Abstract: The long-lived and recently modified (LLRM) partitioning is a system and method for partitioning slowly changing aged data for a given year into one long-lived partition (LLP) and multiple recently modified partitions (RMP) where the RMP corresponds to a time period of a first time granularity. Each record has a start and end time to indicate its lifetime. A record in a RMP is modified at a timepoint corresponding to the time period of a RMP. RMP records can be sampled every second, minute, hour or day. The LLP holds records whose lifetime is greater than the time period of a first time granularity. Queries pertaining to a given timepoint is performed on data that is a union of the LLP and the RMP pertaining to that timepoint instead of the unpartitioned table thus leading to shorter query processing time and better utilization of memory storage.

Abstract: A customized industrial graph knowledge base for an industrial operation includes a graph database storing nodes of multiple dimensions predefined according to the nature and characteristics of the industrial operation. The nodes are extracted from baseline, domain-specific, and implementation specific industrial knowledge data sources using various analytics for structured and unstructured data. The customized industrial graph knowledge base further includes edges representing relationships between nodes that are either inter-dimensional or intra-dimensional. The importance of each node to the industrial operation is further quantified using a graph model and is included in the graph database as a composite filtering parameter.

Abstract: An information processing device includes an acquiring unit, a shaping unit, and an integration unit. The acquiring unit is configured to acquire operation history information from plural systems, respectively. The shaping unit is configured to shape each operation history information acquired by the acquiring unit into operation history information in a unified format. The integration unit is configured to arrange and integrate the operation history information after being shaped by the shaping unit in chronological order.

Abstract: Methods, systems, and computer program products for domain aware explainable anomaly and drift detection for multi-variate raw data using a constraint repository are provided herein. A computer-implemented method includes obtaining a set of data and information indicative of a domain of said set of data; obtaining constraints from a domain-indexed constraint repository based on said set of data and said information, wherein the domain-indexed constraint repository comprises a knowledge graph having a plurality of nodes, wherein each node comprises an attribute associated with at least one of a plurality of domains and constraints corresponding to the attribute; detecting anomalies in said set of data based on whether portions of said set of data violate said retrieved constraints; generating an explanation corresponding to each of the anomalies that describe the attributes corresponding to the violated constraints; and outputting an indication of the anomalies and the corresponding explanation.

Abstract: A system includes an interface and a processor. The interface is configured to receive a table indication of a data table and to receive a transaction indication to perform a transaction. The processor is configured to determine a current position N in a transaction log; determine a current state of the metadata; determine a read set associated with a transaction; attempt to write an update to the transaction log associated with a next position N+1; in response to a transaction determination that a simultaneous transaction associated with the next position N+1 already exists, determine a set of updated files; and in response to a determination that there is not an overlap between the read set associated with the current transaction and the set of updated files associated with the simultaneous transaction, attempt to write the update to the transaction to the transaction log associated with a further position N+2.

Abstract: A computer system performs decentralized collection and aggregation of data. A plurality of requests for data are transmitted, by a data collection server, to a corresponding plurality of remote servers, wherein the plurality of remote server submit responses to the plurality of requests for data to a distributed data structure. The distributed data structure is accessed, by the data collection server, to retrieve the plurality of responses to the plurality of requests. The plurality of responses are aggregated, by the data collection server, into a message and the message is transmitted to an entity requesting data collection. Embodiments of the present invention further include a method and program product for performing decentralized collection and aggregation of data in substantially the same manner described above.

Abstract: Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Abstract: The disclosure relates to a method for operating a data storage system that includes a plurality of data stores. In the method, IoT data generated at a specific data point are stored in at least one data store, and at least one data store for storing the IoT data is selected automatically based on metadata, wherein the metadata are associated with a data point. The disclosure also relates to a computer program configured to carry out the method, and a data storage system that works according to the method.

Abstract: Machine learning is utilized to analyze respective execution times of a plurality of tasks in a job performed in a distributed computing system to determine that a subset of the plurality of tasks are straggler tasks in the job, where the distributed computing system includes a plurality of computing devices. A supervised machine-learning algorithm is performed using a set of inputs including performance attributes of the plurality of tasks, where the supervised machine learning algorithm uses labels generated from determination of the set of straggler tasks, the performance attributes include respective attributes of the plurality of tasks observed during performance of the job, and applying the supervised learning algorithm results in identification of a set of rules defining conditions, based on the performance attributes of the plurality of tasks, indicative of which tasks will be straggler tasks in a job. Rule data is generated to describe the set of rules.

Abstract: Techniques and solutions are provided for metadata-driven data maintenance. One or more data object queries are obtained from one or more data object frameworks. One or more sets of data objects are received based on the one or more data object queries. One or more data object nets are built based on the one or more sets of data objects and the one or more data object frameworks and respectively associated with one or more processes. The one or more data object nets and their associated processes are analyzed. Data object maintenance is performed on the data objects of the one or more data object nets based on the analysis of the one or more data object nets and their associated processes.

Abstract: Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store, wherein the ERs are generated, updated/modified, and/or accessed via a graph-based domain ontology.

Abstract: Disclosed are interest tag determining method and device for accurately promoting information for users in a group based on interest tags of the group. This includes obtaining a group identifier and a corresponding group name, obtaining user identifiers corresponding to the group identifier and first user contents of the user identifiers, concatenating the first user contents of the user identifiers corresponding to the group identifier to obtain a first group content of the group identifier, calculating probabilities that the first group content is under the topic identifiers, and determining interest tags of the group identifier according to the probabilities that the first group content is under the topic identifiers and an association relationship between the topic identifiers and the interest tags that is determined through pre-training.

Abstract: A metadata table manager receives a request for time series data associated with a device, where the request comprises a device identifier associated with the device, and where the time series data comprises a most recently received data element associated with the device. The metadata table manager determines a metadata table that associates the device identifier with one or more time periods during which data associated with the device has been received, and accesses a metadata table entry for the device identifier that includes an indication of a number of data elements received at the most recent time period of the one or more time periods. The metadata table manager queries a time series data store for the first time series data based on the first time period, and outputs a portion of the first time series data, wherein the portion at least comprises the most recently received data element.

Abstract: In one aspect, a data item is written to a data chunk of a stream segment. The stream segment includes a stream segment header and a plurality of data chunks. A first identifier of the data item is written to the stream segment header of the stream segment. A second identifier of the data item is written to a header of the data chunk of the stream segment. In another aspect, a stream segment header of a stream segment is queried with a query identifier. The querying identifies if any of the data items in the data chunks of the stream segment have the query identifier. If any of the data items in the data chunks of the stream segment have the query identifier, the data chunks of the stream segment are queried with the query identifier to identify which data chunks of the stream segment have the query identifier.

Abstract: Provided is a method for domain name ranking. An example method includes receiving Domain Name System (DNS) data, which includes domain names. The DNS data is processed to obtain multiple metric values for each of the domain names. The metric values can include a query count (QC), a client count (CC), and a network count (NC). The method proceeds with calculating a score for each of the domain names based on the metric values. The calculation can be performed using the following equation: Score=NC·CC·(1+log(QC)). Furthermore, the method ranks the domain names based on the score for each of the domain names. The ranking can be based on normalization of the scores or based on converting the scores into respective percentile ranks.

Abstract: Systems and methods for synthesizing cloud snapshots of production data. A client system includes data and snapshots may be taken of that data. These snapshots are transferred to the cloud and hydrated to block devices that are similar to the devices on which the production data exists. Snapshots are then taken of the block devices. The cloud snapshots are the same as and correspond to the locally generated snapshots.

Abstract: Embodiments are directed to managing a data model that includes data objects associated with layers of the data model may be provided. A lineage model that includes lineage objects that has one or more lineage objects associated with a layer may be provided. A lineage object associated with a layer may be activated. A first portion data objects may be determined based on the activated lineage object. An anchor object may be determined from the first portion of data objects. A search of the data model may be employed to provide dependency information based on a position of the anchor data object in the data model. A second portion of data objects may be determined based on the dependency information. A report that includes a list of the second portion of data objects, the dependency information, or the like, may be provided.

Abstract: Systems and methods for discovering applications. An application may be discovered by determining a topology of the application in a containerized platform. Discovering the topology may include determining the components of the application and then determining relationships between the components. The relationships may include whether a component (e.g., a microservice) is user facing and may be based on traffic patterns between the components. The components or microservices may be prioritized or ranked based on the topology. The topology can be used when performing a data protection operation such as a restore operation.

Abstract: A method includes receiving, by a first computing entity of a database system, data that is organized in rows and columns. The method further includes determining, by the first computing entity, one or more key columns from the columns based on a desired sort criteria for the data. The method further includes sorting, by the first computing entity, other columns of the columns based on the one or more key columns to produce sorted other columns. The method further includes sending, by the first computing entity, the one or more key columns to a second computing entity of the database system for storage in a first storage location associated with the second computing entity. The method further includes sending, by the first computing entity, the sorted other columns to the second computing entity for storage in a second storage location associated with the second computing entity.

Abstract: A method, performed by one or more processors, is disclosed, the method comprising receiving a stream of log data from one or more applications and indexing a plurality of different portions of the received stream to respective locations of a cold storage system. The method may also comprise storing, in an index, catalog pointers to the respective locations of the indexed portions in the cold storage system. One or more requests for log data may be received, and the method may also comprise subsequently identifying from the index catalog one or more pointers to respective indexed portions appropriate to at least part of the one or more requests, and sending of the identified one or more indexed portions to one or more hot storage systems each associated with a respective search node for processing of one or more search requests.