Abstract: Various embodiments of the teachings herein include a method for detecting data anomalies. A method may include: receiving test data; and matching the test data with a data rule determined on the basis of historical data having a shared data type with the test data. The data rule contains an antecedent and a consequent predicate set. An intersection of the antecedent predicate set and the consequent predicate set is an empty set. The antecedent predicate set contains at least one antecedent predicate. The consequent predicate set contains at least one consequent predicate. When the data to be tested satisfies all the antecedent predicates in the antecedent predicate set and fails to satisfy at least one consequent predicate in the consequent predicate set, the test data is flagged as anomalous due to failure to satisfy the data rule.

Abstract: Disclosed herein is a software tool that may be used to create and navigate through visualizations of an organization's data as embodied in a digital duplicate. These “smart interactions” may allow a user to visually and intuitively traverse the semantic network to dynamically provide on-demand visualization of the business data. Based on the traversal of the network, the network can be simplified and/or subnetworks can be created. As part of the visualization, “perspectives” can be created based on focal points (i.e., nodes) in the network. The focal points may be used as a starting point for the traversal, and functions (e.g., aggregates) can be dynamically applied to produce subnetworks to provide data for visualizations.

Abstract: Systems and methods of the present disclosure enable database search. The systems and/or methods may include receiving a search query that includes an input document having text. Word embeddings are generated within the input document, where the word embeddings include vector representations of words in the text of the input document. An average input document word embedding vector is determined for the word embeddings of the input document. A set of stored documents is accessed, where each stored document includes a stored text has a particular average stored document word embedding vector. A similarity model is used to determine a similarity metric measuring the similarity between the input document and each stored document based on the average input document word embedding vector and the particular average stored document word embedding vector of each stored document.

Abstract: A data tree for managing data files of a data table and performing one or more transaction operations to the data table is described. The data tree is configured as a KD-epsilon tree and includes a plurality of nodes and edges. A node of the data tree may represent a splitting condition with respect to key-values for a respective key. A leaf node of the data tree may correspond to a data file for a data table that includes a subset of records having key-values that satisfy the condition for the node and conditions associated with parent nodes of the node. A parent node may correspond to a file including a buffer that stores changes to data files reachable by this parent node, and also includes dedicated storage to pointers of the child nodes. By using the data tree, the data processing system may efficiently cluster the data in the data table while reducing the number of data files that are rewritten.

Abstract: Detecting duplicate tables by converting relational tables to knowledge graphs; and mapping nodes in the knowledge graphs to sources in the relational tables. The method may further include applying graph matching to the knowledge graphs; and assessing degree of matching between matched knowledge graphs.

Abstract: A system for distributed storage includes a distributed file system for distributed storage of data, distributed metadata service nodes configured to run in different processes and configured to manage metadata of the data, in which the distributed metadata service nodes are configured with corresponding condition files, control nodes configured to compete for a permission to update the condition file to become a master control node, and obtain an operation permission on the data as the master control. The master control node is configured to utilize the operation on the data to control consistency of the condition files of the distributed metadata service nodes.

Abstract: In a database replication operation, a source end device obtains at least two groups of transaction logs from a log file of a source end database in parallel, where the at least two groups of transaction logs include a first group of transaction logs and a second group of transaction logs. The first group of transaction logs includes at least a first transaction log and a second transaction log that are adjacent to each other, and the second group of transaction logs includes at least a third transaction log and a fourth transaction log that are adjacent to each other, and a generation time point of the second transaction log is earlier than a generation time point of the third transaction log. The source end device then sends the at least two groups of transaction logs to a destination end device.

Abstract: A document retrieval system that retrieves documents, with concepts of the documents taken into account, is provided. The document retrieval system (100) includes an input unit (101), a first processing unit (102), a storage unit (105), a second processing unit (103), and an output unit (104). The input unit (101) has a function of inputting a first document (20), the first processing unit (102) has a function of creating a first graph structure (21) from the first document (20), the storage unit (105) has a function of storing a second graph structure (11), the second processing unit (103) has a function of calculating a similarity between the first graph structure (21) and the second graph structure (11), the output unit (104) has a function of supplying information, the first processing unit (102) has a function of dividing the first document (20) into a plurality of tokens, a node and an edge of the first graph structure (21) have a label, and the label includes the plurality of tokens.

Abstract: Implementations relate to systems and methods for aggregating and presenting data related to geographic locations. Geotag data related to geographic locations and associated features or attributes can be collected to build a regional profile characterizing a set of locations within the region. Geotag data related to the constituent locations, such as user ratings or popularity ranks for restaurants, shops, parks, or other features, sites, or attractions, can be combined to generate a profile of characteristics of locations in the region. The platform can generate recommendations of locations to transmit to the user of a mobile device, based for instance on the location of the device in the region as reported by GPS or other location service and the regional profile. Geotag data can include audio data analyzed using region-specific terms, and user recommendations can be presented via dynamic menus based on regional profiles, user preferences or other criteria.

Abstract: Disclosed herein are systems and methods for graph-based dynamic information retrieval and synthesis, particularly for enhancing the response generation of chatbot applications using large language models (LLMs). The methods involve receiving a user query, generating a query embedding, and accessing a knowledge graph comprising nodes representing documents with associated document embeddings. A set of documents is retrieved based on similarity scores, and a distance matrix is constructed to calculate shortest-path distances between nodes. Documents are clustered to identify a largest cluster from which content is retrieved to formulate a response. The disclosed embodiments improve upon retrieval augmented generation (RAG) by leveraging the hierarchical relationships of a knowledge graph and node interconnectedness to provide contextually coherent information and mitigate the inclusion of information from irrelevant or superficially related documents in LLM responses.

Abstract: Provided are a system for recommending related data based on similarity, and a method thereof, the system including: a data collection device; an event extraction device; a data cleansing device; an event vector generation device; an artificial intelligence learning device; and a similar data recommendation device. The present disclosure is directed to providing a system for recommending related data based on similarity and a method thereof, wherein unstructured open data on a webpage is collected to automatically generate an event label for determining a similarity relation, and an artificial intelligence (AI)-based model is trained to group and recommend semantically similar related data, thereby effectively helping users including data scientists who want to see meaningful results through open data.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for transaction processing. In an example method, a system receives a first transaction. The system is configured as a hybrid database to dynamically switch between a stand-alone mode and a distributed mode. The system determines whether the first transaction is a stand-alone type of transaction or a distributed type of transaction. In response to determining that the first transaction is a stand-alone type of transaction, the system performs one or more operations of the first transaction in the stand-alone mode. In response to determining that the first transaction is a distributed type of transaction, the system performs one or more operations of the first transaction in the distributed mode.

Abstract: A solution for automated column type annotation maps each column contained in a table to a column annotation class. A pre-processor transforms the table into a numerical tensor representation by outputting a sequence of cell tokens for each cell in the table. A table encoder encodes the sequences of cell tokens and a column annotation label for each column into body cell embeddings. A body pooling component processes the body cell embeddings to provide column representations. A classifier classifies the column representations to provide for each column, confidence scores for each column annotation class. The method concludes with comparing the highest confidence score for each column with a threshold, and, if the highest confidence score for each column is above the threshold, annotating each column with the respective column annotation class.

Abstract: Auto-parallel-load techniques are provided for automatically loading database objects from an on-disk database system into an in-memory database system. The auto-parallel-load techniques involve a pipeline that includes several components. In one implementation, each of the pipeline components is configured to receive, extract information from, and add information to, a “state object”. One or more of the pipeline components include logic that is based on the output of a corresponding machine learning model. The machine learning models used by the pipeline components may be trained from training sets from which outliers have been excluded, and may be used as the basis for generating linear models that are used during runtime, to produce estimates that affect the parameters of the auto-parallel-load operation.

Abstract: The present invention is a system and method for creating a digital and searchable inventory of physically stored, items, objects, or documents. Methods and apparatus of the present disclosure may image physical items as a person removes them from a container. This person may also provide verbal descriptions of items being imaged. Artificial intelligence, such as object recognition, optical character recognition, or natural language processing, may then applied to the captured data to classify the items into a searchable hierarchy. The digital classification of the items and their attributes may be linked to a machine-readable ID, such as a barcode or other identifier, that may allow the items scanned to be associated with a specific physical storage device. This may include using identifiers that were originally on a container and by associating new identifiers with the container.

Abstract: Methods and systems for disambiguating musical artist names are disclosed. Musical-artist-release records (MARRs) may be input to a multi-modal artificial neural network (ANN). Each MARR may be associated with a musical release of an artist, and may include a release ID and an artist ID, and release data in categories including music media content and metadata categories including sub-definitive musician name of the artist and release subcategories. All n-tuples of MARRs may be formed, and for each n-tuple, the ANN may be applied concurrently to each MARR to generate a release feature vector (RFV) that includes a set of sub-feature vectors, each characterizing a different category of release data. For each n-tuple, the ANN may be trained to cluster in a multi-dimensional RFV space RFVs of the same artist ID, and to separate RFVs of different artist IDs. The MARRs and their RFVs may be stored in a release database.

Abstract: The present disclosure generally relates to systems and methods for searching media content. In some implementation examples, a search system receives an input query, generates a query embedding of the input query, and generates a bias mitigation transformation associated with a sensitive attribute. Based on the query embedding and the bias mitigation transformation, the search system generates a transformed query embedding that suppresses at least a portion of the query embedding related to the sensitive attribute. Using the transformed query embedding, the search system executes a similarity search in a media embedding model to identify one or more media embeddings that are similar to the transformed query embedding and transmits the one or more media embeddings.

Abstract: According to an aspect, there is provided a computer-implemented method of presenting patient information, the method comprising identifying a first pathway associated with a patient of interest, the first pathway comprising one or more nodes; identifying at least one patient parameter type that is required by a node in the identified first pathway; querying a patient record for the patient of interest to determine if the patient record includes a patient parameter value for the identified at least one patient parameter type; if the patient record includes a patient parameter value for an identified patient parameter type, retrieving the patient parameter value from the patient record and rendering a display including the retrieved patient parameter value; and if the patient record does not include a patient parameter value for an identified patient parameter type, rendering a display including an indication of the identified patient parameter type.

Abstract: Examples described herein generally relate to database systems for storing and processing both small values that are smaller than size of a database column and large objects that exceed the size of the database column. A database management system (DBMS) determines that a value to be stored in a database is a large object having a size larger than a column of the database. The DBMS stores the value as a large object in an external storage associated with a token stored in the column of the database. The token includes information for processing the large object. A vector processing engine associated with the external storage processes the large object based on the information in the token in response to a database command from the DBMS on multiple records represented as a vector.

Abstract: The subject technology receives a set of values for a data set corresponding to a request for determining a Pearson correlation. The subject technology determines a number of rows from the data set. The subject technology determines a number of execution nodes to execute the request. The subject technology determines a set of shards of rows from the data set. The subject technology, for each shard from the set of shards, sends a particular user defined table function (UDTF), including a particular shard of rows, to a different execution node to perform a set of operations. The subject technology provides a set of output values of each particular UDTF corresponding to each shard from the set of shards in a second UDTF. The subject technology sends the second UDTF to a particular execution node to perform an aggregate operation using the set of output values of each particular UDTF.