History-Pattern Encoding for Large-Scale Dynamic Multidimensional Datasets and Its Evaluations
نویسندگان
چکیده
منابع مشابه
Tertiary Storage Organization for Large Multidimensional Datasets
Large multidimensional datasets are found in diverse application areas, such as data warehousing [6], satellite data processing, and high-energy physics [9]. According to current estimates, these datasets are expected to hold terabytes of data. Since these datasets hold mainly historical and aggregate data, their sizes are increasing. Daily accumulation of raw data and jobs generating aggregate...
متن کاملEeective Visualization of Large Multidimensional Datasets
A new method for assisting with the visualization of large multidimensional datasets is proposed. We classify datasets with more than one million elements as large. Multidimensional data elements are elements with two or more dimensions, each of which is at least binary. Multidimensional data visualization involves representation of multidimensional data elements in a low dimensional environmen...
متن کاملE ective Visualization of Large Multidimensional Datasets
A new method for assisting with the visualization of large multidimensional datasets is proposed. We classify datasets with more than one million elements as large. Multidimensional data elements are elements with two or more dimensions, each of which is at least binary. Multidimensional data visualization involves representation of multidimensional data elements in a low dimensional environmen...
متن کاملUsing R-Trees for Interactive Visualization of Large Multidimensional Datasets
Large, multidimensional datasets are difficult to visualize and analyze. Visualization interfaces are constrained in resolution and dimension, so cluttering and problems of projecting many dimensions into the available low dimensions are inherent. Methods of real-time interaction facilitate analysis, but often these are not available due to the computational complexity required to use them. By ...
متن کاملComparisons of Multidimensional Visualization Methods For Very Large Datasets
Three multidimensional visualization methods utilizing nested dimensions namely; trellis-like displays, mosaic plots and TempleMVV graphs are discussed and compared in respect to the insights they provide and their performance. These techniques are applicable when the number of dimensions is no larger than 10 to 20. Only mosaic plots and TempleMVV graphs, can be applied to datasets with large n...
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: IEICE Transactions on Information and Systems
سال: 2016
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2015dap0025