The set of algorithms and techniques used to extract interesting patterns and trends from huge data repos-itories is called Data Mining. Due to the typical com-plexity of computations and the amount of data han-dled, the performance control of Data Mining algo-rithms is still an open problem. Despite the many important results that have been obtained so far for specific cases, a more general framework is needed for the development of Data Mining applications, where performances can be controlled effectively. We propose a research activity aimed at the design of a hardware/software architecture for Data Mining. Such architecture will be based on the generalization of the results found during the design of High Perfor-mance Data Mining Algori...
Data mining is the process of finding useful patterns in large sets of data. These algorithms and te...
Many data-intensive applications exhibit poor temporal and spatial locality and perform poorly on co...
Initial efforts on applying mining techniques on scien-tific simulation datasets has demonstrated th...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...
Abstract—The exponential increase in the generation and collection of data has led us in a new era o...
The problem of devising models and algorithms for high-performance Distributed Data Mining has tradi...
In this age, a huge amount of data is generated every day by human interactions with services. Disco...
Over the past few decades we have witnessed an exponential growth in the amount of data being used i...
This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms...
Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so...
Data Mining draws on many technologies to deliver novel and actionable discoveries from very large c...
In this paper we present G-Net, a distributed algorithm able to infer classifiers from pre-collected...
Systems performing Data Mining analysis are usually dedicated and expensive. They often require spec...
Managing and efficiently analysing the vast amounts of data produced by a huge variety of data sourc...
Abstract: We live in the data age as data storage technologies, hardware and software, have evolved ...
Data mining is the process of finding useful patterns in large sets of data. These algorithms and te...
Many data-intensive applications exhibit poor temporal and spatial locality and perform poorly on co...
Initial efforts on applying mining techniques on scien-tific simulation datasets has demonstrated th...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...
Abstract—The exponential increase in the generation and collection of data has led us in a new era o...
The problem of devising models and algorithms for high-performance Distributed Data Mining has tradi...
In this age, a huge amount of data is generated every day by human interactions with services. Disco...
Over the past few decades we have witnessed an exponential growth in the amount of data being used i...
This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms...
Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so...
Data Mining draws on many technologies to deliver novel and actionable discoveries from very large c...
In this paper we present G-Net, a distributed algorithm able to infer classifiers from pre-collected...
Systems performing Data Mining analysis are usually dedicated and expensive. They often require spec...
Managing and efficiently analysing the vast amounts of data produced by a huge variety of data sourc...
Abstract: We live in the data age as data storage technologies, hardware and software, have evolved ...
Data mining is the process of finding useful patterns in large sets of data. These algorithms and te...
Many data-intensive applications exhibit poor temporal and spatial locality and perform poorly on co...
Initial efforts on applying mining techniques on scien-tific simulation datasets has demonstrated th...