Succinct Data Structures for Retrieval and Approximate Membership (Extended Abstract)

The

retrieval problem

is the problem of associating data with keys in a set. Formally, the data structure must store a function

$f\colon U\to \{0,1\}^r$

that has specified values on the elements of a given set

S

 ⊆ 

U

, |

S

| = 

n

, but may have any value on elements outside

S

. All known methods (e. g. those based on perfect hash functions), induce a space overhead of

Θ

(

n

) bits over the optimum, regardless of the evaluation time. We show that for any

k

, query time

O

(

k

) can be achieved using space that is within a factor 1 + 

e

− 

k

of optimal, asymptotically for large

n

. The time to construct the data structure is

O

(

n

), expected. If we allow logarithmic evaluation time, the additive overhead can be reduced to

O

(loglog

n

) bits whp. A general reduction transfers the results on retrieval into analogous results on

approximate membership

, a problem traditionally addressed using Bloom filters. Thus we obtain space bounds arbitrarily close to the lower bound for this problem as well. The evaluation procedures of our data structures are extremely simple. For the results stated above we assume free access to fully random hash functions. This assumption can be justified using space

o

(

n

) to simulate full randomness on a RAM.