Examinando por Autor "Kuijpers, Bart"
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Ponencia en Congreso Aggregation languages for moving object and places of interest(2008) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel"We address aggregate queries over GIS data and moving object data, where non-spatial information is stored in a data warehouse. We propose a formal data model and query language to express complex aggregate queries. Next, we study the compression of trajectory data, produced by moving objects, using the notions of stops and moves. We show that stops and moves are expressible in our query language and we consider a fragment of this language, consisting of regular expressions to talk about temporally ordered sequences of stops and moves. This fragment can be used not only for querying, but also for expressing data mining and pattern matching tasks over trajectory data."Artículo de Publicación Periódica An algebra for OLAP(2017) Kuijpers, Bart; Vaisman, Alejandro Ariel"Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube, where each cell contains one or more measures can be aggregated along dimensions. Despite the extensive corpus of work in the field, a standard language for OLAP is still needed, since there is no well-defined, accepted semantics, for many of the usual OLAP operations. In this paper, we address this problem, and present a set of operations for manipulating a data cube. We clearly define the semantics of these operations, and prove that they can be composed, yielding a language powerful enough to express complex OLAP queries. We express these operations as a sequence of atomic transformations over a fixed multidimensional matrix, whose cells contain a sequence of measures. Each atomic transformation produces a new measure. When a sequence of transformations defines an OLAP operation, a flag is produced indicating which cells must be considered as input for the next operation. In this way, an elegant algebra is defined. Our main contribution, with respect to other similar efforts in the field is that, for the first time, a formal proof of the correctness of the operations is given, thus providing a clear semantics for them. We believe the present work will serve as a basis to build more solid practical tools for data analysis."Artículo de Publicación Periódica Analysing river systems with time series data using path queries in graph databases(2023) Bollen, Erik; Hendrix, Rik; Kuijpers, Bart; Soliani, Valeria; Vaisman, AlejandroTransportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to represent transportation networks, since these networks can change over time, a temporal graph data model is required to keep track of these changes. In this model, time-series data are represented as properties of nodes in the network, and nodes and edges are timestamped with their validity intervals. In this paper, we show that transportation networks can be represented and queried using temporal graph databases and temporal graph query languages. Many interesting situations can be captured by the temporal paths supported by this model. To achieve the above, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph, redefine temporal paths and study and implement new kinds of paths, namely Flow paths and Backwards Flow paths. Further, we analyze a real-world case, using a portion of the Yser river in the Flanders’ river system in Belgium, where some nodes are equipped with sensors while other ones are not. We model this river as a temporal graph, implement it using real data provided by the sensors, and discover interesting temporal paths based on the electric conductivity parameter, that can be used in a decision support environment, by experts for analyzing water quality across time.Artículo de Publicación Periódica Analytical queries on semantic trajectories using graph databases(2019-10) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel"This article studies the analysis of moving object data collected by location-aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so-called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non-spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real-world public data case study, the article shows that trajectory queries are expressed more naturally on the graph-based representation than over the relational alternative, and perform better in many typical cases."Artículo de Publicación Periódica A data model and query language for spatio-temporal decision support(2010) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel"In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems(GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of intro ducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote Lt, able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet."Ponencia en Congreso Indexing continuous paths in temporal graphs(2022) Kuijpers, Bart; Ribas, Ignacio; Soliani, Valeria; Vaisman, Alejandro Ariel"Temporal property graph databases track the evolution over time of nodes, properties, and edges in graphs. Computing temporal paths in these graphs is hard. In this paper we focus on indexing Continuous Paths, defined as paths that exist continuously during a certain time interval. We propose an index structure called TGIndex where index nodes are defined as nodes in the graph database. Two different indexing strategies are studied. We show how the index is used for querying and also present different search strategies, that are compared and analyzed using a large synthetic graph."Ponencia en Congreso Modeling and querying sensor networks using temporal graph databases(2022) Kuijpers, Bart; Soliani, Valeria; Vaisman, Alejandro Ariel"Transportation networks (e.g., river systems or road net works) equipped with sensors that collect data for several different pur poses can be naturally modeled using graph databases. However, since networks can change over time, to represent these changes appropriately, a temporal graph data model is required. In this paper, we show that sensor-equipped transportation networks can be represented and queried using temporal graph databases and query languages. For this, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph. We redefine temporal paths and study and implement a new kind of path, called Flow path. We take the Flanders’ river system as a use case."Artículo de Publicación Periódica Online analytical processsing on graph data(2020) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel"Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a “Big Data” scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multihypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive."Ponencia en Congreso Performing OLAP over graph data: query language, implementation, and a case study(2017-08) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel"In current Big Data scenarios, traditional data warehousing and Online Analytical Processing (OLAP) operations on cubes are clearly not sufficient to address the current data analysis requirements. Nevertheless, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model. In previous work we proposed a multidimensional (MD) data model for graph analysis, that considers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node- and edge-labelled directed multi-hypergraphs, called graphoids, defined at several different levels of granularity. In this paper we show how we implemented this proposal over the widely used Neo4J graph database, discuss implementation issues, and present a detailed case study to show how OLAP operations can be used on graphs."Artículo de Publicación Periódica Piet: a GIS-OLAP implementation(2007) Vaisman, Alejandro Ariel; Gómez, Leticia Irene; Kuijpers, Bart; Escribano, Ariel"Data aggregation in Geographic Information Systems (GIS) is a desirable feature, although only marginally present in commercial systems, which also fail to provide integration between GIS and OLAP (On Line Analytical Processing). With this in mind, we have developed Piet, a system that makes use of a novel query processing technique: first, a process called sub-polygonization decomposes each thematic layer in a GIS, into open convex polygons; then, another process computes and stores in a database the overlay of those layers for later use by a query processor. We describe the implementation of Piet, and provide experimental evidence that overlay precomputation can outperform GIS systems that employ indexing schemes based on R-trees."Artículo de Publicación Periódica Time-series-based queries on stable transportation networks equipped with sensors(2021) Bollen, Erik; Hendrix, Rik; Kuijpers, Bart; Vaisman, Alejandro Ariel"In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and evaluate its working on a fragment of the Flemish river system that is equipped with sensors that measure the water height at regular moments in time."Artículo de Publicación Periódica Towards the Internet of water: Using graph databases for hydrological analysis on the Flemish river system(2021-07) Bollen, Erik; Hendrix, Rik; Kuijpers, Bart; Vaisman, Alejandro Ariel"The “Internet of Water” project will deploy 2,500 sensors along the Flemish river system, in Belgium. These sensors will be part of a monitoring system. This will produce anenormous amount of data, on which prediction and analysis tasks can be performed. To represent, store, and query river data, relational databases are normally used. However, this choice introduces an “impedance mismatch” between the conceptual representation (typically a graph) and the storage model (relational tables). To solve this problem, this article proposes to use graph databases. The Flemish river system is presented as a use case and the Neo4j graph database and its high-level query language, Cypher, are used for storing and querying the data, respectively. A relational alternative is implemented over the PostgreSQL database. A collection of representative queries of interest for hydrologists is defined over both database implementations."