Abstract: Techniques and solutions are provided for improved data modelling, including techniques that facilitate data model comparison, integration, or deployment. A core data model is created that has a plurality of elements, which can represent semantic concepts, including relationships between semantic concepts. A plurality of domain models are mapped to core data model elements. In particular, when a domain model element is to be created, the element is mapped to a core data model element. When a relation is to be established between two domain model elements, the relation is mapped to a relation type represented in the core data model. Mapping to a core data model can be enforced as part of domain model creation, allowing different domain models to be compared, since domain elements in different domain models that represent a common semantic will be mapped to the same core data model element.

Abstract: Methods, systems, and devices for data management are described. A data management system (DMS) may determine to generate a backup instance of an application that includes multiple user accounts. The DMS may identify a time window for generation of the backup instance based on a set of backup parameters associated with the application. The DMS may calculate respective runtime durations for the user accounts based on respective quantities of data objects in the user accounts and respective backup job types associated with the data objects. The DMS may determine respective values of a semaphore parameter based on the respective runtime durations and the time window, where the semaphore parameter defines a threshold quantity of data objects to be processed in parallel. The DMS may generate the backup instance of the application based on processing the user accounts in accordance with the respective values of the semaphore parameter.

Abstract: A method may include receiving, at a first time, an instruction to execute a stored procedure at a database system, retrieving a schema of a structure of a database used by a management system of a client entity, determining an identity of a management system and a type of data to retrieve for the client entity. The method may also include, responsive to determining the identity and the type of data to retrieve, determining a criteria to use in a dynamic query based on the identity and the type of data to retrieve. The method may also include, responsive to determining the criteria, generating, in the stored procedure, the dynamic query comprising the criteria, and executing the stored procedure to retrieve raw data from the database used by the management system and transform the raw data to transformed data using the dynamic query.

Abstract: In some implementations, a device may store a set of data in a first database associated with a fixed storage duration and a storage parameter that restricts access associated with adding or modifying information stored in the first database. The device may process a subset of data from the set of data, wherein processing the subset of data includes performing one or more data processing operations performed via data processing operation documentation that defines the one or more data processing operations. The device may store the data processing operation documentation in a second database associated with a variable storage duration for data stored in the second database. The device may provide the first database and the second database as data sources for a data searching application to enable the first database and the second database to be searchable via a dashboard associated with the data searching application.

Abstract: A computer-implemented method, according to one approach, includes: receiving a structured log of information, and converting the structured log of information into a filesystem interface. Resources in the information are identified by inspecting the filesystem interface. The identified resources are also translated into an unstructured object by unmarshalling the identified resources. Furthermore, the unstructured object is made available by mapping the unmarshalled resources.

Abstract: Systems, methods, and apparatuses for detecting anomalies using clusters are described. In some examples, a method includes receiving a request to perform anomaly detection using a plurality of clusters; receiving a data point; determining when the received data point is a part of one of the plurality of clusters utilizing a distance to centers of the one or more clusters, wherein: when the received data point is determined to belong to a normal cluster, assigning the received data point to the determined cluster, updating the cluster, and updating a history for the cluster, when the received data point is determined to belong to an anomalous cluster, raising an anomaly, updating the cluster, and updating a history for the cluster, and when the received data point is determined to not belong to any cluster, raising an anomaly.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for server-less complex event processing (CEP). For instance, the method may include hosting a plurality of server-less functions, the plurality of server-less functions including at least a first group of server-less functions, the first group of server-less functions including a driver function and at least one lookup function including a first lookup function; receiving data streams; and processing the data streams, using the driver function and/or the at least one lookup function, to publish a CEP data stream.

Abstract: Methods, systems, and computer-readable storage media for automatic index creation for relational database systems. Query statements from a relational database are processed to generate query patterns from the query statements. Vectorization of the query patterns is performed to transform each query pattern into a numerical vector. A clustering algorithm is executed to cluster the numerical vectors into multiple clusters. Each cluster has a respective cluster center. A frequent query pattern is determined, for at least some of the multiple clusters, that corresponds to a respective cluster center. Active columns in the frequent query patterns are determined and a database index is automatically created for each active column that does not currently have a database index.

Abstract: Examples described herein generally relate to a scalable multi-tier storage system. An entry may be added and/or deleted within the storage system. To delete an entry, the storage system may determine whether the entry corresponds to the file or the directory based on directory metadata, and request deletion of the directory metadata associated with the entry from the directory volume based on determining that the entry corresponds to the directory, and further requesting deletion of the file from a file volume based on a determination that the entry corresponds to the file. To add a file, the storage system may generate directory metadata associated with the entry in the directory volume based on a determination that the entry corresponds to the directory, and may further allocate file metadata in the file volume based on a determination that the entry corresponds to the file.

Abstract: Systems and methods are provided that extract information from IMS log records to reduce the amount of data transmitted and input to an analysis engine. An example method includes writing IMS log records matching log types identified in an extraction list to a file within an IMS control region of a mainframe computer and outside of the IMS control region, and reading records from the file. For each record read, the method may also include extracting fields of interest from the log record based on fields of interest associated with the log type of the log record in the extraction list and a data-to-field mapping for the log type, converting the fields of interest to a predetermined format based on the data-to-field mapping, and writing at least one field of interest to an output file. The method may also include transmitting the output file to an analytics engine for processing.

Abstract: A method comprises storing a plurality of accounts; and generating a hierarchical data structure from the plurality of accounts by: identifying a first set of accounts and a second set of accounts responsive to the first set of accounts and the second set of accounts satisfying a first set of rules; linking the first set of accounts to a first entity profile and the second set of accounts to a second entity profile; and responsive to the first plurality of profile attribute-value pairs and the second plurality of profile attribute-value pairs of the second entity profile satisfying a second set of rules, linking the first entity profile and the second entity profile to a household entity profile by inserting a household identifier into a household attribute-value pair of each account of the first set of accounts and the second set of accounts.

Abstract: Embodiments are directed to managing data. A data model that includes data type nodes and relationship edges may be provided. Other data types and other data type relationships may be provided and included in the data model. If a portion of the nodes in the data model may be downstream of leaf nodes in the graphlet: the data model may be traversed to visit the downstream nodes; shortcut edges may be generated to each downstream node associated with shortcut nodes. If a second portion of the nodes in the data model may be upstream of the leaf nodes: the data model may be traversed upwards from the leaf nodes; other shortcut edges may be generated to each node visited in the upwards traversal associated with shortcut nodes.

Abstract: Whether a document file is tampered and the like can be determined without imposing a burden on a user. A hash value calculation unit 103 adds contents of update to an original document as incremental update in order of update, and calculates a hash value of the document file for each of the original document and a document for adding the updated content and a hash value of a document file the hash value and the document file ID, identifying the document file, of which are registered in a blockchain network. An ID acquisition unit 102 acquires the document file ID identifying a received document file, from the document file. A hash value acquisition unit 105 acquires the hash value stored in the latest block among blocks storing the document file IDs from the blockchain network. A tampering determination unit 106 determines whether the document file is tampered by determining whether the hash value calculated and the hash value acquired from the blockchain network 300 match.

Abstract: Techniques for compressing a data set in a manner such that the data set is represented as a geometric filter are disclosed. Here, the geometric filter has a small memory footprint, thereby enabling the data set to be indexable without consuming a large amount of memory. One bits can be represented within a bit vector portion of the geometric filter or in an index portion of the geometric filter. Techniques are also provided to determine a level of similarity between two data sets, where the data sets have been compressed such that the data sets are representable as geometric filters. The data sets can be indexed without consuming large amounts of memory, and the similarity determination can be performed rapidly. Approximate minimum spanning trees (MSTs) are also generated to find an optimal compression for the data sets, such that the indexes are smaller and searching operations are faster.

Abstract: A system and method to generate search results in response to a search query based on comparisons of embedding vectors. The system and method receive, from an end user system, a search query including a set of keywords associated with the entity. Using a neural network, an embedding vector is identified based on the set of keywords of the search query. The system and method compares the embedding vector associated with the search query to a set of embedding vectors associated with a set of structured data elements relating to the entity. Based on the comparison, a set of matching structured data elements is identified. The system and method generate a search result in response to the search query, wherein the search result includes at least a portion of the set of matching structured data elements. The search result is displayed via an interface of the end user system.

Abstract: Disclosed are techniques for increasing the speed of pairwise comparison operations in a database system. In an embodiment, a method is disclosed comprising receiving a network request identifying a user; identifying a plurality of segments associated with the user; loading a plurality of bitmaps associated with the plurality of segments, each bitmap in the plurality of bitmaps representing a set of users associated with a segment; comparing pairs of bitmaps from the plurality of bitmaps to generate a set of overlaps; filtering the plurality of segments based on the set of overlaps to generate an anonymized set of segments; and returning aggregated data associated with the anonymized set of segments in response to the network request.

Abstract: An information processing device converts a plurality of original data arranged on a preset n-dimensional map into a plurality of new data arranged on a map according to a conversion rule. In the conversion rule, when an original datum at a target position on the map has a value of out-of-criterion, a new datum at the target position is set to the value of the original datum at the target position, and when the original datum at the target position has a value of within-criterion, each of values of one or more original data excluding an original datum having a value of the out-of-criterion from original data included in a reference region is selected to perform majority voting with the values of the one or more original data selected, and the new datum at the target position is set to a value determined by the majority voting.

Abstract: A method of storing segmented video in a database is provided. The method includes obtaining, by one or more computing devices, data indicative of a segment of a video captured by a camera. The method includes storing, by the one or more computing devices, the data indicative of the segment as a record in the database. The method includes determining, by the one or more computing devices, the record has expired based, at least in part, on a timestamp associated with the record. The method includes modifying, by the one or more computing devices, the record in response to determining the record has expired.

Abstract: A method includes obtaining first data expressing a first usage intent; obtaining an answer object; obtaining, in accordance with first access control data, a first visualization data for the answer object; associating, with the first visualization data, a first generation-identifier that corresponds to at least a subset of the first access control data; generating first instructions for rendering the first visualization data on a user device of the first user; receiving a first snapshot of the first visualization data; storing a first association between the first generation-identifier and the first snapshot; obtaining second data expressing a second usage intent from a second user; identifying the answer object responsive to the second data expressing the second usage intent from the second user; and, responsive to second access control data corresponding to the second user matching the first generation-identifier, outputting second instructions for rendering the first snapshot to the second user.

Abstract: A database processing method and an apparatus are provided, and may be applied to a database system. A tree structure is used to represent a join order and used as an input of a neural network, and different first attribute matrices are allocated to different brother nodes in the input tree structure. This helps the neural network comprehensively learn information about the join order, obtain representation information capable of differentiating the join order from another join order, and predict costs of the join order accurately based on the obtained representation information of the join order. Then, an optimizer selects a join order with lowest costs for a query statement based on the costs predicted by a cost prediction module.