Abstract: Index sharding in a low-latency database analysis system includes obtaining index configuration data for indexing constituent data, the constituent data including a plurality of logical tables, and indexing, by an indexing unit, the constituent data by partitioning the constituent data based on a characteristic of the constituent data into at least a first partition and a second partition, segmenting the first partition into a first segment of the first partition, sharding the first segment into a first shard of the first segment of the first partition, segmenting, using hash-partitioning, the second partition into one or more segments of the second partition, and for each segment of the second partition, sharding the segment into one or more respective shards.

Abstract: Methods and systems for large scale data integration in distributed or massively parallel environments comprises a development phase wherein the results of a proposed jobflow can be viewed by the user during development, including the results of upstream units where the data sources and data targets can be any of a variety of different platforms, and further comprises the use of remote agents proximate to those data sources and data targets with direct communication between the associated agents under the direction of a topologically central controller to provide, among other things, improved security, reduced latency, reduced bandwidth requirements, and faster throughput.

Abstract: Lossless switching between search grammars is described herein.

Abstract: Systems and methods for query generation based on a logical data model are described. For example, methods may include accessing a first join graph representing tables in a database; receiving a first query that references data in two or more of the tables of the database; selecting a connected subgraph of the first join graph that includes the two or more tables referenced in the first query; generating multiple leaf queries that reference respective subject tables that are each a root table of the connected subgraph or a table including a measure referenced in the first query; generating a query graph that specifies joining of results from queries based on the multiple leaf queries to obtain a transformed query result; and invoking a transformed query on the database that is based on the query graph and the queries based on the multiple leaf queries to obtain the transformed query result.

Abstract: Systems and methods for natural language question answering are described. For example, methods may include determining a set of candidate database queries, including respective sequences of tokens of a database syntax, based on a string; determining a first score for a first candidate database query from the set of candidate database queries, wherein the first score is based on a match between one or more words of the string and a token of the respective sequence of tokens of the first candidate database query; determining a second score for the first candidate database query, wherein the second score is based on natural language syntax data determined for words of the string; selecting, based on the first score and the second score, the first candidate database query from the set of candidate database queries; and invoking a search of the database using the first candidate database query to obtain search results.

Abstract: Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data d

Abstract: Token based dynamic data indexing with integrated security includes generating attribute tokens based on a database, each attribute token including an ordered sequence of symbols, generating an index indexing the attribute tokens, such that the index includes a root node, a first branch depending from the root node representing a first symbol from a first attribute token, and a security bitmask for the first attribute token, such that a security bitmask for the first branch at the root node is a hierarchical logical disjunction based on the security bitmask for the first attribute token, such that generating a response receiving an information retrieval request indicating a search symbol and a search context, is based on a determination that a horizontal logical disjunction of a vertical logical conjunction of the security bitmask for the first branch and a security bitmask for the information retrieval request indicates authorization.

Abstract: Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data indicating an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a phrase pattern, the phrase fragment including the unresolved data portion, identifying candidate tokens, identifying candidate phrases by traversing a phrase index based on the phrase fragment, wherein identifying the candidate phrases includes, in response to a determination that the phrase index includes an indexed phrase at least partially matching the phrase fragment in accordance with the phrase pattern, the indexed phrase is identified as one of the candidate phrases, identifying candidate resolved-requests, weighting and sorting the candidate tokens, the candidate phrases, and the candidate resolved-request, to obtain sorted candidate resolutions, and outputting one or more of the sorted candidate resolutions for presentation to a user as respective candidates for resolving the unres

Abstract: An information retrieval system converts unstructured ad-hoc search queries into structured search instructions that retrieve data in a structured relational database or an unstructured database. Data from the database is uploaded into a distributed in-memory database system. Tokens are automatically generated based on attributes, measures, and other metadata extracted from the relational database. The tokens are then compared with the non-structured ad-hoc user search queries. The information retrieval system uses the tokens to identify or predict what structured data is associated with user search queries. The tokens guide the user through a set of search terms that the system then uses to generate the structured query instructions. The structured query instructions retrieve specific data and answers from in the database system.

Abstract: A search engine performs semantic analysis of unstructured search terms to generate relational database queries. By understanding the semantics, the search engine more effectively identifies and predicts what information the user is searching for and provides more in-depth user assistance. The search engine uses different ranking schemes to identify which data is most likely associated with search terms and provides both row level and object level data security. The search engine may identify ambiguous search requests and allows the user to select different join paths that create different mappings between the data. The search engine may generate worksheets containing the selected mappings.