Not all facts in a data warehouse are described by the same set of dimensions. However, there can be associations between the data dimensions and other dimensions. By maintaining a set of relationships that are capable of linking the dimensional keys used in existing data to the keys of an associated dimension, a data transformation can be constructed that summarizes by the original and by the associated dimensions in feeds in an analytical data mart (cube) that includes all the dimensions. This cube can then be consolidated and analyzed in a slice-and-dice fashion as though all the dimensions were independent. Data transformed in this manner can be analyzed alongside data from a source that is keyed by all of the dimensions.

In accordance with an embodiment, described herein are systems and methods for supporting slowly changing dimensions and metadata versioning in a multidimensional database, comprising. A system can comprise a computer that includes one or more microprocessors, and a multidimensional database server executing on the computer, wherein the multidimensional database server supports at least one hierarchical structure of data dimensions. A data dimension can slowly change over time. When such changes occur, metadata associated with the data dimension can be updated. Advantageously, a current snapshot of the data structure can allow searching of previous changes to the slowly changing dimension based upon the metadata.

Systems and methods are disclosed for processing and executing queries in a data intake and query system. An indexing system of the data intake and query system receives data and stores at least a portion of it in buckets, which are then stored in a shared storage system. The indexing system merges multiple buckets to generate merged buckets and uploads the merged buckets to the shared storage system.

Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

A transaction request compliant with a first version of a journal schema of a multi-data-store storage system is received at a journal manager. The journal schema indicates attributes of data objects which may be materialized at various data stores of the system. The journal manager stores an entry in the system's journal if the transaction meets acceptance criteria. Writes indicated in the entry are materialized at the data stores after verifying that the entry is compliant with the journal schema. After verifying that member data stores have approved a proposed change to the journal schema, another entry indicating a different version of the journal schema is added to the journal. Client-side components of the system obtain the current version of the journal schema to prepare the transaction requests.

The query method and device suitable for an On-Line Analytical Processing (OLAP) query engine includes a client agent module, a query pattern matching module, a query distributed execution module, and a pre-aggregation module. The query pattern matching module is configured to obtain an MDX query request received by an OLAP query engine and process the MDX query request to generate at least one set of aggregation query sets. The one set of aggregation query sets includes a plurality of aggregation query requests. The query distributed execution module is configured to perform concurrent processing on the plurality of aggregation query requests. The aggregation query requests are arranged corresponding to the aggregation query results. An efficient OLAP query execution engine can deal with complex OLAP queries of various reporting system. Therefore, the execution efficiency of MDX query can be significantly enhanced, and analysis requests of the reporting systems are rapidly responded.

A system for updating a hypercube includes an interface and a processor. The interface is configured to receive an indication to update a cell of the hypercube. The processor is configured to determine a primary dimension value associated with the cell; determine a group of dependencies based at least in part on the primary dimension value, wherein a dependency of the group of dependencies comprises one or more primary dimension values and a pattern; for the dependency of the group of dependencies, determine a set of source locations based at least in part on the one or more primary dimension values and the pattern; and mark the set of source locations as invalid.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

A spreadsheet supports formulas in cells that trigger queries of a data source. The parameters for queries can include or depend on values in other cells in the spreadsheet. Thus, the precise query submitted to the data source is dynamic, being dependent on the data and formulas in the spreadsheet. Furthermore, on receiving the query results, they are added to cells in the spreadsheet, which can be parameters for other queries defined in other cells. Changing the value of a single cell can automatically trigger an update of an arbitrarily deep hierarchy of calculations that can include an arbitrary number of data source queries.