Querying heterogeneous data in an in-situ unified agile system

Data integration provides a unified view of data by combining different data sources. In today’s multi-disciplinary and collaborative research environments, data is often produced and consumed by various means, multiple researchers operate on the data in different divisions to satisfy various research requirements, and using different query processors and analysis tools. This makes data integration a crucial component of any successful data intensive research activity. The fundamental difficulty is that data is heterogeneous not only in syntax, structure, and semantics, but also in the way it is accessed and queried. We introduce QUIS (QUery In-Situ), an agile query system equipped with a unified query language and a federated execution engine. It is capable of running queries on heterogeneous data sources in an in-situ manner. Its language provides advanced features such as virtual schemas, heterogeneous joins, and polymorphic result set representation. QUIS utilizes a federation of agents to transform a given input query written in its language to a (set of) computation models that are executable on the designated data sources. Federative query virtualization has the disadvantage that some aspects of a query may not be supported by the designated data sources. QUIS ensures that input queries are always fully satisfied. Therefore, if the target data sources do not fulfill all of the query requirements, QUIS detects the features that are lacking and complements them in a transparent manner. QUIS provides union and join capabilities over an unbound list of heterogeneous data sources; in addition, it offers solutions for heterogeneous query planning and optimization. In brief, QUIS is intended to mitigate data access heterogeneity through query virtualization, on-the-fly transformation, and federated execution. It offers in-Situ querying, agile querying, heterogeneous data source querying, unifeied execution, late-bound virtual schemas, and Remote execution

Multi-sensory Integration for a Digital Earth Nervous System

The amount of geospatial data is increasing, but interoperability issues hinder integrated discovery, view and analysis. This paper suggests an illustrative and extensible solution to some of the underlying challenges, by extending a previously suggested Digital Earth Nervous System with multi-sensory integration capacities. In doing so, it proposes the combination of multiple ways of sensing our environment with a memory for storing relevant data sets and integration methods for extracting valuable information out of the rich inputs. Potential building blocks for the implementation of such an advanced nervous system are sketched and briefly analysed. The paper stimulates more detailed considerations by concluding with challenges for future research and requesting a multidisciplinary development approach – including computer sciences, environmental sciences, cognitive and neurosciences, as well as engineering.JRC.H.6-Digital Earth and Reference Dat

Multi-sensory Integration for a digital earth nervous system

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The amount of geospatial data is increasing, but interoperability issues hinder integrated discovery, view and analysis. This paper suggests an illustrative and extensible solution to some of the underlying challenges, by extending a previously suggested Digital Earth Nervous System with multi-sensory integration capacities. In doing so, it proposes the combination of multiple ways of sensing our environment with a memory for storing relevant data sets and integration methods for extracting valuable information out of the rich inputs. Potential building blocks for the implementation of such an advanced nervous system are sketched and briefly analysed. The paper stimulates more detailed considerations by concluding with challenges for future research and requesting a multidisciplinary development approach – including computer sciences, environmental sciences, cognitive and neurosciences, as well as engineering

New Generation Sensor Web Enablement

Many sensor networks have been deployed to monitor Earth’s environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Knowledge hypergraph based-approach for multi-source data integration and querying : Application for Earth Observation domain

Early warning against natural disasters to save lives and decrease damages has drawn increasing interest to develop systems that observe, monitor, and assess the changes in the environment. Over the last years, numerous environmental monitoring systems and Earth Observation (EO) programs were implemented. Nevertheless, these systems generate a large amount of EO data while using different vocabularies and different conceptual schemas. Accordingly, data resides in many siloed systems and are mainly untapped for integrated operations, insights, and decision making situations. To overcome the insufficient exploitation of EO data, a data integration system is crucial to break down data silos and create a common information space where data will be semantically linked. Within this context, we propose a semantic data integration and querying approach, which aims to semantically integrate EO data and provide an enhanced query processing in terms of accuracy, completeness, and semantic richness of response. . To do so, we defined three main objectives. The first objective is to capture the knowledge of the environmental monitoring domain. To do so, we propose MEMOn, a domain ontology that provides a common vocabulary of the environmental monitoring domain in order to support the semantic interoperability of heterogeneous EO data. While creating MEMOn, we adopted a development methodology, including three fundamental principles. First, we used a modularization approach. The idea is to create separate modules, one for each context of the environment domain in order to ensure the clarity of the global ontology’s structure and guarantee the reusability of each module separately. Second, we used the upper-level ontology Basic Formal Ontology and the mid-level ontologies, the Common Core ontologies, to facilitate the integration of the ontological modules in order to build the global one. Third, we reused existing domain ontologies such as ENVO and SSN, to avoid creating the ontology from scratch, and this can improve its quality since the reused components have already been evaluated. MEMOn is then evaluated using real use case studies, according to the Sahara and Sahel Observatory experts’ requirements. The second objective of this work is to break down the data silos and provide a common environmental information space. Accordingly, we propose a knowledge hypergraphbased data integration approach to provide experts and software agents with a virtual integrated and linked view of data. This approach generates RML mappings between the developed ontology and metadata and then creates a knowledge hypergraph that semantically links these mappings to identify more complex relationships across data sources. One of the strengths of the proposed approach is it goes beyond the process of combining data retrieved from multiple and independent sources and allows the virtual data integration in a highly semantic and expressive way, using hypergraphs. The third objective of this thesis concerns the enhancement of query processing in terms of accuracy, completeness, and semantic richness of response in order to adapt the returned results and make them more relevant and richer in terms of relationships. Accordingly, we propose a knowledge-hypergraph based query processing that improves the selection of sources contributing to the final result of an input query. Indeed, the proposed approach moves beyond the discovery of simple one-to-one equivalence matches and relies on the identification of more complex relationships across data sources by referring to the knowledge hypergraph. This enhancement significantly showcases the increasing of answer completeness and semantic richness. The proposed approach was implemented in an open-source tool and has proved its effectiveness through a real use case in the environmental monitoring domain

CRISTAL: A practical study in designing systems to cope with change

Software engineers frequently face the challenge of developing systems whose requirements are likely to change in order to adapt to organizational reconfigurations or other external pressures. Evolving requirements present difficulties, especially in environments in which business agility demands shorter development times and responsive prototyping. This paper uses a study from CERN in Geneva to address these research questions by employing a 'description-driven' approach that is responsive to changes in user requirements and that facilitates dynamic system reconfiguration. The study describes how handling descriptions of objects in practice alongside their instances (making the objects self-describing) can mediate the effects of evolving user requirements on system development. This paper reports on and draws lessons from the practical use of a description-driven system over time. It also identifies lessons that can be learned from adopting such a self-describing description-driven approach in future software development. © 2014 Elsevier Ltd

Web-Interface for querying and visualizing Alcoholic Liver Disease Patients’ data from database using GraphQL

Ο αλκοολισμός αποτελεί́ ένα από τα σοβαρότερα και συχνότερα προβλήματα που αντιμετωπίζουν οι σύγχρονες κοινωνίες. 5%-10% του πληθυσμού στις ευρωπαϊκές χώρες κάνει κατάχρηση αλκοόλ, με την παρατεταμένη κατανάλωση αλκοόλ να επιφέρει ίνωση και κίρρωση του ήπατος (αλκοολική νόσος, Alcohol Liver Disease, ALD). Η αλκοολική νόσος συνίσταται στην ανάπτυξη του λιπώδους ήπατος, στην αλκοολική ηπατίτιδα, και τελικά στην κίρρωση του ήπατος. Τα πρώτα στάδια της ίνωσης και της αλκοολικής ηπατίτιδας είναι ασυμπωματικά ενώ όταν τελικά εκδηλωθεί η νόσος, η κλινική εικόνα είναι οξεία. Στην κλινική πράξη η διάγνωση της ALD βασίζεται στο ιστορικό χρήσης αλκοόλ, στην συμπτωματολογία του ασθενούς, και σε εργαστηριακές εξετάσεις (π.χ. ηπατικά ένζυμα, αρτηριακή πίεση, γλυκόζη αίματος, κ.α.). Η διπλωματική εργασία αποσκοπεί στη δημιουργία μιας βάσης δεδομένων για την συλλογή και ταξινόμηση όλων των εργαστηριακών, κλινικών, κ.α. εξετάσεων των ασθενών. Η αναζήτηση δεδομένων και δημιουργία γραφημάτων γίνεται σε πραγματικό χρόνο μέσω της χρήσης GraphQL επερωτήσεων. Η σχεδίαση της διεπαφής λαμβάνει υπόψη την αλλαγή των δεδομένων καθώς επίσης και την επαναχρησιμοποίηση σε διαφορετικού είδους δεδομένα από άλλα πειράματα και τη χρήση από άλλα υπολογιστικά συστήματα. Με αυτό το βιοπληροφορικό εργαλείο θα απλοποιηθεί η διαδικασία επιλογής δεδομένων, ανάλυσης και προβολής με χρήση γραφημάτων και διαγραμμάτων όλων των δεδομένων από ιατρούς και ερευνητές. Αυτό έχει ως αποτέλεσμα το εργαλείο να διευκολύνει την καθημερινότητα των ιατρών και ερευνητών ώστε να επικεντρώνονται περισσότερο στην ουσία της έρευνας, δηλαδή στην εξαγωγή συμπερασμάτων για τις βασικότερες κατηγορίες των δεδομένων που οδηγούν τους ασθενείς στην πάθηση της αλκοολικής ηπατικής νόσου, και λιγότερο στις διαδικασίες.Alcoholism is one of the most serious and most common problems faced by modern societies. Approximately, 5%-10% of the population in European countries do alcohol abuse, with prolonged alcohol consumption causing liver fibrosis and cirrhosis (alcoholic liver disease, ALD). Alcoholic disease is the development of fatty liver, alcoholic hepatitis, and finally cirrhosis of the liver. The early stages of fibrosis and alcoholic hepatitis are symptomless, and when the disease is finally manifested, the clinical picture is acute. In clinical practice, the diagnosis of ALD is based on the historical alcohol ingestion, patient symptomatology and laboratory tests (e.g. liver enzymes, blood pressure, blood glucose, etc.). The dissertation aims to create a database for the collection and classification of all laboratorial, clinical, etc. examinations of patients. Data search and graph plots and charts are created in real-time with the use of GraphQL queries and middleware query caching. The design process of the interface takes into account data changes as well as reusability of this tool in different kind of data from other tests or experiments and can be used in all types computing systems as it is containerized and responsive. This bioinformatic tool will help physicians and researchers to simplify the process of data selection, analysis and visualization by using graphs and diagrams of all data. As a result, the tool facilitates the day-to-day physicians and researchers schedule and as has the effect of letting them focus more on the essence of research, i.e. to draw conclusions about the main categories of information that lead patients to alcoholic liver disease, and less on processes