Scientific Workflows: Past, Present and Future

International audienceThis special issue and our editorial celebrate 10 years of progress with data-intensive or scientific workflows. There have been very substantial advances in the representation of workflows and in the engineering of workflow management systems (WMS). The creation and refinement stages are now well supported, with a significant improvement in usability. Improved abstraction supports cross-fertilisation between different workflow communities and consistent interpretation as WMS evolve. Through such re-engineering the WMS deliver much improved performance, significantly increased scale and sophisticated reliability mechanisms. Further improvement is anticipated from substantial advances in optimisation. We invited papers from those who have delivered these advances and selected 14 to represent today's achievements and representative plans for future progress. This editorial introduces those contributions with an overview and categorisation of the papers. Furthermore, it elucidates responses from a survey of major workflow systems, which provides evidence of substantial progress and a structured index of related papers. We conclude with suggestions on areas where further research and development is needed and offer a vision of future research directions

Semantically-aware data discovery and placement in collaborative computing environments

As the size of scientific datasets and the demand for interdisciplinary collaboration grow in modern science, it becomes imperative that better ways of discovering and placing datasets generated across multiple disciplines be developed to facilitate interdisciplinary scientific research. For discovering relevant data out of large-scale interdisciplinary datasets. The development and integration of cross-domain metadata is critical as metadata serves as the key guideline for organizing data. To develop and integrate cross-domain metadata management systems in interdisciplinary collaborative computing environment, three key issues need to be addressed: the development of a cross-domain metadata schema; the implementation of a metadata management system based on this schema; the integration of the metadata system into existing distributed computing infrastructure. Current research in metadata management in distributed computing environment largely focuses on relatively simple schema that lacks the underlying descriptive power to adequately address semantic heterogeneity often found in interdisciplinary science. And current work does not take adequate consideration the issue of scalability in large-scale data management. Another key issue in data management is data placement, due to the increasing size of scientific datasets, the overhead incurred as a result of transferring data among different nodes also grow into a significant inhibiting factor affecting overall performance. Currently, few data placement strategies take into consideration semantic information concerning data content. In this dissertation, we propose a cross-domain metadata system in a collaborative distributed computing environment and identify and evaluate key factors and processes involved in a successful cross-domain metadata system with the goal of facilitating data discovery in collaborative environments. This will allow researchers/users to conduct interdisciplinary science in the context of large-scale datasets that will make it easier to access interdisciplinary datasets, reduce barrier to collaboration, reduce cost of future development of similar systems. We also investigate data placement strategies that involve semantic information about the hardware and network environment as well as domain information in the form of semantic metadata so that semantic locality could be utilized in data placement, that could potentially reduce overhead for accessing large-scale interdisciplinary datasets

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

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Query-Time Data Integration

Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to traditional up-front integration. It aims at enabling users to issue ad-hoc queries on their own data as if all potential other data sources were already integrated, without declaring specific sources and mappings to use. Automated data search and integration methods are then coupled directly with query processing on the available data. The ambiguity and uncertainty introduced through fully automated retrieval and mapping methods is compensated by answering those queries with ranked lists of alternative results. Each result is then based on different data sources or query interpretations, allowing users to pick the result most suitable to their information need. To this end, this thesis makes three main contributions. Firstly, we introduce a novel method for Top-k Entity Augmentation, which is able to construct a top-k list of consistent integration results from a large corpus of heterogeneous data sources. It improves on the state-of-the-art by producing a set of individually consistent, but mutually diverse, set of alternative solutions, while minimizing the number of data sources used. Secondly, based on this novel augmentation method, we introduce the DrillBeyond system, which is able to process Open World SQL queries, i.e., queries referencing arbitrary attributes not defined in the queried database. The original database is then augmented at query time with Web data sources providing those attributes. Its hybrid augmentation/relational query processing enables the use of ad-hoc data search and integration in data analysis queries, and improves both performance and quality when compared to using separate systems for the two tasks. Finally, we studied the management of large-scale dataset corpora such as data lakes or Open Data platforms, which are used as data sources for our augmentation methods. We introduce Publish-time Data Integration as a new technique for data curation systems managing such corpora, which aims at improving the individual reusability of datasets without requiring up-front global integration. This is achieved by automatically generating metadata and format recommendations, allowing publishers to enhance their datasets with minimal effort. Collectively, these three contributions are the foundation of a Query-time Data Integration architecture, that enables ad-hoc data search and integration queries over large heterogeneous dataset collections

Radar satellite imagery for humanitarian response. Bridging the gap between technology and application

This work deals with radar satellite imagery and its potential to assist of humanitarian operations. As the number of displaced people annually increases, both hosting countries and relief organizations face new challenges which are often related to unclear situations and lack of information on the number and location of people in need, as well as their environments. It was demonstrated in numerous studies that methods of earth observation can deliver this important information for the management of crises, the organization of refugee camps, and the mapping of environmental resources and natural hazards. However, most of these studies make use of -high-resolution optical imagery, while the role of radar satellites is widely neglected. At the same time, radar sensors have characteristics which make them highly suitable for humanitarian response, their potential to capture images through cloud cover and at night in the first place. Consequently, they potentially allow quicker response in cases of emergencies than optical imagery. This work demonstrates the currently unused potential of radar imagery for the assistance of humanitarian operations by case studies which cover the information needs of specific emergency situations. They are thematically grouped into topics related to population, natural hazards and the environment. Furthermore, the case studies address different levels of scientific objectives: The main intention is the development of innovative techniques of digital image processing and geospatial analysis as an answer on the identified existing research gaps. For this reason, novel approaches are presented on the mapping of refugee camps and urban areas, the allocation of biomass and environmental impact assessment. Secondly, existing methods developed for radar imagery are applied, refined, or adapted to specifically demonstrate their benefit in a humanitarian context. This is done for the monitoring of camp growth, the assessment of damages in cities affected by civil war, and the derivation of areas vulnerable to flooding or sea-surface changes. Lastly, to foster the integration of radar images into existing operational workflows of humanitarian data analysis, technically simple and easily-adaptable approaches are suggested for the mapping of rural areas for vaccination campaigns, the identification of changes within and around refugee camps, and the assessment of suitable locations for groundwater drillings. While the studies provide different levels of technical complexity and novelty, they all show that radar imagery can largely contribute to the provision of a variety of information which is required to make solid decisions and to effectively provide help in humanitarian operations. This work furthermore demonstrates that radar images are more than just an alternative image source for areas heavily affected by cloud cover. In fact, what makes them valuable is their information content regarding the characteristics of surfaces, such as shape, orientation, roughness, size, height, moisture, or conductivity. All these give decisive insights about man-made and natural environments in emergency situations and cannot be provided by optical images Finally, the findings of the case studies are put into a larger context, discussing the observed potential and limitations of the presented approaches. The major challenges are summarized which need be addressed to make radar imagery more useful in humanitarian operations in the context of upcoming technical developments. New radar satellites and technological progress in the fields of machine learning and cloud computing will bring new opportunities. At the same time, this work demonstrated the large need for further research, as well as for the collaboration and transfer of knowledge and experiences between scientists, users and relief workers in the field. It is the first extensive scientific compilation of this topic and the first step for a sustainable integration of radar imagery into operational frameworks to assist humanitarian work and to contribute to a more efficient provision of help to those in need.Die vorliegende Arbeit beschäftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur Unterstützung humanitärer Einsätze. Die jährlich zunehmende Zahl an vertriebenen oder geflüchteten Menschen stellt sowohl Aufnahmeländer als auch humanitäre Organisationen vor große Herausforderungen, da sie oft mit unübersichtlichen Verhältnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von Flüchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlässliche Angaben über Anzahl und Aufenthaltsort der Geflüchteten und ihrer natürlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, während bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfügen gerade Radarsatelliten über Eigenschaften, die hilfreich für humanitäre Einsätze sein können, allen voran ihre Unabhängigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in Krisenfällen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur Unterstützung humanitärer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen für ausgewählte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche Ansprüche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und räumlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von Flüchtlingslagern zur Abschätzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von Flüchtlingslagern aufgezeigt. Darüber hinaus werden existierende oder erprobte Ansätze für die Anwendung im humanitären Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von Flüchtlingslagern, zur Ermittlung von Schäden an Gebäuden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende Abläufe oder Arbeitsroutinen in der humanitären Hilfe anhand technisch vergleichsweise einfacher Ansätze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestützte Kartierung von entlegenen Gebieten zur Unterstützung von Impfkampagnen, die Identifizierung von Veränderungen in Flüchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und Komplexitätsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten für die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstützen. Darüber hinaus wird in dieser Arbeit deutlich, dass Radardaten für humanitäre Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von Oberflächen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, Größe oder Höhe, sind sie optischen Daten überlegen und daher für viele Anwendungsbereiche im Kontext humanitärer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukünftiger Entwicklungen und Herausforderungen diskutiert. So versprechen neue Radarsatelliten und technologische Fortschritte im Bereich der Datenverarbeitung großes Potenzial. Gleichzeitig unterstreicht die Arbeit einen großen Bedarf an weiterer Forschung, sowie an Austausch und Zusammenarbeit zwischen Wissenschaftlern, Anwendern und Einsatzkräften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein für eine langfristige Integration von Radardaten in operationelle Abläufe dienen, um humanitäre Arbeit zu unterstützen und eine wirksame Hilfe für Menschen in Not ermöglichen

Similarity search and mining in uncertain spatial and spatio-temporal databases

Both the current trends in technology such as smart phones, general mobile devices, stationary sensors and satellites as well as a new user mentality of utilizing this technology to voluntarily share information produce a huge flood of geo-spatial and geo-spatio-temporal data. This data flood provides a tremendous potential of discovering new and possibly useful knowledge. In addition to the fact that measurements are imprecise, due to the physical limitation of the devices, some form of interpolation is needed in-between discrete time instances. From a complementary perspective - to reduce the communication and bandwidth utilization, along with the storage requirements, often the data is subjected to a reduction, thereby eliminating some of the known/recorded values. These issues introduce the notion of uncertainty in the context of spatio-temporal data management - an aspect raising an imminent need for scalable and flexible data management. The main scope of this thesis is to develop effective and efficient techniques for similarity search and data mining in uncertain spatial and spatio-temporal data. In a plethora of research fields and industrial applications, these techniques can substantially improve decision making, minimize risk and unearth valuable insights that would otherwise remain hidden. The challenge of effectiveness in uncertain data is to correctly determine the set of possible results, each associated with the correct probability of being a result, in order to give a user a confidence about the returned results. The contrary challenge of efficiency, is to compute these result and corresponding probabilities in an efficient manner, allowing for reasonable querying and mining times, even for large uncertain databases. The paradigm used to master both challenges, is to identify a small set of equivalent classes of possible worlds, such that members of the same class can be treated as equivalent in the context of a given query predicate or data mining task. In the scope of this work, this paradigm will be formally defined, and applied to the most prominent classes of spatial queries on uncertain data, including range queries, k-nearest neighbor queries, ranking queries and reverse k-nearest neighbor queries. For this purpose, new spatial and probabilistic pruning approaches are developed to further speed up query processing. Furthermore, the proposed paradigm allows to develop the first efficient solution for the problem of frequent co-location mining on uncertain data. Special emphasis is taken on the temporal aspect of applications using modern data collection technologies. While the aforementioned techniques work well for single points of time, the prediction of query results over time remains a challenge. This thesis fills this gap by modeling an uncertain spatio-temporal object as a stochastic process, and by applying the above paradigm to efficiently query, index and mine historical spatio-temporal data.Moderne Technologien, z.B. Sattelitentechnologie und Technologie in Smart Phones, erzeugen eine Flut räumlicher Geo-Daten. Zudem ist in der Gesellschaft ein Trend zu beobachten diese erzeugten Daten freiwillig auf öffentlich zugänglichen Plattformen zur Verfügung zu stellen. Diese Datenflut hat immenses Potential, um neues und nützliches Wissen zu entdecken. Diese Daten sind jedoch grundsätzlich unsichere räumliche Daten. Die Unsicherheit ergibt sich aus mehreren Aspekten. Zum einen kommt es bei Messungen grundsätzlich zu Messungenauigkeiten, zum anderen ist zwischen diskreten Messzeitpunkten eine Interpolation nötig, die zusätzliche Unsicherheit erzeugt. Auerdem werden die Daten oft absichtlich reduziert, um Speicherplatz und Transfervolumen einzusparen, wodurch weitere Information verloren geht. Diese Unsicherheit schafft einen sofortigen Bedarf für skalierbare und flexible Methoden zur Verwaltung und Auswertung solcher Daten. Im Rahmen dieser Arbeit sollen effektive und effiziente Techniken zur Ähnlichkeitssuche und zum Data Mining bei unsicheren räumlichen und unsicheren räumlich-zeitlichen Daten erarbeitet werden. Diese Techniken liefern wertvolles Wissen, das auf verschiedenen Forschungsgebieten, als auch bei industriellen Anwendungen zur Entscheidungsfindung genutzt werden kann. Bei der Entwicklung dieser Techniken gibt es zwei Herausforderungen. Einerseits müssen die entwickelten Techniken effektiv sein, um korrekte Ergebnisse und Wahrscheinlichkeiten dieser Ergebnisse zurückzugeben. Andererseits müssen die entwickelten Techniken effizient sein, um auch in sehr großen Datenbanken Ergebnisse in annehmbarer Zeit zu liefern. Die Dissertation stellt ein neues Paradigma vor, das beide Herausforderungen meistert. Dieses Paradigma identifiziert mögliche Datenbankwelten, die bezüglich des gegebenen Anfrageprädikats äquivalent sind. Es wird formal definiert und auf die relevantesten räumlichen Anfragetypen angewendet, um effiziente Lösungen zu entwickeln. Dazu gehören Bereichanfragen, k-Nächste-Nachbarnanfragen, Rankinganfragen und Reverse k-Nächste-Nachbarnanfragen. Räumliche und probabilistische Pruningkriterien werden entwickelt, um insignifikante Ergebnisse früh auszuschlieen. Zudem wird die erste effiziente Lösung für das Problem des "Spatial Co-location Minings" auf unsicheren Daten präsentiert. Ein besonderer Schwerpunkt dieser Arbeit liegt auf dem temporalen Aspekt moderner Geo-Daten. Während obig genannte Techniken dieser Arbeit für einzelne Zeitpunkt sehr gut funktionieren, ist die effektive und effiziente Verwaltung von unsicheren räumlich zeitlichen Daten immer noch ein weitestgehend ungelöstes Problem. Diese Dissertation löst dieses Problem, indem unsichere räumlich-zeitliche Daten durch stochastische Prozesse modeliert werden. Auf diese stochastischen Prozesse lässt sich das oben genannte Paradigma anwenden, um unsichere räumlich-zeitliche Daten effizient anzufragen, zu indexieren, und zu minen

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