The role of big data analytics in Internet of Things

Spatiotemporal deformation characteristics and triggering factors of Baijiabao landslide in Three Gorges Reservoir region, China

Quick attribute reduct algorithm for neighborhood rough set model

A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Systems and methods for query evaluation over distributed linked data stores

Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Ontologically driven procedure coding

In a method for storing data in a relational database system using a processor, a collection of values is assigned to a structure dictionary, each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection. and the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, automatic detection of frequencies of values occurring in data, analysis of a history of queries, and predetermined information about structures relevant to data content that is stored in the system. For each structure, forming a structure match list from ordinal numbers of values matching the structure, and a structure sub-collection from values matching the structure, using the processor. An outlier match list is formed from ordinal numbers of values that are not null and do not match any structure in the structure dictionary, and an outlier sub-collection is formed from values that are not null and do not match any structure in the structure dictionary. The collection of values is stored in the relational database in a form of structure match lists, structure sub-collections, outlier match list and outlier sub-collection.

System and method for storing data in a relational database

We discuss the usage of the paradigms of rough sets and granular computing in the core components of the Infobright's database engine. Having data stored in the form of compressed blocks of attribute values, our query execution methods utilize compact information about those blocks' contents instead of brute-force data decompression. The paper contains examples of algorithms implemented with the aim to minimize the need of accessing the compressed data in such operations as filtering, joining and aggregating.

/pdf/infobright-analytic-database-engine-using-rough-sets-and-2krc3c65zl.pdf

Infobright Analytic Database Engine Using Rough Sets and Granular Computing

A method of resolving data queries in a data processing system. The method comprises receiving in the data processing system a data query, where the data processing system stores a plurality of information units describing pluralities of data elements, a first information unit having a retrieval subunit that includes information for retrieving all unique data elements in a first plurality of data elements and a summary subunit including summarized information about data elements in the first plurality of data elements. The method further includes deriving, via the data processing system, a result of the data query, wherein the result of the data query comprises a plurality of new data elements. The data processing system uses summary subunits of information units to select a set of information units describing data elements that are sufficient to resolve the data query.

System and method for granular scalability in analytical data processing

A Rough-Columnar RDBMS Engine -- A Case Study of Correlated Subqueries.

In a method for managing metadata in a relational database system using a processor, the metadata is created in a form of rough values corresponding to collections of values, wherein each rough value represents summarized information about values, the values are elements of the corresponding collection of values, and each rough value is substantially smaller than the corresponding collection of values. A collection of values is assigned to a structure dictionary, wherein each of the values represents the value of a row for an attribute and has a unique ordinal number within the collection, and wherein the structure dictionary contains structures defined based on at least one of interaction with a user of the system via an interface, automatic detection of structures occurring in data, and predetermined information about structures relevant to data content that is stored in the system. A match granule is formed, and for each structure in the structure dictionary, a structure granule is formed. Information represented by the match granule and the structure granules is summarized to form a rough value.

System and method for managing metadata in a relational database

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