Abstract: Systems and methods are disclosed for processing queries against a common storage utilizing dynamically allocated partitions operating on one or more worker nodes. The common storage can include one or more data stores, which collectively contain a data set divided across multiple buckets of data. To query the common storage, a query coordinator can retrieve metadata regarding the multiple buckets, in order to determine a subset of buckets that are potentially relevant to a query. The query coordinator can then dynamically allocate partitions operating on worker nodes to retrieve and intake individual buckets of the subset into a phased search process. The dynamic allocation can be selected to maximize parallelization of the buckets across partitions, thus increasing a speed at which the common storage can be searched.

Abstract: Systems and methods are disclosed for processing and executing queries against one or more dataset sources, where the queries identify a set of data to be processed and a manner of processing the set of data. To query the dataset sources, a query coordinator generates a query processing scheme that includes a dynamic allocation of multiple layers of partitions. The query is then executed based on the query processing scheme.

Abstract: Systems and methods are disclosed for processing queries against one or more dataset sources. The system tracks query resource data and resource utilization data. The query-resource usage data can indicate resources used to execute queries. The node resource utilization data can indicate current utilization of nodes in the system. Upon receipt of a query that identifies a set of data to be processed and a manner of processing the set of data, the system can use the query-resource usage data and the resource utilization data to define a query processing scheme. The query can then be executed using the query processing scheme. In some cases, the query coordinator can dynamically allocate partitions operating on worker nodes to execute the query.

Abstract: Systems and methods are disclosed for processing queries against one or more dataset sources utilizing dynamically allocated partitions operating on one or more worker nodes. The results of the processing are stored in a dataset destination. The queries can identify data in the one or more dataset sources for processing and a manner for processing the data. In addition, the queries can identify the dataset destination for storing results of the query. To process the query, a query coordinator can dynamically allocate partitions operating on worker nodes to retrieve data for processing, process the data, and communicate the data to the dataset sources. In addition, the query coordinator can dynamically allocate partitions based on an identification of the dataset destination.

Abstract: Systems and methods are disclosed for processing queries against an external data source utilizing dynamically allocated partitions operating on one or more worker nodes. The external data source can include data that has not been processed by the system. To query the external data source, a query coordinator can generate a subquery for the external data source based on determined functionality of the data source. The subquery can identify data in the external data source for processing and a manner for processing the data. In addition, the query coordinator can dynamically allocate partitions operating on worker nodes to retrieve and intake results of the subquery. In some cases, number of partitions allocated can be based on a number of partitions supported by the external data source.

Abstract: Systems and methods are disclosed for utilizing an ingested data buffer operating according to a publish-subscribe messaging model as an intake mechanism for a query system. Data from various sources can be placed into the data buffer according to different topics. Indexers can subscribe to these topics in order to ingest the data into the system for long-term storage and later search. In addition, worker nodes may directly subscribe to the topics to enable continuous or streaming searching of the data, without delays that may be caused by ingestion of the data at an indexer. When a request for a streaming search is received, a query coordinator can determine a number of message queues on the data buffer that contain potentially relevant messages. The query coordinator can then dynamically allocate partitions operating on worker nodes to retrieve and intake messages from the message queues into a phased search process.

Abstract: Systems and methods are disclosed for processing queries against multiple dataset sources. One dataset source can include indexers that index and store data. The system can receive a query that identifies a set of data to be processed and a manner of processing the set of data. The set of data can include a first dataset that is accessible by one or more indexers and a second dataset that is accessible by one or more other dataset sources. A query coordinator can define a query processing scheme for obtaining and processing the set of data that includes a dynamic allocation of multiple layers of partitions. The partitions can operate on multiple worker nodes. The query can then be executed based on the query processing scheme.

Abstract: Data sets are analyzed to discover trends and determine predictive data models. A data model is determined based on analyzing the data set with a specific algorithm from a set of different analytical algorithms. The data model is stored in a structural file format. The data model is converted into an instantiated object model with the use of a predefined object model. The instantiated object model is converted into an in-database analysis model to score new data within a database system. The scoring is based on the logic in the data model. The in-database analysis model is stored on a database server part of a database system to provide analytical functionality defined in the data model. The new data can be scored with the in-database analysis model. The new data is not extracted from the database system and the scoring is accomplished in-memory.

Abstract: The present disclosure provides systems, devices, and methods for a fast-turnaround, minimally invasive, and/or cost-effective assay for screening diseases, such as genetic disorders and/or pathogens, in subjects.

Abstract: Method and systems may be used to provide search results in response to plain language queries from a database. A search query may be received and a database may be queried. A metadata search of the database data structures may be performed to determine a search set based on the search query. Using the search set, a data search may be performed to determine the database data structures for a result set. Result data structures may be generated based on the result set. The result data structures may be iteratively provided to a client to display the search results responsive to the search query quickly. The relevant data may be retrieved from the database in response to a selection. In some instances, the results retrieved from the database in response to a selection of a result data structure may be limited based on a count parameter.

Abstract: In one embodiment, data records associated with attributes are received. A check is made to determine whether the data records are greater than a maximum data record limit of a graph. Further, when the maximum number of data records in the bin is less than or equal to the maximum data record limit of the graph, the data records are retrieved and presented in the graph. When the data records are greater than the maximum data record limit of the graph, the data records are grouped into bins based on initial bin sizes corresponding to the plurality of attributes. Furthermore, weighted densities of the bins are determined using a maximum number of data records associated with a bin and a maximum data record limit of the bin. Further, the graph is rendered to present the weighted densities of the bins using a randomization technique to analyze the data records.

Abstract: Methods, computer-readable media, and systems for managing business objects data sources. A search query that includes multiple query terms is received. Each query term at least partially represents metadata associated with one of multiple business objects data sources that each stores multiple data items. Multiple search index documents are searched to identify one or more business objects data sources that are each associated with metadata at least partially represented by each query term. Multiple metadata tables are searched to identify metadata associated with each identified business objects data source. The identified business objects data sources are searched for data items that satisfy the identified metadata. Representations of the data items and the metadata are provided in response to receiving the search query.

Abstract: Disclosed are methods and systems for improving indexing throughput. The methods and systems involve receiving one or more documents for indexing, categorizing the one or more documents based on a document type, a document size and a processing priority, assigning buckets to the categorized one or more documents according to the document type, the document size and the processing priority and scheduling the buckets for processing based on a document type priority, a bucket type and number of threads available to process the buckets.

Abstract: Techniques allow automatic identification of statistically significant attribute combinations in a dataset, and provide users with an understanding thereof including starting points for further analysis. Statistically significant combinations may be obtained from large data sets by limiting combinations to four or fewer attributes. The combinations obtained may be ranked to differentiate patterns, e.g. according to factors such as error ratio, decision tree depth, occurrences, and number of attributes. Still further insights may be achieved by ranking attributes according to the number of statistically significant combinations in which they appear. For useful visualization of statistically significant information within the patterns, only those having at least one measure/numeric may analyzed for further insight (e.g. by an outlier algorithm) and presented as output in a chart (e.g. pie, bar) form.

Abstract: Methods, non-transitory computer-readable media, and systems for managing business objects data sources. Multiple business objects data sources, each storing multiple data items, are accessed. For each data source, multiple computer-searchable index documents and multiple metadata tables, including master tables and mapping tables, are generated. The multiple computer-searchable index documents and the multiple metadata tables are provided to perform a search for data items in the multiple business objects data sources.

Abstract: Various embodiments of systems and methods for synchronizing a primary repository and a secondary repository in a business infrastructure are disclosed. Content of artifacts present in the primary repository and the secondary repository are indexed. Based upon a search criteria received on the secondary repository, an integrated search service provided on the secondary repository is invoked to perform a combinatorial query on the indexed content and retrieve an identifier of each artifact corresponding to the search criteria. Based upon relevancy parameters, the retrieved identifiers are ranked and rendered as a categorized table including a first column and a second column for identifiers present in the primary repository and the second repository respectively. Based upon a user action received on the ranked identifiers in the first column of the categorized table, the associated artifacts are determined and downloaded from the primary repository to the secondary repository.

Abstract: A user uploads date sets through a client to a database. The data sets are provisioned in the database for in-database searching. The data sets are evaluated and classifications for the columns of the tables that include the data set are detected. Columns content may be classified into different analysis types, aggregation types, formats, categories, hierarchies, etc. Metadata is generated based on the evaluation of the data sets. A schema is used to store the metadata that describes the detected classification of the columns. The schema is stored in the database and is used when a search in the database is performed.

Abstract: Data sets are analyzed to discover trends and determine predictive data models. A data model is determined based on analyzing the data set with a specific algorithm from a set of different analytical algorithms. The data model is stored in a structural file format. The data model is converted into an instantiated object model with the use of a predefined object model. The instantiated object model is converted into an in-database analysis model to score new data within a database system. The scoring is based on the logic in the data model. The in-database analysis model is stored on a database server part of a database system to provide analytical functionality defined in the data model. The new data can be scored with the in-database analysis model. The new data is not extracted from the database system and the scoring is accomplished in-memory.

Abstract: Method and systems may be used to provide search results in response to plain language queries from a database. A search query may be received and a database may be queried. A metadata search of the database data structures may be performed to determine a search set based on the search query. Using the search set, a data search may be performed to determine the database data structures for a result set. Result data structures may be generated based on the result set. The result data structures may be iteratively provided to a client to display the search results responsive to the search query quickly. The relevant data may be retrieved from the database in response to a selection. In some instances, the results retrieved from the database in response to a selection of a result data structure may be limited based on a count parameter.

Abstract: Techniques allow automatic identification of statistically significant attribute combinations in a dataset, and provide users with an understanding thereof including starting points for further analysis. Statistically significant combinations may be obtained from large data sets by limiting combinations to four or fewer attributes. The combinations obtained may be ranked to differentiate patterns, e.g. according to factors such as error ratio, decision tree depth, occurrences, and number of attributes. Still further insights may be achieved by ranking attributes according to the number of statistically significant combinations in which they appear. For useful visualization of statistically significant information within the patterns, only those having at least one measure/numeric may analyzed for further insight (e.g. by an outlier algorithm) and presented as output in a chart (e.g. pie, bar) form.