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.

Random graphs

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.

/pdf/mining-time-changing-data-streams-a9kghvc13p.pdf

Mining time-changing data streams

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.

/pdf/taming-the-underlying-challenges-of-reliable-multihop-2e2c4yefix.pdf

Taming the underlying challenges of reliable multihop routing in sensor networks

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].

/pdf/data-streams-algorithms-and-applications-5fo85u5tcy.pdf

Data streams: algorithms and applications

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)

/pdf/handbook-of-constraint-programming-53srutguo2.pdf

Handbook of Constraint Programming

We consider the fundamental problem of managing a bounded size queue buffer where traffic consists of packets of varying size, where each packet requires several rounds of processing before it can be transmitted from the queue buffer. The goal in such an environment is to maximize the overall size of packets that are successfully transmitted. This model is motivated by the ever-growing ubiquity of network processors architectures, which must deal with heterogeneously-sized traffic, with heterogeneous processing requirements. Our work addresses the tension between two conflicting algorithmic approaches in such settings: the tendency to favor packets with fewer processing requirements, thus leading to fast contributions to the accumulated throughput, as opposed to preferring packets of larger size, which imply a large increase in throughput at each step. We present a model for studying such systems, and present competitive algorithms whose performance depend on the maximum size a packet may have, and maximum amount of processing a packet may require. We further provide lower bounds on algorithms performance in such settings.

Balancing Work and Size with Bounded Buffers

We study the online list update problem under the advice model of computation. Under this model, an online algorithm receives partial information about the unknown parts of the input in the form of some bits of advice generated by a benevolent offline oracle. We show that advice of linear size is required and sufficient for a deterministic algorithm to achieve an optimal solution or even a competitive ratio better than 15/14. On the other hand, we show that surprisingly two bits of advice is sufficient to break the lower bound of 2 on the competitive ratio of deterministic online algorithms and achieve a deterministic algorithm with a competitive ratio of $1.\bar{6}$ . In this upper-bound argument, the bits of advice determine the algorithm with smaller cost among three classical online algorithms.

On the List Update Problem with Advice

The list update problem was first studied by McCabe [47] more than 45 years ago under distributional analysis in the context of maintaining a sequential file. In 1985, Sleator and Tarjan [55] introduced the competitive ratio framework for the study of worst case behavior on list update algorithms. Since then, many deterministic and randomized online algorithms have been proposed and studied under this framework. The standard model as originally introduced has a peculiar cost function for the rearrangement of the list after each search operation. To address this, several variants have been introduced, chiefly the MRM model (Martinez and Roura, [46]; Munro, [49]), the paid exchange model, and the compression model. Additionally, the list update problem has been studied under locality of reference assumptions, and several models have been proposed to capture locality of input sequences. This survey gives a brief overview of the main list update algorithms, the main alternative cost models, and the related results for list update with locality of reference. Open problems and directions for future work are included.

A Survey of Algorithms and Models for List Update

In this paper we investigate parallel searches on m concurrent rays for a point target t located at some unknown distance along one of the rays. A group of p agents or robots moving at unit speed searches for t. The search succeeds when an agent reaches the point t. Given a strategy S the competitive ratio is the ratio of the time needed by the agents to find t using S and the time needed if the location of t had been known in advance. We provide a strategy with competitive ratio of 1 + 2(m/p - 1)(m/(m - p))m/p and prove that this is optimal. This problem has applications in multiple heuristic searches in AI as well as robot motion planning. The case p = 1is known in the literature as the cow path problem.

Online Parallel Heuristics and Robot Searching under the Competitive Framework

We consider the problem of managing a bounded size First-In-First-Out (FIFO) queue buffer, where each incoming unit-sized packet requires several rounds of processing before it can be transmitted out. Our objective is to maximize the total number of successfully transmitted packets. We consider both push-out (when a policy is permitted to drop already admitted packets) and non-push-out cases. We provide worst-case guarantees for the throughput performance of our algorithms, proving both lower and upper bounds on their competitive ratio against the optimal algorithm, and conduct a comprehensive simulation study that experimentally validates predicted theoretical behavior.

Alejandro López-Ortiz

Papers

Balancing Work and Size with Bounded Buffers

On the List Update Problem with Advice

A Survey of Algorithms and Models for List Update

Online Parallel Heuristics and Robot Searching under the Competitive Framework

Online Scheduling FIFO Policies with Admission and Push-Out