A critical issue in representing, querying and mining data streams consists of the fact that they are intrinsically multi-level and multidimensional in nature, hence they require to be analyzed by means of multi-level and multi-resolution (analysis) models accordingly. Furthermore, it is a matter of fact to note that enormous data flows generated by a collection of stream sources naturally require to be processed by means of advanced analysis/mining models, beyond traditional solutions provided by primitive SQL-based DBMS interfaces, and very often high-performance computational infrastructures, like Data Grids, are advocated to provide the necessary support to this end
Abstract — Traditional databases store sets of relatively static records without the concept of time...
We present the design and development of a data stream system that captures data uncertainty from da...
The term Online Analytical Mining, coined in 1997 by J. Han [9], refers to solutions that integrate ...
Real-time surveillance systems and other dynamic environments often generate tremendous (potentially...
Abstract. Much effort has been put into building data streams management systems for querying data s...
"OLAP" or multi-dimensional analysis workloads present a number of interesting challenges ...
Data mining aims at discovering valid, novel and potentially useful patterns from data. Over last tw...
Abstract — Data stream is a continuous, real time, multidimensional, ordered sequence of data elemen...
A novel framework for OLAP over uncertain and imprecise multidimensional data streams is introduced ...
Every day, huge volumes of sensory, transactional, and web data are continuously generated as stream...
With the development of computing systems in every sector of activity, more and more data is now ava...
Mining data streams has recently become an important and challenging task for a wide range of applic...
Data stream processing has gained increasing popularity in the last few years as an effective paradi...
Conventional data mining deals with static data stored on disk, for example, using the current state...
The recent advances in hardware and software have enabled the capture of different measurements of d...
Abstract — Traditional databases store sets of relatively static records without the concept of time...
We present the design and development of a data stream system that captures data uncertainty from da...
The term Online Analytical Mining, coined in 1997 by J. Han [9], refers to solutions that integrate ...
Real-time surveillance systems and other dynamic environments often generate tremendous (potentially...
Abstract. Much effort has been put into building data streams management systems for querying data s...
"OLAP" or multi-dimensional analysis workloads present a number of interesting challenges ...
Data mining aims at discovering valid, novel and potentially useful patterns from data. Over last tw...
Abstract — Data stream is a continuous, real time, multidimensional, ordered sequence of data elemen...
A novel framework for OLAP over uncertain and imprecise multidimensional data streams is introduced ...
Every day, huge volumes of sensory, transactional, and web data are continuously generated as stream...
With the development of computing systems in every sector of activity, more and more data is now ava...
Mining data streams has recently become an important and challenging task for a wide range of applic...
Data stream processing has gained increasing popularity in the last few years as an effective paradi...
Conventional data mining deals with static data stored on disk, for example, using the current state...
The recent advances in hardware and software have enabled the capture of different measurements of d...
Abstract — Traditional databases store sets of relatively static records without the concept of time...
We present the design and development of a data stream system that captures data uncertainty from da...
The term Online Analytical Mining, coined in 1997 by J. Han [9], refers to solutions that integrate ...