TY - GEN
T1 - Data streams and data synopses for massive data sets
AU - Matias, Yossi
PY - 2005
Y1 - 2005
N2 - With the proliferation of data intensive applications, it has become necessary to develop new techniques to handle massive data sets. Traditional algorithmic techniques and data structures are not always suitable to handle the amount of data that is required and the fact that the data often streams by and cannot be accessed again. A field of research established over the past decade is that of handling massive data sets using data synopses, and developing algorithmic techniques for data stream models. We will discuss some of the research work that has been done in the field, and provide a decades' perspective to data synopses and data streams.
AB - With the proliferation of data intensive applications, it has become necessary to develop new techniques to handle massive data sets. Traditional algorithmic techniques and data structures are not always suitable to handle the amount of data that is required and the fact that the data often streams by and cannot be accessed again. A field of research established over the past decade is that of handling massive data sets using data synopses, and developing algorithmic techniques for data stream models. We will discuss some of the research work that has been done in the field, and provide a decades' perspective to data synopses and data streams.
UR - https://www.scopus.com/pages/publications/33646404342
U2 - 10.1007/11564096_6
DO - 10.1007/11564096_6
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AN - SCOPUS:33646404342
SN - 3540292438
SN - 9783540292432
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 8
EP - 9
BT - Machine Learning - ECML 2005
PB - Springer Verlag
T2 - 16th European Conference on Machine Learning, ECML 2005
Y2 - 3 October 2005 through 7 October 2005
ER -