Evaluating Relational Ranking Queries Involving both Text Attributes and Numeric Attributes
نویسندگان
چکیده
منابع مشابه
Evaluating Relational Ranking Queries Involving Both Text Attributes and Numeric Attributes
In many database applications, ranking queries may reference both text and numeric attributes, where the ranking functions are based on both semantic distances/similarities for text attributes and numeric distances for numeric attributes. In this paper, we propose a new method for evaluating such type of ranking queries over a relational database. By statistics and training, this method builds ...
متن کاملDetecting Errors in Numeric Attributes
To detect errors in numeric data, this paper proposes numeric functional dependencies (NFDs), a class of dependencies that allow us to specify arithmetic relationships among numeric attributes. We show that NFDs subsume conditional functional dependencies (CFDs); hence, we can catch data inconsistencies, numeric or not, in a uniform logic framework by using NFDs as data quality rules. Better st...
متن کاملSOAP: Efficient Feature Selection of Numeric Attributes
The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. Depending on the method to apply: starting point, search organization, evaluation strategy, and the stopping criterion, there is an added cost to the classification algorithm that we are going...
متن کاملMining Optimized Support Rules for Numeric Attributes
Mining association rules on large data sets have received considerable attention in recent years. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. O...
متن کاملCost Sensitive Discretization of Numeric Attributes
Many algorithms in decision tree learning have not been designed to handle numerically-valued attributes very well. Therefore, discretization of the continuous feature space has to be carried out. In this article we introduce the concept of cost-sensitive discretization as a preprocessing step to induction of a classifier and as an elaboration of the error-based discretization method to obtain ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Software Engineering and Applications
سال: 2012
ISSN: 1945-3116,1945-3124
DOI: 10.4236/jsea.2012.512b018