Data streams are one of the most relevant new data sources, they refer to flows of data that come at a very high rate. Let us consider a stock-exchange market, where n different stocks with p considered attributes (e.g. price, quantity, seller/buyer id, . . .) are negotiated all day long. The distinguishing feature in data streams analysis is that the focus is on transient relations. The present paper proposes a visualization tool exploiting Multidimensional Data Analisis (MDA) techniques to represent the evolving association structures among attributes over different time-frames. The general aim is to detect the stability of the deviation from indipendence in the occurrence of an observed set of attributes stored as binary stream.
Binary data flow visualization onfactorial axes
IODICE D'ENZA, Alfonso
2007-01-01
Abstract
Data streams are one of the most relevant new data sources, they refer to flows of data that come at a very high rate. Let us consider a stock-exchange market, where n different stocks with p considered attributes (e.g. price, quantity, seller/buyer id, . . .) are negotiated all day long. The distinguishing feature in data streams analysis is that the focus is on transient relations. The present paper proposes a visualization tool exploiting Multidimensional Data Analisis (MDA) techniques to represent the evolving association structures among attributes over different time-frames. The general aim is to detect the stability of the deviation from indipendence in the occurrence of an observed set of attributes stored as binary stream.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
