Elsa Negre

Recommending Multidimensional Queries

Interactive analysis of datacube, in which a user navigates a cube by launching a sequence of queries is often tedious since the user may have no idea of what the forthcoming query should be in his current analysis. To better support this process we propose in this paper to apply a Collaborative Work approach that leverages former explorations of the cube to recommend OLAP queries. The system that we have developed adapts Approximate String Matching, a technique popular in Information Retrieval, to match the current analysis with the former explorations and help suggesting a query to the user. Our approach has been implemented with the open source Mondrian OLAP server to recommend MDX queries and we have carried out some preliminary experiments that show its efficiency for generating effective query recommendations.

A framework for recommending OLAP queries

An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.

Triple Helix in Smart Cities: A Literature Review about the Vision of Public Bodies, Universities, and Private Companies

Smart city is a recent topic, aiming at improving the quality of life of citizens in urban areas. Born like a bottom-up trend, it is now becoming crucial in urban planning in large cities all over the world. The smart city success depends on the synergic action by the triple helix key actors: public bodies, universities, and private companies. However, not ever these actors share the same smart city vision. This paper aims at individuating similarities and differences in key actors smart city vision, by a large and deep literature review on both scientific papers and practitioner or institutional reports.

Mining user competency from semantic trace

In order to achieve individual or collective goals, users in informational environments collaborate to integrate intellectual resources and knowledge. Thanks to informational environments, users can better organize, realize and record collaboration. Every activity produces a set of traces. Such traces can be recorded and classified, based on a model of traces. With the help of a model of competency, these traces also contribute to evaluate the competency of users on certain subjects. In this article, we propose a semantic model of traces and analyze classified traces by means of TF-IDF. We also considered the impact of time on the decreasing importance of traces. We show how to offer users recommendations and decision aid.

Cold-start recommender system problem within a multidimensional data warehouse

Data warehouses store large volumes of consolidated and historized multidimensional data for analysis and exploration by decision-makers. Exploring data is an incremental OLAP (On-Line Analytical Processing) query process for searching relevant information in a dataset. In order to ease user exploration, recommender systems are used. However when facing a new system, such recommendations do not operate anymore. This is known as the cold-start problem. In this paper, we provide recommendations to the user while facing this cold-start problem in a new system. This is done by patternizing OLAP queries. Our process is composed of four steps: patternizing queries, predicting candidate operations, computing candidate recommendations and ranking these recommendations.

Predicting a Social Network Structure Once a Node Is Deleted

Social networks are dynamic structures in which entities and links appear and disappear for different reasons. Starting from the observation that each entity has a more or less important role within the network, the objective of this article is to propose a method which exploits the role played by nodes to predict the new structure of a social network once one entity disappears. The role of a node in the network is expressed in terms of the number of interactions it has with the rest of the network. Two roles are considered: the leader and the mediator with their corresponding measure: the degree centrality and the betweenness centrality.

Recommendations to Improve the Smartness of a City

The concept of “smart city” has not yet been clearly defined. However, there are six characteristics/categories for classifying this kind of cities and compare them: smart economy , smart mobility, smart environment, smart people , smart living and smart governance. However, being “smart” is a challenge increasingly important for many cities or communities. This is of particular interest in the domain of Information and Communications Technology (ICT) and for such systems where there are economic, social, and other issues. To the best of our knowledge, there are no studies that attempt to help identifying the actions to be implemented to improve the smartness of a city. Recommending such actions is an emerging and promising field of investigation. Usually, recommender systems try to predict the rating that a user would give to an item (such as music, books, …) he has not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user’s social environment (collaborative filtering approaches). In this chapter, we present a framework for a recommender system for cities. The scope of this research work is to take advantage from recognized “smart cities” and to make same actions for city who wants to become “smart”. The followed method is: having a list of characteristics of a “smart city”, and having a city which wants to become “smart”, which actions must be implemented to become “smart” regarding the characteristics of “smartness”. This framework uses the actions already implemented in smart cities to enhance the smartness of a given city. The main idea is to recommend to the city the actions already implemented in those smart cities that are similar (the similarity between two cities is based on some indicators such as air quality, water consumption, etc.) as the actions to be implemented in the said city. This is done by (1) Pre-treating the indicators values of a given smart city category (only one among the six), (2) Matching the indicators corresponding to this category, (3) Returning to the city the actions to be implemented in a given order (according to the preferences of the city which needs help, for example). Thus, the city will be able to improve its smartness.

A Knowledge-Based Conceptual Vision of the Smart City

The term smart city is a fuzzy concept, not well defined in theoretical researches nor in empirical projects. Several definitions, different from each other, have been proposed. However, all agree on the fact that a Smart City is an urban space that tends to improve the daily life (work, school,) of its citizens (broadly defined). This is an improvement from different points of view: social, political, economic, governmental,... This paper goes beyond this definition and proposes a knowledge-based conceptual vision of the smart city, centered on peoples information and knowledge of people, in order to improve decision-making processes and enhance the value-added of business processes of the modern city.

Decision Support for Disaster Risk Management: Integrating Vulnerabilities into Early-Warning Systems

Despite the potential of new technologies and the improvements of early-warning systems since the 2004 Tsunami, damage and harm caused by disasters do not stop to increase. There is a clear need for better integrating the fragmented landscape of researchers and practitioners working on different aspects of decision support for disaster risk reduction and response. To demonstrate and discuss the advantages of integrated systems, we will focus in this paper on vulnerabilities and early-warning systems. While vulnerabilities are mostly used to allocate risk management resources (preparedness), early-warning systems are designed to initiate the response phase. Indicator models have been used as a part of disaster risk reduction frameworks, and in the design of early-warning systems. In this paper we analyse the commonalities and differences between both, and outline how an integrated system that understands vulnerability assessments as part of both risk reduction programs and early-warning shall be designed in future.

Evaluation of user satisfaction with OLAP recommender systems: an application to RecoOLAP on a agricultural energetic consumption datawarehouse

OLAP and datawarehouse DW systems are technologies intended to support the decision-making process, enabling the analysis of a substantial volume of data. One of the goals of recommender systems is to help users navigate large amounts of data. OLAP recommender systems have recently been proposed in the literature because the multidimensional analysis process is often tedious because the user may not know what the forthcoming query should be. User satisfaction with these systems has not yet been investigated. Thus, this work is the first study of the usefulness of OLAP recommender systems from the decision makers point of view. Indeed, to the best of our knowledge, although several works have proposed OLAP recommender systems, they did not evaluate them against real-world data and users. With our experiments on a spatial DW concerning agricultural energetic consummation issued from the Energetic French Project.