Computerized system and method for performing parameterization of columns in a virtual semantic layer

The disclosed systems and methods provide a novel framework that parameterizes columns in a templated virtual semantic layer. The disclosed framework enables inter- and cross-column relationships between stored data within a SQL database to be determined and stored, and then leveraged at query time to enable an optimized search of the data within the database. The disclosed framework enables a database to be construed, modified and/or configured according to a hierarchy, and/or with types of metadata, that corresponds to the determined column relationships. This not only reduces the amount of data stored and reduces the time to process queries, but also enables a more streamlined approach for searches to be constructed and executed, which can increase the performance of the database and the operations of systems that are backed by the database.

FIELD

The present disclosure relates generally to query optimization, and more particularly to providing a virtual semantic layer that uses templated Structured Query Language (SQL) to execute parameterized queries for an optimized database search.

BACKGROUND

According to ANSI® (American National Standards Institute), SQL is the standard language for relational database management systems. SQL statements are used to perform tasks, such as, but not limited to, retrieve data from a database, update data on a database, and the like. The standard SQL commands such as “Select,” “Insert,” “Update,” “Delete,” “Create,” and “Drop” can be used for most common and proprietary database systems.

SUMMARY

One of the functions of business intelligence tools and data analytic applications is to simplify data that is consumed and analyzed by end users. A primary mechanism to perform this is a semantic layer. Semantic layers present data as a set of tables with measures (e.g., numeric facts about data that can be aggregated) and dimensions (e.g., attributes that describe data). While tables, measures and dimensions are exposed through the semantic layer to users as simple names, they often map to complex formulas, functions, rules and queries that translate a complex physical model to the simpler semantic model.

In a virtual semantic layer, the translation between the complex physical and simple logical models can be performed at query time through a set of predefined rules. A representation of the rules is a set of predefined SQL fragments for the definition of tables, measures, dimensions and the relationships between tables (e.g., joins). In some embodiments, the semantic layer can stitch the fragments together into complete queries depending on the table and columns selected in a given client query. Because the queries are dynamic, the fragments can combine native SQL with templated variables for column and table names. As discussed in more detail below, the variables can be substituted by SQL generation logic with aliases that allow the logical models to construct more complex queries than the physical names would allow.

Semantic layers are intended to simplify the presentation of data to end users. Semantic layers provide users with functionality to construct queries by selecting the table, measures and dimensions that are desired to be explored. The selected columns can then translated into a more complex native SQL query against an underlying database.

One of the challenges with this approach is that the cardinality of columns (measures and dimensions) can get large over time, which can lead to inaccuracies in the way data is managed, hosted, integrated with other data (e.g., nested relationships), thereby causing inaccurate and/or inefficient search processing.

By way of a non-limiting example, consider the task of exposing a “revenue” measure to end users, then adding varying currency values so users can select specific revenues in a target currency. Currently, there are 180 currencies worldwide, and many, if not all, require different measures, which the user can select from.

Under current approaches, it is unwieldy to build navigation around these varying currency values because there is no hierarchy or metadata to reduce the list of currency items from which a user can select a measure. The disclosed systems and methods address these technical shortcomings, among others, by providing a simplified and computationally efficient approach that can parameterize the measure column with an argument (or function, used interchangeably) for currency (a “currency argument”). In this way, the user can select the measure (revenue), and then can select the argument (currency). To date, there is no virtual semantic layer that supports parameterized columns, as discussed herein.

Therefore, the disclosed systems and methods provide a novel framework that enables the parameterization of columns within a virtual semantic layer. As discussed in more detail below, the disclosed framework enables the parameterization to be resolved at query time leading to more accurate and optimized search system. It should be understood that tables, in addition to columns, can also be parameterized in a similar manner as discussed herein without departing from the scope of the instant disclosure. Thus, while the discussion herein will focus on column parameterization, it should not be construed as limiting to the functionality and/or applicability to alternative embodiments.

In accordance with one or more embodiments, the present disclosure provides computerized methods for a novel framework that executes parameterized queries within a virtual sematic layer.

In accordance with one or more embodiments, the present disclosure provides a non-transitory computer-readable storage medium for carrying out the above mentioned technical steps of the framework's functionality. The non-transitory computer-readable storage medium has tangibly stored thereon, or tangibly encoded thereon, computer readable instructions that when executed by a device (e.g., a client device) cause at least one processor to perform a method for a novel and improved framework that executes parameterized queries within a virtual sematic layer.

In accordance with one or more embodiments, a system is provided that comprises one or more computing devices configured to provide functionality in accordance with such embodiments. In accordance with one or more embodiments, functionality is embodied in steps of a method performed by at least one computing device. In accordance with one or more embodiments, program code (or program logic) executed by a processor(s) of a computing device to implement functionality in accordance with one or more such embodiments is embodied in, by and/or on a non-transitory computer-readable medium.

DESCRIPTION OF EMBODIMENTS

For purposes of this disclosure, a “wireless network” should be understood to couple client devices with a network. A wireless network may employ stand-alone ad-hoc networks, mesh networks, Wireless LAN (WLAN) networks, cellular networks, or the like. A wireless network may further employ a plurality of network access technologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd, 4thor 5thgeneration (2G, 3G, 4G or 5G) cellular technology, mobile edge computing (MEC), Bluetooth, 802.11b/g/n, or the like. Network access technologies may enable wide area coverage for devices, such as client devices with varying degrees of mobility, for example.

In short, a wireless network may include virtually any type of wireless communication mechanism by which signals may be communicated between devices, such as a client device or a computing device, between or within a network, or the like.

A client device may vary in terms of capabilities or features. Claimed subject matter is intended to cover a wide range of potential variations, such as a web-enabled client device or previously mentioned devices may include a high-resolution screen (HD or 4K for example), one or more physical or virtual keyboards, mass storage, one or more accelerometers, one or more gyroscopes, global positioning system (GPS) or other location-identifying type capability, or a display with a high degree of functionality, such as a touch-sensitive color 2D or 3D display, for example.

Certain embodiments will now be described in greater detail with reference to the figures. In general, with reference toFIG.1, a system100in accordance with an embodiment of the present disclosure is shown.FIG.1shows components of a general environment in which the systems and methods discussed herein may be practiced. Not all the components may be required to practice the disclosure, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of the disclosure. As shown, system100ofFIG.1includes local area networks (“LANs”)/wide area networks (“WANs”)—network105, wireless network110, mobile devices (client devices)102-104and client device101.FIG.1additionally includes a variety of servers, such as content server106and application (or “App”) server108.

One embodiment of mobile devices102-104may include virtually any portable computing device capable of receiving and sending a message over a network, such as network105, wireless network110, or the like. Mobile devices102-104may also be described generally as client devices that are configured to be portable. Thus, mobile devices102-104may include virtually any portable computing device capable of connecting to another computing device and receiving information, as discussed above.

Mobile devices102-104also may include at least one client application that is configured to receive content from another computing device. In some embodiments, mobile devices102-104may also communicate with non-mobile client devices, such as client device101, or the like. In one embodiment, such communications may include sending and/or receiving messages, searching for, viewing and/or sharing memes, photographs, digital images, audio clips, video clips, or any of a variety of other forms of communications.

Client devices101-104may be capable of sending or receiving signals, such as via a wired or wireless network, or may be capable of processing or storing signals, such as in memory as physical memory states, and may, therefore, operate as a server.

Wireless network110is configured to couple mobile devices102-104and its components with network105. Wireless network110may 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 mobile devices102-104.

Network105is configured to couple content server106, application server108, or the like, with other computing devices, including, client device101, and through wireless network110to mobile devices102-104. Network105is enabled to employ any form of computer readable media or network for communicating information from one electronic device to another.

The content server106may include a device that includes a configuration to provide any type or form of content via a network to another device. Devices that may operate as content server106include personal computers, desktop computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, servers, and the like. Content server106can further provide a variety of services that include, but are not limited to, email services, instant messaging (IM) services, streaming and/or downloading media services, search services, photo services, web services, social networking services, news services, third-party services, audio services, video services, SMS services, MMS services, FTP services, voice over IP (VOIP) services, or the like.

In some embodiments, content server106can be, or may be coupled or connected to, a third party server that stores online advertisements for presentation to users. In some embodiments, various monetization techniques or models may be used in connection with sponsored advertising, including advertising associated with user data, as discussed below, where ads can be modified and/or added to content based on the personalization of received content using the locally accessible user profile.

In some embodiments, users are able to access services provided by servers106and/or108. This may include in a non-limiting example, search servers, authentication servers, email servers, social networking services servers, SMS servers, IM servers, MMS servers, exchange servers, photo-sharing services servers, and travel services servers, via the network105using their various devices101-104.

In some embodiments, applications, such as, but not limited to, news applications, mail applications, instant messaging applications, blog, photo or social networking applications, search applications, and the like, can be hosted by the application server108, or content server106and the like.

Thus, the application server108and/or content server106, for example, can store various types of applications and application related information including application data and other various types of data related to the content and services in an associated database107, as discussed in more detail below. Embodiments exist where the network105is also coupled with/connected to a Trusted Search Server (TSS) which can be utilized to render content in accordance with the embodiments discussed herein. Embodiments exist where the TSS functionality can be embodied within servers106and/or108.

Moreover, althoughFIG.1illustrates servers106and108as single computing devices, respectively, the disclosure is not so limited. For example, one or more functions of servers106and/or108may be distributed across one or more distinct computing devices. Moreover, in one embodiment, servers106and/or108may be integrated into a single computing device, without departing from the scope of the present disclosure.

FIG.2is a schematic diagram illustrating a client device showing an example embodiment of a client device that may be used within the present disclosure. Client device200may include many more or less components than those shown inFIG.2. However, the components shown are sufficient to disclose an illustrative embodiment for implementing the present disclosure. Client device200may represent, for example, client devices101-104discussed above in relation toFIG.1.

As shown in the figure, Client device200includes a processing unit (CPU)222in communication with a mass memory230via a bus224. Client device200also includes a power supply226, one or more network interfaces250, an audio interface252, a display254, a keypad256, an illuminator258, an input/output interface260, a haptic interface262, an optional global positioning systems (GPS) receiver264and a camera(s) or other optical, thermal or electromagnetic sensors266. Device200can include one camera/sensor266, or a plurality of cameras/sensors266, as understood by those of skill in the art. Power supply226provides power to Client device200.

Client device200may optionally communicate with a base station (not shown), or directly with another computing device. Network interface250is sometimes known as a transceiver, transceiving device, or network interface card (NIC).

Audio interface252can be arranged to produce and receive audio signals such as, for example, the sound of a human voice. Display254can, but is not limited to, a include a touch sensitive screen arranged to receive input from an object such as a stylus or a digit from a human hand. Keypad256can comprise any input device arranged to receive input from a user. Illuminator258may provide a status indication and/or provide light.

Client device200also comprises input/output interface260for communicating with external devices. Input/output interface260can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like. Haptic interface262is arranged to provide tactile feedback to a user of the client device.

Optional GPS transceiver264can determine the physical coordinates of Client device200on the surface of the Earth. In some embodiments however, Client device200may through other components, provide other information that may be employed to determine a physical location of the device, including for example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory230includes a RAM232, a ROM234, and other storage means. Mass memory230stores a basic input/output system (“BIOS”)240for controlling low-level operation of Client device200. The mass memory also stores an operating system241for controlling the operation of Client device200

Memory230further includes one or more data stores, which can be utilized by Client device200to store, among other things, applications242and/or other information or data. For example, data stores may be employed to store information that describes various capabilities of Client device200. The information may then be provided to another device based on any of a variety of events, including being sent as part of a header (e.g., index file of the HLS stream) during a communication, sent upon request, or the like. At least a portion of the capability information may also be stored on a disk drive or other storage medium (not shown) within Client device200.

Applications242may include computer executable instructions which, when executed by Client device200, transmit, receive, and/or otherwise process audio, video, images, and enable telecommunication with a server and/or another user of another client device. Applications242may further include search client245that is configured to send, to receive, and/or to otherwise process a search query and/or search result.

Having described the components of the general architecture employed within the disclosed systems and methods, the components' general operation with respect to the disclosed systems and methods will now be described below.

FIG.3is a block diagram illustrating the components for performing the systems and methods discussed herein.FIG.3includes query engine300, network315and database320. The query engine300can be a special purpose machine or processor and could be hosted by a network server (e.g., cloud web services server(s)), messaging server, application server, content server, social networking server, web server, search server, content provider, third party server, user's computing device, and the like, or any combination thereof.

According to some embodiments, query engine300can be embodied as a stand-alone application that executes on a networking server. In some embodiments, the query engine300can function as an application installed on the user's device, and in some embodiments, such application can be a web-based application accessed by the user device over a network. In some embodiments, the query engine300can be configured and/or installed as an augmenting script, program or application (e.g., a plug-in or extension) to another application or portal data structure.

The database320can be any type of database or memory, and can be associated with a content server on a network (e.g., content server, a search server or application server) or a user's device (e.g., device101-104or device200fromFIGS.1-2). Database320comprises a dataset of data and metadata associated with local and/or network information related to users, services, applications, content, content and/or service providers, third party websites and the like.

In some embodiments, such information can be stored and indexed in the database320independently and/or as a linked or associated dataset. An example of this is look-up table (LUT). As discussed above, it should be understood that the data (and metadata) in the database320can be any type of information and type, whether known or to be known, without departing from the scope of the present disclosure.

As discussed above, with reference toFIG.1, the network315can be any type of network such as, but not limited to, a wireless network, a local area network (LAN), wide area network (WAN), the Internet, or a combination thereof. The network315facilitates connectivity of the query engine300, and the database of stored resources320. Indeed, as illustrated inFIG.3, the query engine300and database320can be directly connected by any known or to be known method of connecting and/or enabling communication between such devices and resources.

The principal processor, server, or combination of devices that comprise hardware programmed in accordance with the special purpose functions herein is referred to for convenience as query engine300, and includes request module302, definition module304, validation module306and resolution module308. It should be understood that the engine(s) and modules discussed herein are non-exhaustive, as additional or fewer engines and/or modules (or sub-modules) may be applicable to the embodiments of the systems and methods discussed. The operations, configurations and functionalities of each module, and their role within embodiments of the present disclosure will be discussed below.

Turning toFIG.4, Process400details non-limiting example embodiments for the parameterization of columns within a templated virtual sematic layer. Engine300, as mentioned above and evident from the discussion below, enables the parameterization of columns, which can be utilized during generation of a query and during execution of the query (e.g., at query time).

A data schema is a design or configuration of a database that represents the storage of the data within the database. At a high level, the data schema can describe both the organization of data and the relationships between the data stored in the database. This relationship can provide information indicating which data is necessary for other functions, forms or types of data, and how they are connected to each other. For example, a data schema can describe a table and its columns. Each column can have a name and a data type (e.g., integer, text, and the like).

Currently, there are two kinds of general data schemas in a virtual semantic layer: 1) a logical model (i.e., what is exposed to end users), and 2) a physical model (i.e., what is stored in the database).

In a virtual semantic layer, a logical model can also have a definition, referred to as a native SQL fragment. The native SQL fragment can refer to: i) zero or more other columns in the logical model (e.g., Logical Column References); ii) zero or more columns in the physical model of the current table (Physical Column Reference); iii) zero or more columns in a different logical model that requires a table join to access (Logical Join Reference); and iv) zero or more columns in a different physical model that requires a table join to access (Physical Join Reference).

Because a column definition can reference other column definitions, each column definition can be represented as a tree (e.g., a column reference tree) where each node in the tree can be a column definition, and each edge can be a reference to another column. Column references in a virtual semantic layer can be specified as template variables in a column definition.

Currently, there are four kinds of general template variables in a virtual semantic layer: i) logical column references; ii) physical column references; iii) logical join reference; and iv) physical join reference.

In some embodiments, a logical column reference is a variable name that refers to another logical column in the semantic model. In some embodiments, the template can have the form: {{logicalName}}.

In some embodiments, a physical column reference is a variable name that refers to a physical database column. In some embodiments, the template can have the form: {{$physicalName}}, where the “$” denotes the physical database.

In some embodiments, a logical join reference is a variable name that refers to a logical column in a different semantic model that requires a table join to access. In some embodiments, the template can have the form {{joinName1.joinName2.logicalName}}.

In some embodiments, a physical join reference is a variable name that refers to a physical column in a different semantic model that requires a table join to access. In some embodiments, the template can have the form {{joinName1.joinName2.joinNameN.$physicalName}}.

As discussed herein, the disclosed framework adds a fifth kind of templated expression (and associated rules), which can expand column arguments and lead to the never-before expressed relationships between columns of a table or database. In some embodiments, therefore, a parameterized column can be added which enables a more efficient search compilation and/or search execution at query time.

According to some embodiments, Steps402-404of Process400can be performed by request module302of query engine300; Steps406-408can be performed by definition module304; Step410(and its sub-steps inFIG.5) can be performed by validation module306; and Step412can be performed by resolution module308.

Process400begins with Step402where a request to access a database is received. In some embodiments, the request corresponds to a user desiring to generate a query. In some embodiments, the request can comprise information related to, but not limited to, a variable, type of data, a value of data, and the like, or some combination thereof. For example, the request can be generated by an analyst providing information related to a type of currency and its value.

In some embodiments, the database includes a plurality of electronically stored information organized into columns and/or tables, as discussed above.

In some embodiments, this request can be in accordance with a search for or a request for an electronic resource stored in a database. In some embodiments, the request can be associated with a specific query and/or an overall configuration of query log information stored in a database. In some embodiments, this request can be based on an analysis or technician associated with a service or content provider requesting input to configure and/or inspect a database. In some embodiments, the requested database can be associated with a particular service and/or house/store particular types of data (e.g., revenues, currencies, click rates for advertising partners, and the like).

In Step404, based on the request from Step402, the database is identified. In some embodiments, the identification of the database can also enable access. For example, engine300may be granted write access (e.g., administrator privileges) to perform the disclosed column parameterization.

In Step406, engine300identifies (or defines) the column arguments that are referenced by, identified by or determined to correspond to the request. For example, if the request corresponds to a currency and a currency value, then the columns identified can correspond to currency types and currency values.

According to some embodiments, each column in a virtual semantic layer can include and/or be extended to include zero or more arguments. An argument (or argument definition) can include information related to: i) argument name; ii) argument data type (e.g., number, text, and the like); iii) default value; iv) whether or not the argument is required; and/or iv) metadata related to the argument (e.g., legal values, description, type-ahead search location(s), and the like).

Therefore, in some embodiments, Step406can involve identifying a set of arguments for a set of identified columns, and/or arguments for each identified column that correspond to the specifically identified information in the request.

In Step408, engine identifies (or defines) argument templates for the identified columns (from Step406). According to some embodiments, an argument template can include information related to a column reference and a column argument reference.

In some embodiments, a column reference(s) can appear in a templated SQL fragment(s) for a column definition(s) (e.g., a templated native SQL fragment that defines a measure or dimension) and a join definition(s) (e.g., a templated native SQL fragment that joins two tables together (which represents the ON clause in SQL).

In some embodiments, a column argument reference can take one of two forms and be defined as follows: {{$$column.arg.argumentName}} or {{sql table=‘tableName’ column=‘columnName[arg1:value1][arg2:value2]’}}.

{{$$column.arg.argumentName}} references an argument value by its current name. The substituted argument value has either been supplied by the end user or is set to its default value.

{{sql table=‘tableName’ column=‘columnName[arg1:value1][arg2:value2]’}} references another logical column and supplies some or all of its arguments. This form can be useful when a column definition references another column definition and the value of some or all of the argument values are to be overridden (or edited, as discussed below). It should be noted that any argument not specified is assumed to be provided from the calling context (e.g., the referencing column or user query).

According to some embodiments, for columns in a semantic layer, the arguments of the columns may also be defined. According to some embodiments, the definition of the argument can be based on the measure definition that leverages the column argument. In some embodiments, such argument template can be defined as follows:{name: highScoretype: INTEGERdefinition: ‘{{$$column.args.aggregation}}({{$highScore}})’arguments: [name: aggregationtype: TEXTvalues: [SUM, MIN, MAX]default: ‘SUM’}]}

By way of a non-limiting example, below is an example of a column argument in a join clause:{name: totalCosttype: MONEYdefinition: ‘SUM({{$cost}})*{{rates.currentRate}}’arguments: [{name: currencytype: TEXTtableSource: rates.id#### If there is no default value, the argument is required.## This argument is referenced in the join on clause so no default should exist here##}{name: formattype: TEXTdefault: ‘$0.00’#### No tableSource or values indicates context aware filtering is not supported.##}]}

In some embodiments, a join definition for the above example can be as follows:{name: ratesto: currencyRateskind: toOnetype: left#### This join is parameterized on a particular column's arguments.##definition: ‘{{rates.$id}}={{$$column.args.currency}}AND {{rates.$date}}={{date}}’}

According to some embodiments, when defining a parameterized column that references another parameterized column, all required arguments in the dependent column must be defined in the referencing column (e.g., there are no implicit or inherited arguments). This has a number of advantages with respect to argument overrides. For example, it provides the ability to override the default value of a dependent column argument with a new default value. Another advantage is that it provides the ability the ability to remove a required argument from a dependent column by pinning it to a new default value. And, yet another advantage is that it provides the ability to remove a required argument from a dependent column by pinning it to a fixed, non-default value. In some embodiments, overriding the type of an argument can be forbidden and can result in an error during configuration processing.

In Step410, engine300performs semantic layer validation based on the identified column arguments and argument templates (from Steps406-408). According to some embodiments, the validation processing performed by engine300can involve determining that each argument is unique; that is, that each argument is referencing another column or argument in another column. In some embodiments, this can involve determining that the “argument name” of the argument is unique (or not shared by another argument in the same column).

Some embodiments of the processing of Step410are discussed herein in relation toFIG.5, which provides Steps502-508that are sub-steps of Step410.

In Step502, a column within a set of columns (from Step408) are identified, and the arguments of the column are identified. The processing of Step502-508can be performed for each column in the set (e.g., each column that is to be parameterized, as discussed above).

In Step504, the information of the arguments (or argument definitions) are analyzed, and engine300determines whether the definitions of the arguments are unique (e.g., are different from other arguments in a same column, for example). If they are different, then processing proceeds from Step504to Step506where the parameterization of the semantic layer is validated.

If engine300determines that the arguments are not unique, then engine300proceeds to Step508where the argument(s), or at least a portion of the argument's definition, can be edited (or modified, or overridden, as discussed above). Upon modification, in some embodiments, validation of the modified definitions can be performed in a similar manner as discussed above in relation to Steps504-506.

According to some embodiments, a semantic layer definition is often a separate artifact that can be created and maintained independently from other components of an analytic system. Every edit may need to be validated on demand or before it is deployed to ensure correctness.

By way of a non-limiting example embodiments, according to some embodiments of the processing of Step410, validation can involve determining whether argument names are unique for a given column.

For example, if a column definition (column A) references another column (column B), and column B has a required argument (argument X), column A must either: i) also define argument X; or ii) expand column B directly with the {{sql . . . }} template syntax and supply the value for argument X (e.g., Step508).

In another non-limiting example, if a column definition (column A) references another column (column B), and both column A and column B have the same argument definition (argument X), argument X must have the same data type in both definitions.

At the conclusion of the processing of Steps502-510ofFIG.5, the sematic layer can be considered validated (e.g., through determinations that column arguments are valid and unique, as discussed above), and processing continues from Step410to Step412.

In Step412, engine300performs template resolution processing on the validated semantic layer. Step412ensures that there are no conflicts with arguments within the semantic layer and/or parameterized columns and their respective arguments. According to some embodiments, template resolution processing performed by engine300can be an iterative process that can expand a reference tree for a (validated or parameterized) column.

In some embodiments, Step412can involve engine300identifying the validated semantic layer and its columns, as discussed above in relation to Step410, then identifying the reference tree for each identified column. Engine300can then perform the resolution processing of Step412by first analyzing a column's reference tree to identify which template variables (or column arguments) are in need of expansion, then on a subsequent traversal(s), expanding those identified variables. In some embodiments, subsequent traversals of the reference tree may be required to ensure that all variables of need of expansion are identified and expanded appropriately. With each pass, any unexpanded template variables in the current definition can be expanded, and the resolution processing of Step412is completed when the reference tree has been traversed and no more variables are identified or determined as requiring expansion. Thus, in some embodiments, Step412can be recursively performed until it is determined that resolution is complete.

In some embodiments, a column argument (or template variable) can be provided in one of several ways. In some embodiments, i) an argument can be pinned in the semantic layer definition using the {{sql . . . }} template function; ii) an argument can be provided by the end user in a request or query (e.g., Step402); iii) an argument can be provided as a default value.

According to some embodiments, from the perspective of column definition expansion performed during Step412's processing, an argument value is assigned in the following priority order: i) argument pinned in the calling context. ({{sql . . . }} template function); ii) argument set in the calling context (e.g., user query or referencing column default value); and iii) default argument value in the current column definition.

Thus, according to some embodiments, as engine300traverses the columns of the semantic layer that are validated in Step410, engine300can expand upon the arguments/variables that are need of expansion based upon the above argument expansions. The result of the resolution processing of the validated columns (of Step410) is the parameterization of columns within the virtual semantic layer. In some embodiments, as mentioned above, this can be performed in response to a request from a user, and/or can be performed at query time in response to a specific query for information housed within a database.

Therefore, the steps and sub-steps of Process400enables columns to be parameterized for efficient query generation and search optimization. Engine300's execution of Process400's steps (and sub-steps) enables a database and/or its associated semantic layer to be construed, modified and/or configured according to a hierarchy, and/or with types of metadata, that corresponds to the determined column relationships. This not only reduces the amount of data stored and reduces the time to process queries, but also enables a more streamlined approach for searches to be constructed and executed, which can increase the performance of the database and the operations of systems that are backed by the database.

For the purposes of this disclosure the term “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein and/or a consumer of data supplied by a data provider. By way of example, and not limitation, the term “user” or “subscriber” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session, or can refer to an automated software application which receives the data and stores or processes the data.