A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a “compression unit” using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Time-series data indicating a temporal variation of an index, which indicates a usage state of each of resources that are used by multiple processes, is acquired, and an operation-data matrix including vectors is generated based on the time-series data such that each of the vectors indicates the time-series data at a predetermined time interval and includes as an element the index indicating the usage state of one of the resources at the predetermined time interval. A basis matrix including a predetermined number of basis vectors is generated by performing nonnegative matrix factorization on the operation-data matrix. Component values, which respectively correspond to the resources, indicated by each of the predetermined number of the basis vectors are extracted, and information on the extracted component values is output as usage states of the resources that are used by each of the multiple processes.

A method for traversing hierarchical data is provided. The method may include generating, based on a source table stored in a database, an index for traversing a graph corresponding to the source table. The source table may identify a parent node for each node in the graph. The generating of the index may include iterating over the source table to generate an inner node map. The inner node map may include at least one mapping identifying one or more children nodes descending from an inner node in the graph. The graph may be traversed based at least on the index. The index may enable the graph to be traversed depth first starting from a root node of the graph and continuing to a first child node descending from the root node of the graph. Related systems and articles of manufacture, including computer program products, are also provided.

Methods, apparatus and computer program product for generating tiered search index fields based on a divided group-based communication data corpus in a group-based communication platform are described herein. In some embodiments, the system provides for receiving a group-based communication data corpus, generating a retrieval score, and assigning each group-based communication data object associated with a retrieval score. Each group-based communication data object may meet or exceed a retrieval score threshold of a high retrieval probability corpus. Each group-based communication data object associated with a retrieval score below the retrieval score threshold may be assigned to a low retrieval probability corpus. High and a low retrieval probability search index fields may be generated and associated with the high and low retrieval probability corpus.

Methods, apparatus and computer program product for generating tiered search index fields based on a divided group-based communication data corpus in a group-based communication platform are described herein. In some embodiments, the system provides for receiving a group-based communication data corpus, generating a retrieval score, and assigning each group-based communication data object associated with a retrieval score. Each group-based communication data object may meet or exceed a retrieval score threshold of a high retrieval probability corpus. Each group-based communication data object associated with a retrieval score below the retrieval score threshold may be assigned to a low retrieval probability corpus. High and a low retrieval probability search index fields may be generated and associated with the high and low retrieval probability corpus.

Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

Methods for hybrid job processing may include receiving raw data records stored within a plurality of tables from a plurality of systems of record at a raw data layer within a data exchange. Methods may include generating, based on a data model, a list of dependencies between the plurality of tables. Each table included in a second subset of the plurality of tables may be dependent on at least one table included in a first subset of the plurality of tables. Methods may include processing the first subset of the plurality of tables concurrently with one another. The processing includes modeling the raw data records and transmitting the modeled data records to the model data layer. Methods may include processing each table included in the second subset after completion of processing of the table included in the first subset from which the table in the second subset depends on.

A computer system is configured to receive a plurality of previous user selections by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to determine weights for the various database fields included in the previous user selections and rank subsequent search results for a subsequent search of the database based on the determined weights, where the one or more weights affect a ranking of a search result based on a match associated with the particular database field. The computer system is further configured to receive customized search result layout settings specifying that one or more specified database fields are displayed to the user when the search results are displayed, where one or more weights for the particular database field are based on the customized search result layout settings.

A system and method for generating automated response to an input query received from a user in a human-machine interaction environment are described. The system may comprise an external memory wherein the data is stored and segregated into a plurality of segments in the hierarchical structure. The system may further comprise a processor and a memory coupled with the processor. The processor may execute a plurality of modules stored in the memory. A segment identification module may be configured to identify a relevant segment, from the plurality of segments, matching with a user input. A relevant data determination module may be configured to determine relevant data within the relevant segment matching with the user input. A response generation module may be configured to generate a response with respect to the user input based upon the relevant data.

Methods, systems, and computer program products for providing a contextual dialogue framework over dynamic tables are provided herein. A computer-implemented method includes maintaining a context space for a natural language conversation of a user, wherein the context space comprises a dynamic set of one or more tables used for processing at least one query of the natural language conversation; obtaining an additional table associated with an additional query of the natural language conversation; discovering one or more implicit links between the additional table and the dynamic set of tables; updating the context space with the one or more implicit links; and answering the additional query based at least in part on the updated context space.