Abstract: Methods and systems for producing graphene from spent lithium-ion batteries are disclosed. One method includes applying an acid leaching solution to an anode of a lithium-ion battery to produce expanded graphite, applying a hydrothermal process to the expanded graphite to produce purified graphite, and subjecting the purified graphite to a shear mixing process to produce dispersed graphene. In some examples, the shear mixing process is combined with a hydrogen passivation process, which collectively improves each of graphene quality, graphene conversion rate, and graphene production efficiency.

Abstract: A database is selected from a plurality of databases included in the distributed database cluster. A shard map of the database is acquired, wherein the shard map includes a distribution of a plurality of shards on the plurality of nodes, and one or more nodes of the plurality of nodes each includes at least one shard of an old version of the database to be upgraded to a new version. A sustainable serviceability of the database corresponding to each of the one or more nodes is predicted according to the shard map based on the assumption that the at least one shard of each of the one or more nodes has been upgraded from the old version to the new version.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: A database is selected from a plurality of databases included in the distributed database cluster. A shard map of the database is acquired, wherein the shard map includes a distribution of a plurality of shards on the plurality of nodes, and one or more nodes of the plurality of nodes each includes at least one shard of an old version of the database to be upgraded to a new version. A sustainable serviceability of the database corresponding to each of the one or more nodes is predicted according to the shard map based on the assumption that the at least one shard of each of the one or more nodes has been upgraded from the old version to the new version.

Abstract: Provided are techniques for supporting a join operation against multiple NoSQL databases. A search request with a field and a value for the field is received, wherein the search request is to be issued against a plurality of database divisions formed from NoSQL databases. A simple database search is performed against a structured index to generate a first result set. An extended database search is performed against a linear database division index to generate a second result set by: identifying a relationship for the field that identifies related fields and identifying documents for the field and the related fields using a linear database division index. The first result set and the second result set are merged to form a merged result set of documents. The documents from the merged result set are displayed in response to the search request.

Abstract: Provided are techniques for supporting a join operation against multiple NoSQL databases. A search request with a field and a value for the field is received, wherein the search request is to be issued against a plurality of database divisions formed from NoSQL databases. A simple database search is performed against a structured index to generate a first result set. An extended database search is performed against a linear database division index to generate a second result set by: identifying a relationship for the field that identifies related fields and identifying documents for the field and the related fields using a linear database division index. The first result set and the second result set are merged to form a merged result set of documents. The documents from the merged result set are displayed in response to the search request.

Abstract: Provided are techniques for supporting a join operation against multiple NoSQL databases. A search request with a field and a value for the field is received, wherein the search request is to be issued against a plurality of database divisions formed from NoSQL databases. A simple database search is performed against a structured index to generate a first result set. An extended database search is performed against a linear database division index to generate a second result set by: identifying a relationship for the field that identifies related fields and identifying documents for the field and the related fields using a linear database division index. The first result set and the second result set are merged to form a merged result set of documents. The documents from the merged result set are displayed in response to the search request.

Abstract: Provided are techniques for supporting a join operation against multiple NoSQL databases. A search request with a field and a value for the field is received, wherein the search request is to be issued against a plurality of database divisions formed from NoSQL databases. A simple database search is performed against a structured index to generate a first result set. An extended database search is performed against a linear database division index to generate a second result set by: identifying a relationship for the field that identifies related fields and identifying documents for the field and the related fields using a linear database division index. The first result set and the second result set are merged to form a merged result set of documents. The documents from the merged result set are displayed in response to the search request.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: An index for large databases is disclosed. Data is grouped into clusters and the clusters are grouped into levels of detail. Analysis results are determined based on progressive data sampling. Sampling is conducted based on the level of detail required and/or the resources (time or computing resources) that are available. Larger, more concentrated clusters, at higher levels of detail, are sampled more sparsely. Smaller, more diffuse clusters, at lower levels of detail, are sampled more intensively. Analysis results, including outlier data, include proportional representation from the whole database up to the level of detail required. Results are quickly determined with specified degree of accuracy, based on initial sampling, and are refined with subsequent sampling.

Abstract: An index for large databases is disclosed. Data is grouped into clusters and the clusters are grouped into levels of detail. Analysis results are determined based on progressive data sampling. Sampling is conducted based on the level of detail required and/or the resources (time or computing resources) that are available. Larger, more concentrated clusters, at higher levels of detail, are sampled more sparsely. Smaller, more diffuse clusters, at lower levels of detail, are sampled more intensively. Analysis results, including outlier data, include proportional representation from the whole database up to the level of detail required. Results are quickly determined with specified degree of accuracy, based on initial sampling, and are refined with subsequent sampling.

Abstract: An index for large databases is disclosed. Data is grouped into clusters and the clusters are grouped into levels of detail. Analysis results are determined based on progressive data sampling. Sampling is conducted based on the level of detail required and/or the resources (time or computing resources) that are available. Larger, more concentrated clusters, at higher levels of detail, are sampled more sparsely. Smaller, more diffuse clusters, at lower levels of detail, are sampled more intensively. Analysis results, including outlier data, include proportional representation from the whole database up to the level of detail required. Results are quickly determined with specified degree of accuracy, based on initial sampling, and are refined with subsequent sampling.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.