A Survey of UserCentric Data Warehouses: From Personalization to Recommendation”, The

ABSTRACT Providing a customized support for the OLAP brings tremendous challenges to the OLAP technology. Standing at the crossroads of the preferences and the data warehouse, two emerging trends are pointed out; namely: (i) the personalization and (ii) the recommendation. Although the panoply of the proposed approaches, the user-centric data warehouse community issues have not been addressed yet. In this paper we draw an overview of several user centric data warehouse proposals. We also discuss the two promising concepts in this issue, namely, the personalization and the recommendation of the data warehouses. We compare the current approaches among each others with respect to some criteria

On-line analytical processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft

Personnalisation d'analyses décisionnelles sur des données multidimensionnelles

This thesis investigates OLAP analysis personalization within multidimensional databases. OLAP analyse is modeled through a graph where nodes represent the analysis contexts and graph edges represent the user operations. The analysis context regroups the user query as well as result. It is well described by a specific tree structure that is independent on the visualization structures of data and query languages. We provided a model for user preferences on the multidimensional schema and values. Each preference is associated with a specific analysis context. Based on previous models, we proposed a generic framework that includes two personalization processes. First process, denoted query personalization, aims to enhancing user query with related preferences in order to produce a new one that generates a personalized result. Second personalization process is query recommendation that allows helping user throughout the OLAP data exploration phase. Our recommendation framework supports three recommendation scenarios, i.e., assisting user in query composition, suggesting the forthcoming query, and suggesting alternative queries. Recommendations are built progressively basing on user preferences. In order to implement our framework, we developed a prototype system that supports query personalization and query recommendation processes. We present experimental results showing the efficiency and the effectiveness of our approaches.Le travail présenté dans cette thèse aborde la problématique de la personnalisation des analyses OLAP au sein des bases de données multidimensionnelles. Une analyse OLAP est modélisée par un graphe dont les noeuds représentent les contextes d'analyse et les arcs traduisent les opérations de l'utilisateur. Le contexte d'analyse regroupe la requête et le résultat. Il est décrit par un arbre spécifique qui est indépendant des structures de visualisation des données et des langages de requête. Par ailleurs, nous proposons un modèle de préférences utilisateur exprimées sur le schéma multidimensionnel et sur les valeurs. Chaque préférence est associée à un contexte d'analyse particulier. En nous basant sur ces modèles, nous proposons un cadre générique comportant deux mécanismes de personnalisation. Le premier mécanisme est la personnalisation de requête. Il permet d'enrichir la requête utilisateur à l'aide des préférences correspondantes afin de générer un résultat qui satisfait au mieux aux besoins de l'usager. Le deuxième mécanisme de personnalisation est la recommandation de requêtes qui permet d'assister l'utilisateur tout au long de son exploration des données OLAP. Trois scénarios de recommandation sont définis : l'assistance à la formulation de requête, la proposition de la prochaine requête et la suggestion de requêtes alternatives. Ces recommandations sont construites progressivement à l'aide des préférences de l'utilisateur. Afin valider nos différentes contributions, nous avons développé un prototype qui intègre les mécanismes de personnalisation et de recommandation de requête proposés. Nous présentons les résultats d'expérimentations montrant la performance et l'efficacité de nos approches. Mots-clés: OLAP, analyse décisionnelle, personnalisation de requête, système de recommandation, préférence utilisateur, contexte d'analyse, appariement d'arbres de contexte

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Leverage web analytics for real time website browsing recommendations

Trabalho apresentado no 5th World Conference on Information Systems and Technologies (WorldCIST’17), 11-13 de abril 2017, Porto Santo, Madeira PortugalAs a websites’ structure grow it is paramount to accommodate the alignment of user needs and experience with the overall websites’ purposes. Toward this requirement, the proposed website navigation recommendation system suggests to users, pages that might be of her interest based on past successful navigation patterns of overall site’s usage. Most of existing recommendation systems adopts traditionally one of the web mining branches. We take a different stance, on web mining usage, and alternatively considered the real time enactment of web analytic tools supported analysis given their current maturity and affordances. On this basis we provide a model, its implementation and evaluation for navigation based recommendations generation and delivery. The developed prototype adopted a SaaS orientation to promote the underlying functionalities integration within any website. Preliminary evaluation’s results seem to favor the validation of the present contribution rational.N/

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach