International Business Machines Corporation

Rui Wang

Jing James Xu

Si Er Han

Lei Gao

Dong Hai Yu

Xiao Ming Ma

Xue Ying Zhang

Ji Hui Yang

Shuo Li

Xiaobo Wang

Sheng Yan Sun

Ying Chen

Kai Lin Li

Fan Wang

Jun Song Wang

Li Cao

Bin He

James J Rhodes

Chun Lei Xu

A Peng Zhang

Yao Dong Liu

Ana Lelescu

Jiang Bo Kang

Jin Rong Wang

Bo Song

A computer implemented method for managing buffer pool usage includes identifying a database comprising one or more buffer pools and a set of database objects, adding a virtual layer between the set of database objects and the one or more buffer pools, wherein the virtual layer is configured to manage buffer pool resource utilization, managing, using the added virtual layer, buffer pool resource utilization with respect to the set of database objects, wherein the managing comprises defining buffer pool usage corresponding to the set of database objects, dynamically mapping database objects to the buffer pools based, at least in part, on the defined buffer pool usage, analyzing, by one or more processors, queues to identify updated buffer pool usage, and adjusting database object allocation according to the identified updated resource usage. A computer program product and computer system corresponding to the method are also disclosed.

Buffer pool resource allocation

An approach is provided in which the approach builds a combination model that includes a normal status model and an abnormal status model. The normal status model is built from a set of time-sequenced normal status records and the abnormal status model is built from a set of time-sequenced abnormal status records. The approach computes a set of time-sequenced coefficient combination values of the normal status model and the abnormal status model based on applying a set of fitting coefficient characteristics to the normal status model and the abnormal status model. The approach performs goal seek analysis on a system using the combination model and the set of time-sequenced coefficient combination values.

Goal seek analysis based on status models

A machine translation system, a ChatOps system, a method for a context-aware language machine identification, and computer program product. One embodiment of the machine translation system may include a density calculator. The density calculator may be adapted to calculate a part of speech (POS) density for a plurality of word tokens in an input text, calculate a knowledge density for the plurality of word tokens, and calculate an information density for the plurality of word tokens using the POS density and the knowledge density. In some embodiments, the machine translation system may further comprise a sememe attacher and a context translator.

Context-aware machine language identification

Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.

Deep forest model development and training

Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

Compatible and secure software upgrades

In an approach to efficient model adjustment utilizing a dual model system, one or more computer processors create a subset of a dataset utilizing a trained primary model; create a secondary model based on the created subset of the dataset; calculate a confidence of a case utilizing the trained primary model, wherein the confidence is a robustness indicator of a model indicating a capacity of the model to meet or exceed performance when applied to the dataset; responsive to the calculated confidence not exceeding a confidence threshold, generate a score of the case utilizing the created secondary model; responsive to an incorrect classification, update the created subset of the dataset with the case; retrain the secondary model utilizing the updated subset of the dataset.

Dual model incremental learning

Facilitating localization of code defect of an application includes receiving a set of element-value pairs generated by running the application with a test case. Further differences are identified between the set of element-value pairs and a baseline data result for the test case. Tree maps associated with respective elements are displayed in the set of element-value pairs, each of the tree maps representing relationship of code entities of the application related to its associated element, wherein one or more of the tree maps are marked out to show the differences thereby identifying potential defective codes of the application that have caused the differences.

Facilitating localization of code defect

A method of data loading for large information warehouses includes performing checkpointing concurrently with data loading into an information warehouse, the checkpointing ensuring consistency among multiple tables; and recovering from a failure in the data loading using the checkpointing. A method is also disclosed for performing versioning concurrently with data loading into an information warehouse. The versioning method enables processing undo and redo operations of the data loading between a later version and a previous version. Data load failure recovery is performed without starting a data load from the beginning but rather from a latest checkpoint for data loading at an information warehouse level using a checkpoint process characterized by a state transition diagram having a multiplicity of states; and tracking state transitions among the states using a system state table.

Failure recovery and error correction techniques for data loading in information warehouses

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Xian, China

Rui Wang