In this paper, we introduce LAICOS, a social Web search engine as a contribution to the growing area of Social Information Retrieval (SIR). Social information and personalization are at the heart of LAICOS. On the one hand, the social context of documents is added as a layer to their textual content traditionally used for indexing to provide Personalized Social Document Representations. On the other hand, the social context of users is used for the query expansion process using the Personalized Social Query Expansion framework (PSQE) proposed in our earlier works. We describe the different components of the system while relying on social bookmarking systems as a source of social information for personalizing and enhancing the IR process. We show how the internal structure of indexes as well as the query expansion process operated using social information.

We present in this paper a contribution to IR modeling by proposing a new ranking function called SoPRa that considers the social dimension of the Web. This social dimension is any social information that surrounds documents along with the social context of users. Currently, our approach relies on folksonomies for extracting these social contexts, but it can be extended to use any social meta-data, e.g. comments, ratings, tweets, etc. The evaluation performed on our approach shows its benefits for personalized search.

In this paper, we present a contribution to IR modeling. We propose an approach that computes on the fly, a Personalized Social Document Representation (PSDR) of each document per user based on his social activities. The PSDRs are used to rank documents with respect to a query. This approach has been intensively evaluated on a large public dataset, showing significant benefits for personalized search.

We propose a new approach for social and personalized query expansion using social structures in the Web 2.0. While focusing on social tagging systems, the proposed approach considers (i) the semantic similarity between tags composing a query, (ii) a social proximity between the query and the user profile, and (iii) on the fly, a strategy for expanding user queries. The proposed approach has been evaluated using a large dataset crawled from del.icio.us.

In order to measure and assess the quality of GIS, there exist a sparse offer of tools, providing specific functions with their own interest but are not sufficient to deal with broader user's requirements. Interoperability of these tools remains a technical challenge because of the heterogeneity of their models and access patterns. On the other side, quality analysts require more and more integration facilities that allow them to consolidate and aggregate multiple quality measures acquired from different observations or data sources, in using/combining seamlessly different quality tools. Clearly, there is a gap between users's requirements and the spatial data quality market. This demo paper will illustrate GQBox, a geographic quality (tool)box. GQBox supplies a standards-based generic meta model that supports the definition of quality goals and metrics, and it provides a service-based infrastructure that allows interoperability among several quality tools.

In this paper, we propose an approach for approximate matching of OWL-S process model. We also propose a similarity measure that captures structural and semantic differences between two process models. To do so, we reduce the process matching to a graph matching problem and we adapt existing algorithms for this purpose.

Profiles and contexts are the main concepts used by modern applications (e.g. e-commerce and recommender systems) to adapt content delivery services to the users' needs, preferences and environment. Although the definitions of the two terms slightly differ from one application to another, there is a general agreement to distinguish them and use them separately or jointly in a given application. When used jointly, the relationship between the two concepts remains often unclear. This paper aims at providing a personalization model that encompasses profile, context, and a formal relationships between the two. This relationship, called con-textualization, is represented by a set of ranked mappings, automatically extracted from a usage history (log file of user actions). Profile, context and contextualization constitute three structuring elements over which any personalized system should be built. The proposal is supported by a design platform which helps in instantiating profiles and contexts and in generating contextual mappings between them. An instantiation of the meta model is given for an advanced recommender system, called context-aware recommender system (or CARS for short). This instantiation is followed by an experiment highlighting the benefit of contextualization.

With the growing importance of processes in current information systems and service oriented architectures, there is an increasing need for automatic techniques allowing to compare process models. Examples of such applications are numerous: delta analysis, version management, compatibility and replaceability analysis of business protocols, behavior based service discovery. When comparing two process models, first a mapping between their activities have to be found, identifying activities that are either equal or similar.

Finding useful services is a challenging and important task in several applications. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs. In this paper, we argue that in several applications (services having multiple and dependent operations and scientific workflows), the service discovery should be based on the specification of service behavior. The idea behind is to develop matching techniques that operate on behavior models and allow delivery of approximate matches and evaluation of semantic distance between these matches and the user requirements. To do so, we reduce the problem of behavioral matching to a graph matching problem and adapt existing algorithms for this purpose. To validate our approach, we developed a BPEL ranking platform that allows to find in a service repository, a set of service candidates satisfying user requirements, and then, to rank these candidates using a behavioral-based similarity measure.

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs possibly enhanced with some ontological knowledge. Recent works have demonstrated that this approach is not sufficient to discover relevant components. Motivated by these concerns, we have developed BeMatch platform for ranking web services based on behavior matchmaking. We developed matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. We exemplify our approach for behavioral services matchmaking by describing two demonstration scenarios for matchmaking BPEL and WSCL protocols, respectively. A demo scenario is also described concerning the tool for evaluating the effectiveness of the behavioral matchmaking method.

Access to relevant information, adapted to user's needs, preferences and environment, is a challenge in many applications running in content delivery platforms, like IPTV, VoD and mobile Video. In order to provide users with personalized content, applications use various techniques such as content recommendation, content filtering, preference-driven queries, etc. These techniques exploit different knowledge organized into profiles and contexts. However, there is not a common understanding of these concepts and there is no clear foundation of what a personalized access model should be. This paper contributes to this concern by providing, through a meta model, a clear distinction between profile and context, and by providing a set of services which constitutes a basement to the definition of a personalized access model (PAM). Our PAM definition allows applications to interoperate in multiple personalization scenarios, including, preference-based recommendation, context-aware content delivery, personalized access to multiple contents, etc. Concepts and services proposed are tightly defined with respect to real applications requirements provided by Alcatel-Lucent.

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs. Recent works have demonstrated that this approach is not sufficient to discover relevant components. In this paper we argue that, in many situations, the service discovery should be based on the specification of service behavior (in particular, the conversation protocol). The idea behind is to develop matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. To do so, we reduce the problem of behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose. A prototype is presented (available as a Web service) which takes as input two conversation protocols and evaluates the semantic distance between them; the prototype provides also the script of edit operations that can be used to alter the first model to render it identical with the second one

A Data Warehouse (DW) is a large collection of data integrated from multiple distributed autonomous databases and other information sources. A DW can be seen as a set of materialized views defined over the remote source data. Until now research work on DW design is restricted to quantitatively selecting view sets for materialization. However, quality issues in the DW design are neglected. In this paper we suggest a novel statement of the DW design problem that takes into account quality factors. We design a DW system architecture that supports performance and data consistency quality goals. In this framework we present a high level approach that allows to check whether a view selection guaranteeing a data completeness quality goal also satisfies a data currency quality goal. This approach is based on an AND/OR dag representation for multiple queries and views. It also allows determining the minimal change propagation frequencies that satisfy the data currency quality goal along with the optimal query evaluation and change propagation plans. Our results can be directly used for a quality driven design of a DW.

Article A general framework for the view selection problem for data warehouse design and evolution Share on Authors: Dimitri Theodoratos Department of EE&CS, National Technical University of Athens, Greece Department of EE&CS, National Technical University of Athens, GreeceView Profile , Mokrane Bouzeghoub Laboratoire PRiSM Université de Versailles, France Laboratoire PRiSM Université de Versailles, FranceView Profile Authors Info & Claims DOLAP '00: Proceedings of the 3rd ACM international workshop on Data warehousing and OLAPNovember 2000 Pages 1–8https://doi.org/10.1145/355068.355309Online:01 November 2000Publication History 37citation1,787DownloadsMetricsTotal Citations37Total Downloads1,787Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A Multi-Source Information System is composed of a set of independent data sources and a set of views defined as queries over these data sources. This problem is dramatically increased in evolving information systems, such as data warehouses and Web systems, where several views are daily defined or modified by users who are not aware of the detailed metadata describing data sources and their interrelationships. The purpose of the paper is to propose a design aid which helps in the definition of a user view (or query) from its schema and some integrity constraints. Our approach defines a solution space which provides the set of potential queries that correspond to the user view. The approach is based on the existence of metadata describing individual sources, on semantic assertions describing inter-source similarities between concepts, and on some heuristics which reduce the size of the solution space.