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: 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: 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: 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: 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: 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: 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.

Abstract: In an aspect, provided is a method comprising receiving a data model, partitioning a first table in the data model into a first plurality of blocks of rows, generating a first plurality of indexlets, the first plurality of indexlets comprising a first plurality bidirectional indexes, each of the first plurality of bidirectional indexes being generated based on a corresponding one of the first plurality of blocks of rows.

Abstract: Methods, systems, devices, and computer-readable media used by a cloud data management system for collecting data from accounts hosted by a cloud-based data warehousing system on different cloud platforms or in different cloud regions of a cloud platform. Collection of data in the multi-cloud platform and/or multi-cloud region environments may be facilitated by the on-demand creation of one or more data collection accounts. Based on the collected data, one or more data warehouse resources not managed by the cloud data management system may be discovered, configured, and onboarded to the cloud data management system.

Abstract: A fast processing method of massive time-series data based on aggregated edges and time-series aggregated edges implements fast and real-time processing based on an association of a graph data structure under a massive data pattern. In terms of incremental flow calculation based on time windows, the present disclosure proposes an innovative data structure of “aggregated edge” and “time-series aggregated edge”, which is suitable for data modeling of real-time dynamic graph. The present disclosure introduces a time-series graph query language, which adds descriptive semantics of time series information, and not only supports basic queries based on vertices, edges, and attributes, but also can implement users' graph queries for index calculation results in a time window. The graph query includes graph matching and graph filtering. The present disclosure is especially suitable for fields such as marketing and real-time risk control based on massive data mining and has good timeliness control and high scalability.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.

Abstract: A method includes generating a predictive model mapping batch size to estimated memory utilization based on a plurality of prior batch sizes applied for performing a corresponding plurality of prior data format conversions, and further based on a plurality of prior actual memory utilization occurring in performing the corresponding plurality of prior data format conversions. A next batch size is automatically selected for performance of a subsequent data format conversion based on the predictive model and a current memory availability. A batch of data for performance of the subsequent data format conversion is identified based on the batch of data having the next batch size. The subsequent data format conversion is performed the batch of data, and an actual amount of memory utilized in performing the subsequent data format conversion is determined. The predictive model is updated based on the next batch size and the actual amount of memory utilized.

Abstract: A device may receive a first telemetry data entry associated with an attribute and store a record associated with the first telemetry data entry, wherein the record identifies a first context value associated with the attribute. The device may log a first timestamp of the first telemetry data entry in a lookup table, wherein the lookup table includes a mapping of the attribute to the first context value and to the first timestamp. The device may receive a second telemetry data entry associated with the attribute and may determine, from the mapping, that the second telemetry data entry is associated with a second context value that is different from the first context value. The device may determine whether a second timestamp, of the second telemetry data entry, is before the first timestamp. The device may perform an action based on whether the second timestamp is before the first timestamp.