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

Leveraging query logs for user-centric OLAP

OLAP (On-Line Analytical Processing), the process of efficiently enabling common analytical operations on the multidimensional view of data, is a corner stone of Business Intelligence.While OLAP is now a mature, efficiently implemented technology, very little attention has been paid to the effectiveness of the analysis and the user-friendliness of this technology, often considered tedious of use.This dissertation is a contribution to developing user-centric OLAP, focusing on the use of former queries logged by an OLAP server to enhance subsequent analyses. It shows how logs of OLAP queries can be modeled, constructed, manipulated, compared, and finally leveraged for personalization and recommendation.Logs are modeled as sets of analytical sessions, sessions being modeled as sequences of OLAP queries. Three main approaches are presented for modeling queries: as unevaluated collections of fragments (e.g., group by sets, sets of selection predicates, sets of measures), as sets of references obtained by partially evaluating the query over dimensions, or as query answers. Such logs can be constructed even from sets of SQL query expressions, by translating these expressions into a multidimensional algebra, and bridging the translations to detect analytical sessions. Logs can be searched, filtered, compared, combined, modified and summarized with a language inspired by the relational algebra and parametrized by binary relations over sessions. In particular, these relations can be specialization relations or based on similarity measures tailored for OLAP queries and analytical sessions. Logs can be mined for various hidden knowledge, that, depending on the query model used, accurately represents the user behavior extracted.This knowledge includes simple preferences, navigational habits and discoveries made during former explorations,and can be it used in various query personalization or query recommendation approaches.Such approaches vary in terms of formulation effort, proactiveness, prescriptiveness and expressive power:query personalization, i.e., coping with a current query too few or too many results, can use dedicated operators for expressing preferences, or be based on query expansion;query recommendation, i.e., suggesting queries to pursue an analytical session,can be based on information extracted from the current state of the database and the query, or be purely history based, i.e., leveraging the query log.While they can be immediately integrated into a complete architecture for User-Centric Query Answering in data warehouses, the models and approaches introduced in this dissertation can also be seen as a starting point for assessing the effectiveness of analytical sessions, with the ultimate goal to enhance the overall decision making process

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

A client focused business intelligence & analytics solution for the hospitality sector

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceOne of the greatest needs of today's business is to know the customer or the type of customer it wants to reach, which makes a customer database a strategic weapon and one of the most important investments a company can make. The business world is becoming more competitive every day, we are constantly overwhelmed with advertisements of products we may like, product promotions we usually buy or discounts on the next purchase if we subscribe to the company’s newsletter. All of this creates a client customization, and any company that is not able to do this cannot keep up with its competition. This report details the project developed at Pestana Hotel Group, which consisted of a Business Intelligence solution, more specifically the development of a customer database with the creation of two tabular models using SQL Server tools, one specific for loyal customers and another, more general, with information about all Pestana customers, and two Power BI reports that allow the visualization of the information obtained in an effective and simplified way. This report contains a literature review that situates the reader on the subject addressed in this project, a chapter dedicated to the data modeling used to create the tabular models, and another on the creation of the reports.Uma das maiores necessidades dos negócios atuais é conhecer o seu cliente ou o tipo de cliente que quer atingir, o que torna uma base de dados de cliente uma arma estratégica e um dos mais importantes investimentos. O mundo empresarial está cada dia mais competitivo, somos constantemente assoberbados com anúncios de produtos que podemos gostar, promoções de produtos que costumamos comprar ou descontos na próxima compra caso subscrevamos a newsletter. Tudo isto cria uma personalização para o cliente, e qualquer empresa que não o consiga fazer não conseguirá acompanhar a concorrência. Este relatório detalha o projeto feito no Pestana Hotel Group, que consistiu numa solução de Business Intelligence, mais especificamente na construção de uma base de dados do cliente com a criação de dois modelos tabulares através de ferramentas do SQL Server, um específico para clientes fidelizados e outro mais geral com informação sobre todos os clientes Pestana, e dois relatórios em Power BI que permitem a visualização da informação obtida de uma forma eficaz e simplificada. O relatório contém uma revisão de literatura que situa o leitor sobre os assuntos abordados neste projeto, um capítulo dedicado à modelação dos dados de forma a criar os modelos tabulares e outro sobre a criação dos relatórios

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

Proactive Supply Chain Performance Management with Predictive Analytics

Today’s business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment