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

List update algorithms have been widely used as subroutines in compression schemas, most notably as part of Burrows-Wheeler compression. We performed an experimental comparison of various list update algorithms both as stand alone compression mechanisms and as a second stage of the BWT-based compression. We considered the following list update algorithms: move-to-front (MTF), sort-by-rank (SBR), frequency-count (FC), timestamp (TS), and transpose (TR) on text files of the Calgary Corpus. Our results showed that TR and FC usually outperform MTF and TS if we do not use BWT. This is in contrast with competitive analysis in which MTF and TS are superior to TS and FC. After BWT, MTF and TS have comparable performance and always outperform FC and TR. Our experiments are consistent with the intuition that BWT increases locality of reference and the predicted result from the locality of reference model of the work of Angelopoulos et al. (2008).

List Update Algorithms for Data Compression

In this paper, we address the problem of designing an interruptible system in a setting which involves n problem instances. The system schedules executions of a contract algorithm (which offers a trade-off between allowable computation time and quality of the solution) in m identical parallel processors. When an interruption occurs, the system outputs the currently best solution to each of the n problem instances. The quality of this output is then compared to the best possible algorithm that has foreknowledge of the interruption time and must, likewise, produce solutions to all n problem instances. This extends the well-studied setting in which only one problem instance is queried at interruption time. In this work, we first introduce new measures for evaluating the performance of interruptible systems in this setting. In particular, we propose the deficiency of a schedule as a performance measure that meets the requirements of the problem at hand. We then present a schedule whose performance we prove that is within a small factor from optimal in the general, multiprocessor setting. We also show several lower bounds on the deficiency of schedules on a single processor. More precisely, we prove a general lower bound of 
$$(n+1)/n$$

, an improved lower bound for the two-problem setting (
$$n=2$$

), and a tight lower bound for the class of round-robin schedules. Our techniques can also yield a simpler, alternative proof of the main result of Bernstein et al. (in: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), pp 1211–1217, 2003) concerning the performance of cyclic schedules in multiprocessor environments.

https://hal.archives-ouvertes.fr/hal-02986029/document

Interruptible algorithms for multiproblem solving

In this paper we present the first algorithm with optimal average-case and close-to-best known worst-case performance for the classic on-line problem of bin packing. It has long been observed that known bin packing algorithms with optimal average-case performance were not optimal in the worst-case sense. In particular First Fit and Best Fit had optimal average-case ratio of 1 but a worst-case competitive ratio of 1.7. The wasted space of First Fit and Best Fit for a uniform random sequence of length $n$ is expected to be $\Theta(n^{2/3})$ and $\Theta(\sqrt{n} \log ^{3/4} n)$, respectively. The competitive ratio can be improved to 1.691 using the Harmonic algorithm; further variations of this algorithm can push down the competitive ratio to 1.588. However, Harmonic and its variations have poor performance on average; in particular, Harmonic has average-case ratio of around 1.27. In this paper, first we introduce a simple algorithm which we term Harmonic Match. This algorithm performs as well as Best Fit on average, i.e., it has an average-case ratio of 1 and expected wasted space of $\Theta(\sqrt{n} \log ^{3/4} n)$. Moreover, the competitive ratio of the algorithm is as good as Harmonic, i.e., it converges to $ 1.691$ which is an improvement over 1.7 of Best Fit and First Fit. We also introduce a different algorithm, termed as Refined Harmonic Match, which achieves an improved competitive ratio of $1.636$ while maintaining the good average-case performance of Harmonic Match and Best Fit. Finally, our extensive experimental evaluation of the studied bin packing algorithms shows that our proposed algorithms have comparable average-case performance with Best Fit and First Fit, and this holds also for sequences that follow distributions other than the uniform distribution.

An All-Around Near-Optimal Solution for the Classic Bin Packing Problem.

We give experimental evidence for the benefits of order-preserving compression in sorting algorithms. While, in general, any algorithm might benefit from compressed data because of reduced paging requirements, we identified two natural candidates that would further benefit from order-preserving compression, namely string-oriented sorting algorithms and word-RAM algorithms for keys of bounded length. The word-RAM model has some of the fastest known sorting algorithms in practice. These algorithms are designed for keys of bounded length, usually 32 or 64 bits, which limits their direct applicability for strings. One possibility is to use an order-preserving compression scheme, so that a bounded-key-length algorithm can be applied. For the case of standard algorithms, we took what is considered to be the among the fastest nonword RAM string sorting algorithms, Fast MKQSort, and measured its performance on compressed data. The Fast MKQSort algorithm of Bentley and Sedgewick is optimized to handle text strings. Our experiments show that order-compression techniques results in savings of approximately 15p over the same algorithm on noncompressed data. For the word-RAM, we modified Andersson's sorting algorithm to handle variable-length keys. The resulting algorithm is faster than the standard Unix sort by a factor of 1.5X. Last, we used an order-preserving scheme that is within a constant additive term of the optimal Hu--Tucker, but requires linear time rather than O(mlog m), where m = vΣv is the size of the alphabet.

/pdf/fast-string-sorting-using-order-preserving-compression-2rey3jm7oz.pdf

Fast string sorting using order-preserving compression

At any given time, the traffic on the network can be described using a traffic matrix. Entry ai,j in the matrix denotes the traffic originating in i with destination j currently in the network. As traffic demands are dynamic, the matrix itself is ever changing. Traditionally network capacity has been deployed so that it can support any traffic matrix with high probability, given the known traffic distribution patterns. Recently the need for resilience and reliabilibility of the network for mission critical data has brought the need for backbone capacity that can support all traffic matrices. In this talk we give an overview of the state of the art on networks and routing schemes with this property.

Alejandro López-Ortiz

Papers

List Update Algorithms for Data Compression

Interruptible algorithms for multiproblem solving

An All-Around Near-Optimal Solution for the Classic Bin Packing Problem.

Fast string sorting using order-preserving compression

Valiant load balancing, benes networks and resilient backbone design