Secure and privacy-preserving storage of digital data typically requires encrypting it, where the retrieval will man-date its decryption. The overhead of these encryption/decryption requirements introduce some latency, which might be limiting user experience on massive volumes for real-time applications. Reducing this computational load have been studied previously by using lightweight algorithms or selective/partial encryption schemes. In this work, we propose using a recently introduced privacy-preserving coding method [1] (COCOON’2023) to reduce this load and observe that the number of encryption/decryption operations can be reduced by more than 75%, which can be a decent relief especially for real-world applications. We particularly consider privacy requirements of some applications on multimedia data, most typically images and videos.

We revisit the Contextual Pattern Matching Problem, defined as follows: preprocess a text T[1, n], so that given a query consisting of a string P and a length P, the occurrences of all distinct strings XPY where |X|=|Y|=P can be reported. This problem was introduced by Navarro, who presented an O($\overline{r}\log(n/\overline{r}))$ space data structure, where $\overline{r}$ is the maximum of the number of runs in the BWT of the text $\mathrm{T}[1,n]$ and its reverse. His solution reports all c contextual occurrences in $O(|P|+c\log n)$ time. However, the only known bounds on $\overline{r}$ are $\overline{r}=O(r\log^{2}n)$ where r is the number of runs in the BWT of T, making it desirable to avoid using structures with space dependent on $\overline{r}$. We demonstrate that this is possible without a significant sacrifice in query time by providing an $O(r\log(n/r))$ space solution that answers queries in $O(|P|+c\log P\cdot\log(n/r))$ time.

Two equal-length strings X and Y over an alphabet Σ of size σ are a parameterized match iff X can be transformed to Y by renaming the character X[i] to the character Y[i] for 1 ≤ i ≤ |X| using a one-to-one function from the set of characters in X to the set of characters in Y. The parameterized text indexing problem is defined as: Index a text T of n characters over an alphabet set Σ of size σ, such that whenever a pattern P[1, p] comes as a query, we can report all occ parameterized occurrences of P in T. A position i ϵ [1, n] is a parameterized occurrence of P in T, iff P and T[i,(i+p-1)] are a parameterized match. We study an interesting generalization of this problem, where the pattern contains one wildcard character φ ∉ Σ that matches with any other character in Σ. Therefore, for a pattern P[1, p] = P1φP2, our task is to report all positions i in T, such that the string P_1 P_2 and the string obtained by concatenating T[i,(i+|P1|-1)] and T[(i+|P1|+1),(i+p-1)] are a parameterized match. We show that such queries can be answered in optimal O(p+occ) time per query using an O(n log n) space index. We then show how to compress our index into O(n log σ) space but with a higher query cost of O(p(log log n+logσ)+occ logσ).

In this paper, we consider a variant of the color range reporting problem called color reporting with frequencies. Our goal is to pre-process a set of colored points into a data structure, so that given a query range Q, we can report all colors that appear in Q, along with their respective frequencies. In other words, for each reported color, we also output the number of times it occurs in Q. We describe an external-memory data structure that uses O(N(1+log^2D/log N)) words and answers one-dimensional queries in O(1 +K/B) I/Os, where N is the total number of points in the data structure, D is the total number of colors in the data structure, K is the number of reported colors, and B is the block size. Next we turn to an approximate version of this problem: report all colors sigma that appear in the query range; for every reported color, we provide a constant-factor approximation on its frequency. We consider color reporting with approximate frequencies in two dimensions. Our data structure uses O(N) space and answers two-dimensional queries in O(log_B N +log^*B + K/B) I/Os in the special case when the query range is bounded on two sides. As a corollary, we can also answer one-dimensional approximate queries within the same time and space bounds.

Galled treesare a class of tree-like phylogenetic networks in which the loops are not overlapping with each other. They are popularly used among biologists to represent the evolutionary history between a set of species. In this paper, we propose a compact encoding for the structure of a galled tree, and show that with our encoding, the treecontainment problemon a galled tree can be solved in optimal time.

On a given vector X = (x1, x2,..., xn) of integers, the range selection (i, j, k) query is finding the k-th smallest integer in (xi, xi+1,..., xj) for any (i, j, k) such that 1 ≤ i ≤ j ≤ n, and 1 ≤ k ≤ j - i + 1. Previous studies on the problem kept X intact and proposed data structures that occupied additional O(n · log n) bits of space over the X itself that answer the queries in logarithmic time. In this study, we replace X and encode all integers in it via a single wavelet tree by using S = n · log u + Σ∀ilog xi+ o(n · log u + Σ∀ilog xi) bits, where u is the number of distinct ⌊log xi⌋ values observed in X. Notice that u is at most 32 (64) for 32-bit (64-bit) integers and when xi> u, the space used for xiin the proposed data structure is less then the Elias-δ coding of xi. Besides data-aware coding of X, the range selection is performed in O(log u + log x') time where x' is the k-th smallest integer in the queried range. This somewhat adaptive result interestingly achieves the range selection regardless of the size of X, and totally depends on the actual answer of the query. In summary, to the best of our knowledge, we present the first algorithm using data-aware space and time for the general range selection problem.

Orthogonal range reporting is one of the classic and most fundamental data structure problems. (2,1,1) query is a 3 dimensional query with two-sided constraint on the first dimension and one sided constraint on each of the 2nd and 3rd dimension. Given a set of N points in three dimension, a particular formulation of such a (2,1,1) query (known as four-sided range reporting in three-dimension) asks to report all those K points within a query region [a, b]X(-infinity, c]X[d, infinity). These queries have overall 4 constraints. In Word-RAM model, the best known structure capable of answering such queries with optimal query time takes O(N log^{epsilon} N) space, where epsilon>0 is any positive constant. It has been shown that any external memory structure in optimal I/Os must use Omega(N log N/ log log_B N) space (in words), where B is the block size [Arge et al., PODS 1999]. In this paper, we study a special type of (2,1,1) queries, where the query parameters a and c are the same i.e., a=c. Even though the query is still four-sided, the number of independent constraints is only three. In other words, one constraint is shared. We call this as a Shared-Constraint Range Reporting (SCRR) problem. We study this problem in both internal as well as external memory models. In RAM model where coordinates can only be compared, we achieve linear-space and O(log N+K) query time solution, matching the best-known three dimensional dominance query bound. Whereas in external memory, we present a linear space structure with O(log_B N + log log N + K/B) query I/Os. We also present an I/O-optimal (i.e., O(log_B N+K/B) I/Os) data structure which occupies O(N log log N)-word space. We achieve these results by employing a novel divide and conquer approach. SCRR finds application in database queries containing sharing among the constraints. We also show that SCRR queries naturally arise in many well known problems such as top-k color reporting, range skyline reporting and ranked document retrieval.

Given an array A[1...n] of n distinct elements from the set {1, 2, ..., n} a range maximum query RMQ(a, b) returns the highest element in A[a...b] along with its position. In this paper, we study a generalization of this classical problem called Categorical Range Maxima Query (CRMQ) problem, in which each element A[i] in the array has an associated category (color) given by C[i] ∈ [σ]. A query then asks to report each distinct color c appearing in C[a...b] along with the highest element (and its position) in A[a...b] with color c. Let pc denote the position of the highest element in A[a...b] with color c. We investigate two variants of this problem: a threshold version and a top-k version. In threshold version, we only need to output the colors with A[pc] more than the input threshold τ, whereas top-k variant asks for k colors with the highest A[pc] values. In the word RAM model, we achieve linear space structure along with O(k) query time, that can report colors in sorted order of A[•]. In external memory, we present a data structure that answers queries in optimal O(1+k/B) I/O's using almost-linear O(n log* n) space, as well as a linear space data structure with O(log* n + k/B) query I/Os. Here k represents the output size, log* n is the iterated logarithm of n and B is the block size. CRMQ has applications to document retrieval and categorical range reporting -- giving a one-shot framework to obtain improved results in both these problems. Our results for CRMQ not only improve the existing best known results for three-sided categorical range reporting but also overcome the hurdle of maintaining color uniqueness in the output set.

Hon et al. (2011) recently proposed a variant of suffix tree, called circular suffix tree, and showed that it can be compressed into succinct space and can be used to solve the circular dictionary matching problem efficiently. Although there are several efficient construction algorithms for the suffix tree in the literature, none of them can be applied directly to construct circular suffix tree due to the different nature of the patterns being indexed. Here, we give the first construction algorithm for the circular suffix tree, which takes O(n log n) time and requires O(n log σ + d log n)$ bits of working space, where n denotes the total length of the patterns in the dictionary, d denotes the number of patterns, and s denotes the alphabet size.

Let D = {d1, d2,...dD} be a given collection of D string documents of total length n, our task is to index D, such that whenever a pattern P (of length p) and an integer k come as a query, those k documents in which P appears the most number of times can be listed efficiently. In this paper, we propose a compressed index taking 2|CSA| + D logn/D + O(D) + o(n) bits of space, which answers a query with O(tsalog k logϵn) per document report time. This improves the O(tsalog k log1+ϵn) per document report time of the previously best-known index with (asymptotically) the same space requirements [Belazzougui and Navarro, SPIRE 2011]. Here, |CSA| represents the size (in bits) of the compressed suffix array (CSA) of the text obtained by concatenating all documents in V, and tsais the time for decoding a suffix array value using the CSA.

The existing heuristics for top-k join queries, aiming to minimize the scan-depth, rely heavily on scores and correlation of scores. It is known that for uniformly random scores between two relations of length n, scan-depth of √kn is required. Moreover, optimizing multiple criteria of selections that are anti-correlated may require scan-depth up to (n + k)/2. We build a linear space index, which in anticipation of worst-case queries maintains a subset of answers. Based on this, we achieve Õ(√kn) join trials i.e., average case performance even for the worst-case queries. The experimental evaluation shows superior performance against the well-known Rank-Join algorithm.

Given a set D of d patterns of total length n, the dictionary matching problem is to index D such that for any query text T, we can locate the occurrences of any pattern within T efficiently. This problem can be solved in optimal O(|T|+occ) time by the classical AC automaton (Aho and Corasick, 1975) where occ denotes the number of occurrences. The space requirement is O(n) words. In the {approximate} dictionary matching problem with one error, we consider a substring of T[i..j] an occurrence of P whenever the edit distance between T[i..j] and P is at most one. For this problem, the best known indexes are by Cole et al. (2004), which requires O(n+ d\log{d}) words of space and reports all occurrences in O(|T|\log{d}\log{\log{d}}+occ) time, and by Ferragina et al. (1999), which requires O(n^{1+\epsilon}) words of space and reports all occurrences in O(|T|\log\log n + occ) time. Recently, there have been successes in compressing the dictionary matching index while keeping the query time optimal (Belazzougui, 2010, Hon et al., 2010). However, a compressed index for approximate dictionary matching problem is still open. In this paper, we propose the first such index which requires an optimal nH_k+O(n)+o(n\log\sigma)-bit index space, where H_k denotes the kth-order empirical entropy of \D, and \sigma is the size of alphabet set from which all the characters in \D and T are drawn. The query time of our index is O(σ|T|log3n log \ log n + occ).

Property matching is a biologically motivated problem where the task is to find those occurrences of an online pattern P in a string text T (of size n), such that the matched part in T satisfies some conceptual property. The property of a string is a set π of (possibly overlapping) intervals {(s1, f1), (s2, f2),⋯} corresponding to the part of T, and an occurrence of a pattern P at T[i..(i + |P| - 1)] is a valid output under the property π only if T[i..(i + |P| - 1)] is completely contained in some interval (sj, fj) ∈ π. Algorithmically this problem can be solved in time linear to the size of text. Amir et al. (2008) introduced the indexing version of this problem, where they preprocess the text in O(n log σ + n log log n) time and maintain an O(n log n) bits index, named Property Suffix Tree (PST), where σ denotes the alphabet size. PST can perform property matching in optimal O(|P| log σ + occπ) time, where occπis the number of occurrences of P in T which satisfies the property. Later, Iliopoulos and Rahman (2008) proposed an alternative index which can be constructed in linear time. Recently Kopelowitz (2010) considered the dynamic version of this problem where intervals can be added or deleted. However, all these indexes requires space of O(n log n) bits, which can be much more than the size of the text (n log σ bits). In this paper, we propose the first index for property matching which takes space close to the entropy compressed space requirement of the text. Our compressed index takes |CSA| + n(2 + ∈ + o(1)) bits space and can perform query answering in O(t(|P|) + 1/∈ occπtSA) time, where |CSA| is the size of compressed suffix array (CSA), t(|P|) and tSAare the time for searching a pattern of length |P| and the time for computing the suffix array value using CSA, respectively, and e is a constant. We also introduce a dynamic index, taking |CSA| + n(2 + ∈ + o(1)) + O(|π|log n) bits of space, which can perform query answering in O(t(|P|) + 1/∈ occπ(log n/log log n + tSA) log n) time and can update (insert or delete) an interval (s, f) in O((f - s + 1)(log n + log |π| + tSA)) time.

Inverted indexes are the most fundamental and widely used data structures in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often applied to further reduce the space requirement of these lists. However, the index has a shortcoming, in that only predefined pattern queries can be supported efficiently. In terms of string documents where word boundaries are undefined, if we have to index all the substrings of a given document, then the storage quickly becomes quadratic in the data size. Also, if we want to apply the same type of indexes for querying phrases or sequence of words, then the inverted index will end up storing redundant information. In this paper, we show the first set of inverted indexes which work naturally for strings as well as phrase searching. The central idea is to exclude document d in the inverted list of a string P if every occurrence of P in d is subsumed by another string of which P is a prefix. With this we show that our space utilization is close to the optimal. Techniques from succinct data structures are deployed to achieve compression while allowing fast access in terms of frequency and document id based retrieval. Compression and speed trade-offs are evaluated for different variants of the proposed index. For phrase searching, we show that our indexes compare favorably against a typical inverted index deploying position-wise intersections. We also show efficient top-k based retrieval under relevance metrics like frequency and tf-idf.