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Running analytics computation inside database engines through the use of UDFs (User Defined Functions) has been extensively investigated, but not yet become a scalable approach due to two major limitations. One limitation lies in that the existent UDFs are not relation-in, relation-out and schema-aware, unable to model complex applications, and cannot be composed with relational operators in a SQL...
Contemporary data warehouses now represent some of the world’s largest databases. As these systems grow in size and complexity, however, it becomes increasingly difficult for brute force query processing approaches to meet the performance demands of end users. Certainly, improved indexing and more selective view materialization are helpful in this regard. Nevertheless, with warehouses moving into...
While in the past the BI market was strictly dominated by closed source and commercial tools, the last few years were characterized by the birth of open source solutions: first as single BI tools, and later as complete BI platforms. An Open Source BI platform provides a full spectrum of BI capabilities within a unified system that reduces the overhead for the development and management of each application,...
Business users define calculated facts based on the dimensions and facts contained in a data warehouse. These business calculation definitions contain necessary knowledge regarding quantitative relations for deep analyses and for the production of meaningful reports. The business calculation definitions are implementation and widely organization independent. But no automated procedures facilitating...
The appropriate choice of a method for imputation of missing data becomes especially important when the fraction of missing values is large and the data are of mixed type. The proposed dynamic clustering imputation (DCI) algorithm relies on similarity information from shared neighbors, where mixed type variables are considered together. When evaluated on a public social science dataset of 46,043 mixed...
Within data mining, clustering can be considered the most important unsupervised learning problem which deals with finding a structure in a collection of unlabeled data. Generally, clustering refers to the process of organizing objects into groups whose members are similar. Among clustering approaches, those methods based on probabilistic models have been extensively developed, such as Naïve Bayes...
The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local feature descriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information...
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