We generalize Cuckoo Hashing [23] to d-ary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes and the expected amortized insertion time is constant. This is the first dictionary that has worst case constant access time and expected constant update time, works with (1 + ffl) n space, and supports satellite information. Experiments indicate that d = 4 choices suffice for ffl 0:03. We also describe variants of the data structure that allow the use of hash functions that can be evaluted in constant time.

Suffix trees are by far the most important data structure in stringology, with myriads of applications in fields like bioinformatics and information retrieval. Classical representations of suffix trees require O(n log n) bits of space, for a string of size n. This is considerably more than the nlog 2 σ bits needed for the string itself, where σ is the alphabet size. The size of suffix trees has been a barrier to their wider adoption in practice. Recent compressed suffix tree representations require just the space of the compressed string plus Θ(n) extra bits. This is already spectacular, but still unsatisfactory when σ is small as in DNA sequences. In this paper we introduce the first compressed suffix tree representation that breaks this linear-space barrier. Our representation requires sublinear extra space and supports a large set of navigational operations in logarithmic time. An essential ingredient of our representation is the lowest common ancestor (LCA) query. We reveal important connections between LCA queries and suffix tree navigation.

We propose new succinct representations of ordinal trees, which have been studied extensively. It is known that any n-node static tree can be represented in 2n + o(n) bits and various operations on the tree can be supported in constant time under the word-RAM model. However the data structures are complicated and difficult to dynamize. We propose a simple and flexible data structure, called the range min-max tree, that reduces the large number of relevant tree operations considered in the literature, to a few primitives that are carried out in constant time on sufficiently small trees. The result is extended to trees of arbitrary size, achieving 2n + O(n/polylog(n)) bits of space. The redundancy is significantly lower than any previous proposal. For the dynamic case, where insertion/deletion of nodes is allowed, the existing data structures support very limited operations. Our data structure builds on the range min-max tree to achieve 2n + O(n / log n) bits of space and O(log n) time for all the operations. We also propose an improved data structure using 2n+O(n loglog n / logn) bits and improving the time to O(log n / loglog n) for most operations. 1

We propose new succinct representations of ordinal trees, which have been studied extensively. It is known that any n-node static tree can be represented in 2n + o(n) bits and a large number of operations on the tree can be supported in constant time under the word-RAM model. However existing data structures are not satisfactory in both theory and practice because (1) the lower-order term is Ω(nlog log n / log n), which cannot be neglected in practice, (2) the hidden constant is also large, (3) the data structures are complicated and difficult to implement, and (4) the techniques do not extend to dynamic trees supporting insertions and deletions of nodes. We propose a simple and flexible data structure, called the range min-max tree, that reduces the large number of relevant tree operations considered in the literature to a few primitives, which are carried out in constant time on sufficiently small trees. The result is then extended to trees of arbitrary size, achieving 2n + O(n/polylog(n)) bits of space. The redundancy is significantly lower than in any previous proposal, and the data structure is easily implemented. Furthermore, using the same framework, we derive the first fully-functional dynamic succinct trees. 1

Abstract. Operations rank and select over a sequence of symbols have many applications to the design of succinct and compressed data structures to manage text collections, structured text, binary relations, trees, graphs, and so on. We are interested in the case where the collections can be updated via insertions and deletions of symbols. Two current solutions stand out as the best in the tradeoff of space versus time (considering all the operations). One by Mäkinen and Navarro achieves compressed space (i.e., nH0 + o(n log σ) bits) and O(log nlog σ) worst-case time for all the operations, where n is the sequence length, σ is the alphabet size, and H0 is the zero-order entropy of the sequence. The other log σ log log n solution, by Lee and Park, achieves O(log n(1 +)) amortized time and uncompressed space, i.e. nlog σ +O(n)+o(nlog σ) bits. In this paper we show that the best of both worlds can be achieved. We log σ combine the solutions to obtain nH0+o(nlog σ) bits of space and O(log n(1+)) worst-case time log log n for all the operations. Apart from the best current solution, we obtain some byproducts that might be

Abstract. k-ary trees are a fundamental data structure in many textprocessing algorithms (e.g., text searching). The traditional pointer-based representation of trees is space consuming, and hence only relatively small trees can be kept in main memory. Nowadays, however, many applications need to store a huge amount of information. In this paper we present a succinct representation for dynamic k-ary trees of n nodes, requiring 2n + nlog k + o(nlog k) bits of space, which is close to the information-theoretic lower bound. Unlike alternative representations where the operations on the tree can be usually computed in O(log n) time, our data structure is able to take advantage of asymptotically smaller values of k, supporting the basic operations parent and child in O(log k+log log n) time, which is o(log n) time whenever log k = o(log n). Insertions and deletions of leaves in the tree are supported log k in O((log k + log log n)(1 +)) amortized time. Our rep-log (log k+log log n) resentation also supports more specialized operations (like subtreesize, depth, etc.), and provides a new trade-off when k = O(1) allowing faster updates (in O(log log n) amortized time, versus the amortized time of O((log log n) 1+ǫ), for ǫ> 0, from Raman and Rao [21]), at the cost of slower basic operations (in O(log log n) time, versus O(1) time of [21]). 1

Operations rank and select over a sequence of symbols have many applications to the design of succinct and compressed data structures managing text collections, structured text, binary relations, trees, graphs, and so on. We are interested in the case where the collections can be updated via insertions and deletions of symbols. Two current solutions stand out as the best in the tradeoff of space versus time (when considering all the operations). One solution, by Mäkinen and Navarro, achieves compressed space (i.e., nH0 +o(n log σ) bits) and O(log n log σ) worst-case time for all the operations, where n is the sequence length, σ is the alphabet size, and H0 is the zero-order entropy of the sequence. The other solution, by Lee and log σ Park, achieves O(log n(1 + log log n)) amortized time and uncompressed space, i.e. n log2 σ +O(n)+o(n log σ) bits. In this paper we show that the best of both worlds can be achieved. We combine the solutions to obtain nH0 + o(n log σ) bits of space log σ log log n and O(log n(1 +)) worst-case time for all the operations. Apart from the best current solution to the problem, we obtain several byproducts of independent interest applicable to partial sums, text indexes, suffix arrays, the Burrows-Wheeler transform, and others.

Many applications dealing with large data structures can benefit from keeping them in compressed form. Compression has many benefits: it can allow a representation to fit in main memory rather than swapping out to disk, and it improves cache performance since it allows more data to fit into the cache. However, a data structure is only useful if it allows the application to perform fast queries (and updates) to the data.

We consider the problem of maintaining a dynamic dictionary T of keys and associated data for which both the keys and data are bit strings that can vary in length from zero up to the length w of a machine word. We present a data structure for this variable-bit-length dictionary problem that supports constant time lookup and expected amortized constant time insertion and deletion. It uses O(m + 3n − n log 2 n) bits, where n is the number of elements in T, and m is the total number of bits across all strings in T (keys and data). Our dictionary uses an array A[1... n] in which locations store variable-bit-length strings. We present a data structure for this variable-bit-length array problem that supports worst-case constant-time lookups and updates and uses O(m + n) bits, where m is the total number of bits across all strings stored in A. The motivation for these structures is to support applications for which it is helpful to efficiently store short varying length bit strings. We present several applications, including representations for semi-dynamic graphs, order queries on integers sets, cardinal trees with varying cardinality, and simplicial meshes of d dimensions. These results either generalize or simplify previous results.

In a recent article, we presented a succinct representation of triangulations that supports efficient navigation operations. Here this representation is improved to allow for efficient local updates of the triangulations. Precisely, we show how a succinct representation of a triangulation with m triangles can be maintained under vertex insertions in O(1) amortized time and under vertex deletions/edge flips in O(lg 2 m) amortized time. Our structure achieves the information theory bound for the storage for the class of triangulations with a boundary, requiring asymptotically 2.17m + o(m) bits, and supports adjacency queries between triangles in O(1) time (an extra amount of O(g lg m) bits are needed for representing triangulations of genus g surfaces).