Spiros Athanasiou

Towards GeoSpatial semantic data management: strengths, weaknesses, and challenges ahead

An immense wealth of data is already accessible through the Semantic Web and an increasing part of it also has geospatial context or relevance. Although existing technology is mature enough to integrate a variety of information from heterogeneous sources into interlinked features, it still falls behind when it comes to representation and reasoning on spatial characteristics. It is only lately that several RDF stores have begun to accommodate geospatial entities and to enable some kind of processing on them. To address interoperability, the OGC has recently adopted the GeoSPARQL standard, which defines a vocabulary for representing geometric types in RDF and an extension to the SPARQL language for formulating queries. In this paper, we provide a comprehensive review of the current state-of-the-art in geospatially-enabled semantic data management. Apart from an insightful analysis of the available architectures in industry and academia, we conduct an evaluation study on prominent RDF stores with geospatial support. We also compare their performance and attested capabilities to renowned DBMSs widely used in geospatial applications. We introduce a methodology suitable to assess RDF stores for robustness against large geospatial datasets, and also for expressiveness on a variety of queries involving both spatial and thematic criteria. As our findings demonstrate, the potential for query optimization, advanced indexing schemes, and spatio-semantic extensions is significant. Towards this goal, we point out several challenging issues for joint research by the GIS and Semantic Web communities.

Exposing INSPIRE on the Semantic Web

The INSPIRE Directive by the European Commission sets the legal and technical foundations towards interoperable Spatial Data Infrastructures (SDIs) across Europe. EU member states are already providing such services for several geospatial data themes (e.g.,?transportation networks, administrative units). Unfortunately, the INSPIRE ecosystem has been largely disjoint from the Semantic Web, without any means to repurpose existing SDIs as high-quality data sources, and thus multiply their value through interlinking, reasoning and inferencing. In this paper, we introduce a methodology that can assist stakeholders in exposing INSPIRE-aligned SDIs on the Semantic Web according to the recent GeoSPARQL standard. We develop methods for discovering INSPIRE data through a virtual SPARQL endpoint over existing INSPIRE catalogue services. Further, we implement a suite of tools for automatically transforming INSPIRE data and metadata into RDF triples with geometries. The compiled geographic and thematic information can then be loaded into semantic repositories for querying or interlinked with other data. Our open-source solutions essentially repurpose existing INSPIRE SDIs, so as to promote uptake and facilitate their reuse in practice. Finally, as a case study, we report our experience in validating this approach on a real-world SDI with publicly available data for Greece in order to expose its contents through (Geo)SPARQL endpoints.

OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap

In this demonstration, we present OSMRec, a command line utility and JOSM plugin for automatic recommendation of tags (categories) on newly created spatial entities in OpenStreetMap (OSM). JOSM allows downloading parts of OSM, editing the map (e.g. inserting, deleting, annotating with tags spatial entities) and re-uploading the updated part back on OSM. OSMRec plugin exploits already annotated entities within OSM to train category classification models and utilizes these models in order to recommend OSM categories for newly inserted spatial entities in OSM.

A MapReduce based k-NN joins probabilistic classifier

Water management field has concentrated great interest, with the potential to affect the long term well-being, the societal economy and security. In parallel, it imposes specific research challenges which have not been already met, due to the lack of fine-grained data. Knowledge extraction and decision making for efficient management in the energy field has attracted a lot of interest in Big Data research. However, the water domain is strikingly absent, with minimal focused work on data exploitation and useful information extraction. The goal of this work is to discover persistent and meaningful knowledge from water consumption data and provide efficient and scalable big data management and analysis services. We propose a novel methodology which exploits machine learning techniques and introduces a robust probabilistic classifier which is able to operate on data of arbitrary dimensionality and of huge volume. It also provides added value services and new operation models for the water management domain, inducing sustainable behavioural changes for consumers, which can further raise social awareness. It does so through a new k-Nearest Neighbour based algorithm, developed in a parallel and distributed environment, which operates over Big Data and discovers useful knowledge about consumption classes and other water related attitudinal properties. A detailed experimental evaluation assesses the effectiveness and efficiency of the algorithm on prediction precision along with the provision of analytics. The results show that this method is prosperous and provides accurate and interesting results that allow us to identify useful characteristics, not only for the households, but also for the water utilities.

FAGI-gis: A Tool for Fusing Geospatial RDF Data

In this demonstration, we present FAGI-gis, a tool for fusing geospatial RDF data. FAGI-gis is the core component of the FAGI framework, which handles all the steps of the fusion process of two interlinked RDF datasets in order to produce an integrated, aligned and richer dataset that combines data and metadata from both initial datasets. In the demonstation, we showcase how a user can use FAGI-giss map based UI to perform several fusion actions on linked geospatial entities, considering both spatial and non-spatial properties of them.

FAGI-tr: A Tool for Aligning Geospatial RDF Vocabularies

In this paper, we present FAGI-tr, a tool for aligning RDF vocabularies with respect to their geospatial aspect. The tool provides a framework for (a) loading a source and a target geospatial RDF dataset, (b) identifying vocabularies for representing geospatial RDF data, (c) selecting, from both datasets, the representations to be considered for processing, (d) selecting a target vocabulary and transforming all geospatial triples from both datasets into the respective format and (e) outputting the two datasets for further processing. The outcome of the process is datasets that follow exactly the same vocabulary and, also, are cleansed from possible duplicate triples containing geospatial metadata, which is the case when an RDF dataset adopts more than one vocabularies to describe spatial data. The tool is tested with DBpedia data and performs rather efficiently.

FAGI: A Framework for Fusing Geospatial RDF Data

In this paper, we present FAGI, a framework for fusing geospatial RDF data. Starting from two interlinked datasets, FAGI handles all the steps of the fusion process, producing an integrated, richer dataset that combines entities and attributes from both initial ones. In contrast to existing approaches and tools, which deal either with RDF fusion or with spatial conflation, FAGI specifically addresses the fusion of geospatial RDF data. We describe the main components of the framework and their functionalities, which include aligning dataset vocabularies, processing geospatial features, applying -manually or automatically- fusion strategies, and recommending link creation or rejection between RDF entities, with emphasis on their geospatial properties.

Unsupervised Disaggregation of Low Granularity Resource Consumption Time Series

Resource consumption is typically monitored at a single point that aggregates all activities of the household in one time series. A key task in resource demand management is disaggregation; an operation that decomposes such a composite time series in the consumption parts that comprise it, thus, extracting detailed information about how and when resources were consumed. Current state-of-the-art disaggregation methods have two drawbacks: (a) they mostly work for frequently sampled time series and (b) they require supervision (that comes in terms of labelled data). In practice, though, sampling is not frequent and labelled data are often not available. With this problem in mind, in this paper, we present a method designed for unsupervised disaggregation of consumption time series of low granularity. Our method utilizes a stochastic model of resource consumption along with empirical findings on consumption types (e.g., average volume) to perform disaggregation. Experiments with real world resource consumption data demonstrate up to 85% Recall in identifying different consumption types.

FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins

Efficient management and analysis of large volumes of data is a demanding task of increasing scientific and industrial importance, as the ubiquitous generation of information governs more and more aspects of human life. In this article, we introduce FML-kNN, a novel distributed processing framework for Big Data that performs probabilistic classification and regression, implemented in Apache Flink. The framework’s core is consisted of a k-nearest neighbor joins algorithm which, contrary to similar approaches, is executed in a single distributed session and is able to operate on very large volumes of data of variable granularity and dimensionality. We assess FML-kNN’s performance and scalability in a detailed experimental evaluation, in which it is compared to similar methods implemented in Apache Hadoop, Spark, and Flink distributed processing engines. The results indicate an overall superiority of our framework in all the performed comparisons. Further, we apply FML-kNN in two motivating uses cases for water demand management, against real-world domestic water consumption data. In particular, we focus on forecasting water consumption using 1-h smart meter data, and extracting consumer characteristics from water use data in the shower. We further discuss on the obtained results, demonstrating the framework’s potential in useful knowledge extraction.

Indexing Geolocated Time Series Data

Time series associated with specific locations, such as visitor check-ins or sensor readings, have increased in size and popularity in several domains. Although several works have focused on efficient time series similarity search, there has been limited attention to the inherent challenge that geolocated time series introduce for hybrid queries on both spatial proximity and time series similarity. To efficiently process such queries, we propose a hybrid index, called TSR-tree, which extends the R-tree by introducing appropriate bounds for the time series indexed at each node. This reduces node accesses during query evaluation by simultaneously pruning the search space in the spatial domain and the time series domain while traversing the index. We also present an optimized version, the BTSR-tree, which uses tighter bounds by bundling together similar time series in each node. We describe how these indices can be used to efficiently evaluate different variants of hybrid queries combining spatial and time series filtering or ranking. Finally, we experimentally evaluate our work using real-world datasets from diverse domains, demonstrating a speed-up of 1.5 to 5 times in hybrid query workloads against the baseline R-tree method.