Decentralized learning enables collaborative machine learning with enhanced privacy by allowing participants to train models locally and share updates for aggregation instead of sharing raw data. However, such systems are vulnerable to poisoning attacks that may compromise the learning process. This threat becomes even more severe when combined with sybil attacks, where adversaries contribute numerous malicious updates with minimal effort. To overcome this challenge, particularly in the permissionless setup, we propose SyDeLP, a blockchain-enabled protocol for decentralized learning. SyDeLP integrates byzantine tolerant aggregation for poisoning mitigation with a Verifiable Delay Function to counter sybil attacks requiring Proofs of Work (PoW) to participate. Honest behavior is incentivized by dynamically reducing PoW difficulty, decreasing the computational burden for honest nodes over time. Empirical evaluations conducted on a benchmark dataset across three types of poisoning attacks demonstrate that SyDeLP consistently outperforms existing solutions in terms of resilience.

Connected vehicles generate a vast amount of data and share it with external entities such as the cloud, neighboring vehicles, Road-Side Units (RSUs), and other third-party services in a Vehicle to Everything (V2X) setting. This data is vulnerable and can lead to the leakage of personal information of vehicle owners, such as driving habits, travel routes, and identity theft, among others. Moreover, with the implementation of the General Data Protection Regulation (GDPR), it becomes imperative to empower users with control over their data and the ability to choose whom they share it with. To address this objective, we present a protocol that utilizes a combination of sticky policies and a proxy re-encryption scheme. This protocol ensures that user-defined access controls on the data persist even when crossing organizational boundaries and addresses the confidentiality, integrity, and accountability of vehicle data. Furthermore, we assess our protocol using a semi-honest threat model and analyze its vulnerabilities. Lastly, we perform a quantitative analysis of the data flow model to observe the system's performance.

Modern service-based systems are characterized by applications composed of heterogeneous services provided by multiple, untrusted providers, and deployed along the (multi-) cloud-edge continuum. This scenario of increasing pervasiveness, complexity, and multi-party service recruitment urgently calls for solutions to increase applications privacy and security, on the one hand, and guarantee that applications behave as expected and support a given set of non-functional requirements, on the other hand. Certification schemes became the widespread means to answer this call, but they still build on old-fashioned assumptions that hardly hold in today’s services world. They assume that all actors involved in a certification process are trusted "by definition", meaning that certificates are supposed to be correct and be safely usable for decision-making, such as certification-based service selection and composition. In this paper, we depart from such unrealistic assumptions and define the first certification scheme that is completely transparent to the involved actors and significantly more resistant to misbehavior (e.g., collusion). We design a blockchain-based architecture to support our scheme, redefining the actors and their roles. The quality and performance of our scheme are evaluated in a case study scenario.

The overweight and obesity rate is increasing for decades worldwide. Healthy nutrition is, besides education and physical activity, one of the various keys to tackle this issue. In an effort to increase the availability of digital, healthy recommendations, the scientific area of food recommendation extends its focus from the accuracy of the recommendations to beyond-accuracy goals like transparency and healthiness. To address this issue a data basis is required, which in the ideal case encompasses user-item interactions like ratings and reviews, food-related information such as recipe details, nutritional data, and in the best case additional data which describes the food items and their relations semantically. Though several recipe recommendation data sets exist, to the best of our knowledge, a holistic large-scale healthiness-aware and connected data sets have not been made available yet. The lack of such data could partially explain the poor popularity of the topic of healthy food recommendation when compared to the domain of movie recommendation. In this paper, we show that taking into account only user-item interactions is not sufficient for a recommendation. To close this gap, we propose a connected data set called HUMMUS (Health-aware User-centered recoMMendation and argUment-enabling data Set) collected from Food.com containing multiple features including rich nutrient information, text reviews, and ratings, enriched by the authors with extra features such as Nutri-scores and connections to semantic data like the FoodKG and the FoodOn ontology. We hope that these data will contribute to the healthy food recommendation domain.

International audience

Over the last decade, pedestrian navigation services are gaining a high interest in guiding and tracking the user at any place. Outdoor, GPS persists as the navigation standard. Nevertheless, indoor navigation services, mainly developed to meet the need of a single floor building, are still under development stage. This paper introduces a novel algorithm that computes and displays the pathway, in a continuous manner, between two different points (i.e. source point and destination point) located inside a multi-floor building or outdoor. The calculated pathway is composed of two parts; the first part is displayed in the source floor and the second part is viewed in the destination floor. In cases where the source point and the destination point are in different floors and after displaying the first part of the pathway, the proposed algorithm automatically detects if the user is close to the destination floor based on the difference of altitudes. Therefore, the first part of the pathway is dropped and the second part is displayed. We conducted several real experiments, inside the campus of the University of Passau in Germany, to evaluate the performance of the proposed algorithm. They yielded promising results in terms of continuity of navigation service and we achieved an accuracy in estimating the floor equal to 0.093 m.

A challenge in designing cooperative distributed systems is to develop feasible and cost-effective mechanisms to foster cooperation among selfish nodes, i.e., nodes that strategically deviate from the intended specification to increase their individual utility. Finding a satisfactory solution to this challenge may be complicated by the intrinsic characteristics of each system, as well as by the particular objectives set by the system designer. Our previous work addressed this challenge by proposing RACOON, a general and semi-automatic framework for designing selfishness-resilient cooperative systems. RACOON relies on classical game theory and a custom built simulator to predict the impact of a fixed set of selfish behaviours on the designer's objectives. In this paper, we present RACOON++, which extends the previous framework with a declarative model for defining the utility function and the static behaviour of selfish nodes, along with a new model for reasoning on the dynamic interactions of nodes, based on evolutionary game theory. We illustrate the benefits of using RACOON++ by designing three cooperative systems: a peer-to-peer live streaming system, a load balancing protocol, and an anonymous communication system. Extensive experimental results using the state-of-the-art PeerSim simulator verify that the systems designed using RACOON++ achieve both selfishness-resilience and high performance.

The advent of mobile applications collecting and exploiting the location of users opens a number of privacy threats. To mitigate these privacy issues, several protection mechanisms have been proposed this last decade to protect users' location privacy. However, these protection mechanisms are usually implemented and evaluated in monolithic way, with heterogeneous tools and languages. Moreover, they are evaluated using different methodologies, metrics and datasets. This lack of standard makes the task of evaluating and comparing protection mechanisms particularly hard. In this paper, we present ACCIO, a unified framework to ease the design and evaluation of protection mechanisms. Thanks to its Domain Specific Language, ACCIO allows researchers and practitioners to define and deploy experiments in an intuitive way, as well as to easily collect and analyse the results. ACCIO already comes with several state-of-the-art protection mechanisms and a toolbox to manipulate mobility data. Finally, ACCIO is open and easily extensible with new evaluation metrics and protection mechanisms. This openness, combined with a description of experiments through a user-friendly DSL, makes ACCIO an appealing tool to reproduce and disseminate research results easier. In this paper, we present ACCIO's motivation and architecture, and demonstrate its capabilities through several use cases involving multiples metrics, state-of-the-art protection mechanisms, and two real-life mobility datasets collected in Beijing and in the San Francisco area.

With the advent of wearable and smartphone technologies, the need for pedestrian navigation services has increased over the last decade. Outdoor, the GPS remains currently the navigation standard. However, indoor navigation is in a development stage and suffers from many limitations, mainly related to the used positioning technologies and techniques and to the problem of finding the path from the origin to the destination. Previous studies have been limited to either positioning services or navigation services within a single floor building. The focus of this work is to develop a multi-floor pedestrian navigation service. This prototype is composed of two main modules. First, a positioning module, is conceived to compute the mobile unit (MU) position using the pedestrian dead reckoning (PDR) and the received signal strength indicator (RSSI) fingerprinting positioning techniques. Second, a navigation module, is designed to identify the rooms' numbers inside a building and to generate the graph for the whole building. The performance of our prototype is evaluated in the building IT Zentrum/International House on the campus of the University of Passau in Germany.

Participatory sensing is an emerging paradigm in which citizens voluntarily use their mobile phones to capture and share sensed data from their surrounding environment in order to monitor and analyze some phenomena. Participating users can disrupt the system by contributing corrupted, fabricated, or erroneous data. Different reputation systems have been proposed to monitor participants' behavior and to estimate their honesty. There are some attacks that were not considered by the existing reputation systems in this context including corruption, collusion, and on-off attack. In this paper, we propose a more robust and efficient reputation system designed for these applications. Our reputation system incorporates a mechanism to defend against those attacks. Experimental results indicate that our system can tolerate up to 60% of colluding adversaries involved in the sensing campaign. This enables our system to aggregate the data more accurately compared with the state-of-the art. Moreover, the system can detect on-off attackers even if they strategically contribute some good data with high probability (e.g. 0.8).

Crowdsourcing has emerged as a promising approach for obtaining services and data in a short time and at a reasonable budget. However, the quality of the output provided by the crowd is not guaranteed, and must be controlled. This quality control usually relies on worker screening or contribution reviewing at the cost of additional time and budget overheads. In this paper, we propose to reduce these overheads by leveraging the system history. We describe an offline learning algorithm that groups tasks from history into homogeneous clusters and learns for each cluster the worker features that optimize the contribution quality. These features are then used by the online targeting algorithm to select reliable workers for each incoming task. The proposed method is compared to the state of the art selection methods using real world datasets. Results show that we achieve comparable, and in some cases better, output quality for a smaller budget and shorter time.

Selfishness is one of the key problems that confronts developers of cooperative distributed systems (e.g., file-sharing networks, voluntary computing). It has the potential to severely degrade system performance and to lead to instability and failures. Current techniques for understanding the impact of selfish behaviours and designing effective countermeasures remain manual and time-consuming, requiring multi-domain expertise. To overcome these difficulties, we propose SEINE, a simulation framework for rapid modelling and evaluation of selfish behaviours in a cooperative system. SEINE relies on a domain-specific language (SEINE-L) for specifying selfishness scenarios, and provides semi-automatic support for their implementation and study in a state-of-the-art simulator. We show in this paper that (1) SEINE-L is expressive enough to specify fifteen selfishness scenarios taken from the literature, (2) SEINE is accurate in predicting the impact of selfishness compared to real experiments, and (3) SEINE substantially reduces the development effort compared to traditional manual approaches.

Quite recently, considerable attention has been paid to pedestrian navigation services, since they provide people with guidance and tracking information everywhere especially in an unfamiliar environment. In the literature, indoor and outdoor navigation has been widely investigated. However, studies on the continuity of pedestrian navigation service, still lacking. This paper presents a novel positioning technology selection algorithm able to integrate the pedestrian dead reckoning (PDR) technique, for short-term, through the navigation process either to improve the calculation of the mobile unit's (MU) position and/or to maintain the continuity of the service. The integration of the PDR is inferred based on the fuzzy logic approach. In fact, the proposed algorithm defines a fuzzy variable called “margin” which identifies four different states, namely, confidence, intermediate, confusion and critic. The PDR technique is only incorporated in the last two states in two ways: either fused with the used radio frequency (RF) positioning technology or stand-alone if no RF technology is available. The algorithm performance is evaluated based on an empirical study.

The integration of privacy into reputation systems is a crucial need for building secure and reliable participatory sensing applications. Participants are given the assurance that their privacy is preserved even if they contribute some personal sensitive data. In addition, reputation systems allow an application server to monitor participants' behaviors and evict those who provide the system with corrupted data. However, this integration requires achieving seemingly conflicting objectives. Reputation systems monitor participants behaviors along subsequent interactions. Whereas, one of the major objectives of privacy preserving systems is to unlink subsequent interactions. In this paper, we define a new attack (RR attack), which exploits this conflict in order to detect the succession of contributions provided by the same participant and to subsequently re-identify his original identity. We show that using this attack, more than 35% of contributions can be associated to their successive contributions in each campaign. We then propose PrivaSense as a new privacy preserving reputation system that integrates both reputation and privacy such that their objectives are simultaneously achieved. Experimental results are conducted using a real data-set. These results show that PrivaSense decreases by up to 80% the number of contributions linked to their original providers.

Opportunistic networks suffer some coarse characteristics as frequent disruptions, high loss ratios and uncertain data delivery. The transmission of video content even worsens the delivery problem because of the large size and continuous nature of the medium. Therefore, loss concealment is inducted as a part of the solution, alongside other delivery enhancement mechanisms. Loss concealment techniques are usually used Forwards, where the source must spend extra processing and transmission volume to enable the receiver to recover lost data parts. These techniques are not only associated with prolonged latencies and video encoding manipulations, but they are also sensitive to the loss pattern, which might leave the receiver in passive suspension if a key part for recovery was missing. In this work, we propose a Backward loss concealment mechanism, which allows the receiver to react to a certain amount of loss. Missing parts of the scalable video stream are compensated by a composite solution that encloses 1) video frame loss error concealment, and 2) network demands, which are initiated and cast in search for missing parts from other nodes in the network. Simulation-driven experiments prove the applicability of the backward mechanism, with a limited capability depending on the target application requirements.

With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing of their location information. To deal with this issue, several location privacy protection mechanisms (LPPMs) have been proposed in the past years. However, each of these mechanisms comes with different configuration parameters that have a direct impact both on the privacy guarantees offered to the users and on the resulting utility of the protected data. In this context, it can be difficult for non-expert system designers to choose the appropriate configuration to use. Moreover, these mechanisms are generally configured once for all, which results in the same configuration for every protected piece of information. However, not all users have the same behaviour, and even the behaviour of a single user is likely to change over time. To address this issue, we present in this paper ALP (which stands for Adaptive Location Privacy), a new framework enabling the dynamic configuration of LPPMs. ALP can be used in two scenarios: (1) offline, where ALP enables a system designer to choose and automatically tune the most appropriate LPPM for the protection of a given dataset, (2) online, where ALP enables the user of a crowd sensing application to protect consecutive batches of her geolocated data by automatically tuning a given LPPM to fulfil a set of privacy and utility objectives. We evaluate ALP on both scenarios with two real-life mobility datasets and two state-of-the-art LPPMs. Our experiments show that the adaptive LPPM configurations found by ALP outperform static configurations in terms of trade-off between privacy and utility.

An increasing amount of mobility data is being collected every day by different means, e.g., By mobile phone operators. This data is sometimes published after the application of simple anonymization techniques, which might lead to severe privacy threats. We propose in this paper a new solution whose novelty is two-fold. Firstly, we introduce an algorithm designed to hide places where a user stops during her journey (namely points of interest), by enforcing a constant speed along her trajectory. Secondly, we leverage places where users meet to take a chance to swap their trajectories and therefore confuse an attacker.

A challenge in designing a peer-to-peer (P2P) system is to ensure that the system is able to tolerate selfish nodes that strategically deviate from their specification whenever doing so is convenient. In this paper, we propose RACOON, a framework for the design of P2P systems that are resilient to selfish behaviours. While most existing solutions target specific systems or types of selfishness, RACOON proposes a generic and semi-automatic approach that achieves robust and reusable results. Also, RACOON supports the system designer in the performance-oriented tuning of the system, by proposing a novel approach that combines Game Theory and simulations. We illustrate the benefits of using RACOON by designing two P2P systems: a live streaming and an anonymous communication system. In simulations and a real deployment of the two applications on a testbed comprising 100 nodes, the systems designed using RACOON achieve both resilience to selfish nodes and high performance.

Video transmission in opportunistic networks is very challenging due to the network coarse characteristics of frequent disruptions and uncertain data delivery. This is on the one hand, and the large size and continuous nature of videos on the other hand. In a previous work of Klaghstan et. al (2013), they presented a solution based on Scalable Video Coding (SVC), which aims at improving the user viewing experience assessed by means of playout delay and perceived quality. While this scheme obtained promising experimental results, it suffered from a non-negligible loss rate, which they explained by the still large size of the used SVC layers as the transmission unit. To address this issue, in this paper we investigate reducing the granularity of transmission units down to Network Abstraction Layer (NAL) units that compose SVC layers. The proposed solution packetizes NAL units into larger chunks in an adaptive way, dynamically taking into account environmental information gathered by each node (contact times with other nodes). Simulation-driven experimental results report a 100% playout availability and a significant improvement in the received quality level with no setback in delivery times.

A prediction-based routing protocol for mobile delay tolerant networks functions by forwarding a message from one intermediate node to another if the latter has higher probability of encountering the destination node. However, this process compromises the privacy of the nodes by revealing their mobility patterns. In this paper, we propose a privacy preserving prediction-based routing protocol that forwards messages by comparing information about communities of nodes instead of individual nodes. Specifically, it compares the maximum probability that a node in the community of a potential intermediate node will encounter the destination node. We present theoretical security analyses as well as practical performance evaluations. Our simulations on a well established community-based mobility model demonstrate that our protocol has comparable performance to existing prediction-based protocols. Yet our protocol is the only one that preserves the privacy of nodes.

Opportunistic networks are generally characterized by a low performance due to user mobility and non end-to-end communications, which makes it challenging to use them in scenarios involving the transmission of large data, such as video transmission. This article presents simulated experiments of transmitting different video sequences in opportunistic networks, with the goal of enhancing user video viewing experience. We consider this subjective concept objectively as a trade-off between playout delay and the delivered quality level. To obtain the best trade-off, we propose using Scalable Video Coding, which divides a video into multiple unequally important layers, providing different levels of quality. This property is exploited to transmit video layers with unequal degrees of redundancy proportionally to their degree of importance. Experimental results show that this approach enables the video to become more quickly playable at the destination with a low starting quality but progressively being enhanced, providing a better viewing-experience.

Peer rewiring aims to update peer's contacts to other peers in a network. It is one of the classic approaches to semantic overlay networks(SON), the network organization where peers with semantically similar content are clustered together. To perform peer rewiring, each peer periodically collects the information of other peers in the network and uses them to update its existing contacts. While the information are collected either by random or by similarity-based gradient strategy in the literature, we propose a centrality-based approach to improve the efficiency of peer rewiring. The centrality is defined as peer's capability to access other peers in its neighborhood in SON. By integrating centrality in the similarity-based gradient strategy, more similar peers are expected to be collected in a shorter time. We test our proposal by adding new peers in an unstructured P2P network with more than 20000 peers, where similar peers are already clustered up. The proposed approach greatly improves the rewiring efficiency of the new peers, comparing to similarity-based gradient strategy(the state-of-the-art approach).

Users hesitate to submit negative feedback in reputation systems due to the fear of retaliation from the recipient user. A privacy preserving reputation protocol protects users by hiding their individual feedback and revealing only the reputation score. We present a privacy preserving reputation protocol for the malicious adversarial model. The malicious users in this model actively attempt to learn the private feedback values of honest users as well as to disrupt the protocol. Our protocol does not require centralized entities, trusted third parties, or specialized platforms, such as anonymous networks and trusted hardware. Moreover, our protocol is efficient. It requires an exchange of messages, where and are the number of users in the protocol and the environment, respectively.

This paper presents our latency hiding framework for access to big data in a constrained digital ecosystem with application to digital medical archives. Aiming to enhance ubiquitous access of big data such as patient-oriented access of medical archives, we apply complex/multi-context prefetching to reduce latency thereby improving response time. We propose a formal model for prefetch requests rate and network workload or stress bound that takes into account a diverse set of constraints a digital ecosystem could be in. In addition to that, components of our latency hiding framework such as a generic multi-context functional architecture, use case model, medical database model with emphasis on API (abstracted patient information) and a high-level system architecture have been designed. The development of a complex or multi-context prefetch algorithm that uses a patient's chief complaints, slackness sensitivity, popular content tag, user specified contexts and constraints is underway. A prototype system will also be developed to validate the proposed solutions. Moreover, input and output metrics will be developed to gauge the efficiency and effectiveness of the prefetch algorithm under development.

In a Mobile Ad-hoc NETwork (MANET), information sharing is usually performed by distributing advertisements and queries. In order not to load environments with unnecessary traffic, an advertisement policy should be designed according to users' information consumptions and provisions, which are limited by users' stay-times and their contexts. In this paper, we propose an algorithm to parameterize advertisement policies according to users' stay-times and contexts.

The progress in mobile devices and wireless communication technologies magnifies the importance of opportunistic networks like Mobile Ad-hoc NETworks (MANETs). In MANETs, information sharing is performed by distributing advertisements and queries. In order not to load the environment with unnecessary traffic, file advertisement and query resolution should be performed according to the interests of users. In this paper, we propose algorithms to identify and estimate users' interests. Experimentations are conducted to evaluate the performanace of the proposed algorithms on a mobile phone and a PC.

Data replication can improve data availability in mobile networks; but due to typical resource limitations in such environments, specific replication mechanisms are needed. In this paper, we propose CReaM, user-Centric REplicAtion Model for mobile environment. This model puts users at the centre by letting them determine the amount of resources they are willing to share. CReaM is suitable for dynamic environments where each node needs a certain level of decision autonomy. In this paper, we present CReaM's general principles and focus on one of its core function, which is its autonomic behavior that generates replication requests based on resources monitoring and user settings.

The secure sum protocol is a well-known protocol for computing the sum of private inputs from distributed entities such that the inputs remain private. In this paper we present protocols for computing reputation in a privacy preserving manner that are inspired by the secure sum protocol. We provide a protocol that is secure under the semi-honest adversarial model as well as one that is secure under the stronger non-disruptive malicious model. Although the protocols are inspired by secure sum, they do not suffer from the issues that plague secure sum. Our protocols are resilient against colluding entities, which secure sum is not. The protocols that we develop are also efficient. We require an exchange of O(n) messages under the semi-honest model, where n is the number of feedback providers in the protocol. This is the same complexity offered by secure sum. Our protocol for the non-disruptive malicious model exchanges O(N log n) messages, where N is the number of entities in the system.

Vehicular ad hoc networks are currently attracting the attention of researchers and automobile manufacturers. Disseminating and routing messages in such environments present big challenges due to the very high mobility and the frequent changes of network topology. In this paper, a novel geocast routing protocol is proposed, named DG-CastoR protocol and especially tailored for Infotainment applications in VANETs. It aims to build a virtual community based on future locations prediction of the mobile nodes in the network. We call this community a Rendez-vous group where the nodes may meet in the near future. However, the query is only disseminated between the nodes belonging to the same Rendez-Vous group. Simulation results show that, compared to multicast protocols, DG-CastoR is a robust in managing a large number of nodes in VANETs. It also reduces the traffic load by avoiding the unnecessary packet flooding all over the network.

Abstract Distributed systems like grids support diverse models of distributed computation and need to operate large data entities in a distributed way. A significant quantity of this data are used only for a limited period of time. Caching is recognized as one of the most effective techniques to manage temporary data and collaborative cache is traditionally proposed to scale cache capabilities in distributed environments. Grid needs to manage dynamically different models of computation with different data access patterns. In this paper, we propose a basic infrastructure for the management of collaborative caches that permits to operate and control dynamically different cache mechanisms and cache schemes in grid. Beside traditional collaborative caching where the cooperation is often limited to data resolution, in our infrastructure the collaborative cache capacities are extended to operate and manage these distributed temporal data. Our proposition is composed of a reference cache model that defines four layers for the management of collaborative cache; an information model that represents the main cache elements and their activity; and a set of operations to request specific tasks to monitor, operate, and coordinate a generic collaborative cache system. Implementation issues of a prototype in Globus Toolkit 4 are discussed. Copyright © 2007 John Wiley & Sons, Ltd.

Grid computing exists now at a widely distributed scale.After 10 years of internationally combined efforts to develop from a vision to existing middlewares, we face now a large number of applications being deployed and taking benefit from the Grids.Almost all these applications handle data, at different semantic levels, from raw data coming from sensors in particle physics to rich data in the healthgrids.Most of the time, the procedure is ad hoc: The need to handle these data has been mainly seen as a constraint, and little effort has been put in their smart management in the early stages of the grid evolution.Data were present in raw files, or in databases, may be distributed databases, but with little concerns from the application developer who focused (and that is normal) on the core development of the process of the data.In the close past, the Grid community has been developing specialized services to handle data in a simpler and more smart way: OGSA-DAI allows for instance to access several data sources with a common programming interface, giving the developer also the possibility to add treatment on the data retrieved, to anonymize it, to cache it, or to replicate it.Efforts have been put to access and process the data, not in planning optimization or distributed balanced queries, which are core distributed databases services.Leading databases companies have publicized grid-aware databases that mainly are 'old parallel databases' in clusters, not taking much into account the challenges of the grid environment.The database community has been investigating for a long time issues related to the distribution of the data sources, data queries, query plan optimization in parallel systems but has not been really involved up to know in the grid community and development.Among the challenges rising in these environments, we can cite, among others: dynamicity, reliability, security, data availability and transport, data indexing, search and access, etc.These are existing distributed databases challenges revisited with respect to the grid paradigm.This special issue of 'Concurrency and Computation: Practice and Experience' includes a selection of revised papers presented at the VLDB Data Management in Grids workshop, Seoul, Korea, held on 11th September 2006.They illustrate some of the challenges together with current trends of solutions.The 2006 edition of the workshop is the second in a raw, after the success of the 1st edition in Trondheim in 2005.The idea of the workshop collocated with one of the most important conference in databases (VLDB: Very Large Data Bases) is to bring together the experts from the communities to meet, discuss and argue the above stated challenges.In that sense, the objective of the workshop

This paper presents briefly the aims and challenges addressed in the GGM (Grid for Geno-Medicine) project. The idea behind the project is to offer a software infrastructure able to analyze and discover links between distributed medical and genetic data.

Medical data represent tremendous amount of data for which automatic analysis is increasingly needed. Grids are very promising to face today's challenging health issues such as epidemiological studies through large image data sets. However, the sensitive nature of medical data makes it difficult to widely distribute medical applications over computational grids. In this paper, we review fundamental medical data manipulation requirements and we propose a distributed data management architecture that addresses the medical data security and high performance constraints. A prototype is currently being developed inside our laboratories to demonstrate the architecture capability to face realistic distributed medical data manipulation situations.

The need for systems that can store, represent and provide efficient retrieval facilities for images of particular interest is becoming very high in medicine. In this respect, a lot of work has been done to integrate image data in standard data processing environments. The two different approaches that are used for the representation of images are the meta-data and the content-based approaches. Users in medicine need queries that use both content-based and meta-data representations of images or salient objects. In this paper, we first present a global image data model that supports both meta-data and low-level descriptions of images and their salient objects. This allows us to make multi-criteria image retrieval (context-, semantic- and content-based queries). Then, we present an image data repository model that captures all the data described in the model and permits the integration of heterogeneous operations in a DBMS. In particular, content-based operations (content-based join and selection) in combination with traditional ones can be carried out using our model.

We propose a buffer management policy that plans buffer sharing between multimedia presentations. Our experimental results, which are based on realistic distributions, show that the proposed approach can lead to a gain in storage bandwidth of over 15%.

This paper presents the SIRSALE system. SIRSALE is a video management system that allows users to deal with video streams stored in distributed repositories. SIRSALE is based on a modular model that allows adapting the system to deal with specific semantic contexts. Thus SIRSALE allows users to browse documents by structures (shots, scenes, sequences), to annotate the content of the video documents and to query the database by content, by using a graphical tool adapted to the user's specific topic of interest. The system has been demonstrated to professionals with a positive feedback.

Since the last two decades, image database management has been practiced using different image representation methods. In the literature, images are represented using two paradigms: the metadata-based and the content-based representations. Image retrieval using the metadata is done using the traditional database operations. However, image retrieval by its low-level features requires similarity-based operations. Practice has shown that both types of operations are needed for an efficient image database management system. Particularly in medical image databases, such a mixed form of retrieval is very important. We first present a global image data model that supports both metadata and low-level descriptions of images. We illustrate our work with real examples in the medical domain. Then, using an original image data repository model, we show how relational and similarity-based operations can be integrated. Both image and salient object are considered in our model. A prototype called MIMS (medical image management system) has been realized to validate the main aspects of our approach.

Too much information is no information. When the quantity of data becomes too large, users need help and tools to find their path in the information space. Users can browse through Terabytes of data and more, and the difficulty is finding the best way through all possible documents. Our feeling is that a user is not generic and has particular interests. Thus, the percentage of relevant documents is small, so the time to access these documents must be reduced. We propose a collaborative proxy architecture, close to the user, based on "hot" relevance topics, and the dynamic construction of virtual communities. In this framework, the temperature of a document or a subject reflects its current interest among a community. We propose an architecture that allows proxies not only to manage efficiently cached documents but also to exchange documents with other proxies. Furthermore, these proxies manage meta-data about their partial views of the information space. We show how such an approach fits to enhance global efficiency of the Web both in terms of access time to relevant documents and in terms of Web content indexing, adding semantic value where raw byte arrays are often considered.

Content-based image retrieval is one of the current active and promising research topics in image analysis and image management. The work of transferring the results of this research to support images in DBMS has also been initiated by many researchers and commercial systems. We first illustrate the importance of using content-based operations in combination with the traditional database operations. Then, we propose an image repository model that supports content-based operations on image databases. Furthermore, we introduce our novel similarity-based operators on image tables and investigate their properties. Finally, we investigate the use of the novel content-based operators in combination with the relational operators on image and relational tables.

Images are usually retrieved on the basis of either low-level features, such as texture or color; context, such as date of acquisition or author; or high-level features, such as real-world objects and relations. We propose a data model for medical image retrieval. Our model integrates several types of image features and projects them to a set of spaces. Each image feature is then represented by several dimensions. Furthermore, the medical image has an evolutionary content that changes over time and according to several events. Our proposition allows medical users to easily describe the content evolution of many images. To achieve that, a spatial knowledge model is integrated into our approach that verifies the coherence between feature spaces and assists both system and user whenever precision is needed or specific ambiguity occurs. We briefly expose our Web-accessible implementation called MIMS (Medical Image Management System) developed with Java.

The many successful research results in the domain of computer vision have made similarity based data retrieval techniques a promising approach. As a result, the integration of similarity based retrieval techniques of multimedia data into DBMSs is currently an active research issue. We first illustrate the importance of similarity based operations. Then, we present our image data repository model that supports similarity based operations conveniently under an object-relational database paradigm. Furthermore, we present novel similarity based operators on image tables and study their properties. Finally, based on the properties of the operators identified, we derive algebraic rules that are useful for similarity based query optimization and we introduce a cost model for an implementation of one of the major similarity based operators.

International audience

The registration of multiple 3D data sets obtained with a laser range finder is examined. A sensor calibration technique based on the conjunction of a mathematical camera model, the N-planes B-spline (NPBS), with an accurate mechanical calibration setup, is proposed. An algorithm for recovering the rigid transformation (rotation and translation) between the two sets of 3D coordinates obtained by the calibrated imaging range sensor is developed. Its input is sets of points lying on the surface of the object. This input is converted into an octree-spline that allows point-to-surface distances to be computed quickly. A robust nonlinear least-squares minimization technique then finds the optimal pose by minimizing the sum of square distances between the two sets of 3D coordinates. The algorithm has been applied to matching human faces with highly accurate results.>