Showing papers presented at "Dagstuhl Seminar Proceedings in 2005"

Ontology Mapping: The State of the Art

Abstract: Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms

Abstract: In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the ef- fect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the pro- posed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which il- lustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for pref- erence articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample ef- fectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to sup- ply preference information to the GA.

S-Match: an algorithm and an implementation of semantic matching

Abstract: We think of Match as an operator which takes two graph-like structures and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.

The Priority R-Tree: A Practically Efficient and Worst-Case-Optimal R-Tree

Abstract: The query efficiency of a data structure that stores a set of objects, can normally be assessed by analysing the number of objects, pointers etc. looked at when answering a query. However, if the data structure is too big to fit in main memory, data may need to be fetched from disk. In that case, the query efficiency is easily dominated by moving the disk head to the correct locations, rather than by reading the data itself. To reduce the number of disk accesses, once can group the data into blocks, and strive to bound the number of different blocks accessed rather than the number of individual data objects read. An R-tree is a general-purpose data structur that stores a hierarchical grouping of geometric objects into blocks. Many heuristics have been designed to determine which objects should be grouped together, but none of these heuristics could give a guarantee on the resulting worst-case query time. We present the Priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query by accessing $O((N/B)^{1-1/d} + T/B)$ blocks, where $N$ is the number of $d$-dimensional objects stored, $B$ is the number of objects per block, and $T$ is the number of objects whose bounding boxes intersect the query window. This is provably asymptotically optimal. Experiments show that the PR-tree performs similar to the best known heuristics on real-life and relatively nicely distributed data, but outperforms them significantly on more extreme data.

Online Testing of Real-time Systems Using UPPAAL: Status and Future Work

Abstract: We present the development of T-UPPAAL — a new tool for online black-box testing of real-time embedded systems from non-deterministic timed automata specifica- tions. It is based on a sound and complete randomized online testing algorithm and is im- plemented using symbolic state representation and manipulation techniques. We propose the notion of relativized timed input/output conformance as the formal implementation relation. A novelty of this relation and our testing algorithm is that they explicitly take environment assumptions into account, generate, execute and verify the result online using the UPPAAL on-the-fly model-checking tool engine. This paper introduces the principles behind the tool, describes the present implementation status, and future work directions.

Network Discovery and Verification

Abstract: Consider the problem of discovering (or verifying) the edges and non-edges of a network, modelled as a connected undirected graph, using a minimum number of queries. A query at a vertex v discovers (or verifies) all edges and non-edges whose endpoints have different distance from v. In the network discovery problem, the edges and non-edges are initially unknown, and the algorithm must select the next query based only on the results of previous queries. We study the problem using competitive analysis and give a randomized on-line algorithm with competitive ratio O(sqrt(n*log n)) for graphs with n vertices. We also show that no deterministic algorithm can have competitive ratio better than 3. In the network verification problem, the graph is known in advance and the goal is to compute a minimum number of queries that verify all edges and non-edges. This problem has previously been studied as the problem of placing landmarks in graphs or determining the metric dimension of a graph. We show that there is no approximation algorithm for this problem with ratio o(log n) unless P=NP. Furthermore, we prove that the optimal number of queries for d-dimensional hypercubes is Theta(d/log d).

Fair Payments for Efficient Allocations in Public Sector Combinatorial Auctions.

Abstract: Motivated by the increasing use of auctions by government agencies, we consider the problem of fairly pricing public goods in a combinatorial auction. A well-known problem with the incentive-compatible Vickrey-Clarke-Groves (VCG) auction mechanism is that the resulting prices may not be in the core. Loosely speaking, this means the payments of the winners could be so low, that there are losing bidders who would have been willing to pay more than the payments of the winning bidders. Clearly, this ``unfair'' outcome is unacceptable for a public-sector auction. Proxy-based combinatorial auctions, in which each bidder submits several package bids to a proxy, result in efficient outcomes and bidder-Pareto-optimal core-payments by winners, thus offering a viable practical alternative to address this problem. This paper confronts two critical issues facing the proxy-auction. First, motivated to minimize a bidder's ability to benefit through strategic manipulation (through collusive agreement or unilateral action), we demonstrate the strength of a mechanism that minimizes total payments among all possible proxy auction outcomes, narrowing the previously broad solution concept. Secondly, we address the computational difficulties of achieving these outcomes with a constraint-generation approach, promising to broaden the range of applications for which the proxy-auction achieves a comfortably rapid solution.

Security and Trust Issues in Semantic Grids

Abstract: Grid computing allows sharing of services and resources accross institutions. However, current Grid security mechanisms for authentication and authorization are too rigid and they lack the ability to determine how ``trustworthy'' the result obtained from a specific provider is likely to be. This paper describes the different facets associated to Trust and identifies the need for Trust Management approaches in the context of Virtual Organizations lifecycle and resource access control in the Grid.

Assessing Solution Quality in Stochastic Programs.

Abstract: Assessing whether a solution is of high quality (optimal or near optimal) is a fundamental question in optimization. We develop Monte Carlo sampling-based procedures for assessing solution quality in stochastic programs. Quality is defined via the optimality gap and our procedures' output is a confidence interval on this gap. We review a multiple-replications procedure and then present a result that justifies a computationally simplified single-replication procedure. Even though the single replication procedure is computationally significantly less demanding, the resulting confidence interval may have low coverage for small sample sizes on some problems. We provide variants of this procedure and provide preliminary guidelines for selecting a candidate solution. Both are designed to improve the basic procedure's performance.

Models and Algorithms for Stochastic Online Scheduling.

Abstract: We introduce a model for non-preemptive scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to the classical stochastic scheduling models, we assume that jobs arrive online over time, and there is no knowledge about the jobs that will arrive in the future. The model incorporates both, stochastic scheduling and online scheduling as a special case. The particular setting we analyze is parallel machine scheduling, with the objective to minimize the total weighted completion times of jobs. We propose simple, combinatorial online scheduling policies for that model, and derive performance guarantees that match the currently best known performance guarantees for stochastic parallel machine scheduling. For processing times that follow NBUE distributions, we improve upon previously best known performance bounds, even though we consider a more general setting.

05151 Summary—Annotating, Extracting and Reasoning about Time and Events

Abstract: The main focus of the seminar was on TimeML-based temporal annotation and reasoning. We were concerned with three main points: determining how effectively one can use the TimeML language for consistent annotation, determining how useful such annotation is for further processing, and determining what modifications should be applied to the standard to improve its usefulness in applications such as question-answering and information retrieval.

Foundations of Meta-Pyramids: Languages vs. Metamodels -- Episode II: Story of Thotus the Baboon1

Abstract: Despite the recent interest for Model Driven Engineering approaches, the so-called four-layers metamodelling architecture is subject to a lot of debate. The relationship that exists between a model and a metamodel is often called instanceOf, but this terminology, which comes directly from the object oriented technology, is not appropriate for the modelling of similar meta-pyramids in other domains. The goal of this paper is to study which are the foundations of the meta-pyramids independently from a particular technology. This paper is actually the second episode of the series "From Ancient Egypt to Model Driven Engineering". In the pilot episode, the notion of megamodel was introduced to model essential Model Driven Engineering concepts. The notion of models was thoroughly discussed and only one association, namely RepresentationOf was introduced. In this paper the megamodel is extended with one fundamental relation in order to model the notion of languages and of metamodels. It is shown how Thotus the Baboon helped Nivizeb the priest in designing strong foundations for meta-pyramids. The secrets of some ancient pyramids are revealed.

On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems

Abstract: In this report we describe how continuation methods can be used for the numerical treatment of multi-objective optimization problems (MOPs): starting with a given Karush-Kuhn-Tucker point (KKT-point) x of an MOP, these techniques can be applied to detect further KKT-points in the neighborhood of x. In the next step, again further points are computed starting with these new-found KKT-points, and so on. In order to maintain a good spread of these solutions we use boxes for the representation of the computed parts of the solution set. Based on this background, we propose a new predictor-corrector variant, and show some numerical results indicating the strength of the method, in particular in higher dimensions. Further, the data structure allows for an efficient computation of MOPs with more than two objectives, which has not been considered so far in most existing continuation methods.

Foundations of Model (Driven) (Reverse) Engineering : Models -- Episode I: Stories of The Fidus Papyrus and of The Solarus

Abstract: Model Driven Engineering (MDE) received a lot of attention in the last years, both from academia and industry. However, there is still a debate on which basic concepts form the foundation of MDE. The Model Driven Ar- chitecture (MDA) from the OMG does not provided clear answers to this question. This standard instead provides a complex set of interdependen t technologies. This paper is the first of a series aiming at defining the founda- tions of MDE independently from a particular technology. A megamodel is introduced in this paper and incrementally refined in further papers from the series. This paper is devoted to a single concept, the concept of model, and to a single relation, the RepresentationOf relation. The lack of strong founda- tions for the MDA' 4-layers meta-pyramid leads to a common mockery: "So, MDA is just about Egyptology?!". This paper is the pilot of the series called "From Ancient Egypt to Model Driven Engineering". The various episodes of this series show that Egyptology is actually a good model to study MDE.

Online Scheduling with Bounded Migration

Abstract: Consider the classical online scheduling problem where jobs that arrive one by one are assigned to identical parallel machines with the objective of minimizing the makespan. We generalize this problem by allowing the current assignment to be changed whenever a new job arrives, subject to the constraint that the total size of moved jobs is bounded by~$\beta$ times the size of the arriving job. Our main result is a linear time `online approximation scheme', that is, a family of online algorithms with competitive ratio~$1+\epsilon$ and constant migration factor~$\beta(\epsilon)$, for any fixed~$\epsilon>0$. This result is of particular importance if considered in the context of sensitivity analysis: While a newly arriving job may force a complete change of the entire structure of an optimal schedule, only very limited `local' changes suffice to preserve near-optimal solutions. We believe that this concept will find wide application in its own right. We also present simple deterministic online algorithms with migration factors~$\beta=2$ and~$\beta=4/3$, respectively. Their competitive ratio~$3/2$ beats the lower bound on the performance of any online algorithm in the classical setting without migration. We also present improved algorithms and similar results for closely related problems. In particular, there is a short discussion of corresponding results for the objective to maximize the minimum load of a machine. The latter problem has an application for configuring storage servers that was the original motivation for this work.

Negotiating Trust on the Grid.

Abstract: Grids support dynamically evolving collections of resources and users, usually spanning multiple administrative domains. The dynamic and crossorganizational aspects of Grids introduce challenging management and policy issues for controlling access to Grid resources. In this paper we show how to extend the Grid Security Infrastructure to provide better support for the dynamic and cross-organizational aspects of Grid activities, by adding facilities for dynamic establishment of trust between parties. We present the PeerTrust language for access control policies, which is based on guarded distributed logic programs, and show how to use PeerTrust to model common Grid trust needs.

Average Case and Smoothed Competitive Analysis of the Multi-Level Feedback Algorithm

Abstract: In this paper we introduce the notion of smoothed competitive analysis of online algorithms. Smoothed analysis has been proposed by Spielman and Teng to explain the behaviour of algorithms that work well in practice while performing very poorly from a worst case analysis point of view. We apply this notion to analyze the Multi-Level Feedback (MLF) algorithm to minimize the total flow time on a sequence of jobs released over time when the processing time of a job is only known at time of completion. The initial processing times are integers in the range $[1,2^K]$. We use a partial bit randomization model, i.e., the initial processing times are smoothed by changing the $k$ least significant bits under a quite general class of probability distributions. We show that MLF admits a smoothed competitive ratio of $O((2^k/\psigma)^3 + (2^k/\psigma)^2 2^{K-k}})$, where $\sigma$ denotes the standard deviation of the distribution. In particular, we obtain a competitive ratio of $O(2^{K-k})$ if $\sigma = \Theta(2^k)$. We also prove an $\Omega(2^{K-k})$ lower bound for any deterministic algorithm that is run on processing times smoothed according to the partial bit randomization model. For various other smoothing models, including the additive symmetric smoothing one, which is a variant of the model used by Spielman and Teng, we give a higher lower bound of $\Omega(2^K)$. A direct consequence of our result is also the first average case analysis of MLF. We show a constant expected ratio of the total flow time of MLF to the optimum under several distributions including the uniform one.

An Adaptive Scheme to Generate the Pareto Front Based on the Epsilon-Constraint Method

Abstract: This paper presents a scheme for generating the Pareto front of multiobjective optimization problems by solving a sequence of con- strained single-objective problems. Since the necessity of determining the constraint value a priori can be a serious drawback of the original epsilon-constraint method, our scheme generates appropriate constraint values adaptively during the run. A simple example problem is presented where the running time (measured by the number of constrained single- objective sub-problems to be solved) of the original epsilon-constraint method is exponential in the problem size (number of decision variables), although the size of the Pareto set grows only linearly. For our method we show that | independent of the problem or the problem size | the time complexity isO(k m 1 ), wherek is the number of Pareto-optimal so- lutions to be found and m the number of objectives. Using the algorithm together with a standard ILP solver for the constrained single-objective problems, the exact Pareto front is generated for the three-objective 0/1 knapsack problem with up to 100 decision variables. Links to problem instances and a reference implementation of the algorithm are provided.

N-ary Queries by Tree Automata.

Abstract: Information extraction from semi-structured documents requires to find n-ary queries in trees that define appropriate sets of n-tuples of nodes. We propose new representation formalisms for n-ary queries by tree automata that we prove to capture MSO. We then investigate n-ary queries by unambiguous tree automata which are relevant for query induction in multi-slot information extraction. We show that this representation formalism captures the class of n-ary queries that are finite unions of Cartesian closed queries, a property we prove decidable.

Sublinear Geometric Algorithms

Abstract: We initiate an investigation of sublinear algorithms for geometric problems in two and three dimensions We give optimal algorithms for intersection detection of convex polygons and polyhedra, point location in two-dimensional triangulations and Voronoi diagrams, and ray shooting in convex polyhedra, all of which run in expected time $O(\sqrt{n}\,)$, where $n$ is the size of the input We also provide sublinear solutions for the approximate evaluation of the volume of a convex polytope and the length of the shortest path between two points on the boundary

An improved algorithm for CIOQ switches

Abstract: The problem of maximizing the weighted throughput in various switching settings has been intensively studied recently through competitive analysis. To date, the most general model that has been investigated is the standard CIOQ (Combined Input and Output Queued) switch architecture with internal fabric speedup $S \geq 1$. CIOQ switches, that comprise the backbone of packet routing networks, are $N \times N$ switches controlled by a switching policy that incorporates two components: Admission control and scheduling. An admission control strategy is essential to determine the packets stored in the FIFO queues in input and output ports, while the scheduling policy conducts the transfer of packets through the internal fabric, from input ports to output ports. The online problem of maximizing the total weighted throughput of CIOQ switches was recently investigated by Kesselman and Ros\'{e}n [SPAA03]. They presented two different online algorithms for the general problem that achieve non-constant competitive ratios (linear in either the speedup or the number of distinct values, or logarithmic in the value range). We introduce the first constant-competitive algorithm for the general case of the problem, with arbitrary speedup and packet values. Specifically, our algorithm is $8$-competitive, and is also simple and easy to implement.

Average-Case Competitive Analyses for Ski-Rental Problems

Abstract: Let $s$ be the ratio of the cost for purchasing skis over the cost for renting them. Then the famous result for the ski-rental problem shows that skiers should buy their skis after renting them $(s-1)$ times, which gives us an optimal competitive ratio of $2-\frac{1}{s}$. In practice, however, it appears that many skiers buy their skis before this optimal point of time and also many skiers keep renting them forever. In this paper we show that these behaviors of skiers are quite reasonable by using an {\em average-case competitive ratio}. For an exponential input distribution $f(t) = \lambda e^{-\lambda t}$, optimal strategies are (i) if $\frac{1}{\lambda} \leq s$, then skiers should rent their skis forever and (ii) otherwise should purchase them after renting approximately $s^2\lambda \;\;(

The CIDOC CRM, an Ontological Approach to Schema Heterogeneity

Abstract: The creation of the World Wide Web has had a profound impact on the ease with which information can be distributed and presented. Now with more and more information becoming available, there is an increasing demand for targeted global search, comparative studies, data transfer and data migration between heterogeneous sources of cultural and scholarly contents. This requires interoperability not only at the encoding level - a task solved well by XML for instance - but also at the more complex semantics level, where lie the characteristics of the domain. In the meanwhile, the reality of semantic interoperability is getting frustrating. In the cultural area alone, dozens of “standard” and hundreds of proprietary metadata and data structures exist, as well as hundreds of terminology systems. Core systems like the Dublin Core represent a common denominator by far too small to fulfil advanced requirements. Overstretching its already limited semantics in order to capture complex contents leads to further loss of meaning[1]. The CIDOC Conceptual Reference Model (CRM) [2], [3] is a core ontology that aims at enabling information exchange and integration between heterogeneous sources of cultural heritage information, archives and libraries. It provides semantic definitions and clarifications needed to transform disparate, heterogeneous information sources into a coherent global resource, be it within a larger institution, in intranets or on the Internet. Its use comprises: intellectual guidance for conceptual modelling; common upper level for application ontology development; reference ontology for data translation and global schema in federated systems [4], [5], [6]. The CIDOC CRM was developed over the past 8 years by an interdisciplinary working group of the International Committee for Documentation of the International Council of Museums (CIDOC/ICOM) under the scientific lead of ICS-FORTH. It is on vote as Draft ISO standard (ISO/DIS 21127) until February 2005 [7]. The latest version 4.0, which was released on March 12th, 2004, consists of 80 classes and 132 properties. It is developed in the knowledge representation language TELOS, and available in RDFS and other formats.

What is a Model

Abstract: With the recent trend to model driven development a commonly agreed notion of \model" becomes a pivotal issue. However, currently there is little consensus about what exactly a model is and what it is not. Furthermore, basic terms such as \metamodel" are far from being understood in the same way by all members of the modeling community. This article attempts to start establishing a consensus about generally acceptable terminology. Its main contribution is the distinction between two fundamentally different kinds of models, i.e. \type model" versus \token model". The recognition of the fundamental difference in these two kinds of models is crucial to avoid misunderstandings and unnecessary disputes among members of the modeling community.

NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations

Abstract: The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradient- based NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBI's convergence. In order to over- come this problem, a hybridization technique coupled with a partitioning method is proposed.

Ontology Alignment: An annotated Bibliography

Abstract: topics in this research. We talked about types of heterogeneity between ontologies, various mapping representations, classified methods for discovering methods both between ontology concepts and data, and talked about various tasks where mappings are used. In this extended abstract of our talk, we provide an annotated bibliography for this area of research, giving readers brief pointers on representative papers in each of the topics mentioned above. We did not attempt to compile a comprehensive bibliography and hence the list in this abstract is necessarily incomplete. Rather, we tried to sketch a map of the field, with some specific reference to help interested readers in their exploration of the work to-date.

A simple modal logic for belief revision

Abstract: We propose a modal logic based on three operators, representing intial beliefs, information and revised beliefs. Three simple axioms are used to provide a sound and complete axiomatization of the qualitative part of Bayes' rule. Some theorems of this logic are derived concerning the interaction between current beliefs and future beliefs. Information flows and iterated revision are also discussed.

Towards Task-Based Temporal Extraction and Recognition

Abstract: We seek to improve the robustness and portability of temporal information extraction systems by incorporating data-driven techniques. We present two sets of experiments pointing us in this direction. The first shows that machine-learning-based recognition of temporal expressions not only achieves high accuracy on its own but can also improve rule-based normalization. The second makes use of a staged normalization architecture to experiment with machine learned classifiers for certain disambiguation sub-tasks within the normalization task.

TimeBank-Driven TimeML Analysis

Abstract: The design of TimeML as an expressive language for temporal information brings promises, and challenges; in particular, its representa- tional properties raise the bar for traditional information extraction meth- ods applied to the task of text-to-TimeML analysis. A reference corpus, such as TimeBank, is an invaluable asset in this situation; however, certain characteristics of TimeBank—size and consistency, primarily—present chal- lenges of their own. We discuss the design, implementation, and perfor- mance of an automatic TimeML-compliant annotator, trained on TimeBank, and deploying a hybrid analytical strategy of mixing aggressive finite- state processing over linguistic annotations with a state-of-the-art ma- chine learning technique capable of leveraging large amounts of unan- notated data. The results we report are encouraging in the light of a close analysis of TimeBank; at the same time they are indicative of the need for more infrastructure work, especially in the direction of creating a larger and more robust reference corpus. 1

A New Approach on Many Objective Diversity Measurement.

Abstract: In this paper, we introduce two measurements for computing the diversity and spread of non-dominated solutions in the objective space. These measurements compute the angular positions of solutions in the objective space and are able to nd a percentage which indicates the distribution of solutions in the space. Also, because we are able to compute the positions of the solutions, the spread of solutions along the non-dominated front can also be measured. This is more important when we evaluate solutions of a problem with a large number of objectives, the objective space of which cannot be illustrated graphically. These measurements are being examined to measure distribution of several sets of non-dominated solutions in the objective space.