Graham Toppin

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Columnar databases provide a number of benefits with regard to both data storage (e.g.: data compression) and data processing (e.g.: optimized data access, parallelized decompression, lazy materialization of intermediate results). Their characteristics are particularly advantageous for exploratory sessions and ad hoc analytics. The principles of columnar(More)
We discuss how to use techniques from such fields as text processing and knowledge management to better handle text attributes in the Infobright's RDBMS engine. Our approach leads to a rich interface for domain experts who wish to share their knowledge about data content and, on the other hand, it remains unnoticeable to data users. It enables to improve(More)
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(More)
We introduce rough query, which is a new approach to defining and computing SQL approximations. Rough query results are reported by means of simple metadata of attributes in an information system that would be a result of a standard select statement. The proposed approach is already available as an SQL extension in both Infobright Community and Enterprise(More)
We discuss the importance of analytic SQL statements with complex expressions in the business intelligence and knowledge discovery applications. We report the recent improvements of the execution of complex expressions in the Infobright’s RDBMS, which is based on the paradigms of columnar databases and adaptive rough computations over the granulated(More)
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