Results 1  10
of
18
Why simple hash functions work: Exploiting the entropy in a data stream
 In Proceedings of the 19th Annual ACMSIAM Symposium on Discrete Algorithms
, 2008
"... Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can assume that the hash function is truly random, mapping each data item independently and uniformly to the range. This idealiz ..."
Abstract

Cited by 33 (6 self)
 Add to MetaCart
Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can assume that the hash function is truly random, mapping each data item independently and uniformly to the range. This idealized model is unrealistic because a truly random hash function requires an exponential number of bits to describe. Alternatively, one can provide rigorous bounds on performance when explicit families of hash functions are used, such as 2universal or O(1)wise independent families. For such families, performance guarantees are often noticeably weaker than for ideal hashing. In practice, however, it is commonly observed that weak hash functions, including 2universal hash functions, perform as predicted by the idealized analysis for truly random hash functions. In this paper, we try to explain this phenomenon. We demonstrate that the strong performance of universal hash functions in practice can arise naturally from a combination of the randomness of the hash function and the data. Specifically, following the large body of literature on random sources and randomness extraction, we model the data as coming from a “block source, ” whereby
Linear probing with constant independence
 In STOC ’07: Proceedings of the thirtyninth annual ACM symposium on Theory of computing
, 2007
"... Hashing with linear probing dates back to the 1950s, and is among the most studied algorithms. In recent years it has become one of the most important hash table organizations since it uses the cache of modern computers very well. Unfortunately, previous analyses rely either on complicated and space ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
Hashing with linear probing dates back to the 1950s, and is among the most studied algorithms. In recent years it has become one of the most important hash table organizations since it uses the cache of modern computers very well. Unfortunately, previous analyses rely either on complicated and space consuming hash functions, or on the unrealistic assumption of free access to a truly random hash function. Already Carter and Wegman, in their seminal paper on universal hashing, raised the question of extending their analysis to linear probing. However, we show in this paper that linear probing using a pairwise independent family may have expected logarithmic cost per operation. On the positive side, we show that 5wise independence is enough to ensure constant expected time per operation. This resolves the question of finding a space and time efficient hash function that provably ensures good performance for linear probing.
Strongly historyindependent hashing with applications
 In Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
, 2007
"... We present a strongly history independent (SHI) hash table that supports search in O(1) worstcase time, and insert and delete in O(1) expected time using O(n) data space. This matches the bounds for dynamic perfect hashing, and improves on the best previous results by Naor and Teague on history ind ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
We present a strongly history independent (SHI) hash table that supports search in O(1) worstcase time, and insert and delete in O(1) expected time using O(n) data space. This matches the bounds for dynamic perfect hashing, and improves on the best previous results by Naor and Teague on history independent hashing, which were either weakly history independent, or only supported insertion and search (no delete) each in O(1) expected time. The results can be used to construct many other SHI data structures. We show straightforward constructions for SHI ordered dictionaries: for n keys from {1,..., n k} searches take O(log log n) worstcase time and updates (insertions and deletions) O(log log n) expected time, and for keys in the comparison model searches take O(log n) worstcase time and updates O(log n) expected time. We also describe a SHI data structure for the ordermaintenance problem. It supports comparisons in O(1) worstcase time, and updates in O(1) expected time. All structures use O(n) data space. 1
HashBased Techniques for HighSpeed Packet Processing
"... Abstract Hashing is an extremely useful technique for a variety of highspeed packetprocessing applications in routers. In this chapter, we survey much of the recent work in this area, paying particular attention to the interaction between theoretical and applied research. We assume very little bac ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
Abstract Hashing is an extremely useful technique for a variety of highspeed packetprocessing applications in routers. In this chapter, we survey much of the recent work in this area, paying particular attention to the interaction between theoretical and applied research. We assume very little background in either the theory or applications of hashing, reviewing the fundamentals as necessary. 1
Tabulation Based 5Universal Hashing and Linear Probing
"... Previously [SODA’04] we devised the fastest known algorithm for 4universal hashing. The hashing was based on small precomputed4universal tables. This led to a fivefold improvement in speed over direct methods based on degree 3 polynomials. In this paper, we show that if the precomputed tables a ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Previously [SODA’04] we devised the fastest known algorithm for 4universal hashing. The hashing was based on small precomputed4universal tables. This led to a fivefold improvement in speed over direct methods based on degree 3 polynomials. In this paper, we show that if the precomputed tables are made 5universal, then the hash value becomes 5universal without any other change to the computation. Relatively this leads to even bigger gains since the direct methods for 5universal hashing use degree 4 polynomials. Experimentally, we find that our method can gain up to an order of magnitude in speed over direct 5universal hashing. Some of the most popular randomized algorithms have been proved to have the desired expected running time using
Sketching and Streaming HighDimensional Vectors
, 2011
"... A sketch of a dataset is a smallspace data structure supporting some prespecified set of queries (and possibly updates) while consuming space substantially sublinear in the space required to actually store all the data. Furthermore, it is often desirable, or required by the application, that the sk ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
A sketch of a dataset is a smallspace data structure supporting some prespecified set of queries (and possibly updates) while consuming space substantially sublinear in the space required to actually store all the data. Furthermore, it is often desirable, or required by the application, that the sketch itself be computable by a smallspace algorithm given just one pass over the data, a socalled streaming algorithm. Sketching and streaming have found numerous applications in network traffic monitoring, data mining, trend detection, sensor networks, and databases. In this thesis, I describe several new contributions in the area of sketching and streaming algorithms. • The first spaceoptimal streaming algorithm for the distinct elements problem. Our algorithm also achieves O(1) update and reporting times. • A streaming algorithm for Hamming norm estimation in the turnstile model which achieves the best known space complexity.
LINEAR PROBING WITH 5WISE INDEPENDENCE ∗
"... Abstract. Hashing with linear probing dates back to the 1950s, and is among the most studied algorithms for storing (key,value) pairs. In recent years it has become one of the most important hash table organizations since it uses the cache of modern computers very well. Unfortunately, previous analy ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract. Hashing with linear probing dates back to the 1950s, and is among the most studied algorithms for storing (key,value) pairs. In recent years it has become one of the most important hash table organizations since it uses the cache of modern computers very well. Unfortunately, previous analyses rely either on complicated and space consuming hash functions, or on the unrealistic assumption of free access to a hash function with random and independent function values. Carter and Wegman, in their seminal paper on universal hashing, raised the question of extending their analysis to linear probing. However, we show in this paper that linear probing using a 2wise independent hash function may have expected logarithmic cost per operation. Recently, Pǎtra¸scu and Thorup have shown that also 3 and 4wise independent hash functions may give rise to logarithmic expected query time. On the positive side, we show that 5wise independence is enough to ensure constant expected time per operation. This resolves the question of finding a space and time efficient hash function that provably ensures good performance for hashing with linear probing.