the data source for processing, or it may be issued to a presentation server instead. The presentation server requests the BI server 200 to execute the query. The index writer 202 can index the query and the data corresponding to the query in the data index.

[0062] According to one embodiment, the search service 192 may initiate a crawl based on user input. This allows the user to search for data as an unstructured query in a format familiar to most users. The query subsystem may process the user-entered string to determine its semantic meaning, which includes processing the input string into words and comparing these words to a data index to determine the closest term and / or semantic meaning.

[0063] According to one embodiment, upon receiving a data access request / query, the search query rewriter 194 may perform processing to adjust and / or modify the query to search the index. For example, it may process the input defining the query and create a set of terms for searching the index. The index search unit 196 may perform an index search based on one or more terms output by the search query writer unit.

[0064] According to the embodiment, based on the closest term identified by the index search unit, the system can determine the data corresponding to the matching term in the index and provide a query generation unit 198 from the output data. The query generation unit 198 from the output data operates to query and / or retrieve the requested data within the data source.

[0065] According to one embodiment, the query subsystem may perform actions to selectively determine one or more options for displaying the retrieved data in response to the query it has created, such as the type of visualization that best represents the requested data.

[0066] According to the embodiment, the query subsystem may optionally provide the generated queries to the presentation server. The presentation server may provide one or more types of visual representations, perform actions that request the BI server to identify and retrieve data in response to the queries, and provide the retrieved data to the presentation server. The presentation server generates a graphical interface, such as a dashboard or KPI that provides a visual representation of the retrieved data.

[0067] Figures 4 and 5 further illustrate a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0068] As shown in Figures 4 and 5, according to the embodiment, the data analysis system includes a user login and authorization unit 236, an inference engine 238, an automatic card selector 240, a card generation unit 242, a context repository 230, and a controller 234 that facilitates communication with the stored cards 232.

[0069] According to the embodiment, the data analysis system can further improve the dynamic generation of analytical data metrics or smart criteria by leveraging intrinsic and extrinsic contextual information. Contextual information may include, for example, metadata related to a user, their associated client computing device or software, or user interaction with the computing device or software.

[0070] According to the embodiment, essential context information can be any context information specifically selected or specified by the user, such as a user data access request or query including natural language query statements and expressions.

[0071] According to the embodiment, the incidental context information may be any context information not explicitly selected or specified by the user, such as user data access permissions or login credentials, the location of a client computing device (such as indicated by a GPS (Global Positioning System) receiver), or a user team or collaborative research group. The incidental context information may also include aggregated metrics calculated by analyzing the activities of the user community.

[0072] According to the embodiment, the login and user authorization unit facilitates user login to the BI server by, for example, receiving user login information and facilitating the application of appropriate constraints such as user identity verification and data access permissions to the user's client device. User identity and associated data access permissions represent the types of contextual information that the system can use to selectively adjust the content provided via the stored card.

[0073] According to the embodiment, the inference engine facilitates the interpretation of query terms or expressions and includes the use of essential and / or incidental contextual information held in the context repository.

[0074] According to the embodiment, the automatic card selector unit facilitates the mapping of natural language input expressions to, for example, MDX (Multi-Dimensional eXpressions) and the selection of card types according to the mapping of input expressions to database dimensions, measured values, and analytical calculated values ​​supported by the data source.

[0075] According to the embodiment, the card generation unit facilitates the organization of data for use in visualization, includes selecting a visualization according to the type of card determined by the automatic card selector, and collects drawing data to be used to draw the card.

[0076] According to the embodiment, the context information held by the context repository may include dynamic context information. Dynamic context information is context information that changes periodically or daily, including context information that changes in near real-time, such as GPS location information that characterizes a client device. On this client device, the user may communicate with others and interact using data or criteria.

[0077] According to one embodiment, such contextual information can be used to facilitate the system to dynamically or contextually push notifications to the user's device (e.g., the home screen) with appropriate smart criteria so that the home screen is updated periodically or in real time.

[0078] The above examples are provided for the convenience of describing specific embodiments. According to various embodiments, the system can utilize further data crawling / searching capabilities, such as the functionality described in U.S. Patent Application No. 16 / 662,695, entitled "Techniques For Semantic Searching," filed on October 24, 2019, and published on April 16, 2020, as U.S. Patent Application Publication No. 2020 / 0117658.

[0079] Generation of smart standards Figure 6 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0080] As shown in Figure 6, according to the embodiment, a client device having device hardware (e.g., a CPU), a data analytics application, and a user interface can communicate with a data analytics environment via a mobile / web interface. Here, the data analytics environment comprises a data metrics subsystem or equivalent component that provides access to enterprise / other data and enables the use of conditional formatting, and a smart criteria generation unit.

[0081] According to the embodiment, a user can use a client device to interact with a data analytics environment and view, or otherwise access, various data / metrics, such as KPIs or other types of metrics, 54, 56.

[0082] According to the embodiment, the data metrics subsystem enables the mapping of smart criteria to metadata that indicates the scope of data of interest to a user or group of users. The system operates according to the defined smart criteria, monitors the relevant data, and broadcasts metrics, analytics, or other relevant information, such as detected anomalies, trends, or other notable changes in the data, to listeners who subscribe to the smart criteria.

[0083] Figure 7 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0084] As shown in Figure 7, according to the embodiment, such metadata 73 may include, for example, a range, filter, direction, threshold / target, or a representation of other changes in the data that are likely to be of interest to one or more users.

[0085] Figure 8 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0086] As shown in Figure 8, according to the embodiment, if the system observes that a user or a user's community regularly views certain types of data or generally takes action in response to such data, the system may begin to understand the user's (or community's) interest in that data, or it may infer from the user's actions through, for example, user / community-based learning and one or more statistical, machine learning, artificial intelligence, or other computer-automated processes.

[0087] Figure 9 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0088] As shown in Figure 9, according to the embodiment, the system can automatically discover, define, or update smart standards based on its understanding of data and / or information received from the user community, and record such up-to-date knowledge / understandings as up-to-date metadata / rules 75 associated with the smart standards.

[0089] Figure 10 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0090] As shown in Figure 10, according to the embodiment, when the user community provides enough metadata for the system to recognize a smart standard, including metadata 76 or up-to-date metadata / rules 79 associated with the smart standard, one or more smart standards 72, 74 can be generated or updated by the system.

[0091] Figure 11 further illustrates a system for providing dynamically generated analytical data metrics or criteria according to an embodiment.

[0092] As shown in Figure 11, according to the embodiment, once a smart standard is defined and associated with a user or group of users, the smart standard may be provided to that user or other users appropriately on a contextual basis, for example, as one or more visualizations, or may be used, communicated, broadcast, or otherwise provided across different / various types of computing environments, such as mobile devices, web browsers, on-premise systems, or third-party applications.

[0093] Pattern / trend analysis function As described above, according to the embodiment, the data analytics environment may include, or may operate in combination with, a pattern / trend determination function that enables the system to provide users with relevant information in the form of personalized trend metrics or anomaly metrics, for example, specifically for that particular user.

[0094] According to the embodiment, when one or more users access the system and its associated data sources and, for example, execute data access requests / queries, the system can identify and track usage patterns for each user, such as initial or subsequent browsing habits, or habitual / frequently performed actions that can be identified as occurring based on specific data values ​​or changes in data values.

[0095] According to one embodiment, the system can then use the identified user patterns in conjunction with current data values ​​or trends to identify information that is likely to be useful to the user based on the usage patterns, and provide that information as smart criteria.

[0096] According to the embodiment, relevant data / information may be provided to the user if the system determines it to be statistically important or relevant within a particular field. Relationships between metrics may be identified by utilizing usage patterns or user community behavior, and / or the behavior of metrics over a long period of time.

[0097] For example, according to one embodiment, a metric may be associated with metadata that the system can use to identify relationships between two or more metric indicators, or to analyze the values ​​(trends) of a set of metric indicators to identify leading and lagging indicators.

[0098] Optionally, according to the embodiment, the system may identify relationships between metric indicators by analyzing user logs to find the metrics that the user viewed in a short period of time. User logs from multiple users may be used to generate relationships between metric indicators. Relationships, user logs, and / or metric indicator behavior created by explicit user definitions of relationships may then be used by the system to generate smart criteria based on the user's role, user behavior, and / or the user's current view.

[0099] According to the embodiment, the system may include a step for crowdsourcing metric indicators. For example, trend metrics may include metric indicators that are viewed by other users in a particular organization or team. Trend metrics may be identified based on which other users in other organizations are viewing them within their organization as common domain knowledge.

[0100] According to one embodiment, for example, an application / user interface associated with a client device having a data analytics application and a user interface can be updated by the system identifying metric indicators that can be configured to be revealed as smart criteria if desired by the user, as described above.

[0101] According to the embodiment, user configuration of such metrics may include setting personalized thresholds that take precedence over the default values ​​of the metrics. The types of thresholds may include, for example, individual thresholds or benchmarks. Users may configure how notifications of varying severity levels are sent, for example, to mobile devices, email, or voice.

[0102] According to the embodiment, the content with the highest informational value for use in smart criteria may be information about a metric that has undergone a statistically significant change in the metric, or information about a metric that has undergone a statistically significant anomaly over the period in question.

[0103] For example, according to one embodiment, analytical data metrics or criteria that have a trend or trend value over a specific period that does not match the variance of expected values ​​based on predictions made by a multivariate time series model may be identified as an anomaly. Providing this information to the user as a smart criterion, whether or not the user explicitly requests that metric, would be valuable.

[0104] According to the embodiment, data from enterprise data sources is used to identify signal anomalies. The data metrics subsystem may identify, for example, metric indicators that may be exhibiting an anomalous signal caused by a tendency for the metric indicator to deviate from a threshold.

[0105] As an example, according to one embodiment, with respect to a given metric index, the data metrics subsystem may generate a multivariate dynamic dependency model using, for example, a vector autoregressive integrated moving average process or other statistical, machine learning, artificial intelligence, or other computer-automated techniques. Using such a process, a first set of predictions for the values ​​of a first variable or attribute of the metric index are generated based on the dynamic dependency model. The values ​​from the model may be compared to actual values ​​to determine whether the variance of the observed values ​​is significantly different from the predictions.

[0106] According to the embodiment, if the statistical deviation is significant (for example, greater than two standard deviations from the centroid of the variance), the data metrics subsystem identifies the metric as a relevant indicator or indicates that the metric contains important information that should be viewed by a human, and selects the analytical data metric or criterion to be used as a smart criterion. An anomaly is identified when the metric value deviates statistically (for example, by only two standard deviations) from the predicted value across multiple time points. If the metric model suggests a problem or anomaly, the metric may be selected to incorporate the user's smart criterion.

[0107] According to some embodiments, selected analytical data metrics or criteria, such as KPIs, may be provided as smart criteria without further analysis. In some embodiments, the data metrics subsystem may generate an explanation for an anomaly based on a model and include that explanation with the smart criteria. For example, the model of the metric, the current metric value, and the values ​​of the contributing variables at that time may be used to identify attributes that are important to the metric at a relevant time. Once the explanation is generated, the data metrics subsystem may generate advice for addressing the signal anomaly and provide it with the smart criteria.

[0108] According to the embodiment, each action taken by a user in relation to a smart criterion or associated dataset may be recorded by an action recorder. The recorded actions are analyzed, and activity data and contextual data are extracted. Over time, patterns may emerge in such individual user episodes. Data from many users may be similarly acquired and used to generate personalized advice for the user based on the actions of other users.

[0109] According to one embodiment, when providing smart criteria, the system may determine, for example, for each of several analytical data metrics or criteria of the system, which attribute has the highest entropy change, for example, a SHARP (Shapley Additive Explanations) value or other statistical, machine learning, artificial intelligence, or other computer-automated process.

[0110] According to one embodiment, when generating a graphical representation of smart criteria, the system may select analytical data metrics or criteria, KPIs, or cards having the highest (maximum) entropy change identified above for use with the smart criteria. In this way, the system can select relevant content associated with the metric indicators provided to the user.

[0111] The above examples are provided to illustrate specific embodiments. According to various embodiments, the system may utilize additional trend determination functions, such as those described in U.S. Patent Application No. 16 / 586,347, filed on September 27, 2019, and published on April 2, 2020, as U.S. Patent Application Publication No. 2020 / 0104775, entitled "TECHNIQUES FOR DATA-DRIVEN CORRELATION OF METRICS".

[0112] Smart criteria user-defined As described above, according to the embodiment, users may choose to define smart criteria themselves. Alternatively, the data analysis system may employ statistical, machine learning, artificial intelligence, or other computer-automated processes to learn from the user community which aspects of a particular analytical metric or criterion are important, and then gradually update the relevant metadata for use in generating appropriate smart criteria.

[0113] For example, in an embodiment that provides a DV (Data Visualization) environment used when creating visualizations, the user may define smart criteria as explicit definitions from the properties window of those criteria. Other options (e.g., charts / audio) may also create smart criteria that have metadata information pre-populated from visualizations or audio queries. Examples of areas where metadata necessary to create smart criteria can be created are provided below, according to various embodiments.

[0114] From the Properties window: Select a type; specify a range; define a filter; set thresholds, trends, etc.

[0115] From the chart: Right-click the chart; Auto-populate type; Auto-populate range; Auto-populate filter; User sets threshold based on type.

[0116] From the audio: Notify me if sales of a specific product in the Northeast have increased by 10%; if the "inventory quantity on hand" for the product is less than 1000 units, please send me an email.

[0117] In mobile applications (apps): Return from the chart based on the same rules as on the desktop.

[0118] Smart standard notifications / alerts As described above, according to the embodiment, once a smart standard is defined and associated with a user or group of users, the smart standard may be used, transmitted, broadcast, or otherwise provided to the user or other users as appropriate on a context basis, for example, across different / various types of computing environments, such as multiple mobile devices, web browsers, on-premise systems, or third-party applications.

[0119] According to the embodiment, one advantage of defining smart criteria is that, for example, a dashboard can monitor datasets for the user even when the user is not looking at them. The system may evaluate smart criteria based on configured frequency and, if the conditions are met, may create alerts based on the user's profile settings for notifications. Exemplary user-specific notifications may include:

[0120] User profile for configuring notification settings: Default delivery method (SMS, homepage, email). Frequency (daily, weekly, monthly). History management.

[0121] Homepage notifications: A feature that allows users to see pending notifications at a glance (e.g., an icon with the number of notifications). Triggered smart criteria may be displayed on the smart feed (per mobile continuity project).

[0122] Mobile notifications: SMS. For example, text-based alerts with a link back to the Oracle Analytics Cloud homepage notification area. Unique mobile notifications. Notification groups.

[0123] Email Notifications: RichHTML notifications with an attached image and a drill-back to the homepage notification area and / or project canvas, including the smart criteria that triggered the alert. Email notifications are limited to the frequency set in each user's preferences.

[0124] Smart standards and natural language processing As described above, according to the embodiment, the system may interpret user input as, for example, text input related to a specific dataset or its modification, and utilize natural language processing techniques when determining appropriate smart criteria.

[0125] Depending on the embodiment, the Oracle BI Ask framework may be extended to support smart criterion metadata and requerying of values. This extended metadata enables a rich set of highly business-value queries, such as: "Show me all my criteria that are trending downwards"; "Show me all my criteria that are in critical status"; "Show me all my criteria that are within 80% of the target"; "Show me the top 5 criteria that are trending in the right / wrong direction"; "Show me the top 10 criteria that are most far from the target"; "Show me all criteria that are in critical (or deficit) status".

[0126] According to the embodiment, for example, the results of an Ask query may return a list of smart criteria as a result set. For example, the Ask homepage experience may be adapted to display results consisting of numerous smart criteria performance tiles. The user may also be able to sort the results in several ways, for example: sorting / grouping by status (e.g., critical / warning / OK); sorting by distance to goal; sorting by name; sorting by difference from goal, etc.

[0127] According to the embodiment, Yseop's NLG (Natural Language Generation) engine or a similar process may be used to support analytical data metrics or criteria that include richer sentences and observations.

[0128] For example, according to one embodiment, metadata such as "desired direction," "goal / target," and "threshold" would help the NLG engine generate comments about the differences between the direction and the goal.

[0129] Smart-standard visualization As described above, according to the embodiment, conditional formatting may be used, for example, when presenting smart criteria as displayed data metrics or visualizations. Conditional formatting allows the data analytics environment to present information in various different ways within the displayed data visualization, for example, to highlight parts of the visualization to reflect differences in values ​​of relevant data.

[0130] According to the embodiment, the creator of the visualization should have the ability to define smart criteria rules that can be associated with any chart having criteria. Each chart may have multiple "enabled / available" rules, and when the visualization project is then shared in presentation mode, users can toggle rules on or off on the visualization, but they cannot add rules to the visualization.

[0131] According to the embodiment, the interaction with smart criteria is the same as with normal criteria, but visualizations with smart criteria should allow users to enrich the display with extended attributes that include smart criteria. For example, such extended metadata may include: status colors (e.g., red / poor, yellow / warning, green / good); trends with sentiment (e.g., up is good, down is bad); actual values ​​and target values / goal values; differences from the target as a value or percentage (e.g., distance); or changes from "a certain period ago" as a value or percentage.

[0132] Exemplary Smart Criteria Metadata As described above, according to the embodiment, the smart criteria may range to a dataset and may be associated with metadata that indicates an understanding of the ranged data or changes thereto that are of interest to a particular user. The data analysis system may operate according to the smart criteria and associated rules to monitor its associated data and broadcast relevant information to subscribers, such as anomalies, trends, or other notable changes.

[0133] According to the embodiment, smart criteria must be associated with a dataset. If a user creates a project on a dataset and this dataset has smart criteria for the project, the user must have access to those smart criteria within the project.

[0134] The following Example 1 shows a superset of metadata elements for each smart standard type described above, according to the embodiment.

[0135] [Table 1]

[0136] TIFF0007857939000002.tif231155

[0137] Exemplary types of smart standards According to the embodiment, smart criteria can generally be limited to the scope of data. This is useful considering performance and system load. For example, there can be various different types of smart criteria, as follows:

[0138] Explicit smart criteria compare a standard to the result of a simple number or a simple mathematical formula. Comparative smart criteria are criteria similar to explicit smart criteria, but their values ​​are the result of formulas involving other analytical criteria.

[0139] Trend-smart criteria focus on identifying and tracking changes when criteria are trending in the right or wrong direction.

[0140] Statistical smart criteria focus on identifying anomalies and identifying when a criterion value falls outside its normal range.

[0141] The above is provided as an example, and is not intended to limit the embodiments to the specific examples described.

[0142] Explicit smart standards According to the embodiment, explicit smart criteria are useful when a user wants to know (or be alerted) when a smart criterion reaches a specific value that is important to their business. This value may be a simple number, a percentage increase or decrease, or the result of a simple mathematical formula.

[0143] For example, exemplary uses of explicit smart criteria include: User A wants to know when they will reach their $1,000,000 sales target; User A wants to know if the turnover rate in their department is above 6%; User A wants to know if their sales contract closing rate has fallen by more than 10% this quarter; User A wants to know when their sales of a particular product increased by 10% today, and so on.

[0144] According to the embodiment, Table 1 shows the metadata required to support explicit smart criteria.

[0145] [Table 2]

[0146] Comparison of Smart Standards According to the embodiment, the comparative smart criterion is a criterion similar to the explicit smart criterion, but the value used as the target by the smart criterion is the result of a formula obtained by examining another criterion in the system or past values.

[0147] For example, exemplary uses of comparative smart criteria include: User A wants to know when their product sales in the Northeast will reach 80% of their 2019 quota (comparing sales to quota criteria); User A wants to know if their sales have increased by 10% compared to last year's sales (comparing this year's sales to last year's sales); User A wants to know if their spending is exceeding quarterly and departmental budgets, and so on.

[0148] According to the embodiment, Table 2 shows the metadata required to support the comparative smart criteria.

[0149] [Table 3]

[0150] Trend Smart Criteria According to the embodiment, the trend smart criterion tracks how the criterion trends over time. The system should identify (visually and / or notify) when the criterion experiences a significant change that will cause the trend to shift from good to bad.

[0151] For example, exemplary uses of the trend smart criterion include: User A wants to know when their sales will start to decline; User A wants to know when expenses will start to trend in the wrong direction, etc.

[0152] According to the embodiment, Table 3 shows the metadata required to support the trend smart criteria.

[0153] [Table 4]

[0154] Statistical Smart Criteria According to the embodiment, a statistical smart criterion is a criterion similar to a trend smart criterion. However, this relies on the user to have information about when the smart criterion deviates from the standard of that criterion.

[0155] For example, exemplary uses of statistical smart criteria include: User A wants to know when the number of customer complaints falls outside the standard; User A wants to know which criteria may be "out of control"; User A wants to know which criteria trends are likely to change, etc.

[0156] According to the embodiment, Table 4 shows the metadata required to support the statistical smart standard.

[0157] [Table 5]

[0158] Smart standard process control rules As described above, according to the embodiment, the system may operate in accordance with smart criteria and related rules to monitor its related data and broadcast relevant information to subscribers, such as anomalies, trends, or other notable changes.

[0159] Figure 12 shows various examples of rules that may be used by the system when generating and using dynamically generated analytical data metrics or smart criteria according to the embodiment.

[0160] As shown in Figure 12, according to the embodiment, exemplary rules may include, for example, the following:

[0161] Rule #1: Determine a single point within data ranged by a specific smart criterion that exceeds any of the control limits. An exemplary use of this smart criterion and associated rule includes the ability to detect very large / abrupt transitions.

[0162] Rule #2: Determine nine consecutive points on the same side of the centerline within data ranged to a specific smart criterion. Exemplary use of this smart criterion and its associated rules includes the ability to detect small shifts or trends.

[0163] Rule #3: Identify six consecutive points that steadily increase or decrease within data ranged by a specific smart criterion. Exemplary use of this smart criterion and its associated rules includes the ability to detect strong trends.

[0164] Rule #4: Identify 14 or more consecutive points that alternate up and down within data ranged to a specific smart criterion. Exemplary use of this smart criterion and related rules includes the ability to detect systemic influences such as shift work, operators, and suppliers.

[0165] Rule #5: Within data ranged to a specific smart criterion, identify two of three consecutive points that lie on the same side and are at least two standard deviations beyond the centerline. Exemplary use of this smart criterion and related rules includes the ability to detect large changes.

[0166] Rule #6: Within data ranged to a specific smart criterion, determine four out of five consecutive points on the chart that are at least two standard deviations away from the centerline. This example use of the smart criterion and related rules includes the ability to detect moderate changes.

[0167] Rule #7: Identify 15 or more consecutive points within one standard deviation of the centerline within data ranged to a specific smart criterion. Exemplary use of this smart criterion and related rules includes the ability to detect process variability reduction.

[0168] Rule #8: Identify eight or more consecutive points within data ranged to a specific smart criterion that are located on either side of the centerline but are all outside of one standard deviation. Exemplary use of this smart criterion and related rules includes the ability to detect increased process variability.

[0169] The exemplary rules shown and described in Figure 17 are provided for the purpose of illustrating examples of various types of rules that can be used to support the operation of smart standards. Depending on the various embodiments, different / other types of rules may be applied to the data to support various use cases.

[0170] Exemplary visualizations and uses of smart standards Figure 13 illustrates an exemplary use of the smart standard according to an embodiment.

[0171] As shown in Figure 13, according to one embodiment, if the system observes that a particular user or user community regularly reviews a particular type of data, or takes a particular action in response to reviewing such data, the system may begin to understand the user's (or community's) interests in various types of data and may make inferences from the user's actions.

[0172] Figure 14 further illustrates an exemplary use of the smart standard according to an embodiment.

[0173] As shown in Figure 14, according to the embodiment, once a smart standard is defined and associated with a user, the smart standard may be used, communicated, broadcasted, or otherwise provided to the user or other users as, for example, one or more visualizations.

[0174] Figure 15 illustrates another exemplary use of the smart standard according to an embodiment. As shown in Figure 15, according to the embodiment, as described above, the system may observe that a particular user or user community regularly reviews a particular type of data or generally performs actions related to a particular type of data, and generate smart criteria.

[0175] Figure 16 further illustrates another exemplary use of the smart standard according to an embodiment.

[0176] As shown in Figure 16, according to the embodiment, as described above, the smart criteria may be used, communicated, broadcast, or otherwise provided to the user or other users appropriately, for example, as one or more visualizations.

[0177] Exemplary use of smart standards in application and data warehouse environments According to the embodiment, smart standards may be developed for use in analytical application environments, cloud computing, or other types of data analytics environments, and may be developed for use in various types of data warehouse environments or components, such as Oracle ADW (Autonomous Data Warehouse), Oracle ADWC (Autonomous Data Warehouse Cloud), and vertical and / or horizontal business applications.

[0178] According to the embodiment, horizontal business applications can include ERP, HCM, CX, SCM, and EPM, as mentioned above, and can provide a wide range of functions across various corporate organizations. Vertical business applications generally have a narrower scope than horizontal business applications, but provide access to a defined range of data in a vertical direction within an industry. Examples of vertical business applications include medical software or banking software for use within a specific organization.

[0179] As an example, Figure 17 illustrates an exemplary use of smart standards in a data analytics environment according to an embodiment.

[0180] The example shown and described in Figure 17 is provided to illustrate an example of one type of analytical application or data analytics environment. According to the embodiment, the components and processes shown in Figure 17 and further described in relation to various other embodiments can be provided as software or program code executable by a computer system or other type of processing device. For example, the components and processes described herein can be provided by a cloud computing system or other appropriately programmed computer system.

[0181] As shown in Figure 17, according to the embodiment, the analysis environment 300 may be provided by a computer system having computer hardware (e.g., a processor, memory) 301, or if not, it may operate on such a computer system and comprises one or more software components that operate as a control plane 302 and a data plane 304, and provides access to a data warehouse instance 360 ​​and a database 361.

[0182] According to one embodiment, the control plane operates to control cloud or other software products provided in association with a SaaS or cloud environment, such as an Oracle Analytics Cloud environment. For example, according to one embodiment, the control plane may include a console interface 310 that enables access by customers (tenants) and / or the cloud environment having the provisioning component 311.

[0183] According to the embodiment, the console interface may be accessible by the customer (tenant) by operating a GUI (graphical user interface) and / or a CLI (command line interface) or other interface, and / or may include an interface used by the SaaS or cloud environment provider and its customers (tenants).

[0184] According to one embodiment, a provisioning component may be used to update or edit data warehouse instances and / or ETL processes operating in the data plane, for example, by changing or updating the frequency of requests for ETL process execution for a specific customer (tenant).

[0185] According to the embodiment, the data plane may include a data pipeline or processing layer 320 and a data transformation layer 334. Together, these process operational or transactional data from an organization's enterprise software applications or data environment, such as business productivity software applications provisioned in a customer's (tenant's) SaaS environment.

[0186] According to one embodiment, the data transformation layer may include a data model that the system uses to transform the data into a model format that the analytical environment can understand, such as a Knowledge Model (KM) or other types of data models.

[0187] According to the embodiment, a data pipeline or processing system may be scheduled to operate at regular intervals (e.g., hourly / daily / weekly) to extract transactional data from an enterprise software application or data environment, such as a business productivity software application and a corresponding transactional database 306.

[0188] According to one embodiment, after transforming the extracted data, the data pipeline or processing may execute the warehouse load procedure 350 to load the transformed data into a data warehouse instance.

[0189] According to the embodiments, the data warehouse includes a default analytics application schema (referred to herein as the analytics warehouse schema in some embodiments) and may include a customer schema for each customer (tenant) of the system. Different customers of the data analytics environment have different requirements for how their data is classified, aggregated, or transformed to provide data analytics or business intelligence data or to develop software analytics applications.

[0190] In some embodiments, to address various customer requirements regarding how customer data is classified, aggregated, or transformed, the semantic layer 380 may include data defining a semantic model of the customer data. The semantic layer 380 is useful in helping users understand and access the data using widely known business terminology and provides user-defined content to the presentation layer 390.

[0191] According to the embodiment, the presentation layer may enable access to data content using, for example, software analytics applications, user interfaces, dashboards, key performance indicators (KPIs), visualizations, or other types of reports or interfaces that may be provided by products such as Oracle Analytics Cloud.

[0192] According to the embodiment, a user / developer may interact with a client computer device comprising computer hardware (e.g., processor, storage, memory), a user interface, and a software application. A query engine (e.g., OBIS) operates, for example, to provide analytical queries within an Oracle Analytics Cloud environment, pushing operations down to a supported database and translating business user queries into the appropriate database-specific query language.

[0193] Figure 18 shows a process or method for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment, according to an embodiment.

[0194] As shown in Figure 18, according to the embodiment, in step 402, the system enables the user community to access an analytics application environment, cloud computing, or other type of data analytics environment.

[0195] Step 404 involves the system generating smart criteria that leverage, for example, machine learning or artificial intelligence (ML / AI) processes to discover and define KPIs (Key Performance Indicators) based on an understanding of data and / or information received from the user community.

[0196] Step 406, once defined, the smart criteria will continue to monitor its relevant data and broadcast relevant information to subscriber listeners of that data, such as anomalies or trends detected within the data.

[0197] According to various embodiments, the teachings herein may be conveniently implemented by one or more conventional general-purpose or dedicated digital computers, computing devices, machines, or microprocessors, including one or more processors, memory, and / or computer-readable storage media programmed in accordance with the teachings herein. A programmer skilled in the art can readily provide appropriate software coding based on the teachings herein, as will be apparent to a person proficient in software technology.

[0198] In some embodiments, the present invention includes a computer program product which is a non-temporary computer-readable storage medium (or a plurality of computer-readable storage mediums) storing instructions. A computer can be programmed to use the computer program product to perform any of the operations of this teaching. For example, such storage mediums may include, but are not limited to, hard disk drives, hard disks, fixed disks or other electromechanical data storage devices, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, microdrives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems, or other types of storage mediums or devices suitable for temporarily storing instructions and / or data.

[0199] The above description is provided for illustrative and illustrative purposes only. It is not intended to be comprehensive or to limit the scope of protection to the exact form disclosed. Many changes and modifications will be apparent to those skilled in the art.

[0200] For example, some of the examples provided herein illustrate operation with enterprise software applications or data analytics environments such as the Oracle Analytics Cloud environment. However, according to various embodiments, the systems and methods described herein can be used with other types of enterprise software applications / data environments, cloud environments, cloud services, cloud computing, or other computing environments.

[0201] In addition, while some of the examples provided herein illustrate environments such as Oracle BI Server, Oracle BI Ask, and Oracle Day-by-Day, other social / shared information discovery and retrieval environments may be used to receive information from user communities related to accessing and using data. This information can then be used by the system to understand user interests and generate appropriate smart criteria, or to associate specific smart criteria with actions that are likely to be of interest to the user. Furthermore, according to various embodiments, smart criteria may be generated and / or used in other types of social / shared information discovery and retrieval environments, or other types of analytical application environments, cloud computing, or data analytics environments.

[0202] Embodiments have been selected and described to best illustrate the principles of this teaching and their practical applications. This will enable those skilled in the art to understand various embodiments and, in addition, various modifications suitable for specific applications. The scope of this disclosure is indicated by the appended claims and their equivalents.

Claims

1. A system for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment, A computer comprising one or more processors and a data analytics environment including a data analysis system, The aforementioned data analysis system is A crawl subsystem that operates to generate data indexes used by the system when processing received data requests or queries, A query subsystem that operates to provide semantic analysis of the received data request or query using a semantic data model and the data index, A data metric subsystem includes a measurement generator that evaluates changes in data values ​​or data metrics and identifies and tracks the usage patterns of one or more users, including browsing habits or actions identified as occurring based on the changes in said data values ​​or data metrics. The data metrics subsystem automatically generates analytical data metrics that function as smart criteria by observing, based on user / community-based learning, whether specific users or user communities regularly access specific data or specific analytical data metrics, or take action in response to changes in such data or data metrics, and each dynamically generated analytical data metric that functions as a smart criterion is scoped to a dataset and associated with metadata indicating the range of data of interest to the user or group of users. The system operates according to defined smart criteria, monitors its relevant data, and broadcasts analytical information describing the relevant data as a data visualization including one or more detected anomalies, trends, or changes in the data to subscribed listeners.

2. The system according to claim 1, wherein analytical data metrics that function as smart criteria are automatically discovered and determined and updated by the system based on the system observing whether a particular user or user community regularly accesses a particular type of data or a particular analytical data metric, or takes action in response to changes in such data or data metrics.

3. The data analysis system according to claim 2, wherein the data analysis system records the latest state of knowledge associated with the data as the latest state of metadata or rules associated with the analysis data metrics which function as smart criteria.

4. The system according to claim 3, wherein, in accordance with the definition and association of smart standards with a user or group of users, the smart standards are communicated, broadcast, or otherwise provided to the user or other user, including one or more mobile devices, one or more web browsers, one or more on-premise systems, or one or more third-party applications, on a contextual basis.

5. The system according to any one of claims 1 to 4, wherein the system applies conditional formatting to present the analytical data metrics in a visualization or other easily identifiable format.

6. A system method for providing dynamically generated analytical data metrics or criteria for use in a data analytics environment, A computer containing one or more processors provides a data analytics environment that includes a data analysis system, The aforementioned data analysis system is A crawl subsystem that operates to generate data indexes used by the system when processing received data requests or queries, A query subsystem that operates to provide semantic analysis of the received data request or query using a semantic data model and the data index, A data metric subsystem includes a measurement generator that evaluates changes in data values ​​or data metrics and identifies and tracks the usage patterns of one or more users, including browsing habits or actions identified as occurring based on the changes in said data values ​​or data metrics. The data metrics subsystem automatically generates analytical data metrics that function as smart criteria by observing, based on user / community-based learning, whether specific users or user communities regularly access specific data or specific analytical data metrics, or take action in response to changes in such data or data metrics, and each dynamically generated analytical data metric that functions as a smart criterion is scoped to a dataset and associated with metadata indicating the range of data of interest to the user or group of users. A method comprising: a system that operates according to defined smart criteria, monitors its relevant data, and broadcasts analytical information describing the relevant data as a data visualization including one or more detected anomalies, trends, or changes in the data to subscribed listeners.

7. The method according to claim 6, wherein analytical data metrics that function as smart criteria are automatically discovered and determined and updated by the system based on the system observing whether a particular user or user community regularly accesses a particular type of data or a particular analytical data metric, or takes action in response to changes in such data or data metrics.

8. The method according to claim 7, wherein the data analysis system records up-to-date knowledge associated with the data as up-to-date metadata or rules associated with the analysis data metrics that function as smart criteria.

9. The method according to claim 8, wherein, in accordance with the definition and association of smart standards with a user or group of users, the smart standards are communicated, broadcast, or otherwise provided to the user or other user, including one or more mobile devices, one or more web browsers, one or more on-premise systems, or one or more third-party applications, on a contextual basis.

10. The method according to any one of claims 6 to 9, wherein the previous system applies conditional formatting to present the analytical data metrics in a visualization or other easily identifiable format.

11. A program that causes a computer having one or more processors to execute a method, wherein the method is The computer includes providing a data analytics environment that includes a data analysis system, The aforementioned data analysis system is A crawl subsystem that operates to generate a data index used by the computer when processing received data requests or queries, A query subsystem that operates to provide semantic analysis of the received data request or query using a semantic data model and the data index, A data metric subsystem includes a measurement generator that evaluates changes in data values ​​or data metrics and identifies and tracks the usage patterns of one or more users, including browsing habits or actions identified as occurring based on the changes in said data values ​​or data metrics. The data metrics subsystem automatically generates analytical data metrics that function as smart criteria by observing, based on user / community-based learning, whether specific users or user communities regularly access specific data or specific analytical data metrics, or take action in response to changes in such data or data metrics, and each dynamically generated analytical data metric that functions as a smart criterion is scoped to a dataset and associated with metadata indicating the range of data of interest to the user or group of users. The computer is a program that operates according to specified smart criteria, monitors its associated data, and broadcasts analytical information describing the associated data as a data visualization, including one or more detected anomalies, trends, or changes in the data, to subscribed listeners.

12. The program according to claim 11, wherein analytical data metrics that function as smart criteria are automatically discovered and determined and updated by the computer based on the computer observing whether a particular user or user community regularly accesses a particular type of data or a particular analytical data metric, or takes action in response to changes in such data or data metrics.

13. The program according to claim 12, wherein the data analysis system records up-to-date knowledge associated with the data as up-to-date metadata or rules associated with the analysis data metrics that function as smart criteria.

14. The program according to claim 13, wherein, in response to a defined smart standard being associated with a user or group of users, the smart standard is communicated, broadcast, or otherwise provided to the user or other user, including one or more mobile devices, one or more web browsers, one or more on-premise systems, or one or more third-party applications, on a contextual basis.

15. The program according to any one of claims 11 to 14, wherein the computer applies conditional formatting to present the analytical data metrics in a visualization or other easily recognizable format.

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