An approach for managing replicated data is presented. A current usage of resources in a system and a threshold usage of the resources are determined. Based on inter-replica correlation(s) and inter-data correlation(s) specified by a unified replication metadata model, a proper subset of replicas included in a plurality of replicas is indexed by (i) if the current usage is less than the threshold usage, determining an expected additional resource usage due to performing an indexing task online and based on the expected additional resource usage, determining a resource affinity score for performing the indexing task online, or (ii) if the current usage is greater than or equal to the threshold usage, determining an expected resource usage due to performing the indexing task offline and based on the expected resource usage, determining a resource affinity score for performing the indexing task offline.

A method of automated domain knowledge structure generation includes crawling learning materials. The method may include extracting structural information from the learning materials. The method may include extracting knowledge points from the learning materials. The method may include inferring dependency relationships between the knowledge points. The method may include aligning one or more of the knowledge points with one or more of the learning materials. The method may also include generating a domain knowledge structure. The domain knowledge structure may include the extracted knowledge points organized at least partially according to the inferred hierarchy and dependency relationships. The extracted knowledge points may include the aligned learning materials.

Methods, computer program products, and computer systems for configuration management are disclosed. Such methods, computer program products, and computer systems include identifying an associative template node and setting a configuration parameter to a parameter value, based on a template association. The associative template node is a node in a hierarchy of an associative template. The identifying indicates a template association between the associative template node and a node in a hierarchy of hierarchically-organized unstructured data (HOUD). The parameter value is maintained in the associative template node, and the node comprises the configuration parameter.

Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.

A computer-implemented method, according to one approach, includes: receiving one or more streams of system log information from one or more microservices, and comparing the received system log information to compressed templates. One or more blocks of the system log information that correspond to one or more of the compressed templates are identified. All static text is removed from the identified blocks. Moreover, runtime variables from the identified blocks are inserted into respective ones of the compressed templates. The streams of system log information are further updated by replacing the identified blocks with the compressed templates having the runtime variables therein.

The exemplary embodiments may provide a search tool that can locate customer issues in call transcripts and agent notes and can provide an accurate count of how often such issues appear in the call transcripts and agent notes. The exemplary embodiments may improve the speed with which the search of documents is performed. The exemplary embodiments rely upon a document matrix that is computed once for a given corpus of documents and a given vocabulary of the documents. The document matrix may be used across multiple queries. The exemplary embodiments also account for similar terms in processing a query. The exemplary embodiments may use a word coverage factor to improve the relevance of the search results returned by the search tool. The word coverage factor acts as a multiple factor that computes the fraction a query terms that are present in a document.

A system for generating a virtual assistant in a messaging user interface, the system comprising a computing device configured to receive, from a sensor, a change in the biological state of a plurality of users; prioritize an agenda action relating to food delivery of a plurality of agenda actions contained within a user agenda list as a function of the change in the biological state of the plurality of users; initiate a virtual message user interface between a user client device and the computing device; select a conversation profile for the virtual assistant, wherein the conversation profile identifies instructions relating to the food delivery; generate a communication to the user comprising the instructions relating to the food delivery; identify a third-party device as a function of the instructions relating to the food delivery; share information with the third-party device as a function of the instructions relating to the food delivery.

An application receives a natural language query from a user into a cell of a notebook environment and responsively determines a set of precedent cells and a profile of the user. The application determines a portion of the data warehouse graph that corresponds to the natural language query. The application primes the large language model with priming context that is based on the portion of the data warehouse graph that corresponds to the natural language query, the precedent cells from which the code cell depends, and the profile of the user, the priming resulting in a primed large language model. The application inputs the natural language query into the primed large language model and receives, as output from the large language model, a response to the natural language query. The application provides the response to the natural language query to the user.

In some aspects, the techniques described herein relate to a method including: receiving, at a platform, an electronic document and corresponding metadata; encoding the electronic document; sending the encoded document as a byte stream to a unit extraction service; decoding, by the unit extraction service, the byte stream into a string file; standardizing, by the unit extraction service, partition separation characters; determining, by the unit extraction service and based on the partition separation characters, a value of a first key; assigning, by the platform, a value of the corresponding metadata as a value of a second key; indexing the first key, the value of the first key, the second key, and the value of the second key in a search index; and providing a search function via an interface of the platform, wherein the search function searches the search index.

ADVANCED RESPONSE PROCESSING IN WEB DATA COLLECTION discloses processor-implemented apparatuses, methods, and systems of processing unstructured raw HTML responses collected in the context of a data collection service, the method comprising, in one embodiment, receiving raw unstructured HTML documents and extracting text data with associated meta information that may comprise style and formatting information. In some embodiments data field tags and values may be assigned to the text blocks extracted, classifying the data based on the processing of Machine Learning algorithms. Additionally, blocks of extracted data may be grouped and re-grouped together and presented as a single data point. In another embodiment the system may aggregate and present the text data with the associated meta information in a structured format. In certain embodiments the Machine Learning model may be a model trained on a pre-created training data set labeled manually or in an automatic fashion.