New Variants of Pattern Matching with Constants and Variables

Given a text and a pattern over two types of symbols called constants and variables, the parameterized pattern matching problem is to find all occurrences of substrings of the text that the pattern matches by substituting a variable in the text for each variable in the pattern, where the substitution should be injective. The function matching problem is a variant of it that lifts the injection constraint. In this paper, we discuss variants of those problems, where one can substitute a constant or a variable for each variable of the pattern. We give two kinds of algorithms for both problems, a convolution-based method and an extended KMP-based method, and analyze their complexity.Comment: 15 pages, 2 figure

Movement similarity assessment using symbolic representation of trajectories

This paper describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters such as speed, acceleration, or direction. First, a segmentation technique is applied to decompose trajectories into a set of segments with homogeneous characteristics with respect to a particular movement parameter. Each segment is assigned to a movement parameter class, representing the behavior of the movement parameter. Accordingly, the segmentation procedure transforms a trajectory to a sequence of class labels, that is, a symbolic representation. A modified version of edit distance, called Normalized Weighted Edit Distance (NWED) is introduced as a similarity measure between different sequences. As an application, we demonstrate how the method can be employed to cluster trajectories. The performance of the approach is assessed in two case studies using real movement datasets from two different application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three different experiments have been conducted that respond to different facets of the proposed techniques, and that compare our NWED measure to a related method

ModelDrivenGuide: An Approach for Implementing NoSQL Schemas

International audienc

Adding Meaning to Your Steps (Keynote Paper)

Mobility data is becoming an important player in many application domains. Many techniques have been elaborated to extract statistical knowledge from the data sets gathering raw data tracks about the moving objects of interest to an application. These data tracks obey the physical-level specifications of the devices used for data acquisition (GPS, GSM, RFID, smart phones, and other sensors). Nowadays, interest has shifted from raw data tracks analysis to more application-oriented ways of analyzing more meaningful movement records suitable for the specific purposes of the application at hand. This trend has promoted the concept of semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies

Solving Graph Isomorphism using Parameterized Matching

Abstract. We propose a new approach to solve graph isomorphism using parameterized matching. To find isomorphism between two graphs, one graph is linearized, i.e., represented as a graph walk that covers all nodes and edges such that each element is represented by a parameter. Next, we match the graph linearization on the second graph, searching for a bijective function that maps each element of the first graph to an element of the second graph. We develop an efficient linearization algorithm that generates short linearization with an approximation guarantee, and develop a graph matching algorithm. We evaluate our approach experimentally on graphs of different types and sizes, and compare to the performance of VF2, which is a prominent algorithm for graph isomorphism. Our empirical measurements show that graph linearization finds a matching graph faster than VF2 in many cases because of better pruning of the search space. 1 Introduction and Related Wor