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The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB., SCOPUS: ar.j, info:eu-repo/semantics/published

Spatial data warehouses (SDW) and spatial online analytical processing (SOLAP) enhance decision making by enabling spatial analysis combined with multidimensional analytical queries. A SDW is an integrated and voluminous multidimensional database containing both conventional and spatial data. SOLAP allows querying SDWs with multidimensional queries that select spatial data that satisfy a given topological relationship and that aggregate spatial data. Existing SDW and SOLAP applications mostly consider phenomena represented by spatial data having exact locations and sharp boundaries. They neglect the fact that spatial data may be affected by imperfections, such as spatial vagueness, which prevents distinguishing an object from its neighborhood. A vague spatial object does not have a precisely defined boundary and/or interior. Thus, it may have a broad boundary and a blurred interior, and is composed of parts that certainly belong to it and parts that possibly belong to it. Although several real-world phenomena are characterized by spatial vagueness, no approach in the literature addresses both spatial vagueness and the design of SDWs nor provides multidimensional analysis over vague spatial data. These shortcomings motivated the elaboration of this doctoral thesis, which addresses both vague spatial data warehouses (vague SDWs) and vague spatial online analytical processing (vague SOLAP). A vague SDW is a SDW that comprises vague spatial data, while vague SOLAP allows querying vague SDWs. The major contributions of this doctoral thesis are: (i) the Vague Spatial Cube (VSCube) conceptual model, which enables the creation of conceptual schemata for vague SDWs using data cubes; (ii) the Vague Spatial MultiDim (VSMultiDim) conceptual model, which enables the creation of conceptual schemata for vague SDWs using diagrams; (iii) guidelines for designing relational schemata and integrity constraints for vague SDWs, and for extending the SQL language to enable vague SOLAP; (i, Les entrepôts de données spatiales (EDS) et l'analyse en ligne spatiale (ALS) améliorent la prise de décision en permettant l'analyse spatiale combinée avec des requêtes analytiques multidimensionnelles. Un EDS est une base de données multidimensionnelle intégrée et volumineuse qui contient des données classiques et des données spatiales. L'ALS permet l'interrogation des EDS avec des requêtes multidimensionnelles qui sélectionnent des données spatiales qui satisfont une relation topologique donnée et qui agrègent les données spatiales. Les EDS et l'ALS considèrent essentiellement des phénomènes représentés par des données spatiales ayant une localisation exacte et des frontières précises. Ils négligent que les données spatiales peuvent être affectées par des imperfections, comme l'imprécision spatiale, ce qui empêche de distinguer précisément un objet de son entourage. Un objet spatial vague n'a pas de frontière et/ou un intérieur précisément définis. Ainsi, il peut avoir une frontière large et un intérieur flou, et est composé de parties qui lui appartiennent certainement et des parties qui lui appartiennent éventuellement. Bien que plusieurs phénomènes du monde réel sont caractérisés par l'imprécision spatiale, il n'y a pas dans la littérature des approches qui adressent en même temps l'imprécision spatiale et la conception d'EDS ni qui fournissent une analyse multidimensionnelle des données spatiales vagues. Ces lacunes ont motivé l'élaboration de cette thèse de doctorat, qui adresse à la fois les entrepôts de données spatiales vagues (EDS vagues) et l'analyse en ligne spatiale vague (ALS vague). Un EDS vague est un EDS qui comprend des données spatiales vagues, tandis que l'ALS vague permet d'interroger des EDS vagues. Les contributions majeures de cette thèse de doctorat sont: (i) le modèle conceptuel Vague Spatial Cube (VSCube), qui permet la création de schémas conceptuels pour des EDS vagues à l'aide de cubes de données; (ii) le modèle conceptuel Vague Spati, O data warehouse espacial (DWE) é um banco de dados multidimensional integrado e volumoso que armazena dados espaciais e dados convencionais. Já o processamento analítico-espacial online (SOLAP) permite consultar o DWE, tanto pela seleção de dados espaciais que satisfazem um relacionamento topológico, quanto pela agregação dos dados espaciais. Deste modo, DWE e SOLAP beneficiam o suporte a tomada de decisão. As aplicações de DWE e SOLAP abordam majoritarimente fenômenos representados por dados espaciais exatos, ou seja, que assumem localizações e fronteiras bem definidas. Contudo, tais aplicações negligenciam dados espaciais afetados por imperfeições, tais como a vagueza espacial, a qual interfere na identificação precisa de um objeto e de seus vizinhos. Um objeto espacial vago não tem sua fronteira ou seu interior precisamente definidos. Além disso, é composto por partes que certamente pertencem a ele e partes que possivelmente pertencem a ele. Apesar de inúmeros fenômenos do mundo real serem caracterizados pela vagueza espacial, na literatura consultada não se identificaram trabalhos que considerassem a vagueza espacial no projeto de DWE e nem para consultar o DWE. Tal limitação motivou a elaboração desta tese de doutorado, a qual introduz os conceitos de DWE vago e de SOLAP vago. Um DWE vago é um DWE que armazena dados espaciais vagos, enquanto que SOLAP vago provê os meios para consultar o DWE vago. Nesta tese, o projeto de DWE vago é abordado e as principais contribuições providas são: (i) o modelo conceitual VSCube que viabiliza a criação de um cubos de dados multidimensional para representar o esquema conceitual de um DWE vago; (ii) o modelo conceitual VSMultiDim que permite criar um diagrama para representar o esquema conceitual de um DWE vago; (iii) diretrizes para o projeto lógico do DWE vago e de suas restrições de integridade, e para estender a linguagem SQL visando processar as consultas de SOLAP vago no DWE vago; e (iv) o índice VSB-index que aprimora, Doctorat en Sciences de l'ingénieur et technologie, Location of the public defense: Universidade Federal de São Carlos, São Carlos, SP, Brazil., info:eu-repo/semantics/nonPublished

Graphs are widespread structures providing a powerful abstraction for modeling networked data. Large and complex graphs have emerged in various domains such as social networks, bioinformatics, and chemical data. However, current warehousing frameworks are not equipped to handle efficiently the multidimensional modeling and analysis of complex graph data. In this paper, we propose a novel framework for building OLAP cubes from graph data and analyzing the graph topological properties. The framework supports the extraction and design of the candidate multidimensional spaces in property graphs. Besides property graphs, a new database model tailored for multidimensional modeling and enabling the exploration of additional candidate multidimensional spaces is introduced. We present novel techniques for OLAP aggregation of the graph, and discuss the case of dimension hierarchies in graphs. Furthermore, the architecture and the implementation of our graph warehousing framework are presented and show the effectiveness of our approach., SCOPUS: cp.k, info:eu-repo/semantics/published

The web is changing the way in which data warehouses are designed and exploited. Nowadays, for many data analysis tasks, data contained in a conventional data warehouse may not suffice, and external data sources, like the web, can provide useful multidimensional information. Also, large repositories of semantically annotated data are becoming available on the web, opening new opportunities for enhancing current decision-support systems. Representation of multidimensional data via semantic web standards is crucial to achieve such goal. In this paper we extend the QB4OLAP RDF vocabulary to represent balanced, recursive, and ragged hierarchies. We also present a set of rules to obtain a QB4OLAP representation of a conceptual multidimensional model, and a procedure to populate the result from a relational implementation of the multidimensional model. We conclude the paper showing how complex real-world OLAP queries expressed in SPARQL can be posed to the resulting QB4OLAP model. © 2014 Springer International Publishing., SCOPUS: cp.k, info:eu-repo/semantics/published

With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design; and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL); and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses; and novel technologies like MapReduce, column-store databases, and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style., SCOPUS: bk.b, info:eu-repo/semantics/published

The "Open Semantic Cloud for Brussels" (OSCB) project aims at building a platform for linked open data for the Brussels region in Belgium, such that participants can easily publish their data. In OSCB, data providers deliver their data in the form of relational tables or XML documents. These data are mapped to RDF triples using the R2RML mapping language. Since OSCB data are spatiotemporal in nature, we needed to adapt R2RML to be able to produce spatiotemporal linked open data in order to build to a spatial data-enabled SPARQL endpoint where the result of spatiotemporal SPARQL queries can be shown on a map. In this paper we show how we achieved this goal. © 2013 Springer-Verlag., SCOPUS: cp.k, info:eu-repo/semantics/published

The "Open Semantic Cloud for Brussels" (OSCB) project aims at building a platform for linked open data for the Brussels region in Belgium, such that participants can easily publish their data, which can in turn be queried by end users using a web browser to access a SPARQL endpoint. If data are spatial and we want to show them on a map, we need to support this endpoint with an engine that can manage spatial data. For this we chose Strabon, an open source geospatial database management system that stores linked geospatial data expressed in the stRDF format (spatiotemporal RDF) and queries them using stSPARQL (spatiotemporal SPARQL), an extension to SPARQL 1.1. In this paper we show how the SPARQL endpoint is built and the kinds of queries it supports, also providing a wide variety of examples. © 2013 Springer-Verlag., SCOPUS: cp.k, info:eu-repo/semantics/published

The notion of SOLAP (Spatial On-Line Analytical Processing) is aimed at exploring spatial data in the same way as OLAP operates over tables. SOLAP, however, only accounts for discrete spatial data. Current decision support systems are increasingly being needed for handling more complex types of data, like continuous fields, which describe physical phenomena that change continuously in time and/or space (e.g. temperature). Although many models have been proposed for adding spatial (continuous and discrete) information to OLAP tools, no one is general enough to allow users to just perceive data as a cube, and analyze any type of spatial data together with typical alphanumerical discrete OLAP data, using only the classic OLAP operators (e.g. Roll-up, Drill-down). In this paper the authors propose a model and an algebra supporting it, that allow operating over data cubes, independently of the underlying data types and physical data representation. That means, in this approach, the final user only sees the typical OLAP operators at the query level, whereas at lower abstraction levels the authors provide discrete and continuous spatial data support as well as different ways of partitioning the space. As far as the authors are aware of, this is the first proposal, which provides such a general framework for spatiotemporal data analysis. Copyright © 2013, IGI Global., SCOPUS: ar.j, info:eu-repo/semantics/published

Business Process Modeling (BPM) is the activity of representing the processes of an organization, so that they can be analyzed and improved. Nowadays, with increased globalization, BPM techniques are used, for example, to optimize the way in which organizations react to business events, in order to enhance competitiveness. Starting from the underlying notion of workflow modeling, this paper introduces the basic concepts of modeling and implementing business processes using current information technologies and standards, such as Business Process Modeling Notation (BPMN) and Business Process Execution Language (BPEL). We also address the novel, yet growing, topic of Business Process Mining, and point out to open research challenges in the area. © 2013 Springer-Verlag., SCOPUS: cp.k, info:eu-repo/semantics/published

A typical problem in the field of moving object (MO) databases consists in discovering interesting trajectory patterns. To solve this problem, data mining techniques are commonly used. Due to the huge volume of these trajectory data, some form of compression facilitates the data processing. One of such compression techniques is based on the notion of stops and moves. In this approach, a set of places that are relevant to the application, denoted Places of Interest (POIs) is selected. If a moving object spends a pre-defined amount of time in a place of interest, this place is considered a stop for the object's trajectory. Thus, raw trajectories given by (O-{id}, t, x, y)-tuples can be replaced by a sequence of application-relevant stops. This leads to the concept of semantic trajectory, in short, a trajectory obtained by replacing raw trajectory data with a sequence of stops, and enriched with metadata of the POIs corresponding to such stops. We present a language based on regular expressions over constraints, denoted RE-SPaM, that can intensionally express sequential patterns. The constraints in RE-SPaM are defined as conjunctions of equalities over metadata of the POIs. In addition, we introduce a data mining algorithm, based on sequential pattern mining techniques, where uninteresting sequences are pruned in advance making use of the automaton that accepts a RE-SPaM expression. This makes the task of the analyst easier, and the mining algorithm more efficient. We also show that RE-SPaM can be extended to support spatial functions, thus integrating spatial data in a moving object setting (proposals so far only account for the MO trajectories themselves). We denote the resulting language RE-SPaM^{+S}. We show that the overhead of this extension is negligible, due to caching techniques that we explain in the paper. We close the paper with a case study over which we perform experiments to study the main variables that impact the performance of the mining algorithm. ©, SCOPUS: ar.j, info:eu-repo/semantics/published

The lack of an appropriate conceptual model for data warehouses and OLAP systems has led to the tendency to deploy logical models (for example, star, snowflake, and constellation schemas) for them as conceptual models. ER model extensions, UML extensions, special graphical user interfaces, and dashboards have been proposed as conceptual approaches. However, they introduce their own problems, are somehow complex and difficult to understand, and are not always user-friendly. They also require a high learning curve, and most of them address only structural design, not considering associated operations. Therefore, they are not really an improvement and, in the end, only represent a reflection of the logical model. The essential drawback of offering this system-centric view as a user concept is that knowledge workers are confronted with the full and overwhelming complexity of these systems as well as complicated and user-unfriendly query languages such as SQL OLAP and MDX. In this article, the authors propose a user-centric conceptual model for data warehouses and OLAP systems, called the Cube Algebra. It takes the cube metaphor literally and provides the knowledge worker with high-level cube objects and related concepts. A novel query language leverages well known high-level operations such as roll-up, drill-down, slice, and drill-across. As a result, the logical and physical levels are hidden from the unskilled end user. Copyright © 2013, IGI Global., SCOPUS: ar.j, info:eu-repo/semantics/published

Chapter 4, SCOPUS: ch.b, info:eu-repo/semantics/published

Traditional OLAP tools have proven to be successful in analyzing large sets of enterprise data. For today's business dynamics, sometimes these highly curated data is not enough. External data (particularly web data), may be useful to enhance local analysis. In this paper we discuss the extraction of multidimensional data from web sources, and their representation in RDFS. We introduce Open Cubes, an RDFS vocabulary for the specification and publication of multidimensional cubes on the Semantic Web, and show how classical OLAP operations can be implemented over Open Cubes using SPARQL 1.1, without the need of mapping the multidimensional information to the local database (the usual approach to multidimensional analysis of Semantic Web data). We show that our approach is plausible for the data sizes that can usually be retrieved to enhance local data repositories. © 2012 Springer-Verlag., SCOPUS: cp.k, info:eu-repo/semantics/published

On-Line Analytical Processing (OLAP) tools allow querying large multidimensional (MD) databases called data warehouses (DW). OLAP-style data analysis over the semantic web (SW) is gaining momentum, and thus SW technologies will be needed to model, manipulate, and share MD data. To achieve this, the definition of a vocabulary that adequately represents OLAP data is required. Unfortunately, so far, the proposals in this direction have followed different roads. On the one hand, the QB vocabulary (a proposal by the W3C Government Linked Data Working Group) follows a model initially devised for analyzing statistical data, but does not adequately support OLAP multidimensional data. Another recent proposal, the Open Cube vocabulary (OC) follows closely the classic MD models for OLAP and allows implementing OLAP operators as SPARQL queries, but does not provide a mechanism for reusing data already published using QB. In this work, we propose a new vocabulary, denoted QB4OLAP, which extends QB to fully support OLAP models and operators.We show how data already published in QB can be analyzed à la OLAP using the QB4OLAP vocabulary, and vice versa. To this end we provide algorithms that build the structures that allow performing both kinds of analysis, and show that compatibility between QB and QB4OLAP can be achieved at low cost, only adding dimensional information., SCOPUS: cp.p, info:eu-repo/semantics/published

The integration of Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP), denoted SOLAP, is aimed at exploring and analyzing spatial data. In real-world SOLAP applications, spatial and non-spatial data are subject to changes. In this paper we present a temporal query language for SOLAP, called TPiet-QL, supporting so-called discrete changes (for example, in land use or cadastral applications there are situations where parcels are merged or split). TPiet-QL allows expressing integrated GIS-OLAP queries in an scenario where spatial objects change across time. We also present a prototype implementation, and show how this application is used in a real-world scenario: the analysis of protected areas in Uruguay., SCOPUS: cp.p, info:eu-repo/semantics/published

Data Warehouses are a fundamental component of today's Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated research in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP. © 2012 Springer-Verlag., SCOPUS: cp.k, Info:eu-repo/semantics/published, In Marie-Aude Aufaure and Esteban Zimányi, editors, Proceedings of the 1st European Business In-telligence Summer School, eBISS 2011. Springer-Verlag, Paris, France

Mobility data are inherently uncertain due to several contributing factors related to different phases of their life cycle, from acquisition to interpretation. When data are processed, uncertainty propagates to intermediate and final results. Thus, it is important to be aware of uncertainty in trajectory data and explicitly account for it in their modeling and managing. For example, consider a simple scenario where people move around a city and disclose their positions twice an hour; to avoid stalking, the disclosed position is randomly selected from inside a circle with a radius of one kilometer, which contains the position of the user. Not being aware of uncertainty could lead to inconsistent conclusions. For instance, we could erroneously assume that a group of people have met or that someone has visited a privacy-sensitive place. On the contrary, taking uncertainty into account, we can avoid such erroneous conclusions; for example, if someone was farther than one kilometer from the place of an accident, we can certainly assume that this person was not involved in that accident. We next introduce a well-known taxonomy of uncertainty (see Bibliographic Notes section), aimed at clearly defining terms that are often given multiple meanings in the literature. A Taxonomy of Uncertainty The taxonomy we present here considers, at the highest abstraction level, that uncertainty in mobility and geographic information is caused by the complexity of the system conformed by three kinds of entities: human being, earth (i.e. geographic/moving), and computing machinery., SCOPUS: ch.b, info:eu-repo/semantics/published

Business Intelligence (BI) solutions require the design and implementation of complex processes (denoted ETL) that extract, transform, and load data from the sources to a common repository. New applications, like for example, real-time data warehousing, require agile and flexible tools that allow BI users to take timely decisions based on extremely up-to-date data. This calls for new ETL tools able to adapt to constant changes and quickly produce and modify executable code. A way to achieve this is to make ETL processes become aware of the business processes in the organization, in order to easily identify which data are required, and when and how to load them in the data warehouse. Therefore, we propose to model ETL processes using the standard representation mechanism denoted BPMN (Business Process Modeling and Notation). In this paper we present a BPMN-based metamodel for conceptual modeling of ETL processes. This metamodel is based on a classification of ETL objects resulting from a study of the most used commercial and open source ETL tools. © 2012 Springer-Verlag., SCOPUS: cp.k, info:eu-repo/semantics/published, In Alfredo Cuzzocrea and Umeshwar Dayal, editors, Proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2012. Springer-Verlag, Vienna, Austria

On-Line Analytical Processing (OLAP) dimensions are usually modelled as a hierarchical set of categories (the dimension schema), and dimension instances. The latter consist in a set of elements for each category, and relations between these elements (denoted rollup). To guarantee summarizability, a dimension is required to be strict, that is, every element of the dimension instance must have a unique ancestor in each of its ancestor categories. In practice, elements in a dimension instance are often reclassified, meaning that their rollups are changed (e.g. if the current available information is proved to be wrong). After this operation the dimension may become non-strict. To fix this problem, we propose to compute a set of minimal r-repairs for the new non-strict dimension. Each minimal r-repair is a strict dimension that keeps the result of the reclassification, and is obtained by performing a minimum number of insertions and deletions to the instance graph. We show that, although in the general case finding an r-repair is NP-complete, for real-world dimension schemas, computing such repairs can be done in polynomial time. We present algorithms for this, and discuss their computational complexity. © 2011 Springer-Verlag., SCOPUS: cp.k, info:eu-repo/semantics/published

Proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2010, info:eu-repo/semantics/published

ACM GIS 2009, info:eu-repo/semantics/published

SCOPUS: cp.k, info:eu-repo/semantics/published