Attivio, Inc.

Servicenow, Inc.

Other

William Kimble Johnson, Iii

Martin Serrano

Jonathan Hood Young

John E O'Neil

Gregory L George

Sid Probstein

Udayan Das

Rik Tamm-Daniels

Raymond Lau

Benjamin Talcott Borchard

Tim Smith

A search index is generated from one or more data records, wherein the one or more data records have contents in a plurality of different fields. Field information of the one or more data records is stored in the search index as specialized indexed elements, wherein the specialized indexed elements overlap with other indexed elements of the one or more data records. A search query is received from a user allowed to access only a portion of the plurality of different fields. The search query is processed within the portion of the plurality of different fields using the search index including the specialized indexed elements.

Data record search with field level user access control

Documents and data are analyzed to determine one or more data facets. The documents, data and other information contained therein may be presented according to statistically-determined groupings based on the data facets.

Dynamic determination of data facets

A computer system processes a plurality of unrelated database tables to create a join graph where each node in the join graph represents a table. The nodes in the join graph are connected by weighted, directed edges, where each directed edge represents a join from a first column in a first table to a second column in a second table and where the weight of the directed edge represents a predicted level of success in performing the join. The edge weights can be based on a likelihood of finding a value from the first column in the second column. A user selects a subset of the tables, and the system creates a join tree with recommended joins between the tables selected by the user. The recommended joins are used to create a structured query language statement which is executed to return a result to the user.

Automated creation of join graphs for unrelated data sets among relational databases

A distributed system with multiple nodes, each of which processes input data streams according to a set of independent, asynchronous processing components connected in a manner represented by a directed graph, is dynamically updated. Such a dynamic update occurs while the system is processing input data by pausing the input data stream, making changes to the system configuration, and then restarting the input data stream. If the update fails for any reason, the system configuration is rolled back to its prior state.

Dynamic update of a distributed message processing system

Messages are processed to guarantee proper work scheduling and allow for cyclic and conditional processing. A processing node requiring ordered processing of messages is configured to operate within a plurality of successive processing nodes not requiring ordering processing of messages. Nodes upstream of the ordered node (i) monitor skew between the messages being processed and messages that are ordered after the messages still being processed and that have been forwarded downstream of the node and (ii) pause processing upon reaching skew limits. The nodes upstream of the ordered node limit output of messages to be within a delta of other messages as defined by message IDs. In addition, messages removed from a flow path are replaced by Null messages for ordered processing. Child messages are encoded to incorporate parent IDs preserving ordering systems.

Ordered message processing

A data flow processing system represents processing by a directed graph including processing nodes that generate, and process, groups of dependent messages and that can process such groups of messages in order. To identify a group of messages, the system applies message identifiers to messages. Processing of a message may generate child messages. A child message is assigned a message identifier that incorporates the message identifier of the parent message. The message identifier of the parent message is annotated to indicate the number of related child messages. The processing node maintains a buffer to store messages in the group. When a message is received, its message identifier indicates whether it is in a group and information about the group. From this information the processing node determines whether all messages within the group have been received, at which time the processing node can process the messages in order.

Ordered processing of groups of messages

A highly parallel, asynchronous data flow processing system in which processing is represented by a directed graph model, can include processing nodes that generate, and process, groups of dependent messages and that process messages within such groups in order. Other messages can be processed in whatever order they are received by a processing node. To identify a group of dependent messages, message identifiers are applied to a message. Processing of a message may generate child messages. A child message is assigned a message identifier that incorporates the associated message identifier of the parent message. The message identifier of the parent message is annotated to indicate the number of related child messages. When a group of messages is to be processed by a processing node in order, the processing node maintains a buffer in which messages in the group are stored. When a message is received, its message identifier indicates whether it is in a group, its parent node, if any, and the number of child nodes it has if it is a parent node. From this information, it can be determined whether all messages within the group have been received. When all of the messages within the group have been received, the processing node can process the messages in order.

Techniques and systems for indexing and retrieving data and documents stored in a record-based database management system (RDBMS) utilize a search engine interface. Search-engine indices are created from tables in the RDBMS and data from the tables is used to create 'documents' for each record. Queries that require data from multiple tables may be parsed into a primary query and a set of one or more secondary queries. Join mappings and documents are created for the necessary tables. Documents matching the query string are retrieved using the search-engine indices and join mappings.

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William Kimble Johnson, Iii