International Business Machines Corporation

Yi Ming Wang

Wen Jie Wang

Zhong Fang Yuan

Tong Liu

Yang Wang

De Shuo Kong

Jing Bo Jiang

Rui Wang

Lu Yu

Yi Chen Zhong

Kun Yan Yin

Guang Qing Zhong

Xin Peng Liu

Yuan Yuan Li

Xue Ying Zhang

Ling Chen

Rong Fu

Xiang Yu Yang

Jian Dong Yin

He Li

Kang Ed Zhang

Hao Sheng

Zhuo Jp Cai

Deng Xin Luo

Miao Guo

Zheng Jie

Rui W W Wang

Liu Yao He

Hui Dong

Yan Fen Liu

Yan Yu Zhang

The embodiments of the present disclosure disclose a computer-implemented method, computer system and a computer program product for detecting and predicting an abnormal log event. In the method, a current event cluster from a plurality of event clusters for a log line in a log file is determined. The plurality of event clusters include at least one abnormal event cluster. Then, a time of event transition from the current event cluster to at least one abnormal event cluster is predicted.

Abnormal log event detection and prediction

In an approach for proactive service group based auto-scaling, a processor collects usage data generated in one or more services in a container platform. A processor predicts access situation and resource utilization of the one or more services based on the usage data. A processor constructs a dynamic correlation topology among the one or more services based on the access situation and resource utilization. A processor identifies associated services correlated with the one or more services based on the dynamic correlation topology. A processor, in response to a service request exceeding a pre-set threshold, expands the one or more services and associated services.

Proactive auto-scaling

A method, computer program product, and system include a processor(s) generates a representation of microservice communications within a network. The processor(s) updates the representation, based on monitoring deployments of microservices within the network. The processor(s) identifies individual service clusters within the network. The processor(s) selects each cluster from the identified service clusters, and for each cluster, evaluates whether to recommend mergers of a portion of the microservices deployed to each cluster. The processor(s) recommends at least one merger for a given cluster of the identified services clusters. Based on the recommending, the processor(s) generates a what-if analysis for the at least one merger.

Microservice measurement and merging

A method, system, and computer program product for implementing automated pressure level detection and correction is provided. The method includes retrieving from sensors, measurement attributes associated with geological conditions occurring during a mining process. Subsequently, threshold levels configured to activate an alarm associated with measurement attributes exceeding the threshold levels are determined and code is executed with respect to the threshold levels. A combined threshold severity level associated with the safety threshold levels is determined and code is executed with respect to the safety threshold levels. A combined clustering level associated with the safety threshold levels is determined and a difference value between the combined threshold severity level and combined clustering level is generated. A relationship between the difference value and a threshold value is determined and automated software and hardware control systems are enabled for controlling machinery associated with the mining process resulting in operation of the machinery.

Automated pressure level detection and correction

Embodiments of the present invention relate to methods, systems, and computer program products for managing a plurality of live comments. A plurality of live comments is obtained for a video, the plurality of live comments being associated with a plurality of fragments in the video, respectively. A plurality of features are extracted from the plurality of live comments, respectively. A knowledge base is generated for the plurality of live comments based on the plurality of features. With these embodiments, the live comments may be managed in an effective way. Further, the knowledge base may provide answers to a user query.

Live comment management

A computer retrieves data from a database. The computer retrieves a Machine Learning (ML) model trained to generate database queries. The computer applies the ML model to generate a primary database query based, at least in part, on a user inquiry available to the computer. The computer retrieves the primary database query, an initial set of data from a database available to the computer. The computer, in response to retrieving the initial set of data, receives feedback assessing the initial set of data. The computer, in response to receiving the feedback, applies a Natural Language Processing (NLP) model to identify query adjustment content within the feedback. The computer revises the primary database query based, at least in part, on the model adjustment content, to generate a secondary database query. The computer retrieves using the secondary database query, a secondary set of data from the database.

Feedback-updated data retrieval chatbot

An approach for extracting non-textual data from an electronic document is disclosed. The approach includes receiving a request to extract a file and converting the file into pixels. The approach creates a pixel map of the converted file and determines one or more density clusters of the pixel map based on image clustering method. Furthermore, the approach determines one or more coordinates of the one or more density clusters and determines one or more candidate information regions based on the one or more coordinates, density of the one or more density clusters. Finally, the approach extracts one or more textual data based on the one or more candidate information regions and outputs the extracted one or more textual data.

Extracting non-textual data from documents via machine learning

Disclosed embodiments provide techniques for computerized technical support. A knowledge graph for a computer application is established. An input query from a user is processed to extract entities used as action identifiers. One or more nodes within the knowledge graph are identified, along with corresponding relationship edges leading to the nodes. When multiple candidate nodes are found that contain information relevant to the input query, a custom clarification statement is created based on the one or more identified relationship edges. The user provides answers to the clarification statement to narrow down which nodes contain the most relevant information. This process may continue, eliminating nodes based on user responses, until a single node remains, corresponding to an action identifier. The action identifier includes action description information that provides technical assistance to a user.

Dynamic workflow with knowledge graphs

An approach is provided for generating graph database parameter settings. Parameter settings for importing data into a graph database are determined. A speed of importing simulated data into the graph database and a system resource usage are determined by executing an importing of the simulated data using the parameter settings and a simulated hardware environment. A reward associated with the parameter settings is determined. Using a policy network that includes convolutional neural networks and based on the reward and the settings, candidates of adjusted parameter settings are determined. Using a Monte Carlo tree search in multiple iterations to estimate changes in speeds of importing the simulated data and changes in system resource usages for candidates of the adjusted parameter settings, rewards for the candidates are determined. Based on the rewards, a candidate is selected as including final parameter settings that optimize an importing speed and system resource usage.

Cognitively generating parameter settings for a graph database

In embodiments, a method includes receiving a first user input to a chat managed by a multiple-round dialogue system, receiving a second user input to the chat, the second user input following the first user input, and embedding the first and second user inputs into first and second nodes, respectively, each node including a multi-dimensional vector. The method further includes determining, based at least in part on the first node and the second node, if a context of the second user input is the same as a context of the first user input, and, based on the determination, generating a chat reply to the second user input.

Cognitive management of context switching for multiple-round dialogues

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Xi'an, China

Yi Ming Wang