EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

D*R-Tree: un método eficiente para responder consultas espacio-temporales

Existen aplicaciones que requieren manejar objetos espacio-temporales, es decir, objetos cuya posición espacial o forma cambia en distintos instantes de tiempo. Para administrar la información referida a tales cambios se requieren de métodos de acceso espacio-temporal, que permitan además procesar en forma eficiente consultas de tipo espacio-temporal. En general, TimeSlice, Intervalo, Eventos y k son los tipos de consultas para las que podemos encontrar una variedad de métodos, los cuales intentan optimizar el desempeño de las consultas, pero por separado, apuntando a un subconjunto de las antes mencionadas. En este artículo presentamos un método de acceso espacio-temporal, llamado D*R-Tree, que resuelve eficientemente los cuatro tipos de consultas mencionados anteriormente, y mostramos su buen desempeño a través de las evaluaciones experimentales realizadas.Spatio-temporal databases deal with objects that change their location and/or shape over time. Numerous researches have been done in developing spatio-temporal access methods as auxiliary structures to support spatiotemporal queries. The main interest of most applications is to efficiently store and query the positions of these objects. We can find a related rich literature on the subject about the methods for supporting a subset of the following TimeSlice, Events, Interval and Trajectory queries. In this paper we propose a new index structure, the D*R-Tree to efficiently store and retrieve spatio-temporal objects. The main objective of this work is to show a suitable method for supporting all mentioned queries types, with an optimal performance. We propose the index in terms of the basic algorithms for querying. We test our proposal in an extense experimental evaluation with generated data sets. In our tests, the D*R-Tree showed good scalability when increasing the number of objects and time units in the data sets, as well as in query processing, compared with a similar structure.III Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

D*R-Tree: un método eficiente para responder consultas espacio-temporales

Existen aplicaciones que requieren manejar objetos espacio-temporales, es decir, objetos cuya posición espacial o forma cambia en distintos instantes de tiempo. Para administrar la información referida a tales cambios se requieren de métodos de acceso espacio-temporal, que permitan además procesar en forma eficiente consultas de tipo espacio-temporal. En general, TimeSlice, Intervalo, Eventos y k son los tipos de consultas para las que podemos encontrar una variedad de métodos, los cuales intentan optimizar el desempeño de las consultas, pero por separado, apuntando a un subconjunto de las antes mencionadas. En este artículo presentamos un método de acceso espacio-temporal, llamado D*R-Tree, que resuelve eficientemente los cuatro tipos de consultas mencionados anteriormente, y mostramos su buen desempeño a través de las evaluaciones experimentales realizadas.Spatio-temporal databases deal with objects that change their location and/or shape over time. Numerous researches have been done in developing spatio-temporal access methods as auxiliary structures to support spatiotemporal queries. The main interest of most applications is to efficiently store and query the positions of these objects. We can find a related rich literature on the subject about the methods for supporting a subset of the following TimeSlice, Events, Interval and Trajectory queries. In this paper we propose a new index structure, the D*R-Tree to efficiently store and retrieve spatio-temporal objects. The main objective of this work is to show a suitable method for supporting all mentioned queries types, with an optimal performance. We propose the index in terms of the basic algorithms for querying. We test our proposal in an extense experimental evaluation with generated data sets. In our tests, the D*R-Tree showed good scalability when increasing the number of objects and time units in the data sets, as well as in query processing, compared with a similar structure.III Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

GraCT: A Grammar based Compressed representation of Trajectories

We present a compressed data structure to store free trajectories of moving objects (ships over the sea, for example) allowing spatio-temporal queries. Our method, GraCT, uses a $k^2$-tree to store the absolute positions of all objects at regular time intervals (snapshots), whereas the positions between snapshots are represented as logs of relative movements compressed with Re-Pair. Our experimental evaluation shows important savings in space and time with respect to a fair baseline.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094

Towards a compact representation of temporal rasters

Big research efforts have been devoted to efficiently manage spatio-temporal data. However, most works focused on vectorial data, and much less, on raster data. This work presents a new representation for raster data that evolve along time named Temporal k^2 raster. It faces the two main issues that arise when dealing with spatio-temporal data: the space consumption and the query response times. It extends a compact data structure for raster data in order to manage time and thus, it is possible to query it directly in compressed form, instead of the classical approach that requires a complete decompression before any manipulation. In addition, in the same compressed space, the new data structure includes two indexes: a spatial index and an index on the values of the cells, thus becoming a self-index for raster data.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941. Published in SPIRE 201

CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling

In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining a high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To enhance the compression, we incorporate the sparsity of road networks into the data structure. In particular, we present the novel concepts of relative movement labeling and PseudoRank, each contributing to significant reductions in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants