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Alejandro López-Ortiz

Bio: Alejandro López-Ortiz is an academic researcher from University of Waterloo. The author has contributed to research in topics: Competitive analysis & List update problem. The author has an hindex of 33, co-authored 193 publications receiving 3719 citations. Previous affiliations of Alejandro López-Ortiz include Open Text Corporation & University of New Brunswick.


Papers
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Book ChapterDOI
14 Feb 2013
TL;DR: This work considers the problem in conflict-aware multi-channel networks, which can be modeled as undirected graphs in which each edge is labeled with a set of available channels to transmit data between its endpoints.
Abstract: The broadcasting problem asks for the fastest way of transmitting a message to all nodes of a communication network. We consider the problem in conflict-aware multi-channel networks. These networks can be modeled as undirected graphs in which each edge is labeled with a set of available channels to transmit data between its endpoints. Each node can send and receive data through any channel on its incident edges, with the restriction that it cannot successfully receive through a channel when multiple neighbors send data via that channel simultaneously.

3 citations

Posted Content
TL;DR: The $\Pi-Packing with $\alpha()$-Overlap problem is introduced to allow for more complex constraints in the overlap region than those previously studied, and several examples of $\alpha(),$ functions which meet those conditions are given.
Abstract: In earlier versions of the community discovering problem, the overlap between communities was restricted by a simple count upper-bound [17,5,11,8]. In this paper, we introduce the $\Pi$-Packing with $\alpha()$-Overlap problem to allow for more complex constraints in the overlap region than those previously studied. Let $\mathcal{V}^r$ be all possible subsets of vertices of $V(G)$ each of size at most $r$, and $\alpha: \mathcal{V}^r \times \mathcal{V}^r \to \{0,1\}$ be a function. The $\Pi$-Packing with $\alpha()$-Overlap problem seeks at least $k$ induced subgraphs in a graph $G$ subject to: (i) each subgraph has at most $r$ vertices and obeys a property $\Pi$, and (ii) for any pair $H_i,H_j$, with $i eq j$, $\alpha(H_i, H_j) = 0$ (i.e., $H_i,H_j$ do not conflict). We also consider a variant that arises in clustering applications: each subgraph of a solution must contain a set of vertices from a given collection of sets $\mathcal{C}$, and no pair of subgraphs may share vertices from the sets of $\mathcal{C}$. In addition, we propose similar formulations for packing hypergraphs. We give an $O(r^{rk} k^{(r+1)k} n^{cr})$ algorithm for our problems where $k$ is the parameter and $c$ and $r$ are constants, provided that: i) $\Pi$ is computable in polynomial time in $n$ and ii) the function $\alpha()$ satisfies specific conditions. Specifically, $\alpha()$ is hereditary, applicable only to overlapping subgraphs, and computable in polynomial time in $n$. Motivated by practical applications we give several examples of $\alpha()$ functions which meet those conditions.

3 citations

Posted Content
TL;DR: It is proved that all Any-Fit strategies have a competitive ratio of at least μ, where μ is the max/min interval length ratio of jobs, and a simple algorithm called Move To Front (Mtf) which has a competitive ratios of at most 6μ + 8.
Abstract: In Cloud systems, we often deal with jobs that arrive and depart in an online manner. Upon its arrival, a job should be assigned to a server. Each job has a size which defines the amount of resources that it needs. Servers have uniform capacity and, at all times, the total size of jobs assigned to a server should not exceed the capacity. This setting is closely related to the classic bin packing problem. The difference is that, in bin packing, the objective is to minimize the total number of used servers. In the Cloud, however, the charge for each server is proportional to the length of the time interval it is rented for, and the goal is to minimize the cost involved in renting all used servers. Recently, certain bin packing strategies were considered for renting servers in the Cloud [Li et al. SPAA'14]. There, it is proved that all Any-Fit bin packing strategy has a competitive ratio of at least $\mu$, where $\mu$ is the max/min interval length ratio of jobs. It is also shown that First Fit has a competitive ratio of $2\mu + 13$ while Best Fit is not competitive at all. We observe that the lower bound of $\mu$ extends to all online algorithms. We also prove that, surprisingly, Next Fit algorithm has competitive ratio of at most $2 \mu +1$. We also show that a variant of Next Fit achieves a competitive ratio of $K \times max\{1,\mu/(K-1)\}+1$, where $K$ is a parameter of the algorithm. In particular, if the value of $\mu$ is known, the algorithm has a competitive ratio of $\mu+2$; this improves upon the existing upper bound of $\mu+8$. Finally, we introduce a simple algorithm called Move To Front (MTF) which has a competitive ratio of at most $6\mu + 7$ and also promising average-case performance. We experimentally study the average-case performance of different algorithms and observe that the typical behaviour of MTF is distinctively better than other algorithms.

3 citations

Journal ArticleDOI
TL;DR: 3D reconstructing convex polygons and convex polyhedra given the number of visible edges and visible faces in some orthogonal projections is studied and it is shown that the problem becomes NP-hard when the directions are covered by three or more planes.
Abstract: We study the problem of reconstructing convex polygons and convex polyhedra given the number of visible edges and visible faces in some orthogonal projections. In 2D, we find necessary and sufficient conditions for the existence of a feasible polygon of size N and give an algorithm to construct one, if it exists. When N is not known, we give an algorithm to find the maximum and minimum sizes of a feasible polygon. In 3D, when the directions are covered by a single plane we show that a feasible polyhedron can be constructed from a feasible polygon. We also give an algorithm to construct a feasible polyhedron when the directions are covered by two planes. Finally, we show that the problem becomes NP-hard when the directions are covered by three or more planes.

3 citations

Journal ArticleDOI
TL;DR: The $k$-atomicity property can be used to describe the consistency of data operations in large distributed storage systems as well as weak consistency guarantees offered by such systems.
Abstract: The $k$-atomicity property can be used to describe the consistency of data operations in large distributed storage systems. The weak consistency guarantees offered by such systems are seen as a nec...

3 citations


Cited by
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Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations

Proceedings ArticleDOI
26 Aug 2001
TL;DR: An efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner is proposed, called CVFDT, which stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate.
Abstract: Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.

1,790 citations

Proceedings ArticleDOI
05 Nov 2003
TL;DR: This work study and evaluate link estimator, neighborhood table management, and reliable routing protocol techniques, and narrow the design space through evaluations on large-scale, high-level simulations to 50-node, in-depth empirical experiments.
Abstract: The dynamic and lossy nature of wireless communication poses major challenges to reliable, self-organizing multihop networks. These non-ideal characteristics are more problematic with the primitive, low-power radio transceivers found in sensor networks, and raise new issues that routing protocols must address. Link connectivity statistics should be captured dynamically through an efficient yet adaptive link estimator and routing decisions should exploit such connectivity statistics to achieve reliability. Link status and routing information must be maintained in a neighborhood table with constant space regardless of cell density. We study and evaluate link estimator, neighborhood table management, and reliable routing protocol techniques. We focus on a many-to-one, periodic data collection workload. We narrow the design space through evaluations on large-scale, high-level simulations to 50-node, in-depth empirical experiments. The most effective solution uses a simple time averaged EWMA estimator, frequency based table management, and cost-based routing.

1,735 citations

Journal ArticleDOI
TL;DR: Data Streams: Algorithms and Applications surveys the emerging area of algorithms for processing data streams and associated applications, which rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity.
Abstract: In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [1].

1,598 citations

Book
01 Jan 2006
TL;DR: Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.
Abstract: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming - Survey-style chapters - Five chapters on applications Table of Contents Foreword (Ugo Montanari) Part I : Foundations Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) Chapter 3. Constraint Propagation (Christian Bessiere) Chapter 4. Backtracking Search Algorithms (Peter van Beek) Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel) Chapter 7. Tractable Structures for CSPs (Rina Dechter) Chapter 8. The Complexity of Constraint Languages (David Cohen, Peter Jeavons) Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) Chapter 10. Symmetry in Constraint Programming (Ian P. Gent, Karen E. Petrie, Jean-Francois Puget) Chapter 11. Modelling (Barbara M. Smith) Part II : Extensions, Languages, and Applications Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte) Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) Chapter 17. Constraints over Structured Domains (Carmen Gervet) Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh) Chapter 19. Temporal CSPs (Manolis Koubarakis) Chapter 20. Distributed Constraint Programming (Boi Faltings) Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel) Chapter 22. Constraint-Based Scheduling and Planning (Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten) Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) Chapter 24. Configuration (Ulrich Junker) Chapter 25. Constraint Applications in Networks (Helmut Simonis) Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

1,527 citations