Abstract not found

We introduce exponential search trees as a novel technique for converting static polynomial space search structures for ordered sets into fully-dynamic linear space data structures. This leads to an optimal bound of O ( � log n / log log n) for searching and updating a dynamic set X of n integer keys in linear space. Searching X for an integer y means finding the maximum key in X which is smaller than or equal to y. This problem is equivalent to the standard text book problem of maintaining an ordered set. The best previous deterministic linear space bound was O(log n / log log n) due to Fredman and Willard from STOC 1990. No better deterministic search bound was known using polynomial space. We also get the following worst-case linear space trade-offs between the number n, the word length W, and the maximal key U < 2W: O(min{log log n + log log U log n / log W, log log n · log log log U}). These trade-offs are, however, not likely to be optimal. Our results are generalized to finger searching and string searching, providing optimal results for both in terms of n.

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 show that any algorithm computing the median of a stream presented in random order, using polylog(n) space, requires an optimal Ω(log log n) passes, resolving an open question from the seminal paper on streaming by Munro and Paterson, from FOCS 1978. 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

Understanding how a single edge deletion can affect the connectivity of a graph amounts to finding the graph bridges. But when faced with d> 1 deletions, can we establish as easily how the connectivity changes? When planning for an emergency, we want to understand the structure of our network ahead of time, and respond swiftly when an emergency actually happens. We describe a linear-space representation of graphs which enables us to determine how a batch of edge updates can impact the graph. Given a set of d edge updates, in time O(d polylg n) we can obtain the number of connected components, the size of each component, and a fast oracle for answering connectivity queries in the updated graph. The initial representation is polynomial-time constructible. 1.

We present a number of new results on one of the most extensively studied topics in computational geometry, orthogonal range searching. All our results are in the standard word RAM model: 1. We present two data structures for 2-d orthogonal range emptiness. The first achieves O(n lg lg n) space and O(lg lg n) query time, assuming that the n given points are in rank space. This improves the previous results by Alstrup, Brodal, and Rauhe (FOCS’00), with O(n lg ε n) space and O(lg lg n) query time, or with O(n lg lg n) space and O(lg 2 lg n) query time. Our second data structure uses O(n) space and answers queries in O(lg ε n) time. The best previous O(n)-space data structure, due to Nekrich (WADS’07), answers queries in O(lg n / lg lg n) time. 2. We give a data structure for 3-d orthogonal range reporting with O(n lg 1+ε n) space and O(lg lg n+ k) query time for points in rank space, for any constant ε> 0. This improves the previous results by Afshani (ESA’08), Karpinski and Nekrich (COCOON’09), and Chan (SODA’11), with O(n lg 3 n) space and O(lg lg n + k) query time, or with O(n lg 1+ε n) space and O(lg 2 lg n + k) query time. Consequently, we obtain improved upper bounds for orthogonal range reporting in all constant dimensions above 3.

We study the dynamic membership (or dynamic dictionary) problem, which is one of the most fundamental problems in data structures. We study the problem in the external memory model with cell size b bits and cache size m bits. We prove that if the amortized cost of updates is at most 0.999 (or any other constant < 1), then the query cost must be Ω(logb log n (n/m)), where n is the number of elements in the dictionary. In contrast, when the update time is allowed to be 1 + o(1), then a bit vector or hash table give query time O(1). Thus, this is a threshold phenomenon for data structures. This lower bound answers a folklore conjecture of the external memory community. Since almost any data structure task can solve membership, our lower bound implies a dichotomy between two alternatives: (i) make the amortized update time at least 1 (so the data structure does not buffer, and we lose one of the main potential advantages of the cache), or (ii) make the query time at least roughly logarithmic in n. Our result holds even when the updates and queries are chosen uniformly at random and there are no deletions; it holds for randomized data structures, holds when the universe size is O(n), and does not make any restrictive assumptions such as indivisibility. All of the lower bounds we prove hold regardless of the space consumption of the data structure, while the upper bounds only need linear space. The lower bound has some striking implications for external memory data structures. It shows that the query complexities of many problems such as 1D-range counting, predecessor, rank-select, and many others, are all the same

At STOC’06, we presented a new technique for proving cell-probe lower bounds for static data structures with deterministic queries. This was the first technique which could prove a bound higher than communication complexity, and it gave the first separation between data structures with linear and polynomial space. The new technique was, however, heavily tuned for the deterministic worst-case, demonstrating long query times only for an exponentially small fraction of the input. In this paper, we extend the technique to give lower bounds for randomized query algorithms with constant error probability. Our main application is the problem of searching predecessors in a static set of n integers, each contained in a ℓ-bit word. Our trade-off lower bounds are tight for any combination of parameters. For small space, i.e. n 1+o(1) , proving such lower bounds was inherently impossible through known techniques. An interesting new consequence is that for near linear space, the classic van Emde Boas search time of O(lg ℓ) cannot be improved, even if we allow randomization. This is a separation from polynomial space, since Beame and Fich [STOC’02] give a predecessor search time of O(lg ℓ / lg lg ℓ) using quadratic space. We also show a tight Ω(lg lg n) lower bound for 2-dimensional range queries, via a new reduction. This holds even in rank space, where no superconstant lower bound was known, neither randomized nor worst-case. We also slightly improve the best lower bound for the approximate nearest neighbor problem, when small space is available. 1

There is a natural relationship between lower bounds in the multi-pass stream model and lower bounds in multi-round communication. However, this connection is less understood than the connection between single-pass stream computation and one-way communication. In this paper, we consider data-stream problems for which reductions from natural multi-round communication problems do not yield tight bounds or do not apply. While lower bounds are known for some of these data-stream problems, many of these only apply to deterministic or comparison-based algorithms, whereas the lower bounds we present apply to any (possibly randomized) algorithms. Our results are particularly relevant to evaluating functions that are dependent on the ordering of the stream, such as the longest increasing subsequence and a variant of tree pointer jumping in which pointers are revealed according to a post-order traversal. Our approach is based on establishing “pass-elimination” type results that are analogous to the round-elimination results of Miltersen et al. [23] and Sen [29]. We demonstrate our approach by proving tight bounds for a range of data-stream problems including finding the longest increasing sequences (a problem that has recently become very