| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_cce1bf18a448daccdd9f4edb9dcb3fb2 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-01 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-18
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-06315
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2155
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6218
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6259 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-18
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate |
2021-02-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_89a98c0827807794ed7d9bdbf5235225
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d19e95853a6afaf47f9431c1d74e293d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2a166d9228c0b528956801dda680c8e1
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5788f7d11490ca7f5284501781aa84a6 |
| publicationDate |
2022-08-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022253689-A1 |
| titleOfInvention |
Predictive data capacity planning |
| abstract |
Predictive big data capacity planning is described. An example includes instructions for receiving workload data and computing operation data related to workload processing for a customer in a computing infrastructure, the computing infrastructure including one or more clusters, the one or more clusters including one or more data nodes; analyzing the received data to identify relationship information between the workload data and the computing operation data; performing predictive analytics to identify a significant value that relates to performance variations in workload performance or usage pattern characteristics for data growth scale factors in the computing infrastructure; generating a knowledge base based at least in part on the predictive analytics; training a machine learning model based at least in part on the knowledge base; and utilizing the trained machine learning model to generate a computing infrastructure configuration recommendation for the customer. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2023043202-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022269531-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11765100-B1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11789774-B2 |
| priorityDate |
2021-02-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |