A first category encoding data structure encoding map version agnostic identifiers of traversable map elements (TMEs) within a zone of interest and associated with a first category is received. The first category encoding data structure was provided by a network apparatus. The first category encoding data structure is received by a mobile apparatus comprising a processor, memory storing a mobile version of a digital map, and a communication interface. The first category encoding data structure is a probabilistic data structure configured to not provide false negatives. The mobile apparatus determines a respective category associated with each of one or more TMEs within the zone of interest based at least in part on whether the TME satisfies the first category encoding data structure. The mobile apparatus performs at least one navigation function based at least in part on the respective category associated with each of the one or more TMEs within the zone of interest.

Information corresponding to one or more traversable map elements (TMEs) within a zone of interest is accessed from the geographic database. A respective category of a plurality of categories is determined for each of the one or more TMEs based at least in part on the information corresponding to the TME. A first category encoding data structure is generated based at least in part on map version agnostic identifiers corresponding to TMEs determined to be in a first category of the plurality of categories, wherein the first category encoding data structure is a probabilistic data structure configured to not provide false negatives for TMEs within the zone of interest. The first category encoding data structure is provided such that a mobile apparatus receives the first category encoding data structure.

Document retrieval techniques include storing in an index for each archived document a vector of dimension N, based on a query portion of the document and a particular algorithm. An analogy query is received from a requester, indicating a query portion A, a query portion B and a query portion C, each of one or more documents, so that each retrieved document D has a query portion D that is related to C as B is related to A. Vectors A, B and C are determined each based on its query portion and the particular algorithm. A transform from vector A to vector B is determined. An enhanced vector Q is based on the vector C and the transform. Each retrieved document D is based on proximity of a vector of each in the index to the enhanced vector Q; and at least a reference is presented to the requester.

Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Provided are systems and methods for managing storage of machine data. In one embodiment, a method can be provided. The method can include receiving, from one or more data sources, raw machine data; processing the raw machine data to generate processed machine data; storing the processed machine data in a data store; and determining an allocated data size associated with the processed machine data stored in the data store, wherein the allocated data size is the size of the raw machine data corresponding to the processed machine data stored in the data store.

A computer-executable method, system, and computer program product for managing metadata in a distributed data storage system, wherein the distributed data storage system includes a first burst buffer having a key-value store enabled to store metadata, the computer-executable method, system, and computer program product comprising receiving, from a compute node, metadata related to data stored within the distributed data storage system, indexing the metadata at the first burst buffer, and processing the metadata in the first burst buffer.

An index generation device 100 generates key presence information for a plurality of input files when lexical analysis on the plurality of input files are executed, the key presence information including information whether each of a plurality of keys is present in the plurality of input files and presence positions of the respective plurality of keys when the respective plurality of keys are present in the plurality of input files. The index generation device 100 generates index information about the keys and the positions for the plurality of input files based on the key presence information.

A distributed search system can be partitioned into buckets based on entities and time periods. Addresses for the partitions can be formed from entity parameters and time period parameters. An indexing scheme for the partitions can be maintained at one or more search clusters, which may be geographically separate from one another. Consistency can be maintained across the search clusters though routing queries between clusters based at least in part on the status of partitions.

A computer-implemented method according to one embodiment includes identifying at a pre-allocation module a size of object data to be stored within a storage node, identifying at the pre-allocation module file system parameters associated with the storage node, calculating at the pre-allocation module pre-allocated details needed for storing the object data within the storage node, utilizing the size of the object data and the file system parameters associated with the storage node, and sending the object data and the pre-allocated details from the pre-allocation module to the storage node.

A computing server may receive master data, transaction data, and one or more existing process models of a domain. The computing server may aggregate, based on domain knowledge ontology of the domain, the master data and the transaction data to generate a fact table. For example, entries in the fact table may be identified as relevant to the target process model and include attributes and facts that are extracted from master data or transaction data. The computing server may convert the entries in the fact table into vectors. The computing server inputting vectors into one or more machine learning algorithms to generate one or more algorithm outputs. One or more algorithm outputs may correspond to one or more improved process models that are optimized compared to the existing process models. The computing server may provide the improved process model to the domain to replace one of the existing process models.