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Project Description

The following description is an excerpt from the project contract.


  1. Project Summary
  2. Objectives
    1. Introduction
    2. Size and Complexity
    3. Algorithmic Methods
  3. Project Workplan
    1. Introduction
    2. Work Package 1: Massive Data Sets
    3. Work Package 2: Networks and Communication
    4. Work Package 3: Production and Transportation Planning
    5. Work Package 4: Generic Methods
    6. Work Package 5: Experimental Algorithmics
    7. Work Package 6: Project Management, Dissemination, Evaluation

Project Summary

ALCOM-FT brings together eleven of the leading groups in algorithms research in Europe in a project that proposes to discover new algorithmic concepts, identify key algorithmic problems in important applications, and contribute to the accelerated transfer of advanced algorithmic techniques into commercial systems.

The main emphasis of the project is on a novel combination of application oriented research in three important areas - massive data sets, massive and complex communication, and complex problems in production and planning - with innovative methodological work on experimental algorithmics and generic algorithmic methods.



Algorithms and their analysis are at the heart of information technology. Significant advances in algorithms are a crucial factor in such technological developments as (1) modern search-engines which process enormous amounts of data "instantaneously", (2) world-wide communication, and (3) electronic commerce.

The rapid development of the Internet and other information highways is radically changing society. Widespread use of advanced algorithmic techniques in the information infra-structure and in engineering and business applications will lead to more competitive systems and will open opportunities for the design and construction of entirely new systems.

Properly exploiting the revolutionary new technologies will require new algorithmic methods. The consortium behind this project (11 partners in eight EU member states and one NAS country) will co-operate in the rebuilding of the theory and practice of algorithmics, in light of technological developments. We have identified three key challenges and two methodologies as the content of a long-term (or "fundamental") research project, i.e., of a high-risk project of the type in which new insight is discovered, published, applied, and disseminated within a five-to-ten year horizon.

We shall consider problems of size and complexity, specifically within the challenges of "massive data sets," "massive and complex communication," and "complex problems in production and planning." Our methodologies are "experimental algorithmics" and "generic algorithmic methods." Dealing efficiently with size and complexity has always been a primary goal for the theory of algorithmics; however, the actual challenges are in continuous technological revolution, so methodological breakthroughs will be required.

Size and Complexity

Ever larger data sets arise in important applications like Internet-search, data mining, electronic libraries, geographic information systems, computer graphics, scientific computing etc. Archives for satellite images, climate simulation or elementary particle physics already work with petabytes of data, and for many existing and new applications, no size limits are in sight.

Patterns of communication are changing radically. The Internet has dramatically altered the way people share, acquire and exchange information, and technological development will reinforce this change. All-optical networks allow for fast travel of multimedia data, wireless mobile/radio technology allows for forming temporary networks without the aid of an established infrastructure, and satellite networks allow for asymmetric multicast communication of high bandwidth.

Efficient planning in business and society is as important as ever and instances of concrete planning problems are becoming larger and larger and more and more complex. This leads directly to a host of optimization problems, the solutions of which are of significant practical interest and represent a considerable challenge in algorithm design.

Traditional algorithms, traditional network design methods and protocols, and traditional approaches to combinatorial optimization are insufficient for the handling of these problems.

We shall construct new models and algorithms that take into account multiple disks and processors, memory hierarchies, and networks. We shall develop mathematical models of the key properties of new communication systems, and develop algorithmic methods of general applicability for the solutions of the important problems. We shall design, theoretically analyze, and experimentally validate efficient, robust, and stable algorithmic solutions to optimization issues in communication networks and dynamic distributed environments. We have selected important planning problems in the area of production and transportation. We shall develop algorithmic techniques to solve them, implement these techniques, and test them on real-world instances supplied by our industrial contacts.

Many of the algorithms for external-memory problems will be delivered in the form of (extensions to) algorithmic libraries. We shall also produce software for design and resource allocation in networks as well as for the efficient implementation of programs in dynamic distributed environments. We shall build a library containing real-world problem instances in planning. Our software will be made available to a broad spectrum of users, including both industry and academia.

Algorithmic Methods

Theoretical computer science has not only established an impressive body of knowledge about the intrinsic nature of computation, but has also produced a vast number of generic algorithmic methods leading to decisive improvements of the solutions of large scale problems. Important examples of such generic methods are data structuring, on-line and dynamic algorithms, paradigms for parallel and distributed computing, randomization, approximation, etc.

This body of knowledge constitutes the scientific platform on which the consortium meets the challenges of size and complexity. We also realize that the rapid transfer of relevant algorithmic techniques into the applications domain requires an expanded view of the design methodology relative to that of classical algorithmics alone. The objects of interest in traditional algorithmics are algorithms and the techniques for their analysis, whereas there is currently not a well-defined methodology for the study of concrete programs. It is necessary to make programs a first-class object of interest and we expect this expanded view to lead to a methodological breakthrough.

We shall attack the challenges described, lay the foundation for future applications, and establish a solid platform for experimental algorithmics. The vehicle will be continued development of carefully chosen generic methods with special emphasis on randomization, combinatorial optimization and approximation.

We shall produce methodologies for experimental algorithmics as well as a testbed for running reproducible experiments. Also, we shall provide tools for performance prediction, that are capable of informing users about the expected behaviour of their algorithms much more accurately than traditionally rough analysis.

The work on randomization will address the precise quantification of efficiency, of probabilities of failure, and other similar questions, all at a fundamental level. Also, we will produce test sets for experimental algorithmics that are based on randomization.

We will investigate discrete structures that play a central role in our combinatorial optimization problems. The new structural results that we obtain are expected to lead to more efficient algorithms that will serve as building blocks in the overall solutions.

We will design a high-level description language for combinatorial optimization problems, implement it and deliver a complete easy-to-use software system for structured combinatorial optimization problems. We will test our algorithms on recognized test instances and on problems supplied by our industrial contacts.

Project Workplan


The workplan is organized as five mutually related work packages:

  1. Massive Data Sets
  2. Networks and Communication
  3. Production and Transportation Planning
  4. Generic Methods
  5. Experimental Algorithmics.

The first three contain the work related to the challenges of size and complexity, and the last two contain the more methodologically oriented work. We view the relations between the work packages as illustrated by the figure below.

Relations between the work packages

Besides these five work packages, an additional work package for project management, dissemination, and evaluation is included.

Work Package 1: Massive Data Sets

Future technologies allow high performance computation on the massive data sets produced by the information society (e.g., the internet, digital libraries, data warehouses, geographic information systems, or virtual reality). To make this possible we want to devise innovative algorithms which consider multiple disks and processors, memory hierarchies, and networks.
Description of Work
Engineer algorithms for data mining, fundamental data structures, graph problems, and string problems. General techniques for handling parallel disks and processors using realistic machine models. Design and implementation of an experimental platform for external computation. Applying our expertise in parallel computing, probabilistic algorithms, combinatorial optimization, and software libraries will be an important part of the work.

Work Package 2: Networks and Communication

The current explosion of network users will lead to a rapid increase in communications demand for new applications based on wireless technology, high bandwidth services and multimedia. For this reason we foresee that, despite the increase of communication resources, their efficient use will be a key factor of future network management. Many important issues in managing advanced communication networks (including high-speed ATM networks, all-optical and wireless networks, distributed media servers systems) have inherently combinatorial and algorithmic nature. We plan to use advanced algorithmic methods, computational and mathematical techniques and software platforms development to design, analyze and validate efficient algorithmic solutions to such fundamental optimization problems in modern networks and communications.
Description of Work
In this work package, we will design, theoretically analyze and experimentally validate efficient, robust, and stable algorithmic solutions to selected fundamental optimization issues in modern network communications.

We focus on two central issues, efficient communication in modern networks, where we will investigate the efficient use of resources (frequencies, bandwidth) and develop basic protocols (routing, contention resolution) in advanced networks (all-optical, wireless, ATM) and investigate their stability properties; and dynamic distributed environments where we will provide methods for job allocation and load-balancing, as well as efficient data management and contention resolution.

We will also develop integrated software platforms for the implementation and experimental validation of the proposed algorithmic solutions.

Work Package 3: Production and Transportation Planning

More and more, companies become aware that in order to run their business processes as efficiently as possible they must improve the quality of their planning. The planning problems are so complex that algorithmic support is needed to produce a good solution. In co-operation with our industrial contacts we will devise algorithms for a number of real world planning problems in the areas of production and transport.
Description of Work
We will study a number of practical planning problems that we have identified in cooperation with our industrial partners, including job-shop scheduling, dynamic scheduling, facility location, routing on structure networks, and time tabling. We shall develop algorithmic techniques to tackle these problems and implement the solutions.

Work Package 4: Generic Methods

Our main objective is to develop the new algorithmic methods which will be necessary to properly exploit the new technologies which are currently available and becoming available. These methods will include probabilistic methods, optimization methods, and approximation methods, all of which are of vital importance to the rest of the project.
Description of Work
There are strong links between Work Packages 1, 2, and 3, and progress along the lines of a generic methodology. Consequently, a central theme of the project will be research in generic algorithmic methods. The main emphasis will be to develop probabilistic techniques, discrete optimization techniques and approximation techniques. In the course of this work, we will need to use and improve other computational and mathematical techniques such as approximation methods, heuristic methods, computer algebra modeling, derandomization and tractability analysis. Many of the problems that we consider will have an on-line nature (i.e., not all of the data are available at the start of the computation).

Work Package 5: Experimental Algorithmics

The objective of this work package is twofold. First, we will produce methodologies for experimental algorithmics and a testbed for running reproducible experiments. This will be extremely beneficial to researchers and developers interested in practical aspects of algorithms and their implementation issues. Second, we will produce cache-aware algorithms and tools for performance prediction, which are able to predict the behaviour of programs more accurately than classical asymptotic analysis. This will be beneficial to the community of users of algorithmic techniques at large.
Description of Work
Engineering of cache-aware data structures and algorithms. Development of techniques for more realistic performance predictions and for checking of programs. Construction of generators of worst-case, random and simulated real-life problem instances for use in experimental evaluation of algorithms. Development of techniques for visualization of program behaviour. Construction of an experimental testbed - a software tool supporting the experimental evaluation of algorithms, incorporating the results of the work areas mentioned above.

Work Package 6: Project Management, Dissemination, Evaluation

To secure the smooth operation of the project. To ensure proper dissemination of results, and organise self-assessment and evaluation. To represent the project in relation to external partners, including academia, industry and the EU-commission.
Description of work
The project will be led by a Consortium Board, chaired by the Coordinator. The board will appoint a Steering Committee and one or more Work Package Leaders for each Work Package. The board will carry out and/or supervise the following activities: project development, budgeting, and all matters related to project financing; maintenance of the infra-structure of the consortium; organisation of project meetings, workshops, scientific exchanges etc.; management reporting from the project; all other duties related to the general operation and representation of the consortium activities relating to the project.

Maintained by Rolf Fagerberg (rolf@cs.au.dk)
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