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

Personalizing Interactions with Information Systems

Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user’s mental model with the underlying information system’s organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains

Understanding the Business Value Creation Process for Business Intelligence Tools in the UAE

Background: In today’s dynamically changing business environment, organizations are constantly striving to improve their organizational performance, and the use of business intelligence (BI) aids in this process. This is a key reason why BI has captured the attention of many academicians and practitioners. Most of the research on BI highlights its importance and the ways in which it facilitates decision-making; however, there is limited research on how the use of BI tools and applications benefits the organization by enabling it to convert the millions of dollars spent on BI investment into business value. This study validates the “business value of BI” framework by evaluating the business value creation process for BI tools in the UAE. Method: The data for this empirical study were collected via interviews with 15 middle managers from various industries in the UAE, with a focus on BI tools and the use of BI in their organizations. Results: The results show that a business value creation process for BI exists and that it is divided into three subprocesses: the BI conversion process, BI use process, and BI competitive process. Conclusions: The use of BI has become necessary for organizations to compete effectually. The results of this study identify the various BI tools that organizations in the UAE use. Furthermore, the results highlight the business value creation process of BI tools and how the effective use BI offers an information advantage that facilitates decision-making and eventually promotes financial and non-financial benefits for the organization. Available at: https://aisel.aisnet.org/pajais/vol11/iss3/4

A Semantic Approach to Supporting Users in the Selection of Visualizations in Business Intelligence Environments

The amount of data produced and stored in multiple types of distributed data sources is growing steadily. A crucial factor that determines whether data can be analyzed efficiently is the use of adequate visualizations. Almost simultaneously with the ongoing availability of data numerous types of visualization techniques have emerged. Since ordinary business intelligence users typically lack expert visualization knowledge, the selection and creation of visualizations can be a very time- and knowledge-consuming task. To encounter these problems an architecture that aims at supporting ordinary BI users in the selection of adequate visualizations is developed in this thesis. The basic idea is to automatically provide visualization recommendations based on the concrete BI scenario and formalized visualization knowledge. Ontologies that formalize all relevant knowledge play an important role in the developed architecture and are the key to make the knowledge machine-processable

1st doctoral symposium of the international conference on software language engineering (SLE) : collected research abstracts, October 11, 2010, Eindhoven, The Netherlands

The first Doctoral Symposium to be organised by the series of International Conferences on Software Language Engineering (SLE) will be held on October 11, 2010 in Eindhoven, as part of the 3rd instance of SLE. This conference series aims to integrate the different sub-communities of the software-language engineering community to foster cross-fertilisation and strengthen research overall. The Doctoral Symposium at SLE 2010 aims to contribute towards these goals by providing a forum for both early and late-stage Ph.D. students to present their research and get detailed feedback and advice from researchers both in and out of their particular research area. Consequently, the main objectives of this event are: – to give Ph.D. students an opportunity to write about and present their research; – to provide Ph.D. students with constructive feedback from their peers and from established researchers in their own and in different SLE sub-communities; – to build bridges for potential research collaboration; and – to foster integrated thinking about SLE challenges across sub-communities. All Ph.D. students participating in the Doctoral Symposium submitted an extended abstract describing their doctoral research. Based on a good set of submisssions we were able to accept 13 submissions for participation in the Doctoral Symposium. These proceedings present final revised versions of these accepted research abstracts. We are particularly happy to note that submissions to the Doctoral Symposium covered a wide range of SLE topics drawn from all SLE sub-communities. In selecting submissions for the Doctoral Symposium, we were supported by the members of the Doctoral-Symposium Selection Committee (SC), representing senior researchers from all areas of the SLE community.We would like to thank them for their substantial effort, without which this Doctoral Symposium would not have been possible. Throughout, they have provided reviews that go beyond the normal format of a review being extra careful in pointing out potential areas of improvement of the research or its presentation. Hopefully, these reviews themselves will already contribute substantially towards the goals of the symposium and help students improve and advance their work. Furthermore, all submitting students were also asked to provide two reviews for other submissions. The members of the SC went out of their way to comment on the quality of these reviews helping students improve their reviewing skills

1st doctoral symposium of the international conference on software language engineering (SLE) : collected research abstracts, October 11, 2010, Eindhoven, The Netherlands

The first Doctoral Symposium to be organised by the series of International Conferences on Software Language Engineering (SLE) will be held on October 11, 2010 in Eindhoven, as part of the 3rd instance of SLE. This conference series aims to integrate the different sub-communities of the software-language engineering community to foster cross-fertilisation and strengthen research overall. The Doctoral Symposium at SLE 2010 aims to contribute towards these goals by providing a forum for both early and late-stage Ph.D. students to present their research and get detailed feedback and advice from researchers both in and out of their particular research area. Consequently, the main objectives of this event are: – to give Ph.D. students an opportunity to write about and present their research; – to provide Ph.D. students with constructive feedback from their peers and from established researchers in their own and in different SLE sub-communities; – to build bridges for potential research collaboration; and – to foster integrated thinking about SLE challenges across sub-communities. All Ph.D. students participating in the Doctoral Symposium submitted an extended abstract describing their doctoral research. Based on a good set of submisssions we were able to accept 13 submissions for participation in the Doctoral Symposium. These proceedings present final revised versions of these accepted research abstracts. We are particularly happy to note that submissions to the Doctoral Symposium covered a wide range of SLE topics drawn from all SLE sub-communities. In selecting submissions for the Doctoral Symposium, we were supported by the members of the Doctoral-Symposium Selection Committee (SC), representing senior researchers from all areas of the SLE community.We would like to thank them for their substantial effort, without which this Doctoral Symposium would not have been possible. Throughout, they have provided reviews that go beyond the normal format of a review being extra careful in pointing out potential areas of improvement of the research or its presentation. Hopefully, these reviews themselves will already contribute substantially towards the goals of the symposium and help students improve and advance their work. Furthermore, all submitting students were also asked to provide two reviews for other submissions. The members of the SC went out of their way to comment on the quality of these reviews helping students improve their reviewing skills

Using metarules to integrate knowledge in knowledge based systems. An application in the woodworking industry

The current study addresses the integration of knowledge obtained from Data Mining structures and models into existing Knowledge Based solutions. It presents a technique adapted from commonKADS and spiral methodology to develop an initial knowledge solution using a traditional approach for requirement analysis, knowledge acquisition, and implementation. After an initial prototype is created and verified, the solution is enhanced incorporating new knowledge obtained from Online Analytical Processing, specifically from Data Mining models and structures using meta rules. Every meta rule is also verified prior to being included in the selection and translation of rules into the Expert System notation. Once an initial iteration was completed, responses from test cases were compared using an agreement index and kappa index. The problem domain was restricted to remake and rework operations in a cabinet making company. For Data Mining models, 8,674 cases of Price of Non Conformance (PONC) were used for a period of time of 3 months. Initial results indicated that the technique presented sufficient formalism to be used in the development of new systems, using Trillium scale. The use of 50 additional cases randomly selected from different departments indicated that responses from the original system and the solution that incorporated new knowledge from Data Mining differed significantly. Further inspection of responses indicated that the new solution with additional 68 rules was able to answer, although with an incorrect alternative in 28 additional cases that the initial solution was not able to provide a conclusion

Analyzing Impact of Interaction Context in Collaborative Filtering

Kolaborativní filtrování je jednou z nejúspěšnějších technik používaných v doporučovacích systémech. Základní algoritmy využívají historické interakce mezi uživateli a předměty, nicméně doporučovací systémy nasazené v produkčním prostředí mají často k dispozici minimálně jednu další dimenzi dat - časová razítka těchto interakcí. Tyto okolnosti interakcí nazýváme kontextem. Tato práce využívá dosud často opomíjené informace v datech ke zlepšení přesnosti doporučování. Je navrženo několik nových přístupů k začlenění kontextu do tradičních metod kolaborativního filtrování. K evaluaci těchto vylepšení je navržen a implementován testovací framework. Navržené metody jsou rozsáhle testovány na několika datasetech, s různými parametry a kontexty. Výsledky ukazují, že metody beroucí v úvahu kontext vykazují i na převážně statických datasetech zlepšení metriky recall o 5-25 % oproti tradidičním algoritmům kolaborativního filtrování.Collaborative filtering is one of the most successful techniques used in recommender systems. The basic algorithms utilize history of interactions between users and items. However, recommenders deployed in production often have at least one more dimension of data available--timestamp of the interaction. These interaction circumstances are collectively referred to as the context. This thesis exploits the additional information in order to improve overall recommender accuracy. Several novel approaches to incorporating context into traditional collaborative filtering are proposed. An evaluation framework is designed and proposed algorithms are extensively evaluated with different parameters and contexts on multiple datasets. Results show that even mostly static datasets benefit from the proposed context-aware approach. About 5-35 % recall increase was observed in comparison with traditional collaborative filtering algorithms