Adaptive indexing is a concept that considers index creation in databases as a by-product of query processing; as opposed to traditional full index creation where the indexing effort is performed up front before answering any queries. Adaptive indexing has received a considerable amount of attention, and several algorithms have been proposed over the past few years; including a recent experimental study comparing a large number of existing methods. Until now, however, most adaptive indexing algorithms have been designed single-threaded, yet with multi-core systems already well established, the idea of designing parallel algorithms for adaptive indexing is very natural. In this regard, and to the best of our knowledge, only one parallel algorithm for adaptive indexing has recently appeared in the literature: The parallel version of standard cracking. In this paper we describe three alternative parallel algorithms for adaptive indexing, including a second variant of a parallel standard cracking algorithm. Additionally, we describe a hybrid parallel sorting algorithm, and a NUMA-aware method based on sorting. We then thoroughly compare all these algorithms experimentally. Parallel sorting algorithms serve as a realistic baseline for multithreaded adaptive indexing techniques. In total we experimentally compare seven parallel algorithms. The initial set of experiments considered in this paper indicates that our parallel algorithms significantly improve over previously known ones. Our results also suggest that, although adaptive indexing algorithms are a good design choice in single-threaded environments, the rules change considerably in the parallel case. That is, in future highly-parallel environments, sorting algorithms could be serious alternatives to adaptive indexing.

An alternate method for constructing (Hadamard) cocyclic matrices over a finite group G is described. Provided that a homological model φ:B (Z[G]) F H hG for G is known, the homological reduction method automatically generates a full basis for 2-cocycles over G (including 2-coboundaries). From these data, either an exhaustive or a heuristic search for Hadamard cocyclic matrices is then developed. The knowledge of an explicit basis for 2-cocycles which includes 2-coboundaries is a key point for the designing of the heuristic search. It is worth noting that some Hadamard cocyclic matrices have been obtained over groups G for which the exhaustive searching techniques are not feasible. From the computationalcost point of view, even in the case that the calculation of such a homological model is also included, comparison with other methods in the literature shows that the homological reduction method drastically reduces the required computing time of the operations involved, so that even exhaustive searches succeeded at orders for which previous calculations could not be completed. With aid of an implementation of the method in Mathematica, some examples are discussed, including the case of very well-known groups (finite abelian groups, dihedral groups) for clarity.

A conflict-free k-coloring of a graph assigns one of k different colors to some of the vertices such that, for every vertex v, there is a color that is assigned to exactly one vertex among v and v's neighbors. Such colorings have applications in wireless networking, robotics, and geometry, and are well-studied in graph theory. Here we study the natural problem of the conflict-free chromatic number χ CF (G) (the smallest k for which conflict-free k-colorings exist). We provide results both for closed neighborhoods N [v], for which a vertex v is a member of its neighborhood, and for open neighborhoods N (v), for which vertex v is not a member of its neighborhood.For closed neighborhoods, we prove the conflict-free variant of the famous Hadwiger Conjecture: If an arbitrary graph G does not contain K k+1 as a minor, then χ CF (G) ≤ k. For planar graphs, we obtain a tight worst-case bound: three colors are sometimes necessary and always sufficient. In addition, we give a complete characterization of the algorithmic/computational complexity of conflict-free coloring. It is NP-complete to decide whether a planar graph has a conflict-free coloring with one color, while for outerplanar graphs, this can be decided in polynomial time. Furthermore, it is NP-complete to decide whether a planar graph has a conflict-free coloring with two colors, while for outerplanar graphs, two colors always suffice. For the bicriteria problem of minimizing the number of colored vertices subject to a given bound k on the number of colors, we give a full algorithmic characterization in terms of complexity and approximation for outerplanar and planar graphs.For open neighborhoods, we show that every planar bipartite graph has a conflict-free coloring with at most four colors; on the other hand, we prove that for k ∈ {1, 2, 3}, it is NP-complete to decide whether a planar bipartite graph has a conflict-free k-coloring. Moreover, we establish that any general planar graph has a conflict-free coloring with at most eight colors.2. It is NP-complete to decide whether a planar graph has a conflict-free coloring with one color.For outerplanar graphs, this question can be decided in polynomial time.

Hashing is a solved problem. It allows us to get constant time access for lookups. Hashing is also simple. It is safe to use an arbitrary method as a black box and expect good performance, and optimizations to hashing can only improve it by a negligible delta. Why are all of the previous statements plain wrong? That is what this paper is about. In this paper we thoroughly study hashing for integer keys and carefully analyze the most common hashing methods in a five-dimensional requirements space: () data-distribution, () load factor, () dataset size, () read/write-ratio, and () un/successfulratio. Each point in that design space may potentially suggest a different hashing scheme, and additionally also a different hash function. We show that a right or wrong decision in picking the right hashing scheme and hash function combination may lead to significant difference in performance. To substantiate this claim, we carefully analyze two additional dimensions: () five representative hashing schemes (which includes an improved variant of Robin Hood hashing), () four important classes of hash functions widely used today. That is, we consider 20 different combinations in total. Finally, we also provide a glimpse about the effect of table memory layout and the use of SIMD instructions. Our study clearly indicates that picking the right combination may have considerable impact on insert and lookup performance, as well as memory footprint. A major conclusion of our work is that hashing should be considered a white box before blindly using it in applications, such as query processing. Finally, we also provide a strong guideline about when to use which hashing method.

We give an algorithm that determines the number tr(S) of straight line triangulations of a set S of n points in the plane in worst case time O(n 2 2 n ). This is the the first algorithm that is provably faster than enumeration, since tr(S) is known to be Ω(2.43 n ) for any set S of n points. Our algorithm requires exponential space.The algorithm generalizes to counting all triangulations of S that are constrained to contain a given set of edges. It can also be used to compute an optimal triangulation of S (unconstrained or constrained) for a reasonably wide class of optimality criteria (that includes e.g. minimum weight triangulations). Finally, the approach can also be used for the random generation of triangulations of S according to the perfect uniform distribution.The algorithm has been implement and is substantially faster than existing methods on a variety of inputs.