Visually exploring movement data via similarity-based analysis

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for “hands-on” analysis for trajectory data.     

A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem

The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the classic Traveling Salesman Problem (TSP) and one of the most significant stochastic routing problems. In the PTSP, only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm, the classic PSO and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in 13 out of 20 cases the proposed algorithm gives a new best solution.     

Fuzzy control optimized by a Multi-Objective Particle Swarm Optimization algorithm for vibration suppression of smart structures

Smart structures include elements of active, passive or hybrid control. In this paper, a new Multi-Objective Particle Swarm Optimization (MOPSO), with a different velocity equation, for the calculation of the free parameters in active control systems is proposed and tested. A fuzzy control system is considered. Fuzzy control is a suitable tool for the systematic development of nonlinear active control strategies and can be fine tuned if no experience exists or if one designs more complicated control schemes. The usage of MOPSO with a combination of continuous and discrete variables for the optimal design of the controller is proposed. Numerical applications on smart piezoelastic beams are presented.     

Clustering uncertain trajectories

Knowledge discovery in Trajectory Databases (TD) is an emerging field which has recently gained great interest. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic point vector representation of trajectories that encompasses the underlying uncertainty and introduce an effective distance metric to cope with uncertainty. Second, we devise CenTra, a novel algorithm which tackles the problem of discovering the Centroid Trajectory of a group of movements taking into advantage the local similarity between portions of trajectories. Third, we propose a variant of the Fuzzy C-Means (FCM) clustering algorithm, which embodies CenTra at its update procedure. Finally, we relax the vector representation of the Centroid Trajectories by introducing an algorithm that post-processes them, as such providing these mobility patterns to the analyst with a more intuitive representation. The experimental evaluation over synthetic and real world TD demonstrates the efficiency and effectiveness of our approach.     

Recurrent routines: Analyzing and supporting orchestration in technology-enhanced primary classrooms

The increasing presence of multiple Information and Communication Technologies (ICT) in the classroom does not guarantee an improvement of the learning experiences of students, unless it is also accompanied by pedagogically effective orchestration of those technologies. In order to help teachers in this endeavour, it can be useful to understand how this orchestration takes place in real-world classrooms, and to provide teachers with professional development opportunities that can be easily applied to their everyday classroom practice. This paper describes a qualitative field study conducted in five primary school classrooms where a new collaborative software was introduced alongside existing classroom technology. For six months, teachers designed and orchestrated classroom activities in these authentic, technologically-rich settings. The analysis of the resulting activity designs and enactments uncovered a limited set of recurrent elements of teacher practice, or routines. These routines and their graphical representation are posited as a useful analysis tool for researchers in understanding complex teacher practices with ICT. Moreover, the authors propose that these routines offer new opportunities for professional development of teachers in effectively using ICT in their classrooms. Initial uses of these routines in teacher workshops, with encouraging results, are also presented.     

Detecting proximity events in sensor networks

Sensor networks are often used to perform monitoring tasks, such as animal and vehicle tracking, or the surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries, which allow us to report interesting events, observed by nodes in the network that lie within a certain distance from each other. An event is triggered when a user-programmable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks and propose several alternative techniques that differ in the number of messages exchanged by the nodes and the quality of the returned answers. Our solutions utilize a distributed routing index, maintained by the nodes in the network, that is dynamically updated as new observations are obtained by the nodes. This distributed index allows us to efficiently process multiple proximity queries involving several different event types within a fraction of the cost that a straightforward evaluation requires. We present an extensive experimental study to show the benefits of our techniques under different scenarios using both synthetic and real data sets. Our results demonstrate that our algorithms scale better and require significantly fewer messages compared to a straightforward execution of the queries.     

Progressive processing of subspace dominating queries

A top-k dominating query reports the k items with the highest domination score. Algorithms for efficient processing of this query have been recently proposed in the literature. Those methods, either index based or index free, apply a series of pruning criteria toward efficient processing. However, they are characterized by several limitations, such as (1) they lack progressiveness (they report the k best items at the end of the processing), (2) they require a multi-dimensional index or they build a grid-based index on-the-fly, which suffers from performance degradation, especially in high dimensionalities, and (3) they do not support vertically decomposed data. In this paper, we design efficient algorithms that can handle any subset of the dimensions in a progressive manner. Among the studied algorithms, the Differential Algorithm shows the best overall performance.     

ASTERIX: towards a scalable,semistructured data platform for evolving-world models

ASTERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal—the storage and analysis of data pertaining to evolving-world models. We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead.     

Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization

This paper presents the design of a vibration control mechanism for a beam with bonded piezoelectric sensors and actuators. The mechanical modeling of the structure and the subsequent finite element approximation are based on the classical equations of motion, as they are derived from Hamilton’s principle, in connection with simplified modeling of the piezoelectric sensors and actuators. One nature-inspired intelligence method, the Particle Swarm Optimization, is used for the vibration control of the beam. Three different variants of the Particle Swarm Optimization were tested, namely, the simple Particle Swarm Optimization, the inertia Particle Swarm Optimization and the Constriction Particle Swarm Optimization. A linear feedback control law and a quadratic cost function are used, so that the results are comparable with the classical linear quadratic regulator approach. The same problem has been solved with two other stochastic based optimization algorithms, namely a Genetic Algorithm and a Differential Evolution and the results are used for comparison. The numerical simulation shows that sufficient vibration suppression can be achieved by means of this method.