Range query (computer science): Difference between revisions

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===Majority===

Finding frequent elements in a given set of items is one of the most important tasks in data mining. Finding frequent elements might be a difficult task to achieve when most items have similar frequencies. Therefore, it might be more beneficial if some threshold of significance was used for detecting such items. One of the most famous algorithms for finding the majority of an array was proposed by Boyer and Moore <ref>{{cite book |last1=Boyer|first1=Robert S.|date=1991|chapter-url=http://dx.doi.org/10.1007/978-94-011-3488-0_5|pages=105–117|place=Dordrecht|publisher=Springer Netherlands|access-date=2021-12-18|last2=Moore|first2=J. Strother|title=Automated Reasoning |chapter=MJRTY—A Fast Majority Vote Algorithm |series=Automated Reasoning Series |volume=1 |doi=10.1007/978-94-011-3488-0_5 |isbn=978-94-010-5542-0 }}</ref> which is also known as the [[Boyer–Moore majority vote algorithm]]. Boyer and Moore proposed an algorithm to find the majority element of a string (if it has one) in <math>O(n)</math> time and using <math>O(1)</math> space. In the context of Boyer and Moore’sMoore's work and generally speaking, a majority element in a set of items (for example string or an array) is one whose number of instances is more than half of the size of that set. Few years later, Misra and Gries <ref>{{Cite journal|last1=Misra|first1=J.|last2=Gries|first2=David|date=November 1982|title=Finding repeated elements|journal=Science of Computer Programming|volume=2|issue=2|pages=143–152|doi=10.1016/0167-6423(82)90012-0|issn=0167-6423|doi-access=free|hdl=1813/6345|hdl-access=free}}</ref> proposed a more general version of Boyer and Moore's algorithm using <math>O \left ( n \log \left ( \frac{1}{\tau} \right ) \right )</math> comparisons to find all items in an array whose relative frequencies are greater than some threshold <math>0<\tau<1</math>. A range <math>\tau</math>-majority query is one that, given a subrange of a data structure (for example an array) of size <math>|R|</math>, returns the set of all distinct items that appear more than (or in some publications equal to) <math>\tau |R|</math> times in that given range. In different structures that support range <math>\tau</math>-majority queries, <math>\tau </math> can be either static (specified during pre-processing) or dynamic (specified at query time). Many of such approaches are based on the fact that, regardless of the size of the range, for a given <math>\tau</math> there could be at most <math>O(1/\tau)</math> distinct ''candidates'' with relative frequencies at least <math>\tau</math>. By verifying each of these candidates in constant time, <math>O(1/\tau)</math> query time is achieved. A range <math>\tau</math>-majority query is decomposable <ref name=":1">{{Cite book|author=Karpiński, Marek|url=https://worldcat.org/oclc/277046650|title=Searching for frequent colors in rectangles|oclc=277046650}}</ref> in the sense that a <math>\tau</math>-majority in a range <math>R</math> with partitions <math>R_1</math> and <math>R_2</math> must be a <math>\tau</math>-majority in either <math>R_1</math>or <math>R_2</math>. Due to this decomposability, some data structures answer <math>\tau</math>-majority queries on one-dimensional arrays by finding the [[Lowest common ancestor]] (LCA) of the endpoints of the query range in a [[Range tree]] and validating two sets of candidates (of size <math>O(1/\tau)</math>) from each endpoint to the lowest common ancestor in constant time resulting in <math>O(1/\tau)</math> query time.

==== Two-dimensional arrays ====

Gagie et al.<ref>{{cite book |last1=Gagie|first1=Travis |date=2011|chapter-url=http://dx.doi.org/10.1007/978-3-642-24583-1_29 |pages=295–300|place=Berlin, Heidelberg|publisher=Springer Berlin Heidelberg|isbn=978-3-642-24582-4|access-date=2021-12-18|last2=He|first2=Meng|last3=Munro|first3=J. Ian|last4=Nicholson|first4=Patrick K.|title=String Processing and Information Retrieval |chapter=Finding Frequent Elements in Compressed 2D Arrays and Strings |series=Lecture Notes in Computer Science |volume=7024 |doi=10.1007/978-3-642-24583-1_29 }}</ref> proposed a data structure that supports range {{mvar|&<math>\tau;}}</math>-majority queries on an <math>m\times n</math> array {{mvar|<math>A}}</math>. For each query <math>\operatorname{Q}=(\operatorname{R}, \tau)</math> in this data structure a threshold <math>0<\tau<1</math> and a rectangular range <math>\operatorname{R}</math> are specified, and the set of all elements that have relative frequencies (inside that rectangular range) greater than or equal to {{mvar|&<math>\tau;}}</math> are returned as the output. This data structure supports dynamic thresholds (specified at query time) and a pre-processing threshold {{mvar|&<math>\alpha;}}</math> based on which it is constructed. During the pre-processing, a set of ''vertical'' and ''horizontal'' intervals are built on the <math>m \times n</math> array. Together, a vertical and a horizontal interval form a ''block.'' Each block is part of a ''superblock'' nine times bigger than itself (three times the size of the block's horizontal interval and three times the size of its vertical one). For each block a set of candidates (with <math>\tfracfrac{9}{\alpha}</math> elements at most) is stored which consists of elements that have relative frequencies at least <math>\tfracfrac{\alpha}{9}</math> (the pre-processing threshold as mentioned above) in its respective superblock. These elements are stored in non-increasing order according to their frequencies and it is easy to see that, any element that has a relative frequency at least {{mvar|&<math>\alpha;}}</math> in a block must appear its set of candidates. Each {{mvar|&<math>\tau;}}</math>-majority query is first answered by finding the ''query block,'' or the biggest block that is contained in the provided query rectangle in <math>O(1)</math> time. For the obtained query block, the first <math>\tfracfrac{9}{\tau}</math> candidates are returned (without being verified) in <math>O(1/\tau)</math> time, so this process might return some false positives. Many other data structures (as discussed below) have proposed methods for verifying each candidate in constant time and thus maintaining the <math>O(1/\tau)</math> query time while returning no false positives. The cases in which the query block is smaller than <math>1/\alpha</math> are handled by storing <math>\log \left ( \tfracfrac{1}{\alpha} \right )</math> different instances of this data structure of the following form:

<math display="block">\beta=2^{-i}, \;\; i\in \left \{ 1,\dots,\log \left (\frac{1}{\alpha} \right ) \right \}

</math>

where {{mvar|&<math>\beta;}}</math> is the pre-processing threshold of the {{mvar|<math>i}}</math>-th instance. Thus, for query blocks smaller than <math>1/\alpha</math> the <math>\lceil\log (1 / \tau)\rceil</math>-th instance is queried. As mentioned above, this data structure has query time <math>O(1/\tau)</math> and requires <math>O \left ( m n(H+1) \log^2 \left ( \tfracfrac{1}{\alpha} \right ) \right )</math> bits of space by storing a Huffman-encoded copy of it (note the <math>\log\left(\tfracfrac{1}{\alpha}\right)</math> factor and also see [[Huffman coding]]).

==== One-dimensional arrays ====

Chan et al.<ref name=":0">{{cite book |last1=Chan|first1=Timothy M. |date=2012|chapter-url=http://dx.doi.org/10.1007/978-3-642-31155-0_26 |pages=295–306|place=Berlin, Heidelberg|publisher=Springer Berlin Heidelberg|isbn=978-3-642-31154-3|access-date=2021-12-20|last2=Durocher|first2=Stephane|last3=Skala|first3=Matthew|last4=Wilkinson|first4=Bryan T.|title=Algorithm Theory – SWAT 2012 |chapter=Linear-Space Data Structures for Range Minority Query in Arrays |series=Lecture Notes in Computer Science |volume=7357 |doi=10.1007/978-3-642-31155-0_26 }}</ref> proposed a data structure that given a one-dimensional array<math>A</math>, a subrange <math>R</math> of <math>A</math> (specified at query time) and a threshold <math>\tau</math> (specified at query time), is able to return the list of all <math>\tau</math>-majorities in <math>O(1/\tau)</math> time requiring <math>O(n \log n)</math> words of space. To answer such queries, Chan et al.<ref name=":0" /> begin by noting that there exists a data structure capable of returning the ''top-k'' most frequent items in a range in <math>O(k)</math> time requiring <math>O(n)</math> words of space. For a one-dimensional array <math>A[0,..,n-1]</math>, let a one-sided top-k range query to be of form <math>A[0..i] \text { for } 0 \leq i \leq n-1</math>. For a maximal range of ranges <math>A[0..i] \text { through } A[0..j]</math> in which the frequency of a distinct element <math>e</math> in <math>A</math> remains unchanged (and equal to <math>f</math>), a horizontal line segment is constructed. The <math>x</math>-interval of this line segment corresponds to <math>[i,j]</math> and it has a <math>y</math>-value equal to <math>f</math>. Since adding each element to <math>A</math> changes the frequency of exactly one distinct element, the aforementioned process creates <math>O(n)</math> line segments. Moreover, for a vertical line <math>x=i</math> all horizonalhorizontal line segments intersecting it are sorted according to their frequencies. Note that, each horizontal line segment with <math>x</math>-interval <math>[\ell,r]</math> corresponds to exactly one distinct element <math>e</math> in <math>A</math>, such that <math>A[\ell]=e</math>. A top-k query can then be answered by shooting a vertical ray <math>x=i</math> and reporting the first <math>k</math> horizontal line segments that intersect it (remember from above that these line segments are already sorted according to their frequencies) in <math>O(k)</math> time.

Chan et al.<ref name=":0" /> first construct a [[range tree]] in which each branching node stores one copy of the data structure described above for one-sided range top-k queries and each leaf represents an element from <math>A</math>. The top-k data structure at each node is constructed based on the values existing in the subtrees of that node and is meant to answer one-sided range top-k queries. Please note that for a one-dimensional array <math>A</math>, a range tree can be constructed by dividing <math>A</math> into two halves and recursing on both halves; therefore, each node of the resulting range tree represents a range. It can also be seen that this range tree requires <math>O(n \log n)</math> words of space, because there are <math>O(\log n)</math> levels and each level <math>\ell</math> has <math>2^{\ell}</math> nodes. Moreover, since at each level <math>\ell</math> of a range tree all nodes have a total of <math>n</math> elements of <math>A</math> at their subtrees and since there are <math>O(\log n)</math> levels, the space complexity of this range tree is <math>O(n \log n)</math>.

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for <math>0\leq i \leq \log(depth(x))</math> where <math>\operatorname{par}(x)</math> returns the label of the direct parent of node <math>x</math>. Put another way, for each marked node, the set of all paths with a power of two length (plus one for the node itself) towards the root is stored. Moreover, for each <math>P_i(x)</math>, the set of all majority ''candidates'' <math>C_i(x)</math> are stored. More specifically, <math>C_i(x)</math> contains the set of all <math>(\tau/2)</math>-majorities in <math>P_i(x)</math> or labels that appear more than <math>(\tau/2).(2^i+1)</math> times in <math>P_i(x)</math>. It is easy to see that the set of candidates <math>C_i(x)</math> can have at most <math>2/\tau</math> distinct labels for each <math>i</math>. Gagie et al.<ref name=":2"/> then note that the set of all <math>\tau</math>-majorities in the path from any marked node <math>x</math> to one of its ancestors <math>z</math> is included in some <math>C_i(x)</math> (Lemma 2 in <ref name=":2"/>) since the length of <math>P_i(x)</math> is equal to <math>(2^i+1)</math> thus there exists a <math>P_i(x)</math> for <math>0\leq i \leq \log(depth(x))</math> whose length is between <math>d_{xz} \text{ and } 2 d_{xz}</math> where <math>d_{xz}</math> is the distance between x and z. The existence of such <math>P_i(x)</math> implies that a <math>\tau</math>-majority in the path from <math>x</math> to <math>z</math> must be a <math>(\tau/2)</math>-majority in <math>P_i(x)</math>, and thus must appear in <math>C_i(x)</math>. It is easy to see that this data structure require <math>O(n \log n)</math> words of space, because as mentioned above in the construction phase <math>O(\tau n)</math> nodes are marked and for each marked node some candidate sets are stored. By definition, for each marked node <math>O(\log n)</math> of such sets are stores, each of which contains <math>O(1/\tau)</math> candidates. Therefore, this data structure requires <math>O(\log n \times (1/\tau) \times \tau n)=O(n \log n)</math> words of space. Please note that each node <math>x</math> also stores <math>count(x)</math> which is equal to the number of instances of <math>label(x)</math> on the path from <math>x</math> to the root of <math>T</math>, this does not increase the space complexity since it only adds a constant number of words per node.

Each query between two nodes <math>u</math> and <math>v</math> can be answered by using the decomposability property (as explained above) of range <math>\tau</math>-majority queries and by breaking the query path between <math>u</math> and <math>v</math> into four subpaths. Let <math>z</math> be the lowest common ancestor of <math>u</math> and <math>v</math>, with <math>x</math> and <math>y</math> being the nearest marked ancestors of <math>u</math> and <math>v</math> respectively. The path from <math>u</math> to <math>v</math> is decomposed into the paths from <math>u</math> and <math>v</math> to <math>x</math> and <math>y</math> respectively (the size of these paths are smaller than <math>2\lceil 1 / \tau\rceil</math> by definition, all of which are considered as candidates), and the paths from <math>x</math> and <math>y</math> to <math>z</math> (by finding the suitable <math>C_i(x)</math> as explained above and considering all of its labels as candidates). Please note that, boundary nodes have to be handled accordingly so that all of these subpaths are disjoint and from all of them a set of <math>O(1/\tau)</math> candidates is derived. Each of these candidates is then verified using a combination of the <math>labelanc (x, \ell)</math> query which returns the lowest ancestor of node <math>x</math> that has label <math>\ell</math> and the <math>count(x)</math> fields of each node. On a <math>w</math>-bit RAM and an alphabet of size <math>\sigma</math>, the <math>labelanc (x, \ell)</math> query can be answered in <math>O\left(\log \log _{w} \sigma\right) </math> time whilst having linear space requirements.<ref>{{Cite journal|last1=He|first1=Meng|last2=Munro|first2=J. Ian|last3=Zhou|first3=Gelin|date=2014-07-08|title=A Framework for Succinct Labeled Ordinal Trees over Large Alphabets|url=http://dx.doi.org/10.1007/s00453-014-9894-4|journal=Algorithmica|volume=70|issue=4|pages=696–717|doi=10.1007/s00453-014-9894-4|s2cid=253977813 |issn=0178-4617|url-access=subscription}}</ref> Therefore, verifying each of the <math>O(1/\tau)</math> candidates in <math>O\left(\log \log _{w} \sigma\right) </math> time results in <math>O\left((1/\tau)\log \log _{w} \sigma\right) </math> total query time for returning the set of all <math>\tau </math>-majorities on the path from <math>u </math> to <math>v </math>.

==Related problems==