International Business Machines Corporation

Chinese Pla General Hospital

Ping Liang

Shuo Li

Xin Yang

Ke Wei Wei

Jian Wei Zhang

Chen Xin Yu

Kewei Wei

Xiaobo Wang

Yu He

Xiao Feng Meng

Xue Bin Cong

Bing Qing Sun

Ting Xu Guan

Peng Hui Jiang

Ping Wang

Min Li

Jie Yu

Jeffrey William Josten

Fangyi Liu

Ke Chen

Zhigang Cheng

Linan Dong

Xiaoling Yu

Zhiyu Han

Shouchao Wang

Aggregated log index-based log record identification is provided by maintaining log files of a database system on a plurality log storage devices, the log files including log records of changes to database objects, indexing the log files to obtain a collection of log indexes, where the indexing includes, for each log storage device, indexing log records stored on the log storage device to obtain a respective log index, then based on a request, identifying log storage devices that store log records for servicing the request, the log storage devices storing log indexes, of the collection of log indexes, that index an aggregate set of log records on the log storage devices, obtaining the log indexes from the log storage devices, and aggregating the log indexes to provide an aggregated log index that provides an index of the aggregate set of log records on the log storage devices.

Log record identification using aggregated log indexes

A workload is generated to verify a system. The generating includes parsing one or more statements of an input test case to create one or more parsed structures. A comparison is performed of at least one parsed structure of the one or more parsed structures and at least one workload structure of at least one existing workload to identify one or more workload structures as matching the at least one parsed structure. Runtime data relating to at least the one or more workload structures identified as matching is obtained. The workload is created based on at least one matching workload structure of the one or more workload structures identified as matching, the at least one parsed structure of the one or more parsed structures and the runtime data.

Generating workloads for system verification

A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

Method for predicting morphological changes of liver tumor after ablation based on deep learning

A self-learning, self-healing database-management system determines that erroneous input, such as an improper SQL query, has caused a critical database condition. The system parses each input statement into a set of tokens, and then translates each set into lines of a failure script. The script is consolidated by cross-referencing each line to infer relationships between input statements. The system then searches historical database logs for previously entered, error-free input similar to the erroneous input. A degree of similarity between the erroneous input and each previously entered input is determined by a computational method that quantifies similarities between the failure script and each script generated from a previously entered input. The system revises the erroneous input to more closely match the most-similar previously entered input and resubmits the corrected input. The results of the resubmission are used to train the system to more accurately correct future input errors.

Universal self-learning database recovery

In an approach for optimizing query executions a processor receives a request to transfer data from a first table to a second table. A processor detects a relationship between the first table and the second table, wherein the relationship is based on a similarity between the first table and the second table. A processor identifies at least a first access path for transferring the data from the first table to the second table. A processor calculates a first cost of transferring the data from the first table to the second table using the first access path based on the similarity between the first table and the second table, wherein the first cost indicates estimated processor usage to transfer the data.

Enhancements for optimizing query executions

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining data of breaches of a geofence by client computer devices to determine respective positions of the breaches; receiving by a database system transaction data from client computer devices, the transaction data defined by insert query data; changing a structure of an index of a certain table of the database system in response to the receiving; generating logging data in response to the receiving of the transaction data; providing using the logging data prediction specifying data, the prediction specifying data specifying one or more change predicted to occur with respect to the index; and modifying the index in dependence on the prediction specifying data.

Predictive database index modification

In an approach to determining an access method for a partition in a partition table, a computer receives a query and determines if there is a partition table utilized by the query. When there is a partition table utilized by the query, then the computer determines that a partition in the partition table meets the plurality of conditions of the query. The computer collects a plurality of partition level statistics for the partition that meets the plurality of conditions of the query. Additionally, the computer determines, based, at least in part, on the plurality of partition level statistics, a cost for one or more access methods for the partition that meets the plurality of conditions of the query. Furthermore, the computer determines, based, at least in part, on the cost for each access method, an access method for the partition that meets the plurality of conditions of the query.

Partition access method for query optimization

Aspects include a method that includes receiving a structured query language (SQL) database command at a processing device configured to manage a table space of a relational database, the database command including a merge statement. The method also includes identifying a group of target records from the database command, and associating each target record with one of a plurality of portions of the table space, and in response to determining that the group of target records is associated with more than one portion of the table space, sorting the target records into a plurality of sub-groups, each sub-group associated with a different portion of the table space. The method further includes generating a sub-task associated with each sub-group, the sub-task prescribing aspects of performing the merge operation using an associated sub-group respective sub-group, and performing all of the sub-tasks on the memory space in parallel.

Parallel execution of merge operations

A computer-implemented method includes identifying one or more database modification statements and identifying one or more operational unit indicators. The one or more operation unit indicators are caused to be generated by the one or more database modification statements. An anticipated operational size is determined. The anticipated operational size is an estimated total number of the one or more operational unit indicators. An anticipated operational throughput rate is determined. The anticipated operational throughput rate is a rate at which the operational unit indicators are expected to be generated. An anticipated total execution time of the one or more database modification statements is determined based on the anticipated operational size and the anticipated operational throughput rate. A corresponding computer program product and computer system are also disclosed.

Estimating database modification

Embodiments of the present invention provide a method, computer program product, and computer system for masking indexes. The method may include masking the index if the index contention exceeds a defined threshold. The method may also include configuring the mask type for the index to insert the index without masking. In response to determining that the mask type for the index is configured to insert the index without masking, the index is masked according to a set of rules.

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Beijing, China

Ping Liang