Finding and sharing GIS methods based on the questions they answer

Geographic information has become central for data scientists of many disciplines to put their analyses into a spatio-temporal perspective. However, just as the volume and variety of data sources on the Web grow, it becomes increasingly harder for analysts to be familiar with all the available geospatial tools, including toolboxes in Geographic Information Systems (GIS), R packages, and Python modules. Even though the semantics of the questions answered by these tools can be broadly shared, tools and data sources are still divided by syntax and platform-specific technicalities. It would, therefore, be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them. In this article, we systematically investigate the analytic questions that lie behind a range of common GIS tools, and we propose a semantic framework to match analytic questions and tools that are capable of answering them. To support the matching process, we define a tractable subset of SPARQL, the query language of the Semantic Web, and we propose and test an algorithm for computing query containment. We illustrate the identification of tools to answer user questions on a set of common user requests

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A semantic web rule language for geospatial domains

Retrieval of geographically-referenced information on the Internet is now a common activity. The web is increasingly being seen as a medium for the storage and exchange of geographic data sets in the form of maps. The geospatial-semantic web (GeoWeb) is being developed to address the need for access to current and accurate geo-information. The potential applications of the GeoWeb are numerous, ranging from specialised application domains for storing and analysing geo-information to more common applications by casual users for querying and visualising geo-data, e.g. finding locations of services, descriptions of routes, etc. Ontologies are at the heart of W3C's semantic web initiative to provide the necessary machine understanding to the sheer volumes of information contained on the internet. For the GeoWeb to succeed the development of ontologies for the geographic domain are crucial. Semantic web technologies to represent ontologies have been developed and standardised. OWL, the Web Ontology Language, is the most expressive of these enabling a rich form of reasoning, thanks to its formal description logic underpinnings. Building geo-ontologies involves a continuous process of update to the originally modelled data to reflect change over time as well as to allow for ontology expansion by integrating new data sets, possibly from different sources. One of the main challenges in this process is finding means of ensuring the integrity of the geo-ontology and maintaining its consistency upon further evolution. Representing and reasoning with geographic ontologies in OWL is limited. Firstly, OWL is not an integrity checking language due to it's non-unique name and open world assumptions. Secondly, it can not represent spatial datatypes, can not compute information using spatial operators and does not have any form of spatial index. Finally, OWL does not support complex property composition needed to represent qualitative spatial reasoning over spatial concepts. To address OWL's representational inefficiencies, new ontology languages have been proposed based on the intersection or union of OWL (in particular the DL family corresponding to OWL) with logic programs (rule languages). In this work, a new Semantic Web Spatial Rule Language (SWSRL) is proposed, based on the syntactic core of the Description Logic Programs paradigm (DLP), and the semantics of a Logic Program. The language is built to support the expression of geospatial ontological axioms and geospatial integrity and deduction rules. A hybrid framework to integrate both qualitative symbolic information in SWSRL with quantitative, geometric information using spatial datatypes in a spatial database is proposed. Two notable features of SWSRL are 1) the language is based on a prioritised de fault logic that allows the expression of default integrity rules and their exceptions and 2) the implementation of the language uses an interleaved mode of inference for on the fly computation (either qualitative or quantitative) deduction of spatial relations. SWSRL supports an OGC complaint spatial syntax, and a standardised definition of rule meta data. Both features aid the construction, description, identification and categorisation of designed and implemented rules within large rule sets. The language and the developed engine are evaluated using synthetic as well as real data sets in the context of developing geographic ontologies for geographic information retrieval on the Semantic Web. Empirical experiments are also presented to test the scalability and applicability of the developed framework

Algebra of core concept transformations: Procedural meta-data for geographic information

Transformations are essential for dealing with geographic information. They are involved not only in converting between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relations, and a set of basic transformations described in terms of function signatures that use such types. Type inference allows us to enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions. This allows us to automatically infer goal concepts across workflows and to query over such concepts across raster and vector implementations. We evaluate the algebra over a set of expert GIS workflows taken from online tutorials

Formalising cartographic generalisation knowledge in an ontology to support on-demand mapping

This thesis proposes that on-demand mapping - where the user can choose the geographic features to map and the scale at which to map them - can be supported by formalising, and making explicit, cartographic generalisation knowledge in an ontology. The aim was to capture the semantics of generalisation, in the form of declarative knowledge, in an ontology so that it could be used by an on-demand mapping system to make decisions about what generalisation algorithms are required to resolve a given map condition, such as feature congestion, caused by a change in scale. The lack of a suitable methodology for designing an application ontology was identified and remedied by the development of a new methodology that was a hybrid of existing domain ontology design methodologies. Using this methodology an ontology that described not only the geographic features but also the concepts of generalisation such as geometric conditions, operators and algorithms was built. A key part of the evaluation phase of the methodology was the implementation of the ontology in a prototype on-demand mapping system. The prototype system was used successfully to map road accidents and the underlying road network at three different scales. A major barrier to on-demand mapping is the need to automatically provide parameter values for generalisation algorithms. A set of measure algorithms were developed to identify the geometric conditions in the features, caused by a change in scale. From this a Degree of Generalisation (DoG) is calculated, which represents the “amount” of generalisation required. The DoG is used as an input to a number of bespoke generalisation algorithms. In particular a road network pruning algorithm was developed that respected the relationship between accidents and road segments. The development of bespoke algorithms is not a sustainable solution and a method for employing the DoG concept with existing generalisation algorithms is required. Consideration was given to how the ontology-driven prototype on-demand mapping system could be extended to use cases other than mapping road accidents and a need for collaboration with domain experts on an ontology for generalisation was identified. Although further testing using different uses cases is required, this work has demonstrated that an ontological approach to on-demand mapping has promise

Synthesis of Scientific Workflows: Theory and Practice of an Instance-Aware Approach

The last two decades brought an explosion of computational tools and processes in many scientific domains (e.g., life-, social- and geo-science). Scientific workflows, i.e., computational pipelines, accompanied by workflow management systems, were soon adopted as a de-facto standard among non-computer scientists for orchestrating such computational processes. The goal of this dissertation is to provide a framework that would automate the orchestration of such computational pipelines in practice. We refer to such problems as scientific workflow synthesis problems. This dissertation introduces the temporal logic SLTLx, and presents a novel SLTLx-based synthesis approach that overcomes limitations in handling data object dependencies present in existing synthesis approaches. The new approach uses transducers and temporal goals, which keep track of the data objects in the synthesised workflow. The proposed SLTLx-based synthesis includes a bounded and a dynamic variant, which are shown in Chapter 3 to be NP-complete and PSPACE-complete, respectively. Chapter 4 introduces a transformation algorithm that translates the bounded SLTLx-based synthesis problem into propositional logic. The transformation is implemented as part of the APE (Automated Pipeline Explorer) framework, presented in Chapter 5. It relies on highly efficient SAT solving techniques, using an off-the-shelf SAT solver to synthesise a solution for the given propositional encoding. The framework provides an API (application programming interface), a CLI (command line interface), and a web-based GUI (graphical user interface). The development of APE was accompanied by four concrete application scenarios as case studies for automated workflow composition. The studies were conducted in collaboration with domain experts and presented in Chapter 6. Each of the case studies is used to assess and illustrate specific features of the SLTLx-based synthesis approach. (1) A case study on cartographic map generation demonstrates the ability to distinguish data objects as a key feature of the framework. It illustrates the process of annotating a new domain, and presents the iterative workflow synthesis approach, where the user tries to narrow down the desired specification of the problem in a few intuitive steps. (2) A case study on geo-analytical question answering as part of the QuAnGIS project shows the benefits of using data flow dependencies to describe a synthesis problem. (3) A proteomics case study demonstrates the usability of APE as an “off-the-shelf” synthesiser, providing direct integration with existing semantic domain annotations. In addition, a manual evaluation of the synthesised results shows promising results even on large real-life domains, such as the EDAM ontology and the complete bio.tools registry. (4) A geo-event question-answering study demonstrates the usability of APE within a larger question-answering system. This dissertation answers the goals it sets to solve. It provides a formal framework, accompanied by a lightweight library, which can solve real-life scientific workflow synthesis problems. Finally, the development of the library motivated an upcoming collaborative project in the life sciences domain. The aim of the project is to develop a platform which would automatically compose (using APE) and benchmark workflows in computational proteomics

Data Driven Adaptation of Heterogeneous Service-Oriented Processes

Η με βάση τα δεδομένα προσαρμογή διαδικασιών αποτελεί μια επέκταση της έννοιας των Δυναμικών και με βάση τα Δεδομένα Καθοδηγουμενων Συστήματων (DDDAS) όπως αυτά έχουν καθοριστεί από την Δαρεμά. Συγεκριμένα όπως και στα DDDAS συστήματα η προσέγγιση μας επιτρέπει την προσφορά προσαρμοζόμενων διαδικασιών χρησιμοποιώντας διαθέσιμες πληροφορίες και υπηρεσίες. H προσφορά προσαρμοζόμενων διαδικασιών περιλαμβάνει την αναγνώριση και χρήση πιθανών εναλλακτικών μονοπατιών εκτέλεσης (ή διαδρομών) για την επίτευξη των στόχων και υπό-στόχων της κάθε διαδικασίας. Τα εναλλακτικά μονοπάτια λαμβάνουν υπόψη και χρησιμοποιούν σχετικές πληροφορίες ή/και υπηρεσίες (ή συνθέσεις υπηρεσιών). Για την αναζήτηση των πιθανών εναλλακτικών χρησιμοποιούνται τεχνικές από το χώρο της Τεχνητής Νοημοσύνης Σχεδιασμού (AI Planning) και της υπολογιστικής Πλαισίου (Context-Aware computing) κατά τον χρόνο διάθεσης της διαδικασίας. Κατά τον υπολογισμό των πιθανών εναλλακτικών, στόχος της προσέγγισης μας είναι η μείωση των βημάτων εκτέλεσης, δλδ του πλήθους των εργασιών της διαδικασίας που έχουν οριστείIn principle the Data-Driven Process Adaptation (DDPA) approach is based on the concept of Dynamic Data Driven Application Systems (DDDAS) as this is stated by Darema in [8]. In accordance to the DDDAS notion such systems support the utilization of appropriate information at specific decision points so as to make real systems more efficient. In this regard, DDPA accommodates the provision of adaptable service processes by exploiting the use of information available to the process environment in addition to existing services. Adaptation in the context of our approach includes the identification and use of possible alternatives for the achievement of the goals and sub-goals defined in a process; alternatives include the utilization of available related information and/or services (or service chains). Data-Driven adaptation incorporates AI planning and Context-Aware Computing techniques to support the identification of possible alternatives at deployment time. When calculating the possible alternatives the goal of our approach is to reduce the number of steps, i.e. number of process tasks, defined in the original process

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Functional description of geoprocessing services as conjunctive datalog queries

Discovery of suitable web services is a crucial task in Spatial Data Infrastructures (SDI). In this work, we develop a novel approach to the discovery of geoprocessing services (WPS). Discovery requests and Web Processing Services are annotated as conjunctive queries in a logic programming (LP) language and the discovery process is based on Logic Programming query containment checking between these descriptions. Besides the types of input and output, we explicitly formalise the relation between them and hence are able to capture the functionality of a WPS more precisely. The use of Logic Programming query containment allows for effective reasoning during discovery. Furthermore, the relative simplicity of the semantic descriptions is advantageous for their creation by non-logics experts. The developed approach is applicable in the Web Service Modeling Framework (WSMF), a state-of-the-art semantic web service framework