Data Mining-based Fragmentation of XML Data Warehouses

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority

A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks

In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks

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

Intégration holistique et entreposage automatique des données ouvertes

Statistical Open Data present useful information to feed up a decision-making system. Their integration and storage within these systems is achieved through ETL processes. It is necessary to automate these processes in order to facilitate their accessibility to non-experts. These processes have also need to face out the problems of lack of schemes and structural and sematic heterogeneity, which characterize the Open Data. To meet these issues, we propose a new ETL approach based on graphs. For the extraction, we propose automatic activities performing detection and annotations based on a model of a table. For the transformation, we propose a linear program fulfilling holistic integration of several graphs. This model supplies an optimal and a unique solution. For the loading, we propose a progressive process for the definition of the multidimensional schema and the augmentation of the integrated graph. Finally, we present a prototype and the experimental evaluations.Les statistiques présentes dans les Open Data ou données ouvertes constituent des informations utiles pour alimenter un système décisionnel. Leur intégration et leur entreposage au sein du système décisionnel se fait à travers des processus ETL. Il faut automatiser ces processus afin de faciliter leur accessibilité à des non-experts. Ces processus doivent pallier aux problèmes de manque de schémas, d'hétérogénéité structurelle et sémantique qui caractérisent les données ouvertes. Afin de répondre à ces problématiques, nous proposons une nouvelle démarche ETL basée sur les graphes. Pour l'extraction du graphe d'un tableau, nous proposons des activités de détection et d'annotation automatiques. Pour la transformation, nous proposons un programme linéaire pour résoudre le problème d'appariement holistique de données structurelles provenant de plusieurs graphes. Ce modèle fournit une solution optimale et unique. Pour le chargement, nous proposons un processus progressif pour la définition du schéma multidimensionnel et l'augmentation du graphe intégré. Enfin, nous présentons un prototype et les résultats d'expérimentations

Semantic metadata for supporting exploratory OLAP

Cotutela Universitat Politècnica de Catalunya i Aalborg UniversitetOn-Line Analytical Processing (OLAP) is an approach widely used for data analysis. OLAP is based on the multidimensional (MD) data model where factual data are related to their analytical perspectives called dimensions and together they form an n-dimensional data space referred to as data cube. MD data are typically stored in a data warehouse, which integrates data from in-house data sources, and then analyzed by means of OLAP operations, e.g., sales data can be (dis)aggregated along the location dimension. As OLAP proved to be quite intuitive, it became broadly accepted by non-technical and business users. However, as users still encountered difficulties in their analysis, different approaches focused on providing user assistance. These approaches collect situational metadata about users and their actions and provide suggestions and recommendations that can help users' analysis. However, although extensively exploited and evidently needed, little attention is paid to metadata in this context. Furthermore, new emerging tendencies call for expanding the use of OLAP to consider external data sources and heterogeneous settings. This leads to the Exploratory OLAP approach that especially argues for the use of Semantic Web (SW) technologies to facilitate the description and integration of external sources. With data becoming publicly available on the (Semantic) Web, the number and diversity of non-technical users are also significantly increasing. Thus, the metadata to support their analysis become even more relevant. This PhD thesis focuses on metadata for supporting Exploratory OLAP. The study explores the kinds of metadata artifacts used for the user assistance purposes and how they are exploited to provide assistance. Based on these findings, the study then aims at providing theoretical and practical means such as models, algorithms, and tools to address the gaps and challenges identified. First, based on a survey of existing user assistance approaches related to OLAP, the thesis proposes the analytical metadata (AM) framework. The framework includes the definition of the assistance process, the AM artifacts that are classified in a taxonomy, and the artifacts organization and related types of processing to support the user assistance. Second, the thesis proposes a semantic metamodel for AM. Hence, Resource Description Framework (RDF) is used to represent the AM artifacts in a flexible and re-usable manner, while the metamodeling abstraction level is used to overcome the heterogeneity of (meta)data models in the Exploratory OLAP context. Third, focusing on the schema as a fundamental metadata artifact for enabling OLAP, the thesis addresses some important challenges on constructing an MD schema on the SW using RDF. It provides the algorithms, method, and tool to construct an MD schema over statistical linked open data sets. Especially, the focus is on enabling that even non-technical users can perform this task. Lastly, the thesis deals with queries as the second most relevant artifact for user assistance. In the spirit of Exploratory OLAP, the thesis proposes an RDF-based model for OLAP queries created by instantiating the previously proposed metamodel. This model supports the sharing and reuse of queries across the SW and facilitates the metadata preparation for the assistance exploitation purposes. Finally, the results of this thesis provide metadata foundations for supporting Exploratory OLAP and advocate for greater attention to the modeling and use of semantics related to metadata.El processament analític en línia (OLAP) és una tècnica àmpliament utilitzada per a l'anàlisi de dades. OLAP es basa en el model multi-dimensional (MD) de dades, on dades factuals es relacionen amb les seves perspectives analítiques, anomenades dimensions, i conjuntament formen un espai de dades n-dimensional anomenat cub de dades. Les dades MD s'emmagatzemen típicament en un data warehouse (magatzem de dades), el qual integra dades de fonts internes, les quals posteriorment s'analitzen mitjançant operacions OLAP, per exemple, dades de vendes poden ser (des)agregades a partir de la dimensió ubicació. Un cop OLAP va ser provat com a intuïtiu, va ser ampliament acceptat tant per usuaris no tècnics com de negoci. Tanmateix, donat que els usuaris encara trobaven dificultats per a realitzar el seu anàlisi, diferents tècniques s'han enfocat en la seva assistència. Aquestes tècniques recullen metadades situacionals sobre els usuaris i les seves accions, i proporcionen suggerències i recomanacions per tal d'ajudar en aquest anàlisi. Tot i ésser extensivament emprades i necessàries, poca atenció s'ha prestat a les metadades en aquest context. A més a més, les noves tendències demanden l'expansió d'ús d'OLAP per tal de considerar fonts de dades externes en escenaris heterogenis. Això ens porta a la tècnica d'OLAP exploratori, la qual es basa en l'ús de tecnologies en la web semàntica (SW) per tal de facilitar la descripció i integració d'aquestes fonts externes. Amb les dades essent públicament disponibles a la web (semàntica), el nombre i diversitat d'usuaris no tècnics també incrementa signifícativament. Així doncs, les metadades per suportar el seu anàlisi esdevenen més rellevants. Aquesta tesi doctoral s'enfoca en l'ús de metadades per suportar OLAP exploratori. L'estudi explora els tipus d'artefactes de metadades utilitzats per l'assistència a l'usuari, i com aquests són explotats per proporcionar assistència. Basat en aquestes troballes, l'estudi preté proporcionar mitjans teòrics i pràctics, com models, algorismes i eines, per abordar els reptes identificats. Primerament, basant-se en un estudi de tècniques per assistència a l'usuari en OLAP, la tesi proposa el marc de treball de metadades analítiques (AM). Aquest marc inclou la definició del procés d'assistència, on els artefactes d'AM són classificats en una taxonomia, i l'organització dels artefactes i tipus relacionats de processament pel suport d'assistència a l'usuari. En segon lloc, la tesi proposa un meta-model semàntic per AM. Així doncs, s'utilitza el Resource Description Framework (RDF) per representar els artefactes d'AM d'una forma flexible i reusable, mentre que el nivell d'abstracció de metamodel s'utilitza per superar l'heterogeneïtat dels models de (meta)dades en un context d'OLAP exploratori. En tercer lloc, centrant-se en l'esquema com a artefacte fonamental de metadades per a OLAP, la tesi adreça reptes importants en la construcció d'un esquema MD en la SW usant RDF. Proporciona els algorismes, mètodes i eines per construir un esquema MD sobre conjunts de dades estadístics oberts i relacionats. Especialment, el focus rau en permetre que usuaris no tècnics puguin realitzar aquesta tasca. Finalment, la tesi tracta amb consultes com el segon artefacte més rellevant per l'assistència a usuari. En l'esperit d'OLAP exploratori, la tesi proposa un model basat en RDF per consultes OLAP instanciant el meta-model prèviament proposat. Aquest model suporta el compartiment i reutilització de consultes sobre la SW i facilita la preparació de metadades per l'explotació de l'assistència. Finalment, els resultats d'aquesta tesi proporcionen els fonaments en metadades per suportar l'OLAP exploratori i propugnen la major atenció al model i ús de semàntica relacionada a metadades.On-Line Analytical Processing (OLAP) er en bredt anvendt tilgang til dataanalyse. OLAP er baseret på den multidimensionelle (MD) datamodel, hvor faktuelle data relateres til analytiske synsvinkler, såkaldte dimensioner. Tilsammen danner de et n-dimensionelt rum af data kaldet en data cube. Multidimensionelle data er typisk lagret i et data warehouse, der integrerer data fra forskellige interne datakilder, og kan analyseres ved hjælp af OLAPoperationer. For eksempel kan salgsdata disaggregeres langs sted-dimensionen. OLAP har vist sig at være intuitiv at forstå og er blevet taget i brug af ikketekniske og orretningsorienterede brugere. Nye tilgange er siden blevet udviklet i forsøget på at afhjælpe de problemer, som denne slags brugere dog stadig står over for. Disse tilgange indsamler metadata om brugerne og deres handlinger og kommer efterfølgende med forslag og anbefalinger, der kan bidrage til brugernes analyse. På trods af at der er en klar nytteværdi i metadata (givet deres udbredelse), har stadig ikke været meget opmærksomhed på metadata i denne kotekst. Desuden lægger nye fremspirende teknikker nu op til en udvidelse af brugen af OLAP til også at bruge eksterne og uensartede datakilder. Dette har ført til Exploratory OLAP, en tilgang til OLAP, der benytter teknologier fra Semantic Web til at understøtte beskrivelse og integration af eksterne kilder. Efterhånden som mere data gøres offentligt tilgængeligt via Semantic Web, kommer flere og mere forskelligartede ikketekniske brugere også til. Derfor er metadata til understøttelsen af deres dataanalyser endnu mere relevant. Denne ph.d.-afhandling omhandler metadata, der understøtter Exploratory OLAP. Der foretages en undersøgelse af de former for metadata, der benyttes til at hjælpe brugere, og af, hvordan sådanne metadata kan udnyttes. Med grundlag i disse fund søges der løsninger til de identificerede problemer igennem teoretiske såvel som praktiske midler. Det vil sige modeller, algoritmer og værktøjer. På baggrund af en afdækning af eksisterende tilgange til brugerassistance i forbindelse med OLAP præsenteres først rammeværket Analytical Metadata (AM). Det inkluderer definition af assistanceprocessen, en taksonomi over tilhørende artefakter og endelig relaterede processeringsformer til brugerunderstøttelsen. Dernæst præsenteres en semantisk metamodel for AM. Der benyttes Resource Description Framework (RDF) til at repræsentere AM-artefakterne på en genbrugelig og fleksibel facon, mens metamodellens abstraktionsniveau har til formål at nedbringe uensartetheden af (meta)data i Exploratory OLAPs kontekst. Så fokuseres der på skemaet som en fundamental metadata-artefakt i OLAP, og afhandlingen tager fat i vigtige udfordringer i forbindelse med konstruktionen af multidimensionelle skemaer i Semantic Web ved brug af RDF. Der præsenteres algoritmer, metoder og redskaber til at konstruere disse skemaer sammenkoblede åbne statistiske datasæt. Der lægges særlig vægt på, at denne proces skal kunne udføres af ikke-tekniske brugere. Til slut tager afhandlingen fat i forespørgsler som anden vigtig artefakt inden for bruger-assistance. I samme ånd som Exploratory OLAP foreslås en RDF-baseret model for OLAP-forespørgsler, hvor førnævnte metamodel benyttes. Modellen understøtter deling og genbrug af forespørgsler over Semantic Web og fordrer klargørelsen af metadata med øje for assistance-relaterede formål. Endelig leder resultaterne af afhandlingen til fundamenterne for metadata i støttet Exploratory OLAP og opfordrer til en øget opmærksomhed på modelleringen og brugen af semantik i forhold til metadataPostprint (published version

Reusing dynamic data marts for query management in an on-demand ETL architecture

Data analysts working often have a requirement to integrate an in-house data warehouse with external datasets, especially web-based datasets. Doing so can give them important insights into their performance when compared with competitors, their industry in general on a global scale, and make predictions as to sales, providing important decision support services. The quality of these insights depends on the quality of the data imported into the analysis dataset. There is a wealth of data freely available from government sources online but little unity between data sources, leading to a requirement for a data processing layer wherein various types of quality issues and heterogeneities can be resolved. Traditionally, this is achieved with an Extract-Transform-Load (ETL) series of processes which are performed on all of the available data, in advance, in a batch process typically run outside of business hours. While this is recognized as a powerful knowledge-based support, it is very expensive to build and maintain, and is very costly to update, in the event that new data sources become available. On-demand ETL offers a solution in that data is only acquired when needed and new sources can be added as they come online. However, this form of dynamic ETL is very difficult to deliver. In this research dissertation, we explore the possibilities of creating dynamic data marts which can be created using non-warehouse data to support the inclusion of new sources. We then examine how these dynamic structures can be used for query fulfillment andhow they can support an overall on-demand query mechanism. At each step of the research and development, we employ a robust validation using a real-world data warehouse from the agricultural domain with selected Agri web sources to test the dynamic elements of the proposed architecture