We give a 1-pass Õ(m1−2/k)-space algorithm for computing the k-th frequency moment of a data stream for any real k> 2. Together with the lower bounds of [1, 2, 4], this resolves the main problem left open by Alon et al in 1996 [1]. Our algorithm also works for streams with deletions and thus gives an Õ(m1−2/p) space algorithm for the Lp difference problem for any p> 2. This essentially matches the known Ω(m 1−2/p−o(1) ) lower bound of [13, 2]. Finally the update time of our algorithm is Õ(1)
AbstractThe frequency moments of a sequence containingmielements of typei, 1⩽i⩽n, are the numbersFk=...
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of m...
Given a stream with frequency vector f in n dimensions, we characterize the space necessary for appr...
We give a one-pass, O~(m^{1-2/k})-space algorithm for estimating the k-th frequency moment of a data...
In 1999 Alon et al. introduced the still active research topic of approximating the frequency moment...
In this paper we consider the problem of approximating frequency moments in the streaming model. Giv...
In this paper, we provide the first optimal algorithm for the remaining open question from the semin...
We consider the read/write streams model, an extension of the standard data stream model in which an...
Recently, an extension of the standard data stream model has been introduced in which an algorithm c...
Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for...
We consider general update streams, where, the stream is a sequence of updates of the form $(index, ...
The frequency moments of a sequence containing mi elements of type i, for 1 i n, are the numbers Fk ...
We revisit one of the classic problems in the data stream literature, namely, that of estimating the...
We study the classical problem of moment estimation of an underlying vector whose $n$ coordinates ar...
Estimating frequency moments and $L_p$ distances are well studied problems in the adversarial data...
AbstractThe frequency moments of a sequence containingmielements of typei, 1⩽i⩽n, are the numbersFk=...
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of m...
Given a stream with frequency vector f in n dimensions, we characterize the space necessary for appr...
We give a one-pass, O~(m^{1-2/k})-space algorithm for estimating the k-th frequency moment of a data...
In 1999 Alon et al. introduced the still active research topic of approximating the frequency moment...
In this paper we consider the problem of approximating frequency moments in the streaming model. Giv...
In this paper, we provide the first optimal algorithm for the remaining open question from the semin...
We consider the read/write streams model, an extension of the standard data stream model in which an...
Recently, an extension of the standard data stream model has been introduced in which an algorithm c...
Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for...
We consider general update streams, where, the stream is a sequence of updates of the form $(index, ...
The frequency moments of a sequence containing mi elements of type i, for 1 i n, are the numbers Fk ...
We revisit one of the classic problems in the data stream literature, namely, that of estimating the...
We study the classical problem of moment estimation of an underlying vector whose $n$ coordinates ar...
Estimating frequency moments and $L_p$ distances are well studied problems in the adversarial data...
AbstractThe frequency moments of a sequence containingmielements of typei, 1⩽i⩽n, are the numbersFk=...
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of m...
Given a stream with frequency vector f in n dimensions, we characterize the space necessary for appr...