Abstract: A method, system, and computer system for performing an operation with respect to a target table are disclosed. The method includes performing first and second jobs, and persist, in one or more deletion vector files, one or more deletion vectors for corresponding rows of the one or more target table files, and obtaining a resulting table based at least in part on the second job resulting file(s). Performing the first job includes determining a set of matching target table files and storing target table information indicating for each of the set of matching target table files, a particular set of rows having matching rows. Performing the second job includes performing a matching action based on matched rows and one or more deletion of vectors associated with previously removed rows of the matching target table files and obtaining the second job resulting file(s).

Abstract: A method, system, and computer system for performing an operation with respect to a target table are disclosed. The method includes performing first, second and a third jobs, and obtaining a resulting table based at least in part on the second job resulting file(s) and third job resulting file(s). Performing the first job includes determining a set of matching target table files and storing target table information indicating for each of the set of matching target table files, a particular set of rows having matching rows. Performing the second job includes performing a matching action based on matched rows and obtaining the second job resulting file(s). Performing the third job includes determining unmatched rows for target table files and storing the unmatched rows in third job resulting file(s).

Abstract: A method, system, and computer system for performing an operation with respect to a target table are disclosed. The method includes performing first and second jobs, and obtaining other resulting files based at least in part on a second set of unmatched rows among the target table and the source table that results from the first set of unmatched rows having been processed in the second job, and obtaining a resulting table based on (i) second job resulting file(s), and (ii) other resulting files. Performing the first job includes determining a set of matching target table files and storing target table information indicating for each of the set of matching target table files, a particular set of rows having matching rows. Performing the second job includes performing a first matching action based on matched rows and a second matching action based on a subset of unmatched rows.

Abstract: A method, system, and computer system for performing an operation with respect to a target table are disclosed. The method includes performing first and second jobs, obtaining one or more other resulting files based at least in part on unmatched rows, and obtaining a set of processed files based at least in part on performing a post-processing operation with respect to the set of resulting files. The set of processed files has less files than the set of resulting files. Performing the first job includes determining a set of matching target table files and storing target table information indicating for each of the set of matching target table files, a particular set of rows having matching rows. Performing the second job includes performing a matching action based on matched rows and obtaining the second job resulting file(s).

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: The interface is to receive an indication to execute an optimize command. The processor is to receive a file name; determine whether adding a file of the file name to a current bin causes the current bin to exceed a threshold; associate the file with the current bin in response to determining that adding the file does not cause the current bin to exceed the bin threshold; in response to determining that adding the file to the current bin causes the current bin to exceed the bin threshold: associate the file with a next bin, indicate that the current bin is closed, and add the current bin to a batch of bins; determine whether a measure of the batch of bins exceeds a batch threshold; and in response to determining that the measure exceeds the batch threshold, provide the batch of bins for processing.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Abstract: An access control anomaly detection system and method to detect potential anomalies in access control permissions and report those potential anomalies in real time to an administrator for possible action. Embodiments of the system and method input access control lists and semantic groups (or any dataset having binary matrices) to perform automated anomaly detection. This input is processed in three broad phases. First, policy statements are extracted from the access control lists. Next, object-level anomaly detection is performed using thresholds by categorizing outliers in the policies discovered in the first phase as potential anomalies. This object-level anomaly detection can yield object-level security anomalies and object-level accessibility anomalies. Group-level anomaly detection is performed in the third phase by using semantic groups and user sets extracted in first phase to find maximal overlaps using group mapping.

Abstract: Energy saving virtualization technique embodiments are presented that save energy by virtualizing a network-connected client computer's computing session (or a part thereof) as a virtual machine (VM) and then migrating it between the client computer and a VM server, depending on whether the computing session is active or idle. The client computer VM is migrated to the VM server when the client computer is deemed to be idle, and the VM is migrated back when the client computer is deemed to have re-entered an active condition. When the VM has been migrated away from the client computer and is residing on the VM server, the client computer is put into a sleep mode, thereby saving energy. However, the user's computing session remains alive on the VM server so that ongoing network connections and other activity are not disturbed.

Abstract: Energy saving virtualization technique embodiments are presented that save energy by virtualizing a network-connected client computer's computing session (or a part thereof) as a virtual machine (VM) and then migrating it between the client computer and a VM server, depending on whether the computing session is active or idle. The client computer VM is migrated to the VM server when the client computer is deemed to be idle, and the VM is migrated back when the client computer is deemed to have re-entered an active condition. When the VM has been migrated away from the client computer and is residing on the VM server, the client computer is put into a sleep mode, thereby saving energy. However, the user's computing session remains alive on the VM server so that ongoing network connections and other activity are not disturbed.

Abstract: An access control anomaly detection system and method to detect potential anomalies in access control permissions and report those potential anomalies in real time to an administrator for possible action. Embodiments of the system and method input access control lists and semantic groups (or any dataset having binary matrices) to perform automated anomaly detection. This input is processed in three broad phases. First, policy statements are extracted from the access control lists. Next, object-level anomaly detection is performed using thresholds by categorizing outliers in the policies discovered in the first phase as potential anomalies. This object-level anomaly detection can yield object-level security anomalies and object-level accessibility anomalies. Group-level anomaly detection is performed in the third phase by using semantic groups and user sets extracted in first phase to find maximal overlaps using group mapping.