Cross-monotonic cost-sharing methods for connected facility location

We devise cost sharing methods for connected facility location games that are cross-monotonic, competitive and recover a constant fraction of the optimal cost. The novelty of this work is that we use randomized algorithms and that we share the expected cost among the participating users. We also provide a primal-dual cost sharing method for the connected facility location game with opening costs

A neighborhood-based approach for clustering of linked document collections

This technical report addresses the problem of automatically structuring linked document collections by using clustering. In contrast to traditional clustering, we study the clustering problem in the light of available link structure information for the data set (e.g., hyperlinks among web documents or co-authorship among bibliographic data entries). Our approach is based on iterative relaxation of cluster assignments, and can be built on top of any clustering algorithm (e.g., k-means or DBSCAN). These techniques result in higher cluster purity, better overall accuracy, and make self-organization more robust. Our comprehensive experiments on three different real-world corpora demonstrate the benefits of our approach

On fair cost facility location games with non-singleton players

In the fair cost facility location game, players control terminals and must open and connect each terminal to a facility, while paying connection costs and equally sharing the opening costs associated with the facilities it connects to. In most of the literature, it is assumed that each player control a single terminal. We explore a more general version of the game where each player may control multiple terminals. We prove that this game does not always possess pure Nash equilibria, and deciding whether an instance has equilibria is NP-Hard, even in metric instances. Furthermore, we present results regarding the efficiency of equilibria, showing that the price of stability of this game is equal to the price of anarchy, in both uncapacitated and capacitated settings

Non-Cooperative Facility Location Games: a Survey

The Facility Location problem is a well-know NP-Hard combinatorial optimization problem. It models a diverse set of situations where one aims to provide a set of goods or services via a set of facilities F to a set of clients T, also called terminals. There are opening costs for each facility in F and connection costs for each pair of facility and client, if such facility attends this client. A central authority wants to determine the solution with minimum cost, considering both opening and connection costs, in such a way that all clients are attended by one facility. In this survey we are interested in the non-cooperative game version of this problem, where instead of having a central authority, each client is a player and decides where to con- nect himself. In doing so, he aims to minimize his own costs, given by the connection costs and opening costs of the facility, which may be shared among clients using the same facility. This problem has several applications as well, specially in distributed scenarios where a central authority is too expensive or even infeasible to exist. In this paper we present a survey describing different variants of this problem and reviewing several results about it, as well as adapting results from existing literature concerning the existence of equilibria, Price of Anarchy and Price of Stability. We also point out open problems that remain to be addressed.

Overlap-aware global df estimation in distributed information retrieval systems

Peer-to-Peer (P2P) search engines and other forms of distributed information retrieval (IR) are gaining momentum. Unlike in centralized IR, it is difficult and expensive to compute statistical measures about the entire document collection as it is widely distributed across many computers in a highly dynamic network. On the other hand, such network-wide statistics, most notably, global document frequencies of the individual terms, would be highly beneficial for ranking global search results that are compiled from different peers. This paper develops an efficient and scalable method for estimating global document frequencies in a large-scale, highly dynamic P2P network with autonomous peers. The main difficulty that is addressed in this paper is that the local collections of different peers may arbitrarily overlap, as many peers may choose to gather popular documents that fall into their specific interest profile. Our method is based on hash sketches as an underlying technique for compact data synopses, and exploits specific properties of hash sketches for duplicate elimination in the counting process. We report on experiments with real Web data that demonstrate the accuracy of our estimation method and also the benefit for better search result ranking

Reflectance from images: a model-based approach for human faces

In this paper, we present an image-based framework that acquires the reflectance properties of a human face. A range scan of the face is not required. Based on a morphable face model, the system estimates the 3D shape, and establishes point-to-point correspondence across images taken from different viewpoints, and across different individuals' faces. This provides a common parameterization of all reconstructed surfaces that can be used to compare and transfer BRDF data between different faces. Shape estimation from images compensates deformations of the face during the measurement process, such as facial expressions. In the common parameterization, regions of homogeneous materials on the face surface can be defined a-priori. We apply analytical BRDF models to express the reflectance properties of each region, and we estimate their parameters in a least-squares fit from the image data. For each of the surface points, the diffuse component of the BRDF is locally refined, which provides high detail. We present results for multiple analytical BRDF models, rendered at novelorientations and lighting conditions

IO-Top-k: index-access optimized top-k query processing

Top-k query processing is an important building block for ranked retrieval, with applications ranging from text and data integration to distributed aggregation of network logs and sensor data. Top-k queries operate on index lists for a query's elementary conditions and aggregate scores for result candidates. One of the best implementation methods in this setting is the family of threshold algorithms, which aim to terminate the index scans as early as possible based on lower and upper bounds for the final scores of result candidates. This procedure performs sequential disk accesses for sorted index scans, but also has the option of performing random accesses to resolve score uncertainty. This entails scheduling for the two kinds of accesses: 1) the prioritization of different index lists in the sequential accesses, and 2) the decision on when to perform random accesses and for which candidates. The prior literature has studied some of these scheduling issues, but only for each of the two access types in isolation. The current paper takes an integrated view of the scheduling issues and develops novel strategies that outperform prior proposals by a large margin. Our main contributions are new, principled, scheduling methods based on a Knapsack-related optimization for sequential accesses and a cost model for random accesses. The methods can be further boosted by harnessing probabilistic estimators for scores, selectivities, and index list correlations. We also discuss efficient implementation techniques for the underlying data structures. In performance experiments with three different datasets (TREC Terabyte, HTTP server logs, and IMDB), our methods achieved significant performance gains compared to the best previously known methods: a factor of up to 3 in terms of execution costs, and a factor of 5 in terms of absolute run-times of our implementation. Our best techniques are close to a lower bound for the execution cost of the considered class of threshold algorithms

Generalized Incremental Mechanisms for Scheduling Games

We study the problem of devising truthful mechanisms for cooperative cost sharing games that realize (approximate) budget balance and social cost. Recent negative results show that group-strategyproof mechanisms can only achieve very poor approximation guarantees for several fundamental cost sharing games. Driven by these limitations, we consider cost sharing mechanisms that realize the weaker notion of weak groupstrategyproofness. Mehta et al. [Games and Economic Behavior, 67:125–155, 2009] recently introduced the broad class of weakly group-strategyproof acyclic mechanisms and show that several primal-dual approximation algorithms naturally give rise to such mechanisms with attractive approximation guarantees. In this paper, we provide a simple yet powerful approach that enables us to turn any r-approximation algorithm into a r-budget balanced acyclic mechanism. We demonstrate the applicability of our approach by deriving weakly group-strategyproof mechanisms for several fundamental scheduling problems that outperform the best possible approximation guarantees of Moulin mechanisms. The mechanisms that we develop for completion time scheduling problems are the first mechanisms that achieve constant budget balance and social cost approximation factors. Interestingly, our mechanisms belong to the class of generalized incremental mechanisms proposed by Moulin [Social Choice and Welfare, 16:279–320, 1999]