Pawel Terlecki

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Efficient data processing is critical for interactive visualization of analytic data sets. Inspired by the large amount of recent research on column-oriented stores, we have developed a new specialized analytic data engine tightly-coupled with the Tableau data visualization system. The Tableau Data Engine ships as an integral part of Tableau 6.0 and is(More)
This paper examines jumping emerging patterns with negation (JEPNs), i.e. JEPs that can contain negated items. We analyze the basic relations between these patterns and classical JEPs in transaction databases and local reducts from the rough set theory. JEPNs provide an interesting type of knowledge and can be successfully used for classification purposes.(More)
Efficient and convenient handling of heterogeneous data is a current challenge for data management systems. In this paper, we discuss several common relational approaches to represent heterogeneity and argue for a design based on a single wide-table, referred to as a flexible schema. For this scenario, we focus on partial indexation and its support for(More)
Efficient support for applications that deal with data heterogeneity, hierarchies and schema evolution is an important challenge for relational engines. In this paper we show how this flexibility can be handled in Microsoft SQL Server. For this purpose, the engine has been equipped in an integrated package of relational extensions. The package includes(More)
This paper presents the relations between rough set reducts and jumping emerging patterns. Observations are introduced formally and supported by brief examples. Furthermore, we propose practical applications of these observations to the minimal reduct problem and to testing whether a given attribute set is differentiating. We believe that our study can be(More)
Data sets are growing rapidly and there is an attendant need for tools that facilitate human analysis of them in a timely manner. To help meet this need, column-oriented databases (or "column stores") have come into wide use because of their low latency on analytic workloads. Column stores use a number of techniques to produce these dramatic performance(More)
The rapid increase in data volumes and complexity of applied analytical tasks poses a big challenge for visualization solutions. It is important to keep the experience highly interactive, so that users stay engaged and can perform insightful data exploration. Query processing usually dominates the cost of visualization generation. Therefore, in order to(More)