Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 19/005,783 filed on 12/30/2024 in which claims 1-20 are pending in the application, all of which are ready for examination by the examiner.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1 and 18 recite scanning a metastore on the first big data platform based on information about the target directory.
The limitations of scanning…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “scanning…” in the context of these claims encompass the user manually scan a metastore on data platform based on information. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – obtaining…, obtain…, deleting…. The “obtaining”, “obtain”, and “deleting” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “obtaining”, “obtain”, and “deleting” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 2, 11 and 19 recite a name of the target structured data, a storage location of the target structured data in the first storage cluster, a version of the target structured data, a type of the target structured data, or a partition identifier of the target structured data. The limitations only recite additional elements at a high level of generality. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 3 and 20 recite wherein the storage location of the target structured data in the first storage cluster is a storage location corresponding to the target directory. The limitations only recite additional elements at a high level of generality. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 4 recites wherein the second storage cluster belongs to a second data processing system, wherein the second data processing system further comprises a second computing cluster connected to the second storage cluster, and wherein a second big data platform runs in the second computing cluster; and wherein the method further comprises: providing the second computing cluster with the metadata of the target structured data, to enable the second computing cluster to insert the metadata of the target structured data into a metastore on the second big data platform.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – providing…. The “providing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “providing” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 5 recites wherein providing the second computing cluster with the metadata of the target structured data comprises: converting the metadata of the target structured data into data in a first format; and providing the second computing cluster with the data in the first format.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – converting…, providing…. The “converting”, “providing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “converting”, “providing” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 6 recites wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; and wherein providing the second computing cluster with the metadata of the target structured data comprises: sending the metadata of the target structured data to the global message bus.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – providing…. The “providing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “providing” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 7 recites wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; and wherein obtaining the data mobility information that is of-a-the target directory in the file system and that is in the first storage cluster comprises: monitoring the global message bus to obtain the data mobility information that is of the target directory in the file system and that is in the first storage cluster.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – obtaining…. The “obtaining” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “obtaining” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 8 recites subscribing to data mobility information of a plurality of directories in the global file system from the global message bus, wherein the plurality of directories are directories respectively corresponding to the plurality of pieces of structured data in the global file system.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – subscribing…. The “subscribing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “subscribing” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 9 recites wherein the metastore on the first big data platform from which the metadata of the target structured data is deleted is used for a service query for big data, and the service query does not relate to a query for the target structured data. The limitations only recite additional elements at a high level of generality. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 10 recites wherein the data mobility information indicates that data in the target directory is to be migrated to the second storage cluster, and wherein the target directory is a storage directory corresponding to the target structured data in the second storage cluster.
The limitations of indicates…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “indicates…” in the context of these claims encompass the user manually indicate data in directory. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – obtaining…. The “obtaining” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “obtaining” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 12 recites wherein the data in the target directory is from a first storage cluster, wherein the first storage cluster belongs to a first data processing system, wherein the first data processing system further comprises a first computing cluster connected to the first storage cluster, and wherein a first big data platform runs in the first computing cluster; and wherein the method further comprises: receiving the metadata of the target structured data from the first computing cluster.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – receiving…. The “receiving” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receiving” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 13 recites wherein the metadata of the target structured data from the first computing cluster is in a first format; and wherein the method further comprises: converting the metadata of the target structured data in the first format into a format that can be identified by using a metadata service on the second big data platform.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – converting…. The “converting” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “converting” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 14 recites wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; and wherein receiving the metadata of the target structured data from the first computing cluster comprises: receiving the metadata of the target structured data from the first computing cluster through the global message bus.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – receiving…. The “receiving” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receiving” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 15 recites monitoring the global message bus to obtain the data mobility information of the target directory in the file system.
The limitations of monitoring…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “monitoring…” in the context of these claims encompass the user manually monitoring global message bus. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – provides, obtaining…. The “provides”, “obtaining” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “provides”, “obtaining” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 16 recites subscribing to data mobility information of a plurality of directories in the global file system from the global message bus, wherein the plurality of directories are directories respectively corresponding to the plurality of pieces of structured data.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – subscribing…. The “subscribing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “subscribing” is a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 17 recites wherein metastore into which the metadata of the target structured data is inserted is used for a service query for big data, and the service query relates to a query for the target structured data. The limitations only recite additional elements at a high level of generality. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Barker et al. (U.S. Patent 11,210,133; hereinafter “Barker”) in view of Kornacker et al. (U.S. PGPub 2014/0280032; hereinafter “Kornacker”) and further in view of Davis et at. (U.S. PGPub 2014/0006357; hereinafter “Davis”).
As per claims 1 and 18, Barker discloses a data mobility sensing method obtaining, by a data mobility sensing apparatus in a first data processing system, data mobility information that is of a target directory in a file system and that is in a first storage cluster, wherein the first data processing system comprises a first computing cluster and the first storage cluster, wherein the first computing cluster is connected to the first storage cluster, wherein a first big data platform runs in the first computing cluster, wherein the data mobility information indicates that data in the target directory is to be migrated from the first storage cluster to a second storage cluster, and wherein the target directory is a directory, in the file system, corresponding to target structured data on the first big data platform. (See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed, also See col. 18, ll 36-56, wherein connected storage clusters to clients are disclosed, also See Figs. 10-11, col. 52, ll 55-67, wherein workload migration module functions, providing workload mobility between disparate execution environments in which “workload migration module (1002) may reside in a cloud computing environment and may be included as a part of a suite of tools for monitoring and managing one or more storage systems” (col. 52, ll 64-67) are disclosed, also See col. 55, ll 50-67, wherein workload migration module on identifying workload, deploying workload in second environment in which “identifying (1004) a workload (1018a) executing in a first environment (1010) can include identifying (1102), from amongst a plurality of workloads (1018a, 1116, 1118, 1120), a preferred 65 workload (1018a) to migrate. Identifying (1102) a preferred workload (1018a) to migrate…”are disclosed; as taught by Barker.)
However, Barker fails to disclose scanning, by the data mobility sensing apparatus, a metastore on the first big data platform based on information about the target directory, to obtain metadata of the target structured data, wherein the metadata of the target structured data describes an attribute of the target structured data, wherein the metastore on the first big data platform comprises respective metadata of a plurality of pieces of structured data on the first big data platform, and wherein the plurality of pieces of structured data comprise the target structured data.
On the other hand, Kornacker teaches scanning, by the data mobility sensing apparatus, a metastore on the first big data platform based on information about the target directory, to obtain metadata of the target structured data, wherein the metadata of the target structured data describes an attribute of the target structured data, wherein the metastore on the first big data platform comprises respective metadata of a plurality of pieces of structured data on the first big data platform, and wherein the plurality of pieces of structured data comprise the target structured data. (See Figs. 1-2, wherein unified metadata and scheduler, Hive metastore are disclosed, also See paras. 21, 26, 56, wherein low latency (LL) query engine functions on querying unstructured and structured big data, Hive metastore in which “Hive metastore 106 includes information about the data available to the low latency (LL) query engine. Specifically, the Hive metastore includes the table definition, i.e., mapping of the physical data into the logical tables that are exposed (analogous to metadata of a plurality of pieces of structured data on the first big data platform, and wherein the plurality of pieces of structured data comprise the target structured data). The YARN 108 performs job scheduling and cluster resource management. The HDFS name node (NN) 110 includes the details of the distribution of the files across data nodes to optimize local reads… the name node 110 may even include information concerning disk volumes the files sit on, on an individual node” [0026] are disclosed, also See Fig. 3A, paras. 28-30, 42, wherein metadata, hive metastore, Hadoop platform in which “The metadata 222 may include, for example, information such as tables, their partitions, schema-on-read, columns, types, table/block locations, and the like. The metadata 222 may leverage existing Hive metastore, which includes mapping of HBase table, predicates on row key columns mapped into start/stop row, predicates on other columns mapped into single column value filters, and the like (analogous to target structured data describes an attribute of the target structured data)” [0028] are disclosed; as taught by Kornacker.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Kornacker teachings in the Barker system. Skilled artisan would have been motivated to incorporate system for low latency query engine for Apache Hadoop for handling query requests taught by Kornacker in the Barker system for effective workload mobility between disparate execution environments. In addition, both of the references (Barker and Kornacker) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as replication, replication and distribution of data. This close relation between both of the references highly suggests an expectation of success.
However, the combination of Barker and Kornacker fails to disclose after the data in the target directory is migrated out of the first storage cluster, deleting by the data mobility sensing apparatus, the metadata of the target structured data on the first data platform.
On the other hand, Davis teaches after the data in the target directory is migrated out of the first storage cluster, deleting by the data mobility sensing apparatus, the metadata of the target structured data on the first data platform. (See paras. 202-205, 314, wherein migrating data to different cloud storage and deleting the copy from previous cloud storage process in which “migrating a cloud file to a different cloud storage provider and deleting the copy from the previous cloud storage provider involves some additional logistical operations and/or policies to ensure that cloud controllers can still access the cloud file as needed” [0205] are disclosed; as taught by Davis.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Davis teachings in the combination of Barker and Kornacker system. Skilled artisan would have been motivated to incorporate system for restoring an archived file in a distributed filesystem taught by Davis in the combination of Barker and Kornacker system for effective workload mobility between disparate execution environments. In addition, both of the references (Barker, Kornacker, and Davis) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as replication, replication and distribution of data. This close relation between both of the references highly suggests an expectation of success.
As per claims 2, 11, and 19, the combination of Barker, Kornacker, and Davis discloses wherein the metadata of the target structured data comprises one or more of the following: a name of the target structured data,
a storage location of the target structured data in the first storage cluster,
a version of the target structured data,
a type of the target structured data, or
a partition identifier of the target structured data. (See Figs. 2A-G, col. 11, ll 47-67, wherein storage locations in clustered peer-to-peer system are disclosed, also See col. 17, ll 51-57, wherein metadata, log structured merge tree are disclosed, also See col. 37, ll 44-58, wherein big data analytics of advanced analytics, structured data, predictive models are disclosed, also See Table 1, col. 42, ll 47-67, wherein load model, software version, storage capacity, predicted performance load on storage system are disclosed; as taught by Barker.)
As per claims 3 and 20, the combination of Barker, Kornacker, and Davis discloses wherein the storage location of the target structured data in the first storage cluster is a storage location corresponding to the target directory. (See Figs. 2A-G, col. 11, ll 47-67, wherein storage locations in clustered peer-to-peer system in which “Control of storage locations and workloads are distributed across the storage locations in a clustered peer-to-peer system. Tasks such as mediating communications between the various storage nodes, detecting when a storage node has become unavailable, and balancing I/Os (inputs and outputs) across the various storage nodes, are all handled on a distributed basis” are disclosed, also See col. 17, ll 51-57, wherein metadata, log structured merge tree are disclosed, also See col. 37, ll 44-58, wherein big data analytics of advanced analytics, structured data, predictive models are disclosed; as taught by Barker.)
As per claim 4, the combination of Barker and Davis discloses wherein the second storage cluster belongs to a second data processing system, wherein the second data processing system further comprises a second computing cluster connected to the second storage cluster, and wherein a second big data platform runs in the second computing cluster; (See Fig. 2A, col. 13, ll 27-52, wherein multiple clusters having storage nodes are disclosed; as taught by Barker.)
However, Barker fails to disclose wherein the method further comprises: providing the second computing cluster with the metadata of the target structured data, to enable the second computing cluster to insert the metadata of the target structured data into a metastore on the second big data platform.
On the other hand, Kornacker teaches wherein the method further comprises: providing the second computing cluster with the metadata of the target structured data, to enable the second computing cluster to insert the metadata of the target structured data into a metastore on the second big data platform. (See Figs. 1-2, wherein unified metadata and scheduler, Hive metastore are disclosed, also See paras. 21, 26-27, 56, wherein low latency (LL) query engine functions on querying unstructured and structured big data, Hive metastore, YARN functions cluster resource management in which “the Hive metastore includes the table definition, i.e., mapping of the physical data into the logical tables that are exposed. The YARN 108 performs job scheduling and cluster resource management. The HDFS name node (NN) 110 includes the details of the distribution of the files across data nodes to optimize local reads” [0026] are disclosed, also See Fig. 3A, paras. 28-30, 77, wherein metadata, hive metastore, Hadoop platform in which “The metadata 222 may include, for example, information such as tables, their partitions, schema-on-read, columns, types, table/block locations, and the like. The metadata 222 may leverage existing Hive metastore, which includes mapping of HBase table, predicates on row key columns mapped into start/stop row, predicates on other columns mapped into single column value filters, and the like (analogous to target structured data)” [0028] and “some processes or blocks may be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or subcombinations” [0077] are disclosed; as taught by Kornacker.)
See claim 1 for motivation above.
As per claim 5, the combination of Barker, Kornacker, and Davis discloses wherein providing the second computing cluster with the metadata of the target structured data comprises: converting the metadata of the target structured data into data in a first format; (See col. 32, ll 19-33, col. 37, ll 38-58, wherein converting data to a structured form process are disclosed; as taught by Barker.)
and providing the second computing cluster with the data in the first format. (See col. 28, ll 24-40, wherein presenting, retrieving data in same format process are disclosed, also See Fig. 2A, col. 13, ll 27-52, wherein multiple clusters having storage nodes are disclosed; as taught by Barker.)
As per claim 6, the combination of Barker and Davis fails to disclose wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; and wherein providing the second computing cluster with the metadata of the target structured data comprises: sending the metadata of the target structured data to the global message bus.
On the other hand, Kornacker teaches wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; (See paras. 26-27, wherein global system repository features in which “state store 112 is a global system repository which runs on a single node in the cluster…” [0027] are disclosed; as taught by Kornacker.)
and wherein providing the second computing cluster with the metadata of the target structured data comprises: sending the metadata of the target structured data to the global message bus. (See paras. 26-27, wherein global system repository features in which “The YARN 108 performs job scheduling and cluster resource management. The HDFS name node (NN) 110 includes the details of the distribution of the files across data nodes to optimize local reads” [0026] and “state store 112 is a global system repository which runs on a single node in the cluster. The state store 112 in one implementation can be used as a name service. All low latency (LL) query engine daemons, at start up, can register with the state store and get membership information. The membership information can be used to find out about all the low latency (LL) query engine daemons that are running on the cluster…the state store can store and distribute other system information such as load information, diagnostics information, and the like that may be used to improve the functioning and/or performance of the Hadoop cluster” [0027] are disclosed; as taught by Kornacker.)
See claim 1 for motivation above.
As per claim 7, the combination of Barker, Kornacker, and Davis discloses wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; (See Fig. 2B, col. 14, ll 34-49, col. 15, ll 1-4, wherein power distribution bus coupling multiple storage nodes, storage cluster and file system are disclosed, also See col. 16, ll 51-62, wherein file system, unit of distribution of entity in which “data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system. Entities are grouped into sets called authorities. Each authority has an authority owner, which is a storage node that has the exclusive right to update the entities in the authority” are disclosed, also See col. 35, ll 3-5, col. 50, ll 58-63, wherein universal health records and centralized management service are disclosed; as taught by Barker.)
and wherein obtaining the data mobility information that is of the target directory in the file system and that is in the first storage cluster comprises: monitoring the global message bus to obtain the data mobility information that is of the target directory in the file system and that is in the first storage cluster. (See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed, also See col. 18, ll 36-56, wherein connected storage clusters to clients are disclosed, also See Figs. 10-11, col. 52, ll 55-67, wherein workload migration module functions, providing workload mobility between disparate execution environments in which “workload migration module (1002) may reside in a cloud computing environment and may be included as a part of a suite of tools for monitoring and managing one or more storage systems” (col. 52, ll 64-67) are disclosed, also See col. 55, ll 50-67, wherein workload migration module on identifying workload, deploying workload in second environment in which “identifying (1004) a workload (1018a) executing in a first environment (1010) can include identifying (1102), from amongst a plurality of workloads (1018a, 1116, 1118, 1120), a preferred 65 workload (1018a) to migrate. Identifying (1102) a preferred workload (1018a) to migrate…”are disclosed; as taught by Barker.)
As per claim 8, the combination of Barker, Kornacker, and Davis discloses subscribing to data mobility information of a plurality of directories in the global file system from the global message bus, wherein the plurality of directories are directories respectively corresponding to the plurality of pieces of structured data in the global file system. (See Fig. 10, col. 53, ll 34-52, wherein workload migration module functions, monitoring module identifying environments process in which “workload migration module (1002) may alternatively identify (1004) a workload (1018a) executing in a first environment (1010), for example, through the use of an automated monitoring module that identifies environments that are over-subscribed and identifies workloads that may be candidates for migration to reduce the load on a particular environment (1010), through the use of an automated monitoring module that identifies workloads that may benefit from being migrated to a different environment, and in other ways” are disclosed, also See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed; as taught by Barker.)
As per claim 9, the combination of Barker and Davis discloses wherein the first big data platform from which the metadata of the target structured data is deleted is used for a service query for big data, and the service query does not relate to a query for the target structured data. (See col. 52, ll 32-49, wherein workload automatic operations in which “the state of the fleet (906) may be constantly monitored and workloads may be automatically migrated as the state of the fleet (906) changes (e.g., workloads are added, workloads are deleted, storage systems are added to the fleet, storage systems are removed from the fleet, storage systems within the fleet are modified, devices within a particular storage system fail, and so on” are disclosed, also See col. 4, ll 45-65 and col. 23, ll 13-30, wherein querying storage drives process are disclosed, also See col. 31, ll 30-42 and col. 37, ll 38-58, wherein big data analytics are disclosed, also See col. 12, ll 11-18 and col. 18, ll 36-38, wherein in storage cluster run as independent system in one location (analogous to service query does not relate to a query for the target structured data) are disclosed; as taught by Barker.)
However, Barker fails to disclose the metastore.
On the other hand, Kornacker teaches the metastore. (See Fig. 1, paras. 26-27, wherein Hive metastore are disclosed; as taught by Kornacker.)
See claim 1 for motivation above.
As per claim 10, Barker discloses a data mobility sensing method comprising: obtaining, by a data mobility sensing apparatus in a second data processing system, data mobility information of a target directory in a file system, wherein the second data processing system comprises a second computing cluster and a second storage cluster, the second computing cluster is connected to the second storage cluster, and a second big data platform runs in the second computing cluster. (See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed, also See col. 18, ll 36-56, wherein connected storage clusters to clients are disclosed, also See Figs. 10-11, col. 52, ll 55-67, wherein workload migration module functions, providing workload mobility between disparate execution environments in which “workload migration module (1002) may reside in a cloud computing environment and may be included as a part of a suite of tools for monitoring and managing one or more storage systems” (col. 52, ll 64-67) are disclosed, also See col. 55, ll 50-67, wherein workload migration module on identifying workload, deploying workload in second environment in which “identifying (1004) a workload (1018a) executing in a first environment (1010) can include identifying (1102), from amongst a plurality of workloads (1018a, 1116, 1118, 1120), a preferred 65 workload (1018a) to migrate. Identifying (1102) a preferred workload (1018a) to migrate…”are disclosed; as taught by Barker.)
wherein the data mobility information indicates that data in the target directory is to be migrated to the second storage cluster, and wherein the target directory is a storage directory corresponding to the target structured data in the second storage cluster. (See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed, also See col. 18, ll 36-56, wherein connected storage clusters to clients are disclosed, also See Figs. 10-11, col. 52, ll 55-67, wherein workload migration module functions, providing workload mobility between disparate execution environments in which “workload migration module (1002) may reside in a cloud computing environment and may be included as a part of a suite of tools for monitoring and managing one or more storage systems” (col. 52, ll 64-67) are disclosed, also See col. 55, ll 50-67, wherein workload migration module on identifying workload, deploying workload in second environment in which “identifying (1004) a workload (1018a) executing in a first environment (1010) can include identifying (1102), from amongst a plurality of workloads (1018a, 1116, 1118, 1120), a preferred 65 workload (1018a) to migrate. Identifying (1102) a preferred workload (1018a) to migrate…”are disclosed; as taught by Barker.)
However Barker fails to disclose obtaining, by the data mobility sensing apparatus, metadata of target structured data; and wherein the metadata of the target structured data describes an attribute of the target structured data.
On the other hand, Kornacker teaches obtaining, by the data mobility sensing apparatus, metadata of target structured data; (See Fig. 3A, paras. 28-30, 42, wherein metadata, hive metastore, Hadoop platform in which “The metadata 222 may include, for example, information such as tables, their partitions, schema-on-read, columns, types, table/block locations, and the like. The metadata 222 may leverage existing Hive metastore, which includes mapping of HBase table, predicates on row key columns mapped into start/stop row, predicates on other columns mapped into single column value filters, and the like” [0028] are disclosed; as taught by Kornacker.)
and wherein the metadata of the target structured data describes an attribute of the target structured data. (See Fig. 3A, paras. 28-30, 42, wherein metadata, hive metastore, Hadoop platform in which “The metadata 222 may include, for example, information such as tables, their partitions, schema-on-read, columns, types, table/block locations, and the like. The metadata 222 may leverage existing Hive metastore, which includes mapping of HBase table, predicates on row key columns mapped into start/stop row, predicates on other columns mapped into single column value filters, and the like (analogous to target structured data describes an attribute of the target structured data)” [0028] are disclosed; as taught by Kornacker.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Kornacker teachings in the Barker system. Skilled artisan would have been motivated to incorporate system for low latency query engine for Apache Hadoop for handling query requests taught by Kornacker in the Barker system for effective workload mobility between disparate execution environments. In addition, both of the references (Barker and Kornacker) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as replication, replication and distribution of data. This close relation between both of the references highly suggests an expectation of success.
However, the combination of Barker and Kornacker fails to disclose after obtaining the data mobility information of the target directory and obtaining the metadata of the target structured data, inserting, by the data mobility sensing apparatus, the metadata of the target structured data into the second data platform.
On the other hand, Davis teaches after obtaining the data mobility information of the target directory and obtaining the metadata of the target structured data, inserting, by the data mobility sensing apparatus, the metadata of the target structured data into the second data platform. (See paras. 202-205, 314, wherein migrating data to different cloud storage and deleting the copy from previous cloud storage process in which “migrating a cloud file to a different cloud storage provider (analogous to inserting data on second data platform) and deleting the copy from the previous cloud storage provider involves some additional logistical operations and/or policies to ensure that cloud controllers can still access the cloud file as needed” [0205] are disclosed; as taught by Davis.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Davis teachings in the combination of Barker and Kornacker system. Skilled artisan would have been motivated to incorporate system for restoring an archived file in a distributed filesystem taught by Davis in the combination of Barker and Kornacker system for effective workload mobility between disparate execution environments. In addition, both of the references (Barker, Kornacker, and Davis) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as replication, replication and distribution of data. This close relation between both of the references highly suggests an expectation of success.
As per claim 12, the combination of Barker, Kornacker, and Davis discloses wherein the data in the target directory is from a first storage cluster, wherein the first storage cluster belongs to a first data processing system, wherein the first data processing system further comprises a first computing cluster connected to the first storage cluster, and wherein a first big data platform runs in the first computing cluster; (See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed, also See col. 18, ll 36-56, wherein connected storage clusters to clients are disclosed, also See Figs. 10-11, col. 52, ll 55-67, wherein workload migration module functions, providing workload mobility between disparate execution environments in which “workload migration module (1002) may reside in a cloud computing environment and may be included as a part of a suite of tools for monitoring and managing one or more storage systems” (col. 52, ll 64-67) are disclosed, also See col. 55, ll 50-67, wherein workload migration module on identifying workload, deploying workload in second environment in which “identifying (1004) a workload (1018a) executing in a first environment (1010) can include identifying (1102), from amongst a plurality of workloads (1018a, 1116, 1118, 1120), a preferred 65 workload (1018a) to migrate. Identifying (1102) a preferred workload (1018a) to migrate…”are disclosed; as taught by Barker.)
and wherein the method further comprises: receiving the metadata of the target structured data from the first computing cluster. (See Fig. 2A, col. 13, ll 27-52, wherein multiple clusters having storage nodes are disclosed, also See col. 10, ll 46-52 and col. 11, ll 9-16, wherein attaching indexing, metadata are disclosed; as taught by Barker.)
As per claim 13, the combination of Barker, Kornacker, and Davis discloses wherein the metadata of the target structured data from the first computing cluster is in a first format; (See col. 28, ll 24-40, wherein presenting, retrieving data in same format process are disclosed, also See Fig. 2A, col. 13, ll 27-52, wherein multiple clusters having storage nodes are disclosed; as taught by Barker.)
and wherein the method further comprises: converting the metadata of the target structured data in the first format into a format that can be identified by using a metadata service on the second big data platform. (See col. 32, ll 19-33, col. 37, ll 38-58, wherein converting data to a structured form process are disclosed; as taught by Barker.)
As per claim 14, the combination of Barker and Davis fails to disclose wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; and wherein receiving the metadata of the target structured data from the first computing cluster comprises: receiving the metadata of the target structured data from the first computing cluster through the global message bus.
On the other hand, Kornacker teaches wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; (See paras. 26-27, wherein global system repository features in which “state store 112 is a global system repository which runs on a single node in the cluster…” [0027] are disclosed; as taught by Kornacker.)
and wherein receiving the metadata of the target structured data from the first computing cluster comprises: receiving the metadata of the target structured data from the first computing cluster through the global message bus. (See paras. 26-27, wherein global system repository features in which “The YARN 108 performs job scheduling and cluster resource management. The HDFS name node (NN) 110 includes the details of the distribution of the files across data nodes to optimize local reads” [0026] and “state store 112 is a global system repository which runs on a single node in the cluster. The state store 112 in one implementation can be used as a name service. All low latency (LL) query engine daemons, at start up, can register with the state store and get membership information. The membership information can be used to find out about all the low latency (LL) query engine daemons that are running on the cluster…the state store can store and distribute other system information such as load information, diagnostics information, and the like that may be used to improve the functioning and/or performance of the Hadoop cluster” [0027] are disclosed; as taught by Kornacker.)
See claim 10 for motivation above.
As per claim 15, the combination of Barker and Davis fails to disclose wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; and wherein obtaining the data mobility information of the target directory in the file system comprises: monitoring the global message bus to obtain the data mobility information of the target directory in the file system.
On the other hand, Kornacker teaches wherein the file system belongs to a global file system, wherein the first storage cluster and the second storage cluster each store a part of data of the global file system, and wherein the global file system provides a global message bus; (See paras. 26-27, wherein global system repository features in which “state store 112 is a global system repository which runs on a single node in the cluster…” [0027] are disclosed; as taught by Kornacker.)
and wherein obtaining the data mobility information of the target directory in the file system comprises: monitoring the global message bus to obtain the data mobility information of the target directory in the file system. (See paras. 26-27, 31, wherein global system repository features in which “The YARN 108 performs job scheduling and cluster resource management. The HDFS name node (NN) 110 includes the details of the distribution of the files across data nodes to optimize local reads” [0026] and “state store 112 is a global system repository which runs on a single node in the cluster. The state store 112 in one implementation can be used as a name service. All low latency (LL) query engine daemons, at start up, can register with the state store and get membership information. The membership information can be used to find out about all the low latency (LL) query engine daemons that are running on the cluster…the state store can store and distribute other system information such as load information, diagnostics information, and the like that may be used to improve the functioning and/or performance of the Hadoop cluster” [0027] are disclosed; as taught by Kornacker.)
See claim 10 for motivation above.
As per claim 16, the combination of Barker, Kornacker, and Davis discloses subscribing to data mobility information of a plurality of directories in the global file system from the global message bus, wherein the plurality of directories are directories respectively corresponding to the plurality of pieces of structured data. (See Fig. 10, col. 53, ll 34-52, wherein workload migration module functions, monitoring module identifying environments process in which “workload migration module (1002) may alternatively identify (1004) a workload (1018a) executing in a first environment (1010), for example, through the use of an automated monitoring module that identifies environments that are over-subscribed and identifies workloads that may be candidates for migration to reduce the load on a particular environment (1010), through the use of an automated monitoring module that identifies workloads that may benefit from being migrated to a different environment, and in other ways” are disclosed, also See col. 11, ll 47-67, col. 16, ll 46-67, wherein storage clusters, file system, directory, metadata in which “segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system” are disclosed; as taught by Barker.)
As per claim 17, the combination of Barker and Davis discloses wherein the metadata of the target structured data is inserted is used for a service query for big data, and the service query relates to a query for the target structured data. (See col. 52, ll 32-49, wherein workload automatic operations in which “Readers will appreciate that migrating (922) a particular workload (422, 424, 904) among the storage systems ( 406, 408) in the fleet (906) of storage systems (406, 408) in dependence upon the preferred placement for each of the one or more workloads ( 422, 424, 904) may be carried out automatically and without user intervention. For example, the state of the fleet (906) may be constantly monitored and workloads may be automatically migrated as the state of the fleet (906) changes (e.g., workloads are added, workloads are deleted, storage systems are added to the fleet, storage systems are removed from the fleet, storage systems within the fleet are modified, devices within a particular storage system fail, and so on” are disclosed, also See col. 4, ll 45-65 and col. 23, ll 13-30, wherein querying storage drives process are disclosed, also See col. 31, ll 30-42 and col. 37, ll 38-58, wherein big data analytics are disclosed, also See col. 12, ll 11-18 and col. 18, ll 36-38, wherein in storage cluster run as independent system in one location (analogous to service query does not relate to a query for the target structured data) are disclosed; as taught by Barker.)
However, Barker fails to disclose the metastore.
On the other hand, Kornacker teaches the metastore. (See Fig. 1, paras. 26-27, wherein Hive metastore are disclosed; as taught by Kornacker.)
See claim 10 for motivation above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Fernandez et al. (U.S. PGPub 2022/0335005) discloses storage-deferred copying between different file systems.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L. L. H./
Examiner, Art Unit 2153
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153
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