Advertisement

On Embeddability of Buses in Point Sets

  • Till BruckdorferEmail author
  • Michael Kaufmann
  • Stephen G. Kobourov
  • Sergey Pupyrev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9411)

Abstract

Set membership of points in the plane can be visualized by connecting corresponding points via graphical features, like paths, trees, polygons, ellipses. In this paper we study the bus embeddability problem (BEP): given a set of colored points we ask whether there exists a planar realization with one horizontal straight-line segment per color, called bus, such that all points with the same color are connected with vertical line segments to their bus. We present an ILP and an FPT algorithm for the general problem. For restricted versions of this problem, such as when the relative order of buses is predefined, or when a bus must be placed above all its points, we provide efficient algorithms. We show that another restricted version of the problem can be solved using 2-stack pushall sorting. On the negative side we prove the NP-completeness of a special case of BEP.

Keywords

Integer Linear Programming Steiner Tree Restricted Version Euler Diagram Diagonal Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

1 Introduction

Visualization of sets is an important topic in graph drawing and information visualization and the traditional approach relies on representing overlapping sets via Venn diagrams and Euler diagrams [28]. When more than a handful sets are present, however, such diagrams become difficult to interpret and alternative approaches, such as compact rectangular Euler diagrams are needed [27].

Often the geometric position of the elements of the sets are prescribed as points in the plane. The task is to emphasize the sets where the elements belong to. In visualization approaches for set memberships of items on maps, this is done by connecting points from the same set by corresponding lines (LineSets [2]), tree structures (KelpFusion [24]), and enclosing polygons (BubbleSet [11] or MapSets [13]).
Fig. 1.

(a) Fixed positions of points, where points with the same color belong to the same set. (b) A planar bus realization for this setting, while (c) is a non-planar bus realization. (d) A point set without any planar bus realization (Color figure online).

We consider a unified version of the tree-structure approach using a model that has been applied before for drawing orthogonal buses known from VLSI design [22]. Our goal is a membership visualization of points in sets by a tree-structure that consists of a single horizontal segment, called bus, to which all the points from the same set are connected by vertical segments, called connections; see Fig. 1 for planar and non-planar versions. We assume the sets to be given by single-colored points, such that in the final visualization, called bus realization, every point of the same color is connected to exactly one bus associated with this color. The objective is to find a position for each bus, such that crossings of buses with connections are avoided, called planar bus realization. We call this the bus embeddability problem (BEP). Such a simple visualization scheme makes it very easy to recognize the sets and label them, by placing a label inside each bus (if the bus is drawn thick enough), or directly above/next to the bus.

Related Work. Buses have been used, in a more general form, for visualizing degree-restricted hypergraphs. Ada et al. [1] used horizontal and vertical buses in bus realizations, where the points (representing hypervertices contained in at most four hyperedges) were not predefined in the plane. They asked whether a given hypergraph admits a non-planar bus realizations (allowing connections to cross each other) and showed that the problem is NP-complete. In contrast, if a planar embedding is given, a planar bus realization can be constructed on a \({{\mathcal {O}}}(n) \times {{\mathcal {O}}}(n)\) grid in \({{\mathcal {O}}}(n^{3/2})\) time [6]. These types of problems also have connections to rectangular drawings, rectangular duals and visibility graphs, since the edges of the incidence graph of a hypergraph enforce visibility constraints in the bus realizations [29].

Another related approach is visualization based on graph supports of hypergraphs. Here the goal is to connect the vertices in such a way that each hyperedge induces a connected subgraph [8, 20]. Supported hypergraph visualizations inspired edge-bundling and confluent layouts as alternative visualizations for cliques [12, 14].

A solution to the BEP problem can be viewed as planar tree support for hypergraphs, and this problem is related to Steiner trees, where the goal is to connect a set of points in the plane while minimizing the sum of edge lengths in the resulting tree; this is a classic NP-complete problem [15]. Hurtado et al. [18] considered planar supports for hypergraphs with two hyperedges such that the induced subgraph for every hyperedge and the intersection is a Steiner tree. Their objective was to minimize the sum of edge lengths, while allowing degree one or two for the hypervertices. BEP is even more closely related to rectilinear Steiner trees, where the Euclidean distance is replaced by the rectilinear distance; constructing rectilinear Steiner trees is also NP-complete [16]. A single trunk Steiner tree [10] is a path which contains all vertices of degree greater than one. This is a variant that is solvable in linear time. BEP for a single set is the single trunk rectilinear Steiner tree problem, where we ignore the minimization of the sum of the edge lengths. Thus BEP can be seen as a simultaneous single-trunk rectilinear Steiner tree problem. The fact that a bus placement influences the placement of other buses makes the problem hard.

Consider the input to BEP along with a box that encloses all the points. If in BEP the buses extend to the right boundary of this box, or both to the left and right boundary of this box, then this problem corresponds to backbone boundary labeling and can be efficiently solved [4]. In backbone boundary labeling, the problem is to orthogonally connect points by a horizontal backbone segment leading to a label placed at the boundary. In this setting it is always possible to split the problem into two independent subproblems, which is impossible in our case.

BEP is also related to the classical point set embeddability problem, where given a set of points along with a planar graph, we need to determine whether there exists a mapping of vertices to points such that the resulting straight-line drawing is planar. The general decision problem is NP-hard [9]. In the variant of orthogeodesic point set embedding, Katz et al. proved that deciding whether a planar graph can be embedded using only orthogonal edge routing is NP-hard [19].

Our Results. In Sect. 2 we solve BEP when the relative order of the buses is prescribed; we also show that BEP is fixed-parameter tractable (FPT) with respect to the number of colors. In Sect. 3 we formulate an integer linear programming (ILP) formulation for BEP and show some experimental results. In Sect. 4 we restrict BEP (when a bus must be above all its points, or a bus must be either at its topmost or bottommost point) and describe efficient algorithms for these settings. Another restricted version of the problem is shown to be equivalent to the problem of sorting a permutation, which is called 2-stack pushall sorting. Finally we prove that BEP is NP-complete, even for just two points per color, if points may not lie on buses.

2 Preliminaries

We begin with some definitions. Suppose we are given a set of points \(\mathcal{P}=\{p_1,\dots ,p_n\}\) and colors \(\mathcal{C}=\{c_1,\dots ,c_k\}\) together with a function \(f:\mathcal{P} \longrightarrow \mathcal{C}, f(p)=c\). For simplicity, we assume that no two points share a coordinate in the input point set, although in some illustrations the input points might violate this assumption. The bus embeddability problem (BEP) asks, whether there is a planar bus realization with one horizontal bus per color. BEP is a decision problem, but in our descriptions whenever the answer is affirmative we also compute a drawing. We refer to such a drawing as a solution of BEP. In the negative case, we say that BEP has no solution.

A point p has x-coordinate x(p), y-coordinate y(p), and color f(p). In a bus realization we have connections only between a point p and a bus c of the same color, that is, \(c=f(p)\). We denote by \(f^{-1}(c)\) the set of points with color c. Bus c naturally extends from the x-coordinate \(x_l(c) = \min \{x(p) | p \in f^{-1}(c)\}\) of the leftmost point to the x-coordinate \(x_r(c) = \max \{x(p) | p \in f^{-1}(c)\}\) of the rightmost point of \(f^{-1}(c)\). We call \([x_l(c),x_r(c)]\) the span of c, which is predefined by the input points. The y-coordinate of a bus c is denoted by y(c), which is the only parameter to be determined for a solution for BEP.

Note that BEP is trivial when there are at most two colors: it is always possible to place one bus at the top and the other (if exists) at the bottom of the drawing. Thus in the following we assume \(k > 2\). For more than two colors, the relative order of the buses is important; see Fig. 1. Suppose the y-order of the buses is prescribed. The next lemma shows that one can check an existence of a solution for BEP respecting the order.

Lemma 1

There is a \({{\mathcal {O}}}(n \log n)\)-time algorithm that, given an order of buses, tests whether there exists a solution for BEP respecting the order.

Proof

Suppose we are given an order \(c_1 < \dots < c_k\) of the buses from bottom to top. We use discrete values for the y-coordinates increasing from bottom to top, where a unit is 1 / n of the y-distance of two consecutive points. We first present a simpler \({{\mathcal {O}}}(n^2)\)-time algorithm, and then describe how to speed it up.

Recall that the span of every bus is defined by an input point set; hence, we only show how to choose y-coordinates of the buses. The first bus, \(c_1\), is placed at y-coordinate \(y(c_1)=0\), and all the points of color \(c_1\) are connected to the bus. Assume that bus \(c_{i-1}\) is placed at y-coordinate \(y(c_{i-1})\) and is connected to all its points. We place \(c_i\) at \(y(c_i) = y(c_{i-1}) + 1\) unit and check if the bus crosses a previously drawn (vertical) segment. If it does cross a segment, then we shift \(c_i\) one unit upwards by increasing \(y(c_i)\) and repeat the procedure. Once the bus is placed without crossings, we connect it to the corresponding points. Consider the vertical segment of a point p of color \(c_i\). It is easy to see that if \(y(p) \ge y(c_i)\), then the segment cannot cross a previously placed bus \(c_j\) for \(j<i\). If \(y(p) < y(c_i)\) and the vertical segment crosses a bus, then such a crossing is unavoidable in any solution respecting the given order. Hence, we may stop the algorithm reporting that no solution exists. Otherwise, we proceed with the next color.

The above algorithm can easily be implemented in quadratic time. However, we can do better using the following observation: Every bus is placed at its bottommost “valid” y-coordinate, that is, the one that does not produce crossings with previously placed buses. To find such a y-coordinate efficiently for each color, we store all points of the already processed colors in a data structure D that supports the range operation such as “extracting minimum/maximum on a given range”. For every color \(c_i\), we extract a point with the maximum y-coordinate in the range corresponding to the span of \(c_i\). The bus of \(c_i\) is placed at the maximum of the extracted y-coordinate and the y-coordinate of bus \(y(c_{i-1})\). Then all the points of color \(c_i\) are added to D. A balanced tree (e.g., a segment tree) providing logarithmic complexity for insert and extract operations is sufficient for our needs.    \(\square \)

In general the correct order of the buses for a planar bus realization is not known. One can apply Lemma 1 for each of the k! possible bus orders, which yields an \(\widetilde{{{\mathcal {O}}}}(k!)\)-time1 algorithm for BEP. Next, we improve the running time with an algorithm providing deeper insight into the structure of the problem.

Lemma 2

There is a \(\widetilde{{{\mathcal {O}}}}(2^k)\)-time algorithm for BEP.

Proof

We solve a given instance of BEP using dynamic programming. Let us call a state a pair (hB), where \(0 \le h \le n+1\) is an integer and B is a subset of \(\mathcal{C} = \{c_1, \dots , c_k\}\). By a solution for a state (hB) we mean a (planar) bus realization consisting of buses for every color \(c\in B\) such that the topmost bus has y-coordinate h. If such a solution exists, we write \(F(h, B) = {\text {true}}\), and otherwise \(F(h, B) = {\text {false}}\). It is easy to see that a solution for the original BEP problem exists if and only if \(F(h, \mathcal{C})={\text {true}}\) for some \(0\le h \le n+1\).

We reduce the problem to solving it for “smaller” states, that are the states with fewer elements in B. As a base case, we set \(F(h, B) = {\text {true}}\) for all \(0\le h \le n+1\) and \(|B|=1\). To compute a value for a state F(hB) with \(|B|>1\), we consider a color \(c^*\in B\). Let \(h^* = \max \{y(p)| f(p)\in B\setminus \{c^*\} \text { and } x_l(c^*) \le x(p) \le x_r(c^*) \}\), that is, the largest (topmost) y-coordinate of a point of color \(B\setminus \{c^*\}\) laying in the span of \(c^*\). It follows from the proof of Lemma 1 that the bus for \(c^*\) should be placed at y-coordinate \(h^*\). Thus, F(hB) is set to \({\text {true}}\) if (a) \(h \ge h^*\) and (b) there exists a solution for a state \((h', B\setminus \{c^*\})\) for some \(h' < h\). We stress here that in order to compute F(hB), one needs to consider every color of B as a potential \(c^*\). There are \(n2^k\) different states, and a computation for a single state clearly takes a polynomial number of steps.    \(\square \)

The above result shows that the BEP problem is fixed-parameter tractable with respect to k, that is, it can be efficiently solved for a small number of buses. Note that in Sect. 5 we prove that BEP is NP-complete; hence, it is unlikely that a polynomial-time (in terms of k) algorithm exists.

3 An ILP for BEP

In this section we present an integer linear programming (ILP) formulation for BEP that produces a planar bus realization if one exists. The ILP also minimizes the amount of ink in a solution, that is, the sum of all segment lengths.

Lemma 3

A solution for BEP can be computed by an ILP.

Proof

In a preprocessing step we compute the span of every bus \(c \in \mathcal{C}\). As mentioned earlier, it remains to compute the y-coordinate variable y(c) of every bus c. To this end, we introduce a planarity constraint for every point \(p \in \mathcal{P}\) within the span of bus c having a different color. The pairs \((p, c), c \ne f(p)\) are called conflicting. Conflicting pairs (pc) are stored in a matrix \(\mathcal{J}\) and induce the constraint \((y(p)<y(c)\) and \(y(f(p)) < y(c))\) or \((y(p) > y(c)\) and \(y(f(p)) > y(c))\). The matrix \(\mathcal{J}\) can be computed in \({{\mathcal {O}}}(kn)\) time, where n=\(|\mathcal{P}|\) and k=\(|\mathcal{C}|\). In order to minimize the amount of ink, we sum up the lengths of all connections and ignore the lengths of buses, as those are determined by the input.
$$\begin{aligned} \min&\sum _{c \in \mathcal{C}} \sum _{f(p)=c} |y(c) - y(p)|\nonumber \\ s.t.&(y(p) < y(c) \vee y(f(p)) > y(c)) \wedge (y(p) > y(c) \vee y(f(p)) < y(c)) \quad \forall (p,c) \in \mathcal{J}\nonumber \\&0 \le y(c) \le \max _{p \in \mathcal{P}} \{y(p)\} + 1\nonumber \end{aligned}$$
Since absolute value (resp. “or”) needs one more variable and 3 constraints for every point (resp. for every conflicting pair)2, the final ILP has n+k+\(2|\mathcal{J}|\) variables and 3n+k+\(6|\mathcal{J}|\) constraints.    \(\square \)
Fig. 2.

The percentage of solutions for BEP for a random point set of size \(n=kl\) with \(l=2,3,4\) points per color out of \(k=3,\dots ,20\) colors (Color figure online).

In order to get a feeling about the probability that a point set admits a solution of BEP, we ran an experiment with the ILP, implemented with the Gurobi solver [17]. We considered point sets with \(k=3,\dots ,20\) colors and with \(l=2,3,4\) points per color. We randomly placed the points on a \(1024\, \times \, 768\) area. For each pair (lk) we counted the number of BEP solutions out of 100 instances; see Fig. 2. The remaining instances were infeasible. For a fixed number of points, l, the number of solutions for BEP decreases with increasing the number of colors, k. It decreases faster the higher l is. On the other hand for a fixed number of colors, k, the number of solutions for BEP also decreases with increasing number of points, l. Hence, studying two points per color promises to be sufficiently interesting. Thus, as the base case for further analysis, we initially consider two points per color, before dealing with the general case, where in real instances solutions rarely exist. It is possible that much more solutions exist if we allow only few crossings, but all non-planar settings are left as open problems.

4 Efficiently Solvable BEP Variants

In this section we consider three variants of BEP, which can be solved in polynomial time. A bus c is called top (resp., bottom) if all of its points are below (resp., above) the bus, that is, \(y(c) \ge y(p)\) (resp., \(y(c) \le y(p)\)) for all \(p \in f^{-1}(c)\). We distinguish between buses that are above (below) of their points and buses that pass through one of their points. A top-bus is a \(\sqcap \) -bus if \(y(c) > y(p)\) for all \(p \in f^{-1}(c)\) (Fig. 3(a)), while it is a Open image in new window -bus if \(y(c) = y(p)\) for a point p with \(y(p) = \max \{y(q) | q \in f^{-1}(c)\}\) (Fig. 3(c)). Similarly we define a \(\sqcup \) -bus and a Open image in new window -bus; see Fig. 3(b) and (d). A bus, whose type is none of the four types from above, is called a center-bus. The variant of BEP where only buses of the types in Open image in new window are allowed to use is denoted by S-BEP.
Fig. 3.

Illustration of (a) \(\sqcap \)-bus, (b) \(\sqcup \)-bus, (c) Open image in new window -bus, and (d) Open image in new window -bus.

In Sect. 4.1 we study \(\sqcap \)-buses and provide an algorithm for \(\sqcap \)-BEP. The same algorithm obviously solves the \(\sqcup \)-BEP variant. Next we consider Open image in new window -buses and Open image in new window -buses. Note that Open image in new window -BEP and Open image in new window -BEP are trivial, since every Open image in new window -bus (resp., Open image in new window -bus) is uniquely defined by its span and the topmost (bottommost) point. Hence, we investigate and design an efficient algorithm for the Open image in new window -BEP variant. Finally in Sect. 4.3, we examine the general BEP for a specific point set, where all points lie on a diagonal. We show that the variant of the problem is equivalent to a longstanding open problem (resolved very recently) of sorting a permutation with a series of two stacks.

4.1 \(\sqcap \)-BEP

Here, we present an algorithm that decides in polynomial time whether a drawing with \(\sqcap \)-buses exists for a given input, and constructs such a drawing if one exists.

Theorem 1

There exists an \({{\mathcal {O}}}(n \log n)\)-time algorithm for \(\sqcap \)-BEP.

Proof

For ease of presentation, we first assume that the input consists of two points per color, that is, \(k=n/2\), and provide a simple quadratic-time implementation. Later we generalize the algorithm and improve the running time. Intuitively, the algorithm sweeps a line from bottom to top and processes the points in increasing order of y-coordinates. At every step, we keep all the vertical segments of the “active” colors (the ones without a bus) in the correct left-to-right order. If two vertical segments of the same color are adjacent in the order, then we can draw the corresponding bus and remove the color and its vertical segments. Otherwise, all the active vertical segments have to be “grown” until we reach the next point. It is easy to see that a solution exists if and only if the set of active colors is empty after processing all the points.

More formally, the points are processed one-by-one in increasing order of their y-coordinates. The points are stored in an array sorted by x-coordinate, that is, we have \((p_1, \dots , p_n)\) with \(x(p_1) < \dots < x(p_n)\). At each iteration, a new point is inserted into the array in the position determined by its x-coordinate. Then the array is modified (or simplified) so that the pairs of points of the same color that are adjacent in the array are removed. That is, if \(f(p_i) = f(p_{i+1})\) for some \(1 \le i < n\), then we get a new array \((p_1, \dots , p_{i-1}, p_{i+2}, \dots , p_n)\). The simplification is performed as long as the array contains monochromatic adjacent points. After this step the algorithm proceeds with the next point. For every color c, we keep the value \(y^*(c)\), which is equal to the y-coordinate \(y(p), p\in f^{-1}(c')\) of the point of color \(c'\), whose insertion into the array induced the removal of points \(f^{-1}(c)\) from the array. If the algorithm ends up with a non-empty array, then we report that no solution exists. Otherwise, the y-coordinate of the resulting bus of color c is \(y^*(c)+\varepsilon \), where \(\varepsilon > 0\) is sufficiently small to avoid overlaps between the buses. An example of the algorithm is illustrated in Fig. 4.
Fig. 4.

Running the algorithm from Lemma 1 on a given point set with red (R), green (G), blue (B), and white (W) pairs of points. Since the resulting array is not empty, there is no solution for the instance. Notice that removing any of the colors yields an instance with a solution (Color figure online).

Correctness. The correctness follows from the observation that the algorithm chooses the lowest “available” y-coordinate for every bus, that is, the one that does not induce a crossing between the bus and vertical segments of other colors. Indeed, if at any step of the algorithm we get a color pattern \(R, \dots , B, \dots , R\) in the array formed by red (R) and blue (B) points and the second blue point p has not been processed yet, then clearly in any solution the red vertical segments reach the y-coordinate of p. Hence, it is safe to “grow” the segments. On the other hand, if processed points form a color pattern RR (that is, two consecutive points of the same color), then there is a solution connecting the corresponding vertical segments at the current y-coordinate. The two points can be removed from consideration, as they cannot create crossings with the subsequent buses. It is also easy to see that the algorithm minimizes ink of the resulting drawing.

Running Time. At every iteration of the algorithm, we need to insert a new point into the sorted array and then run the simplification procedure. Point insertion takes \({{\mathcal {O}}}(n)\) time and removal of a pair of points from the array can also be done in \({{\mathcal {O}}}(n)\) time. Since every pair is removed only once, the total running time is \({{\mathcal {O}}}(n^2)\).

To get down to \({{\mathcal {O}}}(n \log n)\) time, we use a balanced binary tree instead of an array to store the points. The tree is sorted by the x-coordinates of the points; hence, insertion/removal of a point takes \({{\mathcal {O}}}(\log n)\) time. Note that after inserting/removing a point, the only potential candidate pairs for simplification are the point’s neighbors that can be found in \({{\mathcal {O}}}(\log n)\) time. Again, every point is inserted/removed only once; thus, the total running time is \({{\mathcal {O}}}(n \log n)\).

Finally, we observe that the algorithm can be generalized to handle multiple points per color. To this end, we change the simplification step so that the points are removed only if they form a contiguous subsequence in the array (tree), containing all points of this color. Hence we need to know the number of points for each color, which can be done with a linear-time scan of the input. It is easy to see that the proof of correctness can be appropriately modified and the running time remains the same.    \(\square \)

4.2 Open image in new window -BEP

We present an algorithm that decides in polynomial time whether Open image in new window -BEP has a solution for a given input, and constructs a drawing if one exists.

Theorem 2

There exists an \({{\mathcal {O}}}(n^2)\)-time algorithm for Open image in new window -BEP.

Proof

The span of every bus is predefined by the input, while the y-coordinate has precisely two options. We show that Open image in new window -BEP can be modeled by 2-SAT, and thus is efficiently solvable. For ease of presentation, we first assume that the input consists of two points per color and describe a simple quadratic-time algorithm.

The algorithm creates a variable \(x_c\) for every color \(c \in \mathcal{C}\). The value of \(x_c\) is true if c is a Open image in new window -bus, and it is false if c is a Open image in new window -bus. Then for every pair of colors \(c, c'\), the algorithm creates a clause for the 2-SAT instance when the corresponding buses induce a crossing. Building the clauses with respect to the relative position of points is a straight-forward procedure; 3 examples are illustrated in Fig. 5. We can generalize this idea in a straight-forward manner to the case of more points per color. In the general case the y-coordinate of a bus still has precisely two options. In contrast to the case with two points per color we check several points (not only the leftmost or rightmost point) of color \(c'\) for their position with respect to the points of color c, since points lie not necessarily in corners of the enclosing rectangle.

Correctness. The correctness follows from the complete case analysis.
Fig. 5.

Three examples for creating clauses for two colors black and white.

Running Time. We remark that for the \(n^2/4\) pairs of colors, we create \({{\mathcal {O}}}(n^2)\) clauses, each clause in constant time by a case analysis. This results in a 2-SAT instance with k variables \(x_c, c \in \mathcal{C}\) and \({{\mathcal {O}}}(n^2)\) clauses. We solve this instance in linear time [3] and the solution determines the drawing: c is drawn as a Open image in new window -bus, if the value of \(x_c\) is true, otherwise c is drawn as a Open image in new window -bus.    \(\square \)

4.3 Diagonal BEP

Here we consider a diagonal point set in which all points lie on a single diagonal line and there are two points per color. We assume that the point set is separable, that is, there is a straight line separating every pair of points having the same color; see Fig. 6. This specific arrangement can be naturally described in terms of permutations. Assuming that the colors are numbered from 1 to k in the order along the diagonal from bottom to top, the input is described by a permutation \(\pi =[\pi (1), \dots , \pi (k)]\) on \(\{1, \dots , k\}\). Such an instance is called diagonal \(\pi \)-BEP.

It turns out that this variant of BEP is closely related to the well-studied topic of sorting a permutation with stacks introduced by Knuth in the 1960’s [21]. We next show that diagonal \(\pi \)-BEP has a solution if and only if \(\pi \) can be sorted with 2 stacks in series. The problem of deciding whether a permutation is sortable with 2 stacks in series is a longstanding open problem and it has been conjectured to be NP-complete several times [5]. Only very recently a polynomial-time algorithm has been developed [25, 26]. It is an indication that even the restricted variant of BEP is highly non-trivial. Next we prove the equivalence.

First observe that for a diagonal point set with 2 points per color, a top-bus (bottom-bus) can be transformed to a center-bus. For every color c, there are no points of different color within the span of c above the topmost point of c. Hence, we may only consider center-buses in the variant of BEP. For the 2-stack sorting problem, given a permutation \(\pi \), we want to sort the numbers to the identity permutation \([1,\dots ,k]\) with two stacks \(S_I, S_{II}\) using the following operations:
  • \(\alpha _i:\) read the next element i from input \(\pi \) and push it on the first stack \(S_I\);

  • \(\beta _i:\) pop the topmost element i from \(S_I\) and push it on \(S_{II}\);

  • \(\gamma _i:\) pop the topmost element i from \(S_{II}\) and print it to the output.

Fig. 6.

A diagonal point set with a solution for BEP and the regarding sorting sequence (Color figure online).

To make the equivalence between 2-stack sorting and bus embeddability, we note that the first operation, \(\alpha _i\), corresponds to the left vertical segment of color i, the second one, \(\beta _i\), is the bus of i, while \(\gamma _i\) corresponds to the right vertical segment of the color; see Fig. 6. A crossing in the drawing correspond to an “invalid” sorting operation in which either a non-topmost element is moved from \(S_I\) to \(S_{II}\) (a crossing to the “left” of the diagonal), or a non-topmost element is moved from \(S_{II}\) to the output (a crossing to the “right” of the diagonal). Hence, sorting sequences of the operations for \(\pi \) are in one-to-one correspondence with planar bus realization for the point set. Since the point set is separable, all the elements of \(\pi \) will be pushed to \(S_I\) before any of the elements is popped to the output. This is called 2-stack pushall sorting, see [25] for more details.

Theorem 3

Diagonal \(\pi \)-BEP has a solution if and only if \(\pi \) is 2-stack pushall sortable. This can be checked in \({{\mathcal {O}}}(n^2)\) time.

5 Hardness of BEP

We sketch the idea behind the proof that BEP\(^{\varepsilon }\) for 2 points per color is NP-complete, where BEP\(^{\varepsilon }\) is BEP with minimum distance \(\varepsilon \) of points to their bus as additional input.

We can easily verify a possible solution using Lemma 1; thus BEP\(^{\varepsilon }\) is in the class NP. To prove the hardness of BEP\(^{\varepsilon }\), we reduce from planar 3-SAT [23], which is 3-SAT, where an instance is represented by a graph whose vertices represent variables and clauses and whose edges represent containment of variables in clauses. We replace the vertices and edges by gadgets. We first restrict ourselves to (\(\sqcap , \sqcup \))-BEP and drop the “no points share a coordinate” restriction. Details can be found in [7].

The most important module of the construction is a chain link, which is also a gadget for replacing variables. It consists of two points on a common horizontal line that will be connected by a bus. We replace the edges of the graph by chains consisting of nested chain links and replace the clause vertices by a big construction of points, that allows two specific points to be connected via a bus using only one of three choices. We use the input \(\varepsilon \) to be able to block some choices for this bus. We finally transform the construction into the “no points share a coordinate” setting and allow also center-buses.

Theorem 4

BEP\(^{\varepsilon }\) for 2 points per color is NP-complete.

6 Conclusion and Future Work

We studied bus embeddability, where a set of colored points is covered by a set of horizontal buses, one per color and without crossings. We described an ILP and an FPT algorithm for the general problem and presented polynomial-time algorithms for several restricted versions. The general problem is shown to be NP-complete even for two points per color when points may not lie on buses.

It is still open to determine the complexity of BEP in the following cases:
  • BEP using only center-buses;

  • \((\sqcap , \sqcup )\)-BEP, that is, BEP without center-buses;

  • diagonal BEP with more than 2 points per color;

  • general BEP (in our construction, we use an extra \(\varepsilon \) as a parameter).

A natural generalization would be to allow both horizontal and vertical buses, as in [1, 6]. Another variant might be to consider multi-colored points, where a point has to be connected either to all the buses of its corresponding colors, or to at least one of them. For point sets that have no solution for BEP with only one bus per color, we may allow more than one bus or bound the number of crossings. Possible objectives in these scenarios are to minimize the total number of buses over all colors, to minimize the total number of buses, or to minimize the total number of buses if each tree can connect \(\le \) k unicolored points. These objectives are even interesting if a solution to BEP exists.

Footnotes

  1. 1.

    \(\widetilde{O}\) hides polynomial factors.

  2. 2.

    \(\min \sum |a-b| \Leftrightarrow \min \sum e, e \ge a-b, e \ge b-a, e \ge 0\); \((a<b) \vee (c<d) \Leftrightarrow a-b<eM, c-d<(1-e)M, e \in \{0,1\}, M=\infty \).

References

  1. 1.
    Ada, A., Coggan, M., Marco, P.D., Doyon, A., Flookes, L., Heilala, S., Kim, E., Wing, J.L.O., Préville-Ratelle, L.F., Whitesides, S., Yu, N.: On bus graph realizability. In: Canadian Conference on Computational Geometry, pp. 229–232 (2007)Google Scholar
  2. 2.
    Alper, B., Riche, N.H., Ramos, G., Czerwinski, M.: Design study of LineSets, a novel set visualization technique. IEEE Trans. Visual. Comput. Graph. 17(12), 2259–2267 (2011)CrossRefGoogle Scholar
  3. 3.
    Aspvall, B., Plass, M.F., Tarjan, R.E.: A linear-time algorithm for testing the truth of certain quantified Boolean formulas. Inform. Process. Lett. 8(3), 121–123 (1979)zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Bekos, M.A., Cornelsen, S., Fink, M., Hong, S.-H., Kaufmann, M., Nöllenburg, M., Rutter, I., Symvonis, A.: Many-to-one boundary labeling with backbones. In: Wismath, S., Wolff, A. (eds.) GD 2013. LNCS, vol. 8242, pp. 244–255. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  5. 5.
    Bóna, M.: A survey of stack-sorting disciplines. Electron. J. Comb. 9(2), 16 (2003)Google Scholar
  6. 6.
    Bruckdorfer, T., Felsner, S., Kaufmann, M.: On the characterization of plane bus graphs. In: Spirakis, P.G., Serna, M. (eds.) CIAC 2013. LNCS, vol. 7878, pp. 73–84. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  7. 7.
    Bruckdorfer, T., Kaufmann, M., Kobourov, S., Pupyrev, S.: On embeddability of buses in point sets. CoRR abs/1508.06760 (2015)
  8. 8.
    Buchin, K., van Kreveld, M.J., Meijer, H., Speckmann, B., Verbeek, K.: On planar supports for hypergraphs. J. Graph Algorithms Appl. 15(4), 533–549 (2011)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Cabello, S.: Planar embeddability of the vertices of a graph using a fixed point set is NP-hard. J. Graph Algorithms Appl. 10(2), 353–366 (2006)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Chen, H., Qiao, C., Zhou, F., Cheng, C.K.: Refined single trunk tree: A rectilinear Steiner tree generator for interconnect prediction. In: SLIP, pp. 85–89. ACM (2002)Google Scholar
  11. 11.
    Collins, C., Penn, G., Carpendale, T.: Bubble Sets: Revealing set relations with isocontours over existing visualizations. IEEE Trans. Visual. Comput. Graph. 15(6), 1009–1016 (2009)CrossRefGoogle Scholar
  12. 12.
    Dickerson, M., Eppstein, D., Goodrich, M.T., Meng, J.Y.: Confluent drawings: Visualizing non-planar diagrams in a planar way. J. Graph Algorithms Appl. 9(1), 31–52 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Efrat, A., Hu, Y., Kobourov, S.G., Pupyrev, S.: MapSets: visualizing embedded and clustered graphs. In: Duncan, C., Symvonis, A. (eds.) GD 2014. LNCS, vol. 8871, pp. 452–463. Springer, Heidelberg (2014) Google Scholar
  14. 14.
    Gansner, E.R., Koren, Y.: Improved circular layouts. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 386–398. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  15. 15.
    Garey, M.R., Graham, R.L., Johnson, D.S.: The complexity of computing Steiner minimal trees. SIAM J. Appl. Math. 32(4), 835–859 (1977)zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Garey, M.R., Johnson, D.S.: The rectilinear Steiner tree problem is NP-complete. SIAM J. Appl. Math. 32(4), 826–834 (1977)zbMATHMathSciNetCrossRefGoogle Scholar
  17. 17.
    Gurobi Optimization, I.: Gurobi optimizer reference manual (2015). www.gurobi.com
  18. 18.
    Hurtado, F., Korman, M., van Kreveld, M., Löffler, M., Sacristán, V., Silveira, R.I., Speckmann, B.: Colored spanning graphs for set visualization. In: Wismath, S., Wolff, A. (eds.) GD 2013. LNCS, vol. 8242, pp. 280–291. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  19. 19.
    Katz, B., Krug, M., Rutter, I., Wolff, A.: Manhattan-geodesic embedding of planar graphs. In: Eppstein, D., Gansner, E.R. (eds.) GD 2009. LNCS, vol. 5849, pp. 207–218. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  20. 20.
    Klemz, B., Mchedlidze, T., Nöllenburg, M.: Minimum tree supports for hypergraphs and low-concurrency euler diagrams. In: Ravi, R., Gørtz, I.L. (eds.) SWAT 2014. LNCS, vol. 8503, pp. 265–276. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  21. 21.
    Knuth, D.E.: The Art of Computer Programming, Volume 1. Fundamental Algorithms, 3rd edn. Addison Wesley Longman Publishing Co., Inc., Redwood (1997) Google Scholar
  22. 22.
    Lengauer, T.: VLSI theory. In: van Leeuwen, J. (ed.) Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity (A), pp. 835–868. Elsevier, Amsterdam (1990)Google Scholar
  23. 23.
    Lichtenstein, D.: Planar formulae and their uses. SIAM J. Comput. 11(2), 329–343 (1982)zbMATHMathSciNetCrossRefGoogle Scholar
  24. 24.
    Meulemans, W., Riche, N.H., Speckmann, B., Alper, B., Dwyer, T.: KelpFusion: A hybrid set visualization technique. IEEE Trans. Visual. Comput. Graph. 19(11), 1846–1858 (2013)CrossRefGoogle Scholar
  25. 25.
    Pierrot, A., Rossin, D.: 2-stack pushall sortable permutations. CoRR abs/1303.4376 (2013)
  26. 26.
    Pierrot, A., Rossin, D.: 2-stack sorting is polynomial. In: Mayr, E.W., Portier, N. (eds.) Symposium on Theoretical Aspects of Computer Science. LIPIcs, vol. 25, pp. 614–626. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2014)Google Scholar
  27. 27.
    Riche, N.H., Dwyer, T.: Untangling Euler diagrams. IEEE Trans. Visual. Comput. Graph. 16(6), 1090–1099 (2010)CrossRefGoogle Scholar
  28. 28.
    Simonetto, P., Auber, D., Archambault, D.: Fully automatic visualisation of overlapping sets. Comput. Graph. Forum 28(3), 967–974 (2009)CrossRefGoogle Scholar
  29. 29.
    Tamassia, R., Tollis, I.G.: A unified approach to visibility representations of planar graphs. Discrete Comput. Geom. 1, 321–341 (1986)zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Till Bruckdorfer
    • 1
    Email author
  • Michael Kaufmann
    • 1
  • Stephen G. Kobourov
    • 2
  • Sergey Pupyrev
    • 2
    • 3
  1. 1.Wilhelm-Schickard-Institut für InformatikUniversität TübingenTübingenGermany
  2. 2.Department for Computer ScienceUniversity of ArizonaTucsonUSA
  3. 3.Institute of Mathematics and Computer ScienceUral Federal UniversityYekaterinburgRussia

Personalised recommendations