Abstract: According to one embodiment, a method for detecting, extracting information from, and classifying tables within an original image includes: pre-processing the original image to generate processed image data; detecting one or more tables within the processed image data; extracting the one or more tables from the processed image data; and classifying either: the one or more extracted tables; portions of the one or more extracted tables; or a combination thereof. Additional techniques for pre-processing image data to facilitate detection, extraction of information from, and classification of tables (or portions thereof) are also featured. Corresponding systems and computer program products are included in the scope of the invention. The inventive concepts are also applicable to tabular data arrangements that may not fit a strict definition of a “table.

Abstract: According to one embodiment, a computer-implemented method for classifying one or more tables and/or one or more tabular data arrangements depicted in image data includes: training a machine learning model, using a training dataset representing a plurality of different tables and/or tabular data arrangements, based at least in part on a plurality of recognized textual elements within the training dataset; and outputting a trained classification model based on the training, wherein the trained classification model is configured to classify one or more tables and/or one or more tabular data arrangements represented within a test dataset according to: one or more table classifications; one or more tabular data arrangement classifications; and/or one or more column classifications; and classifying the one or more tables and/or the one or more tabular data arrangements represented within the test dataset using the trained classification model.

Abstract: A computer-implemented method for generating results for a client-requested query involves receiving a query produced by a client communication device, generating a result for the query in response to reception of the query, determining one or more predictive follow-up requests before receiving an actual follow-up request from the client device, and initiating retrieval of information associated with the one or more predictive follow-up requests, and transmitting at least part of the result to the client device, and then transmitting to the client device at least part of the information associated with the one or more predictive follow-up requests.

Abstract: Provided is a method including receiving object IOs for a target device, grouping the object IOs using a first plurality of input parameters, associating a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters, storing a first data entry including the tracking parameter, the first plurality of input parameters, and the performance parameter in a database, extracting a plurality of data entries from the database, the plurality of data entries including the first data entry, training a training model using one or more of the plurality of data entries, cross-validating the training model to determine a degree of error reduction of the training model, performing a model check to compare the training model to an inferencing model, and updating the inferencing model based on the model check.

Abstract: Techniques of content unification are disclosed. In some example embodiments, a computer-implemented method comprises: determining clusters based a comparison of a plurality of audio content using a first matching criteria, each cluster of the plurality of clusters comprising at least two audio content from the plurality of audio content; for each cluster of the plurality of clusters, determining a representative audio content for the cluster from the at least two audio content of the cluster; loading the corresponding representative audio content of each cluster into an index; matching the query audio content to one of the representative audio contents using a first matching criteria; determining the corresponding cluster of the matched representative audio content; and identifying a match between the query audio content and at least one of the audio content of the cluster of the matched representative audio content based on a comparison using a second matching criteria.

Abstract: A method for processing non-standardized data in a relational database may include identifying, within a sequence of operations forming a query pipeline for executing a query, a first operation ingesting a non-standardized data. In response to identifying the first operation ingesting the non-standardized data, a second operation may be inserted before the first operation. The non-standardized data may be output by a third operation preceding the first operation or a source external to the query pipeline. The second operation may serialize the non-standardized data for ingestion by the first operation, for example, by generating a relational table populated by the non-standardized data. The query may be executed by performing the sequence of operations included in the query pipeline. Related systems and computer program products are also provided.

Abstract: This disclosure provides a computer-implemented method, a computer system and a computer program product for database compression oriented to combinations of fields of a database record. One or more combinations of fields of a record of a database are determined that satisfy a frequency criterion indicating that access frequencies of the one or more combinations of fields are higher than an access frequency threshold. The record is reorganized based on the one or more combinations of fields to store fields of each combination of the one or more combinations of fields in a respective contiguous storage space. The reorganized record is compressed by applying a compression scheme to the one or more combinations of fields.

Abstract: Techniques and solutions are described for storing and processing metadata. A target system sends a request to a source system to identify extensions to one or more specified database artefacts. The source system identifies one or more software objects that include metadata extensions to the one or more specified database artefacts and sends at least a portion of such metadata elements to the target system in response to the request. The source system can expose APIs, such as APIs in a standard format, to the target system to assist in requesting and retrieving metadata from the source system.

Abstract: Techniques and systems can analyze information associated with instructions to sort data to determine an identifier common to at least a plurality of individual instructions to sort the data. A correspondence of the identifier to a sort identifier used to sort the data can be determined. Based on the determined correspondence, the techniques and systems can sort the data based on the identifier.

Abstract: A system and method for presenting video asset information to a viewer to assist the view in selecting a video asset for viewing. The video assets can be available from a plurality of different video asset sources, such as VOD (video on demand), PVR (personal video recorders) and broadcast (including over the air, cable, and satellite). Images from the video assets are displayed in a uniform manner, along with information about the video assets. The information includes data in a metadata category. The view can select one of the video assets for viewing, but also can navigate using metadata categories such as genre, actors, director etc. This allows a much easier and natural navigating and selection process for viewers.

Abstract: Materialized views may be generated at a managed materialized view platform that accepts data streams as a source. When updating a materialized view with a data stream as a source, a stored portion of the stream is used to maintain a history of changes to be made. When a join operation needs to utilize the data stream source to determine an update to the materialized view, the data stream is preserved to complete the join and update the materialized view.

Abstract: Systems, computer-implemented methods or computer program products to facilitate receiving results of a semantic structured query language (SQL) query and employing sparse hash-table based sketches to interpret a semantic structured query language (SQL) query result. A computing component stores a first space-efficient structure sketch in a compressed serialize form. The computing component can load a second space-efficient data structure sketch along with the first space-efficient data structure sketch and can compute one or more interpretability scores by extracting co-occurrence information from the first space-efficient data structure sketch. The second space-efficient data structure sketch can include a sketch for containment check.

Abstract: Servicing database requests using canonicalized tables including receiving, from a client computing system, a request for a table from a database; determining that a canonical version of the requested table matches a canonicalized table in a canonical table repository; and providing, to the client computing system, the matching canonicalized table that is transformed based on the received request for the table.

Abstract: A system and method for committing preauthorized database transactions generates a preauthorized database transactions based on a received parameter and a parameter value. The method further includes storing the generated preauthorized database transaction in a temporary storage; accessing a reference database to determine a current value of the received parameter; determining that a current value of the received parameter matches the received parameter value; and committing the preauthorized database transaction to a database.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: Described is a system for producing a causal map from a body of text. The system receives multiple textual documents as input. Pairs of cause-effect phrases are extracted from the textual documents and embedded into a vector space. The embedded data is clustered into clusters using a probabilistic technique. A causal map having nodes and edges is generated from the clusters. Using the causal map, causal connections between clusters are obtained, where each node represents an event and each edge represents a causal relationship between events. The causal map is provided as an interactive graph.

Abstract: The invention relates to computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. Embodiments of the systems and the methods further provide for generating responses to specific questions relating to certain rates and terms, such as LIBOR, as well as complex legal constructs, such as interest rate fallback waterfalls, for risk assessment and mitigation. The present invention performs environmental, social, governance analytics on asset-backed securities including commercial mortgage-backed securities.

Abstract: Incoming data requests from the perspective of the data lake, are gathered and analyzed to determine the usage of the data. Using the perspective of the data lake avoids the technical challenge of analyzing data usage by different computation points, which are at different locations, perform hard-to-track different operations, and are often reachable only through complicated access protocols. Another technical challenge of mapping between an object path and a table path is solved by generating object path datasets and table path datasets at different levels of abstractions. A comparison is performed, iteratively, from a lower level of granularity and the granularity is increased in the progressive steps. Matches from iterations are unionized to generate a final matching data. Observability metrics are generated using final matching data and are used to perform downstream operations such as controlling data table access, moving data tables to cold storage, decommissioning unused pipelines, etc.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: The subject technology receives a command to commit a table in a different table format on an external volume. The subject technology generates a first snapshot of the table on internal storage. The subject technology generates a first list of metadata files on the internal storage. The subject technology generates, based on the first list of metadata files, a first set of metadata files on the internal storage. The subject technology generates a second snapshot of the table on the external volume. The subject technology generates a second list of metadata files on the external volume. The subject technology generates, based on the second list of metadata files, a second set of metadata files on the external volume. The subject technology generates a first set of data files in a different file format on the external volume.