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1. Ranked Document Retrieval in External Memory

   https://doi.org/10.1145/3559763  

   Shah, Rahul ; Sheng, Cheng ; Thankachan, Sharma ; Vitter, Jeffrey ( January 2023 , ACM Transactions on Algorithms)

   The ranked (or top-k) document retrieval problem is defined as follows: preprocess a collection{T₁,T₂,… ,T_d}ofdstrings (called documents) of total lengthninto a data structure, such that for any given query(P,k), wherePis a string (called pattern) of lengthp ≥ 1andk ∈ [1,d]is an integer, the identifiers of thosekdocuments that are most relevant toPcan be reported, ideally in the sorted order of their relevance. The seminal work by Hon et al. [FOCS 2009 and Journal of the ACM 2014] presented anO(n)-space (in words) data structure withO(p+klogk)query time. The query time was later improved toO(p+k)[SODA 2012] and further toO(p/log_σn+k)[SIAM Journal on Computing 2017] by Navarro and Nekrich, whereσis the alphabet size. We revisit this problem in the external memory model and present three data structures. The first one takesO(n)-space and answer queries inO(p/B+ log_Bn + k/B+log^*(n/B)) I/Os, whereBis the block size. The second one takesO(nlog^*(n/B)) space and answer queries in optimalO(p/B+ log_Bn + k/B)I/Os. In both cases, the answers are reported in the unsorted order of relevance. To handle sorted top-kdocument retrieval, we present anO(nlog(d/B))space data structure with optimal query cost.

    

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2. On the Optimal Time/Space Tradeoff for Hash Tables

   Bender, Michael ; Farach-Colton, Martin ; Kuszmaul, John ; Kuszmaul, William ; Liu, Mingmou ( January 2022 , STOC)

   For nearly six decades, the central open question in the study of hash tables has been to determine the optimal achievable tradeoff curve between time and space. State-of-the-art hash tables offer the following guarantee: If keys/values are Θ(logn) bits each, then it is possible to achieve constant-time insertions/deletions/queries while wasting only O(loglogn) bits of space per key when compared to the information-theoretic optimum. Even prior to this bound being achieved, the target of O(log log n) wasted bits per key was known to be a natural end goal, and was proven to be optimal for a number of closely related problems (e.g., stable hashing, dynamic retrieval, and dynamically-resized filters). This paper shows that O(log log n) wasted bits per key is not the end of the line for hashing. In fact, for any k ∈ [log∗ n], it is possible to achieve O(k)-time insertions/deletions, O(1)-time queries, and O(log(k) n) = Ologlog···logn 􏰟 􏰞􏰝 􏰠 k wasted bits per key (all with high probability in n). This means that, each time we increase inser- tion/deletion time by an additive constant, we reduce the wasted bits per key exponentially. We further show that this tradeoff curve is the best achievable by any of a large class of hash tables, including any hash table designed using the current framework for making constant-time hash tables succinct. Our results hold not just for fixed-capacity hash tables, but also for hash tables that are dynamically resized (this is a fundamental departure from what is possible for filters); and for hash tables that store very large keys/values, each of which can be up to no(1) bits (this breaks with the conventional wisdom that larger keys/values should lead to more wasted bits per key). For very small keys/values, we are able to tighten our bounds to o(1) wasted bits per key, even when k = O(1). Building on this, we obtain a constant-time dynamic filter that uses n􏰕logε−1􏰖+nloge+o(n) bits of space for a wide choice of 

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3. Approximate shortest paths and distance oracles in weighted unit-disk graphs

   https://doi.org/10.20382/jocg.v10i2  

   Chan, Timothy M. ; Skrepetos, Dimitrios ( January 2019 , Journal of computational geometry)

   We present the first near-linear-time algorithm that computes a (1+ε)-approximation of the diameter of a weighted unit-disk graph of n vertices. Our algorithm requires O(n log^2 n) time for any constant ε>0, so we considerably improve upon the near-O(n^{3/2})-time algorithm of Gao and Zhang (2005). Using similar ideas we develop (1+ε)-approximate \emph{distance oracles} of O(1) query time with a likewise improvement in the preprocessing time, specifically from near O(n^{3/2}) to O(n log^3 n). We also obtain similar new results for a number of related problems in the weighted unit-disk graph metric such as the radius and the bichromatic closest pair. As a further application we employ our distance oracle, along with additional ideas, to solve the (1+ε)-approximate \emph{all-pairs bounded-leg shortest paths\/} (apBLSP) problem for a set of n planar points. Our data structure requires O(n^2 log n) space, O(loglog n) query time, and nearly O(n^{2.579}) preprocessing time for any constant ε>0, and is the first that breaks the near-cubic preprocessing time bound given by Roditty and Segal (2011). 

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4. Beyond equi-joins: ranking, enumeration and factorization

   https://doi.org/10.14778/3476249.3476306  

   Tziavelis, Nikolaos ; Gatterbauer, Wolfgang ; Riedewald, Mirek ( July 2021 , Proceedings of the VLDB Endowment)

   We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity properties: with n denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for every value of k , the k top-ranked answers are returned in O ( n polylog n + k log k ) time. This is within a polylogarithmic factor of O ( n + k log k ), i.e., the best known complexity for equi-joins, and even of O ( n + k ), i.e., the time it takes to look at the input and return k answers in any order. Our guarantees extend to join queries with selections and many types of projections (namely those called "free-connex" queries and those that use bag semantics). Remarkably, they hold even when the number of join results is n ℓ for a join of ℓ relations. The key ingredient is a novel O ( n polylog n )-size factorized representation of the query output , which is constructed on-the-fly for a given query and database. In addition to providing the first nontrivial theoretical guarantees beyond equi-joins, we show in an experimental study that our ranked-enumeration approach is also memory-efficient and fast in practice, beating the running time of state-of-the-art database systems by orders of magnitude. 

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5. The Maximum Exposure Problem

   Kumar, N ; Sintos, S ; Suri, S. ( September 2019 , Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques)

   Given a set of points P and axis-aligned rectangles R in the plane, a point p ∈ P is called exposed if it lies outside all rectangles in R. In the max-exposure problem, given an integer parameter k, we want to delete k rectangles from R so as to maximize the number of exposed points. We show that the problem is NP-hard and assuming plausible complexity conjectures is also hard to approximate even when rectangles in R are translates of two fixed rectangles. However, if R only consists of translates of a single rectangle, we present a polynomial-time approximation scheme. For general rectangle range space, we present a simple O(k) bicriteria approximation algorithm; that is by deleting O(k2) rectangles, we can expose at least Ω(1/k) of the optimal number of points. 

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