Brigitte Trousse

Smart cities and the future internet: towards cooperation frameworks for open innovation

Cities nowadays face complex challenges to meet objectives regarding socio-economic development and quality of life. The concept of smart cities is a response to these challenges. This paper explores smart cities as environments of open and user-driven innovation for experimenting and validating Future Internet-enabled services. Based on an analysis of the current landscape of smart city pilot programmes, Future Internet experimentally-driven research and projects in the domain of Living Labs, common resources regarding research and innovation can be identified that can be shared in open innovation environments. Effectively sharing these common resources for the purpose of establishing urban and regional innovation ecosystems requires sustainable partnerships and cooperation strategies among the main stakeholders.

Advanced data preprocessing for intersites Web usage mining

Web usage mining applies data mining procedures to analyze user access of Web sites. As with any KDD (knowledge discovery and data mining) process, WUM contains three main steps: preprocessing, knowledge extraction, and results analysis. We focus on data preprocessing, a fastidious, complex process. Analysts aim to determine the exact list of users who accessed the Web site and to reconstitute user sessions-the sequence of actions each user performed on the Web site. Intersites WUM deals with Web server logs from several Web sites, generally belonging to the same organization. Thus, analysts must reassemble the users path through all the different Web servers that they visited. Our solution is to join all the log files and reconstitute the visit. Classical data preprocessing involves three steps: data fusion, data cleaning, and data structuration. Our solution for WUM adds what we call advanced data preprocessing. This consists of a data summarization step, which will allow the analyst to select only the information of interest. Weve successfully tested our solution in an experiment with log files from INRIA Web sites.

WWW Assisted Browsing by Reusing Past Navigations of a Group of Users

In this paper, we present our case-based browsing advisor for the Web, called Broadway. Broadway follows a group of users during their navigations and supports an indirect collaboration to recommend Web pages to visit next. Broadway uses case-based reasoning to reuse precise experiences extracted from past navigations with a time-extended situation assessment, i.e. the recommendations are based mainly on the similarity of ordered sequences of past accessed documents. A first experimental evaluation shows that the system improves the information searching task.

Mining Web Usage Data for Discovering Navigation Clusters

The analysis of a web site based on usage data is an important task as it provides insight into the organization of the site and its adequacy regarding user needs. This allows the relationship between prior categories and user browsing patterns to be explored. In this paper we propose an approach for discovering the profiles of visitor groups. To this end, we begin by mapping user interests into symbolic objects, which is the basis of the Symbolic Data Analysis and represents here a successful interaction of the user with the web site. We then identify groups of users with similar behavior by means of a dynamic clustering approach applying a context dependent dissimilarity measure. The method was applied to identify visitor groups of a web site in the educational domain and also to analyze the traces of different user behavior.

Sequential pattern mining for structure-based XML document classification

This article presents an original supervised classification technique for XML documents which is based on structure only. Each XML document is viewed as an ordered labeled tree, represented by his tags only. Our method has three steps. After a cleaning step, we characterize each predefined cluster in terms of frequent structural subsequences. Then we classify the XML documents based on the mined patterns of each cluster.

Web Usage Mining: Sequential Pattern Extraction with a Very Low Support

The goal of this work is to increase the relevance and the interestingness of patterns discovered by a Web Usage Mining process. Indeed, the sequential patterns extracted on web log files, unless they are found under constraints, often lack interest because of their obvious content. Our goal is to discover minority users’ behaviors having a coherence which we want to be aware of (like hacking activities on the Web site or a users’ activity limited to a specific part of the Web site). By means of a clustering method on the extracted sequential patterns, we propose a recursive division of the problem. The developed clustering method is based on patterns summaries and neural networks. Our experiments show that we obtain the targeted patterns whereas their extraction by means of a classical process is impossible because of a very weak support (down to 0.006%). The diversity of users’ behaviors is so large that the minority ones are both numerous and difficult to locate.

Characterizing visitor groups from web data streams

The efficacy of a Web site is not only measured in terms of the ingoing traffic or the number of users but rather with respect to the knowledge of the user profiles who have visited it. In this paper we propose an approach for discovering the profiles of visitor groups of a Web site. Such knowledge could be especially useful for business applications. To this purpose, we begin by mapping user interests into symbolic objects which represent the user navigational behaviour resulting from a successful interaction with the site. We then identify groups of users with similar behavior by means of a dynamic clustering algorithm applying a proximity function, which is an original point of view. The convergence of the algorithm is guaranteed at the best partitions of the symbolic objects in k classes. We have applied our approach to identify visitor groups of a Web site in the educational domain and also to analyze the traces of different user behaviour. This application allows us to validate the proposed procedure and to suggest it as a useful tool in the Web Usage Mining framework.

Ontology-Based Knowledge Model for Multi-View KDD Process

Knowledge Discovery in Databases KDD is a highly complex, iterative and interactive process that involves several types of knowledge and expertise. In this paper the authors propose to support users of a multi-view analysis a KDD process held by several experts who analyze the same data with different viewpoints. Their objective is to enhance both the reusability of the process and coordination between users. To do so, they propose a formalization of viewpoint in KDD and a Knowledge Model that structures domain knowledge involved in a multi-view analysis. The authors formalization, using OWL ontologies, of viewpoint notion is based on CRISP-DM standard through the identification of a set of generic criteria that characterize a viewpoint in KDD.

A personalized recommender system for travel information

This article concerns an emerging research field related to mobility from the transport point of view, linked to the travel information retrieval. To facilitate such a retrieval, we propose the use of recommender systems in a mobility context: these systems facilitate information retrieval, and help prepare the users trip (pre-trip: choice of the transport mode, schedule, route, time of the trip, ...) and carry it out (on-trip: interactive guidance, way visualization, destination planning). This double impact is rarely exploited today and we propose, after a description of the used technologies, to illustrate the benefits of this new approach on a traditional tourist visit example. The originality of this approach lies in 1) its capacities to adapt the recommendations to the users behavior during his information retrieval correlated to his own movement and 2) the on-line learning capabilities of such a system supporting information retrieval.

Extracting Sequential Patterns for Gene Regulatory Expressions Profiles

In this paper we report outcomes of our computational analysis applied to time-series gene expression data generated by Kagami et al [1]. Gene expression data were generated using Affimetrix chips and validated by quantitative RT-PCR (reverse transcription-polymerase chain reaction) expression analysis of 12 randomly selected and differentially expressed genes. The biotin-labelled cRNA samples generated from mouse cerebella samples were collected at five developmental stages: 1 prenatal (embryonic day 18 or E18) and 4 postnatal at 7, 14, 21 and 56 days (P7, P14, P21 and P56).