Patent ID: 12210650

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail.

Data clean rooms enable two or more parties to share data, while restricting how that data can be used by other parties. In one example scenario, two or more parties wish to combine their respective data without revealing their raw data to each other. For example, two companies may wish to determine how many joint customers they have, but neither company wants to give the other one access to its customer list. A data clean room can be established for processing a join of a customer list from one company with a customer list from the other company, using a field such as mobile phone number or email address as a join key, as an example.

Each company may share its respective customer list with the other company via a data clean room, within which the aforementioned join can be executed, and a total number of rows in the resulting relation can be conveyed back to each party. In that manner, neither company ever has access to the actual customer data on the other's list, but each company can find out the number of common customers between the two companies. In an example such as this, the data clean room may be resident in a database-platform account of either company or in a mutually agreed-upon location that is in neither database-platform account, and each company may confidentially share its customer list (or perhaps just one or more columns of its customer list) with the data clean room, within which the join function may be carried out.

The above-described example relates in many instances to a two-way-sharing model—i.e., each company shares its customer list with the other to at least some extent. There are other scenarios, however, in which the data-sharing model is more of a one-way street. This disclosure includes description of example data-clean-room operation in some such example scenarios. In this disclosure, the sharing relationship is described as being between (i) a company (or organization or an individual, etc.) that is referred to herein as a “data provider” and (ii) a company (or, again, an organization or an individual, etc.) that is referred to herein as a “data consumer.” As one would expect from those names, a given data provider provides data that is consumed by one or more data consumers. In the examples that are primarily described below in connection with the figures, the data provider is a streaming-video platform that presents advertisements (“ads”) in conjunction with the streaming video that it provides, and the data consumer is a particular advertiser that advertises on that streaming service.

Embodiments of the present disclosure are described herein as using data clean rooms that are constructed and operated according to what is referred to herein as “defined access” (or “a defined-access model,” “a defined-access paradigm,” “a defined-access approach,” and/or the like). In at least one embodiment, a data provider creates an application. In some embodiments, the application may be what is referred to in the present disclosure as a “native platform application,” which, as used herein, refers to an application that is “built in” to—i.e., executes on—the herein-described data platform.

In some of the described examples, both the data providers and the data consumers are customers of a common data platform, and accordingly each have a respective customer account (or just “account”) on that data platform. In other embodiments, a given data provider and a given data consumer operate on separate platforms. Either or both of the separate platforms could be platforms operated by the data provider or data consumer themselves, or could be a customer account held by the data provider or the data consumer on another multi-customer data platform. And certainly other architectures are possible as well.

In some example embodiments, a given application may reside in the data-platform account of a data provider (the “data-provider account”), and may include a set of application programming interfaces (APIs) that are associated with various underlying blocks of (e.g., source and/or executable) code provided by the given application. In at least some embodiments, these APIs define how data in the data-provider account (“provider data”) may be accessed by any user that is executing an instance of the given application. The underlying code blocks may perform operations that include, but are not limited to, particular queries, particular query operations (e.g., joins), user-defined functions, other functions, stored procedures, scripts, user-interface elements, secure views, and/or the like. The data provider may share certain data with the application.

The data provider may further permit a data consumer to install an instance of the application. It is noted that there may be multiple data providers, multiple applications provided by a given data provider, multiple data consumers, multiple application instances installed by a given data consumer, and so forth. For simplicity, however, most of the examples that are described in the present disclosure involve a single data provider that has created a single application in the data-provider account of that data provider, and a single data consumer that installs a single instance of that application in the data-consumer account of that data consumer.

Once the data consumer has installed, in the data-consumer account, an instance of the application, the data consumer can thereafter use the one or more APIs provided by the data provider to access the provider data (to the extent permitted by the code underlying the APIs). Because the APIs are created by (or at least for) the provider, the APIs enforce the provider's intended restrictions on how provider data may be used. In at least one embodiment, the APIs themselves are visible to the data-consumer account, whereas the operational logic (e.g., source code, executable code, and/or the like) of the underlying code blocks is not.

In many examples, data consumers combine at least some of their own consumer data with the accessed provider data via the APIs. Thus, in some embodiments, the data provider shares certain provider data with the application, and also shares the application with the data consumer, whereas the data consumer shares at least some of its consumer data with the installed application instance. This arrangement protects the data of both parties, and in particular protects the consumer data, which is only being shared within the data-consumer account with the particular installed instance of the described application. Indeed, in at least one embodiment, the application is constructed such that it is not able to exfiltrate consumer data from the data-consumer account (absent authorization from the data consumer). Moreover, in at least one embodiment, results computed by (or generated by, etc.) a given API are returned only locally within the data-consumer account within which that particular application instance has been installed and is executing.

It is noted that, as used herein, “share” (or “sharing,” etc.) is a broad verb that is intended to include mechanisms such as granting permissions, sending copies, sending links (e.g., customized links), and/or any other mechanism by which access to the party being shared with can be accomplished. In some cases, “sharing” involves granting permissions to one or more objects that may represent, e.g., a database, an application, an application instance, and/or the like.

Additionally, in at least one embodiment, data providers are equipped with one or more tools or other mechanisms that can be used to audit how one or more data consumers are accessing the provider data of that data provider. Some examples of auditable events include API invocations, stored-procedure invocations, accesses of certain tables, accesses of certain views, accesses of certain databases, accesses of certain objects, and/or the like. In some embodiments, data providers have the capability to revoke granted access (at will, or under certain conditions, etc.).

In some embodiments, an audit log (or other record) is generated to record various events. Such an audit log may include details of how the data provider's data was used—e.g., whether a particular column was used as a join key or filter, or directly returned to the data consumer, among other options. An audit log may include computation details, totals, and/or the like. For example, an audit log may include a value such as volume of data produced. Moreover, in embodiments in which a data consumer's query involves the data consumer's own data in addition to the data provider's data, an audit function may record only metrics and events related to the data consumer's access of the data provider's data, but not record metrics and events regarding how the data consumer may or may not have accessed their own data. This may protect privacy and confidentiality of the data consumer's data. Numerous other possibilities could be implemented as well or instead of one or more of the aforementioned options.

FIG.1illustrates an example computing environment100that includes a database system in the example form of a data platform102, in accordance with some embodiments of the present disclosure. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components that are not germane to conveying an understanding of the inventive subject matter have been omitted fromFIG.1. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the computing environment100to facilitate additional functionality that is not specifically described herein. In other embodiments, the computing environment may comprise another type of network-based database system or a cloud data platform.

As shown, the computing environment100comprises the data platform102in communication with a cloud storage platform104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage). The data platform102is a network-based system used for reporting and analysis of integrated data from one or more disparate sources including one or more storage locations within the cloud storage platform104. The cloud storage platform104comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the data platform102.

The data platform102comprises a compute service manager108, an execution platform110, and one or more metadata databases112. The data platform102hosts and provides data reporting and analysis services to multiple client accounts.

The compute service manager108coordinates and manages operations of the data platform102. The compute service manager108also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (also referred to as “virtual warehouses”). The compute service manager108can support any number of client accounts, such as end users providing data storage and retrieval requests, system administrators managing the systems and methods described herein, and other components/devices that interact with compute service manager108.

The compute service manager108is also in communication with a client device114. The client device114corresponds to a user of one of the multiple client accounts supported by the data platform102. A user may utilize the client device114to submit data storage, retrieval, and analysis requests to the compute service manager108.

The compute service manager108is also coupled to one or more metadata databases112that store metadata pertaining to various functions and aspects associated with the data platform102and its users. For example, metadata database(s)112may include a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, metadata database(s)112may include information regarding how data is partitioned and organized in remote data storage systems (e.g., the cloud storage platform104) and local caches.

As discussed herein, a “micro-partition” is a batch storage unit, and each micro-partition has contiguous units of storage. By way of example, each micro-partition may contain between 50 MB and 500 MB of uncompressed data (note that the actual size in storage may be smaller because data may be stored compressed). Groups of rows in tables may be mapped into individual micro-partitions organized in a columnar fashion. This size and structure allows for extremely granular selection of the micro-partitions to be scanned, which can include millions, or even hundreds of millions, of micro-partitions. This granular selection process for micro-partitions to be scanned is referred to herein as “pruning.” Pruning involves using metadata to determine which portions of a table, including which micro-partitions or micro-partition groupings in the table, are not pertinent to a query, avoiding those non-pertinent micro-partitions when responding to the query, and scanning only the pertinent micro-partitions to respond to the query.

Metadata may be automatically gathered on all rows stored in a micro-partition, including: the range of values for each of the columns in the micro-partition; the number of distinct values; and/or additional properties used for both optimization and efficient query processing. In one embodiment, micro-partitioning may be automatically performed on all tables. For example, tables may be transparently partitioned using the ordering that occurs when the data is inserted/loaded. However, it should be appreciated that this disclosure of the micro-partition is exemplary only and should be considered non-limiting. It should be appreciated that the micro-partition may include other database storage devices without departing from the scope of the disclosure. Information stored by a metadata database112(e.g., key-value pair data store) allows systems and services to determine whether a piece of data (e.g., a given partition) needs to be accessed without loading or accessing the actual data from a storage device.

The compute service manager108is further coupled to the execution platform110, which provides multiple computing resources that execute various data storage and data retrieval tasks. The execution platform110is coupled to cloud storage platform104. The cloud storage platform104comprises multiple data storage devices120-1to120-N. In some embodiments, the data storage devices120-1to120-N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices120-1to120-N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices120-1to120-N may be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3™ storage systems, or any other data storage technology. Additionally, the cloud storage platform104may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.

The execution platform110comprises a plurality of compute nodes. A set of processes on a compute node executes a query plan compiled by the compute service manager108. The set of processes can include: a first process to execute the query plan; a second process to monitor and delete cache files using a least recently used (LRU) policy and implement an out-of-memory (OOM) error mitigation process; a third process that extracts health information from process logs and status to send back to the compute service manager108; a fourth process to establish communication with the compute service manager108after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager108and to communicate information back to the compute service manager108and other compute nodes of the execution platform110.

In some embodiments, communication links between elements of the computing environment100are implemented via one or more data communication networks. These data communication networks may utilize any communication protocol and any type of communication medium. In some embodiments, the data communication networks are a combination of two or more data communication networks (or sub-networks) coupled to one another. In alternative embodiments, these communication links are implemented using any type of communication medium and any communication protocol.

The compute service manager108, metadata database(s)112, execution platform110, and cloud storage platform104are shown inFIG.1as individual discrete components. However, each of the compute service managers108, metadata databases112, execution platforms110, and cloud storage platforms104may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations). Additionally, each of the compute service managers108, metadata databases112, execution platforms110, and cloud storage platforms104can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of the data platform102. Thus, in the described embodiments, the data platform102is dynamic and supports regular changes to meet the current data processing needs.

During typical operation, the data platform102processes multiple jobs determined by the compute service manager108. These jobs are scheduled and managed by the compute service manager108to determine when and how to execute the job. For example, the compute service manager108may divide the job into multiple discrete tasks and may determine what data is needed to execute each of the multiple discrete tasks. The compute service manager108may assign each of the multiple discrete tasks to one or more nodes of the execution platform110to process the task. The compute service manager108may determine what data is needed to process a task and further determine which nodes within the execution platform110are best suited to process the task. Some nodes may have already cached the data needed to process the task and, therefore, be a good candidate for processing the task.

Metadata stored in a metadata database112assists the compute service manager108in determining which nodes in the execution platform110have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform110process the task using data cached by the nodes and, if necessary, data retrieved from the cloud storage platform104. It is desirable to retrieve as much data as possible from caches within the execution platform110because the retrieval speed is typically much faster than retrieving data from the cloud storage platform104.

As shown inFIG.1, the computing environment100separates the execution platform110from the cloud storage platform104. In this arrangement, the processing resources and cache resources in the execution platform110operate independently of the data storage devices120-1to120-N in the cloud storage platform104. Thus, the computing resources and cache resources are not restricted to specific data storage devices120-1to120-N. Instead, all computing resources and all cache resources may retrieve data from, and store data to, any of the data storage resources in the cloud storage platform104.

FIG.2is a block diagram illustrating components of the compute service manager108, in accordance with some embodiments of the present disclosure. As shown inFIG.2, the compute service manager108includes an access manager202and a credential management system204coupled to access metadata database206, which is an example of the metadata databases112. Access manager202handles authentication and authorization tasks for the systems described herein. The credential management system204facilitates use of remote stored credentials to access external resources such as data resources in a remote storage device. As used herein, the remote storage devices may also be referred to as “persistent storage devices” or “shared storage devices.”

For example, the credential management system204may create and maintain remote credential store definitions and credential objects (e.g., in the access metadata database206). A remote credential store definition identifies a remote credential store and includes access information to access security credentials from the remote credential store. A credential object identifies one or more security credentials using non-sensitive information (e.g., text strings) that are to be retrieved from a remote credential store for use in accessing an external resource. When a request invoking an external resource is received at run time, the credential management system204and access manager202use information stored in the access metadata database206(e.g., a credential object and a credential store definition) to retrieve security credentials used to access the external resource from a remote credential store.

A request processing service208manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service208may determine the data to process a received query (e.g., a data storage request or data retrieval request). The data may be stored in a cache within the execution platform110or in a data storage device in cloud storage platform104.

A management console service210supports access to various systems and processes by administrators and other system managers. Additionally, the management console service210may receive a request to execute a job and monitor the workload on the system.

The compute service manager108also includes a job compiler212, a job optimizer214, and a job executor216. The job compiler212parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer214determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job optimizer214also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor216executes the execution code for jobs received from a queue or determined by the compute service manager108.

A job scheduler and coordinator218sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform110ofFIG.1. For example, jobs may be prioritized and then processed in that prioritized order. In an embodiment, the job scheduler and coordinator218determines a priority for internal jobs that are scheduled by the compute service manager108ofFIG.1with other “outside” jobs such as user queries that may be scheduled by other systems in the database but may utilize the same processing resources in the execution platform110. In some embodiments, the job scheduler and coordinator218identifies or assigns particular nodes in the execution platform110to process particular tasks. A virtual warehouse manager220manages the operation of multiple virtual warehouses implemented in the execution platform110. For example, the virtual warehouse manager220may generate query plans for executing received queries. The data clean room system230is configured to perform online error checking and offline error checking, as discussed in further detail below.

As illustrated, the compute service manager108includes a configuration and metadata manager222, which manages the information related to the data stored in the remote data storage devices and in the local buffers (e.g., the buffers in execution platform110). The configuration and metadata manager222uses metadata to determine which data files need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer224oversees processes performed by the compute service manager108and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform110. The monitor and workload analyzer224also redistributes tasks, as needed, based on changing workloads throughout the data platform102and may further redistribute tasks based on a user (e.g., “external”) query workload that may also be processed by the execution platform110. The configuration and metadata manager222and the monitor and workload analyzer224are coupled to a data storage device226. Data storage device226represents any data storage device within the data platform102. For example, data storage device226may represent buffers in execution platform110, storage devices in cloud storage platform104, or any other storage device.

As described in embodiments herein, the compute service manager108validates all communication from an execution platform (e.g., the execution platform110) to validate that the content and context of that communication are consistent with the task(s) known to be assigned to the execution platform. For example, an instance of the execution platform executing a query A should not be allowed to request access to data-source D (e.g., data storage device226) that is not relevant to query A. Similarly, a given execution node (e.g., execution node302-1ofFIG.3) may need to communicate with another execution node (e.g., execution node302-2ofFIG.3), but should be disallowed from communicating with a third execution node (e.g., execution node312-1), and any such illicit communication can be recorded (e.g., in a log or other location). Also, the information stored on a given execution node is restricted to data relevant to the current query, and any other data is unusable, rendered so by destruction or encryption where the key is unavailable.

FIG.3is a block diagram illustrating components of the execution platform110ofFIG.1, in accordance with some embodiments of the present disclosure. As shown inFIG.3, the execution platform110includes multiple virtual warehouses, including virtual warehouse1, virtual warehouse2, and virtual warehouse N. Each virtual warehouse includes multiple execution nodes that each include a data cache and a processor. The virtual warehouses can execute multiple tasks in parallel by using the multiple execution nodes. As discussed herein, the execution platform110can add new virtual warehouses and drop existing virtual warehouses in real-time based on the current processing needs of the systems and users. This flexibility allows the execution platform110to quickly deploy large amounts of computing resources when needed without being forced to continue paying for those computing resources when they are no longer needed. All virtual warehouses can access data from any data storage device (e.g., any storage device in cloud storage platform104).

Although each virtual warehouse shown inFIG.3includes three execution nodes, a particular virtual warehouse may include any number of execution nodes. Further, the number of execution nodes in a virtual warehouse is dynamic, such that new execution nodes are created when additional demand is present, and existing execution nodes are deleted when they are no longer useful.

Each virtual warehouse is capable of accessing any of the data storage devices120-1to120-N shown inFIG.1. Thus, the virtual warehouses are not necessarily assigned to a specific data storage device120-1to120-N and, instead, can access data from any of the data storage devices120-1to120-N within the cloud storage platform104. Similarly, each of the execution nodes shown inFIG.3can access data from any of the data storage devices120-1to120-N. In some embodiments, a particular virtual warehouse or a particular execution node may be temporarily assigned to a specific data storage device, but the virtual warehouse or execution node may later access data from any other data storage device.

In the example ofFIG.3, virtual warehouse1includes three execution nodes302-1,302-2, and302-N. Execution node302-1includes a cache304-1and a processor306-1. Execution node302-2includes a cache304-2and a processor306-2. Execution node302-N includes a cache304-N and a processor306-N. Each execution node302-1,302-2, and302-N is associated with processing one or more data storage and/or data retrieval tasks. For example, a virtual warehouse may handle data storage and data retrieval tasks associated with an internal service, such as a clustering service, a materialized view refresh service, a file compaction service, a storage procedure service, or a file upgrade service. In other implementations, a particular virtual warehouse may handle data storage and data retrieval tasks associated with a particular data storage system or a particular category of data.

Similar to virtual warehouse1discussed above, virtual warehouse2includes three execution nodes312-1,312-2, and312-N. Execution node312-1includes a cache314-1and a processor316-1. Execution node312-2includes a cache314-2and a processor316-2. Execution node312-N includes a cache314-N and a processor316-N. Additionally, virtual warehouse3includes three execution nodes322-1,322-2, and322-N. Execution node322-1includes a cache324-1and a processor326-1. Execution node322-2includes a cache324-2and a processor326-2. Execution node322-N includes a cache324-N and a processor326-N.

In some embodiments, the execution nodes shown inFIG.3are stateless with respect to the data being cached by the execution nodes. For example, these execution nodes do not store or otherwise maintain state information about the execution node or the data being cached by a particular execution node. Thus, in the event of an execution node failure, the failed node can be transparently replaced by another node. Since there is no state information associated with the failed execution node, the new (replacement) execution node can easily replace the failed node without concern for recreating a particular state.

Although the execution nodes shown inFIG.3each include one data cache and one processor, alternative embodiments may include execution nodes containing any number of processors and any number of caches. Additionally, the caches may vary in size among the different execution nodes. The caches shown inFIG.3store, in the local execution node, data that was retrieved from one or more data storage devices in cloud storage platform104ofFIG.1. Thus, the caches reduce or eliminate the bottleneck problems occurring in platforms that consistently retrieve data from remote storage systems. Instead of repeatedly accessing data from the remote storage devices, the systems and methods described herein access data from the caches in the execution nodes, which is significantly faster and avoids the bottleneck problem discussed above. In some embodiments, the caches are implemented using high-speed memory devices that provide fast access to the cached data. Each cache can store data from any of the storage devices in the cloud storage platform104.

Further, the cache resources and computing resources may vary between different execution nodes. For example, one execution node may contain significant computing resources and minimal cache resources, making the execution node useful for tasks that require significant computing resources. Another execution node may contain significant cache resources and minimal computing resources, making this execution node useful for tasks that require caching of large amounts of data. Yet, another execution node may contain cache resources providing faster input-output operations, useful for tasks that require fast scanning of large amounts of data. In some embodiments, the cache resources and computing resources associated with a particular execution node are determined when the execution node is created, based on the expected tasks to be performed by the execution node.

Additionally, the cache resources and computing resources associated with a particular execution node may change over time based on changing tasks performed by the execution node. For example, an execution node may be assigned more processing resources if the tasks performed by the execution node become more processor-intensive. Similarly, an execution node may be assigned more cache resources if the tasks performed by the execution node require a larger cache capacity.

Although virtual warehouses1,2, and N are associated with the same execution platform110, the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse1can be implemented by a computing system at a first geographic location, while virtual warehouses2and N are implemented by another computing system at a second geographic location. In some embodiments, these different computing systems are cloud-based computing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown inFIG.3as having multiple execution nodes. The multiple execution nodes associated with each virtual warehouse may be implemented using multiple computing systems at multiple geographic locations. For example, an instance of virtual warehouse1implements execution nodes302-1and302-2on one computing platform at a geographic location and implements execution node302-N at a different computing platform at another geographic location. Selecting particular computing systems to implement an execution node may depend on various factors, such as the level of resources needed for a particular execution node (e.g., processing resource requirements and cache requirements), the resources available at particular computing systems, communication capabilities of networks within a geographic location or between geographic locations, and which computing systems are already implementing other execution nodes in the virtual warehouse.

Execution platform110is also fault tolerant. For example, if one virtual warehouse fails, that virtual warehouse is quickly replaced with a different virtual warehouse at a different geographic location.

A particular execution platform110may include any number of virtual warehouses. Additionally, the number of virtual warehouses in a particular execution platform is dynamic, such that new virtual warehouses are created when additional processing and/or caching resources are needed. Similarly, existing virtual warehouses may be deleted when the resources associated with the virtual warehouse are no longer useful.

In some embodiments, the virtual warehouses may operate on the same data in cloud storage platform104, but each virtual warehouse has its own execution nodes with independent processing and caching resources. This configuration allows requests on different virtual warehouses to be processed independently and with no interference between the requests. This independent processing, combined with the ability to dynamically add and remove virtual warehouses, supports the addition of new processing capacity for new users without impacting the performance.

Further examples of embodiments are described below in connection withFIG.4andFIG.5. In the described example scenario, a data provider and a data consumer are both customers of the data platform102, and each has a respective account (e.g., customer account) with the data platform102. These customer accounts may be maintained by the data platform102in the cloud storage platform104. The example data provider is a streaming-video platform that presents advertisements in connection with presented video. The example data consumer is one particular advertiser that places ads on the streaming-video platform.

FIG.4illustrates an example data-provider data table400and an example data-consumer data table450, in accordance with at least one embodiment. The data-provider data table400may be stored in the data-provider account, whereas the data-consumer data table may be stored in the data-consumer account. The data content and arrangements presented inFIG.4are by way of example and not limitation, as other content and/or arrangements could be used.

The data-provider data table400has a header row and a row for each of a plurality of customers, and further has columns corresponding respectively to a customer ID and an email address. The data-provider data table400also includes columns for an arbitrary number M of advertisements. For each customer, an indication of ‘true’ or ‘false’ indicates whether or not the customer of that row has viewed (or has been presented, etc.) the ad of that column. Respective rows for an arbitrary number N of customers is shown in the data-provider data table400.

The data-consumer data table450has a header row and a row for each of an arbitrary number K customers of the data-consumer, as well as columns corresponding to a customer ID and an email address. The data-consumer data table450further includes an arbitrary number L of columns that respectively contain ‘true’ or ‘false’ to indicate whether or not the customer of that row has purchased the product of that column. For simplicity, ‘AD01’ is an advertisement for ‘PRODUCT01,’ and ‘AD02’ is an advertisement for ‘PRODUCT02.’ The names PRODUCT01and PRODUCT02are simply placeholders, and could just as well represent a given service, a collection of products, and/or the like.

It can be seen from inspection of the data-provider data table400and the data-consumer data table450that there are four common customers between the two tables. In particular, customers1-4in the data-provider data table400correspond respectively to customers46,47,49, and50in the data-consumer data table450. The rows in the data-consumer data table450that correspond to those four example customers are marked with an arrow to the left of each such row. Moreover, it is noted that, in some embodiments, customers will only appear in a given table if they viewed a given ad or bought a given product—thus, more of a transaction-log approach is used in some embodiments. Other approaches could be used as well, and may occur to those of skill in the art having the benefit of the present disclosure.

FIG.5depicts an example defined-access data-clean-room scenario500, in accordance with at least one embodiment. Depicted inFIG.5are representations of (i) a data-provider account502corresponding with the above-described streaming-video platform and (ii) a data-consumer account552corresponding with the above-described advertiser. The data-provider account502includes provider data504and an application506. The provider data504may include the data-provider data table400ofFIG.4. Furthermore, a share512is depicted to represent that at least some of the provider data504is shared with the application506. In this example, the shared data is the data-provider data table400.

Furthermore, the application506includes one or more APIs508that correspond with one or more respective underlying code blocks510. These APIs508and associated underlying code blocks510could provide any of the operations described above, including queries, query operations (e.g., joins), user-defined functions, stored procedures, access to one or more secure views, generation of one or more user-interface elements, and/or the like. In at least one embodiment, the underlying code blocks510contain the source code and/or executable code that actually performs the operations that are accessible via the APIs508.

A share520depicts that the data-provider account502is sharing the application506with the data-consumer account552. In at least one embodiment, this involves permitting the installation in the data-consumer account552of an application instance556of the application506. As can be seen inFIG.5, the application instance556includes one or more APIs558that correspond to the one or more APIs508of the application506. The APIs558respectively provide access to one or more underlying code blocks560, which correspond to the one or more underlying code blocks510in the application506. Whereas the underlying code blocks510(e.g., the underlying source code and/or executable code) are visible to the data-provider account502, the underlying code blocks560are not visible to the data-consumer account552—for this reason, the underlying code blocks560are depicted using dashed outlines inFIG.5.

It can further be seen that the data-consumer account552contains consumer data554which, in this example, includes the above-described data-consumer data table450ofFIG.4. The share562that is depicted inFIG.5represents that the data-consumer account552is sharing at least some of the consumer data554with the application instance556. It is noted with respect to both the provider data504and the consumer data554that their depiction as being respectively within the data-provider account502and the data-consumer account552are illustrative only, and do not reflect an actual storage location.

When the data-consumer account552uses one or more of the APIs558of the application instance556, any output of these operations is depicted as being stored in the consumer data554of the data-consumer account552. The security of the consumer data554is protected in at least two ways: it never leaves the data-consumer account552, and even the resulting output570is locally stored in the data-consumer account552.

FIG.6shows a flow diagram of a method600for providing defined access in the context of a data clean room, in accordance with at least one embodiment. The example method600is described by way of example as being performed by the data platform102, though this is by way of example and not limitation. The method600could be performed by any one or more computing devices that are suitably programmed to perform the described functions.

At operation602, the data platform102creates an application506in the data-provider account502of the data platform102. The application506includes one or more APIs508corresponding to one or more underlying code blocks510.

At operation604, the data platform102shares (at the share512) certain provider data504(e.g., the data-provider data table400) with the application506.

At operation606, the data platform102installs (in association with the share520) an application instance556of the application506in the data-consumer account552of the data platform102. The application instance556includes APIs558that correspond to the APIs508, and that also correspond to the (non-visible) underlying code blocks560, which themselves correspond to the underlying code blocks510.

At operation608, the data platform102shares (at the share562) certain consumer data554(e.g., the data-consumer data table450) with the application instance556.

At operation610, the data platform102invokes one or more of the APIs558of the application instance556of the application506.

At operation612, the data platform102saves the output570of the APIs558locally within the data-consumer account552.

In an example embodiment, an API558may provide to the data-consumer account552a conversion rate that reflects the fraction of customers that viewed a given advertisement—via the streaming-video service associated with the data-provider account502—that actually went ahead and bought the advertised product (or service, etc.). With access to both the data-provider data table400and the data-consumer data table450, the application instance556can compute a conversion rate on a product-by-product basis.

In the example data, it can be seen that advertisement01(corresponding to product01) was viewed by the customers having the email addresses that start with ‘name02’ and ‘name03.’ It can further be seen that the ‘name02’ customer did not buy product01, though the ‘name03’ customer did. A conversion rate of 0.5 (1 out of 2) may be locally returned within the data-consumer account552for product01.

For product02, it can be seen that all four customers that are explicitly listed in the data-provider data table400viewed advertisement02. These four customers have email addresses starting with ‘name01,’ ‘name02,’ ‘name03,’ and ‘name04,’ respectively. It can further be seen that the ‘name01’ customer, the ‘name02’ customer, and the ‘name04’ customer bought product02, whereas the ‘name03’ customer did not. A conversion rate of 0.75 (3 out of 4) may be returned within the data-consumer account552for product02.

The above example shows that some APIs may provide results that are a certain count, average, fraction, percentage, and/or the like that are computed using data from both the provider and the consumer. These operations thus anonymize the data by outputting only a numerical answer without exposing the underlying data from which that answer was computed.

In other cases, a relation (e.g., a table) may be returned locally within the data-consumer account552. Depending on the functionality of the corresponding API, this relation may only be a subset of the consumer data that was shared by the data-consumer account552with the application instance556.

In some embodiments, an API may apply a differential privacy noise parameter to return aggregate results that satisfy a specified epsilon value (i.e., privacy budget). Among other techniques a given API may inject a specific amount of Laplace noise into the aggregate results, although other techniques exist.

Various different embodiments provide advantages over prior implementations. Some such advantages are described below. This list of advantages is intended to be illustrative and not limiting. Other advantages may occur to those of skill in the art having the benefit of the present disclosure.

Embodiments of the present disclosure give data providers flexibility in defining how they want their data to be accessed. For example, a data provider can create stored procedures and user-defined functions to enforce restrictions. One example context in which this may apply is in machine learning. In that context, the contents of a model can potentially reveal sensitive information about individuals, or reveal proprietary information about hyperparameters and other details of how a given model was trained. To limit such exposure, a data provider may wish to allow consumers to access the provider's machine-learning model only in certain ways. For example, a provider may allow a consumer to generate predictions, and optionally to contribute training data, without allowing the consumer to directly inspect the model. In such an embodiment, the provider creates APIs to access the model (i.e., “predict” API and optionally “train” API), and the consumer can only interact with the model via these APIs. Additionally, the provider can limit the number of predictions that the consumer can perform. A relevant use case is fraud detection in financial services: Banks wish to collaborate to build models to detect fraudulent consumers, but may be prevented by regulation and business interests from sharing raw consumer data with one another.

At least one embodiment supports limiting the extent of a consumer's access to data. For example, an embodiment can keep state that tracks how many queries a consumer has issued, or the aggregate amount of data the consumer has retrieved, or the privacy loss metric in differential privacy, as examples. Based on these metrics, the data platform can restrict the consumer's access if too much data has been accessed, in total or over a discrete time period (rate-limiting). Other examples are possible as well.

At least one embodiment supports global collaboration across clouds and geographical regions. When an authorized consumer wants to access data that a provider has shared, the data platform may automatically replicate the data to the region where it is needed, so that the consumer can install it as a native application.

At least one embodiment supports collaboration across X parties, where X can be 2 or greater. In an X-party scenario, one party acts as the consumer, combining data from the X−1 other parties and optionally data from itself.

As mentioned above, in at least one embodiment, usage of installed application instances of applications are auditable. As a first example, a data platform can provide a generic audit mechanism in the form of log of API calls. As a second example, a data platform can provide a logging facility that providers' code can invoke to log use-case-specific context, e.g., how much of the consumer's privacy budget is consumed by the current call.

At least one embodiment is integrated with the data platform's SQL query processing platform, so consumers can directly use the results from clean rooms as inputs to arbitrary computations that the consumer wants to perform.

In some embodiments, data providers can create user interfaces as part of their applications. For example, a provider might wish to share aggregate data about individuals without revealing individual records. The provider can give consumers access to data in the form of a dashboard. The dashboard might include graphs and charts, and provide consumers with ways to customize the dashboard. For example, in an interactive dashboard, the consumer may be able to specify filters, grouping conditions/breakdowns, time ranges, and so forth, to customize the aggregate results that are displayed. In an embodiment, the underlying data for the dashboard comes from APIs of native platform functions as described herein. The APIs may include parameters that the data consumer can set, through the dashboard, to customize the aggregate quantities that are returned. The APIs may also restrict how the data consumer can customize the dashboard. For example, the provider's code may prevent the consumer from setting filter conditions that could uniquely identify an individual.

FIG.7illustrates a diagrammatic representation of a machine700in the form of a computer system within which a set of instructions may be executed for causing the machine700to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,FIG.7shows a diagrammatic representation of the machine700in the example form of a computer system, within which instructions716(e.g., software, a program, an application, an applet, an app, or other executable code), for causing the machine700to perform any one or more of the methodologies discussed herein, may be executed. For example, the instructions716may cause the machine700to execute any one or more operations of any one or more of the methods described herein, by one or more processors. As another example, the instructions716may cause the machine700to implement portions of the data flows described herein. In this way, the instructions716transform a general, non-programmed machine into a particular machine700(e.g., the client device114ofFIG.1, the compute service manager108ofFIG.1, the execution platform110ofFIG.1) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein.

In alternative embodiments, the machine700operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine700may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine700may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a smart phone, a mobile device, a network router, a network switch, a network bridge, or any machine capable of executing the instructions716, sequentially or otherwise, that specify actions to be taken by the machine700. Further, while only a single machine700is illustrated, the term “machine” shall also be taken to include a collection of machines700that individually or jointly execute the instructions716to perform any one or more of the methodologies discussed herein.

The machine700includes processors710, memory730, and input/output (I/O) components750configured to communicate with each other such as via a bus702. In an example embodiment, the processors710(e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor712and a processor714that may execute the instructions716. The term “processor” is intended to include multi-core processors710that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions716contemporaneously. AlthoughFIG.7shows multiple processors710, the machine700may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiple cores, or any combination thereof.

The memory730may include a main memory732, a static memory734, and a storage unit731, all accessible to the processors710such as via the bus702. The main memory732, the static memory734, and the storage unit731comprise a machine storage medium738that may store the instructions716embodying any one or more of the methodologies or functions described herein. The instructions716may also reside, completely or partially, within the main memory732, within the static memory734, within the storage unit731, within at least one of the processors710(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine700.

The I/O components750include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components750that are included in a particular machine700will depend on the type of machine. For example, portable machines, such as mobile phones, will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components750may include many other components that are not shown inFIG.7. The I/O components750are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components750may include output components752and input components754.

The output components752may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), other signal generators, and so forth. The input components754may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components750may include communication components764operable to couple the machine700to a network781via a coupling783or to devices780via a coupling782. For example, the communication components764may include a network interface component or another suitable device to interface with the network781. In further examples, the communication components764may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices780may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a universal serial bus (USB)). For example, as noted above, the machine700may correspond to any one of the client device114, the compute service manager108, and the execution platform110, and may include any other of these systems and devices.

The various memories (e.g.,730,732,734, and/or memory of the processor(s)710and/or the storage unit736) may store one or more sets of instructions716and data structures (e.g., software), embodying or utilized by any one or more of the methodologies or functions described herein. These instructions716, when executed by the processor(s)710, cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, (e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate arrays (FPGAs), and flash memory devices); magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network781may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local-area network (LAN), a wireless LAN (WLAN), a wide-area network (WAN), a wireless WAN (WWAN), a metropolitan-area network (MAN), the Internet, a portion of the Internet, a portion of the public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network781or a portion of the network781may include a wireless or cellular network, and the coupling782may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling782may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.

The instructions716may be transmitted or received over the network781using a transmission medium via a network interface device (e.g., a network interface component included in the communication components764), and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions716may be transmitted or received using a transmission medium via the coupling782(e.g., a peer-to-peer coupling) to the devices780. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions716for execution by the machine700, and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.

In view of the disclosure above, various examples are set forth below. It should be noted that one or more features of an example, taken in isolation or combination, should be considered within the disclosure of this application.Example 1 is a method performed by executing instructions on at least one hardware processor, the method including: creating an application in a data-provider account of a data platform, the application including one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks; sharing provider data with the application in the data-provider account; installing, in a data-consumer account of the data platform, an application instance of the application, the application instance including one or more APIs corresponding to the one or more APIs in the application in the data-provider account; sharing consumer data with the application instance in the data-consumer account; invoking one or more of the APIs of the application instance to execute respective associated underlying code blocks, the respective associated underlying code blocks not being visible to the data-consumer account; and saving output of the one or more respective associated underlying code blocks locally within the data-consumer account.Example 2 is the method of Example 1, where the application instance is, by default, not authorized to exfiltrate consumer data from the data-consumer account.Example 3 is the method of Example 1 or Example 2, where the respective associated underlying code blocks not being visible to the data-consumer account includes a source code of the respective associated underlying code blocks not being visible to the data-consumer account.Example 4 is the method of any of the Examples 1-3, where the saved output includes aggregated output data.Example 5 is the method of Example 4, where the saved output does not include any of the shared provider data.Example 6 is the method of any of the Examples 1-5, where the saved output includes a relation.Example 7 is the method of Example 6, where the relation includes only a subset of the consumer data that was shared with the application instance.Example 8 is a data platform including: at least one hardware processor; and one or more non-transitory computer readable storage media containing instructions that, when executed by the at least one hardware processor, cause the data platform to perform operations including: creating an application in a data-provider account of the data platform, the application including one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks; sharing provider data with the application in the data-provider account; installing, in a data-consumer account of the data platform, an application instance of the application, the application instance including one or more APIs corresponding to the one or more APIs in the application in the data-provider account; sharing consumer data with the application instance in the data-consumer account; invoking one or more of the APIs of the application instance to execute respective associated underlying code blocks, the respective associated underlying code blocks not being visible to the data-consumer account; and saving output of the one or more respective associated underlying code blocks locally within the data-consumer account.Example 9 is the data platform of Example 8, where the application instance is, by default, not authorized to exfiltrate consumer data from the data-consumer account.Example 10 is the data platform of Example 8 or Example 9, where the respective associated underlying code blocks not being visible to the data-consumer account includes a source code of the respective associated underlying code blocks not being visible to the data-consumer account.Example 11 is the data platform of any of the Examples 1-10, where the saved output includes aggregated output data.Example 12 is the data platform of Example 11, where the saved output does not include any of the shared provider data.Example 13 is the data platform of any of the Examples 1-12, where the saved output includes a relation.Example 14 is the data platform of Example 13, where the relation includes only a subset of the consumer data that was shared with the application instance.Example 15 is one or more non-transitory computer readable storage media containing instructions that, when executed by at least one hardware processor of a data platform, cause the data platform to perform operations including: creating an application in a data-provider account of the data platform, the application including one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks; sharing provider data with the application in the data-provider account; installing, in a data-consumer account of the data platform, an application instance of the application, the application instance including one or more APIs corresponding to the one or more APIs in the application in the data-provider account; sharing consumer data with the application instance in the data-consumer account; invoking one or more of the APIs of the application instance to execute respective associated underlying code blocks, the respective associated underlying code blocks not being visible to the data-consumer account; and saving output of the one or more respective associated underlying code blocks locally within the data-consumer account.Example 16 is the one or more non-transitory computer readable storage media of Example 15, where the application instance is, by default, not authorized to exfiltrate consumer data from the data-consumer account.Example 17 is the one or more non-transitory computer readable storage media of Example 15 or Example 16, where the respective associated underlying code blocks not being visible to the data-consumer account includes a source code of the respective associated underlying code blocks not being visible to the data-consumer account.Example 18 is the one or more non-transitory computer readable storage media of any of the Examples 15-17, where the saved output includes aggregated output data.Example 19 is the one or more non-transitory computer readable storage media of Example 18, where the saved output does not include any of the shared provider data.Example 20 is the one or more non-transitory computer readable storage media of any of the Examples 15-19, where the saved output includes a relation.Example 21 is the one or more non-transitory computer readable storage media of Example 20, where the relation includes only a subset of the consumer data that was shared with the application instance.

In at least one embodiment, the application is already in the data-provider account, and need not be created as part of an embodiment.

Copending U.S. Provisional Patent Application No. 63/366,308, entitled “Privacy-Preserving Multi-Party Machine Learning Using a Database Cleanroom” is hereby incorporated herein by reference in its entirety.

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the methods described herein may be performed by one or more processors. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but also deployed across a number of machines. In some embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment, or a server farm), while in other embodiments the processors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This detailed description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim is still deemed to fall within the scope of that claim.