Data Driven Discovery in Astrophysics

We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure

Understanding Semantic Aware Grid Middleware for e-Science

In this paper we analyze several semantic-aware Grid middleware services used in e-Science applications. We describe them according to a common analysis framework, so as to find their commonalities and their distinguishing features. As a result of this analysis we categorize these services into three groups: information services, data access services and decision support services. We make comparisons and provide additional conclusions that are useful to understand better how these services have been developed and deployed, and how similar services would be developed in the future, mainly in the context of e-Science applications

Ontologies and the Semantic Web for Digital Investigation Tool Selection

The nascent field of digital forensics is heavily influenced by practice. Much digital forensics research involves the use, evaluation, and categorization of the multitude of tools available to researchers and practitioners. As technology evolves at an increasingly rapid pace, the digital forensics field must constantly adapt by creating and evaluating new tools and techniques to perform forensic analysis on many disparate systems such as desktops, notebook computers, mobile devices, cloud, and personal wearable sensor devices, among many others. While researchers have attempted to use ontologies to classify the digital forensics domain on various dimensions, no ontology of digital forensic tools has been developed that defines the capabilities and relationships among the various digital forensic tools. To address this gap, this work develops an ontology using Resource Description Framework (RDF) and Ontology Web Language (OWL) which is searchable via SP ARQL ( an RDF query language) and catalogues common digital forensic tools. Following the concept of ontology design patterns, our ontology has a modular design to promote integration with existing ontologies. Furthermore, we progress to a semantic web application that employs reasoning in order to aid digital investigators with selecting an appropriate tool. This work serves as an important step towards building the knowledge of digital forensics tools. Additionally, this research sets the preliminary stage to bringing semantic web technology to the digital forensics domain as well as facilitates expanding the developed ontology to other tools and features, relationships, and forensic techniques

Understanding semantic aware Grid middleware for e-Science

In this paper we analyze several semantic-aware Grid middleware services used in e-Science applications. We describe them according to a common analysis framework, so as to find their commonalities and their distinguishing features. As a result of this analysis we categorize these services into three groups: information services, data access services and decision support services. We make comparisons and provide additional conclusions that are useful to understand better how these services have been developed and deployed, and how similar services would be developed in the future, mainly in the context of e-Science applications

Proceedings of the Automated Reasoning Workshop (ARW 2019)

Preface This volume contains the proceedings of ARW 2019, the twenty sixths Workshop on Automated Rea- soning (2nd{3d September 2019) hosted by the Department of Computer Science, Middlesex University, England (UK). Traditionally, this annual workshop which brings together, for a two-day intensive pro- gramme, researchers from different areas of automated reasoning, covers both traditional and emerging topics, disseminates achieved results or work in progress. During informal discussions at workshop ses- sions, the attendees, whether they are established in the Automated Reasoning community or are only at their early stages of their research career, gain invaluable feedback from colleagues. ARW always looks at the ways of strengthening links between academia, industry and government; between theoretical and practical advances. The 26th ARW is affiliated with TABLEAUX 2019 conference. These proceedings contain forteen extended abstracts contributed by the participants of the workshop and assembled in order of their presentations at the workshop. The abstracts cover a wide range of topics including the development of reasoning techniques for Agents, Model-Checking, Proof Search for classical and non-classical logics, Description Logics, development of Intelligent Prediction Models, application of Machine Learning to theorem proving, applications of AR in Cloud Computing and Networking. I would like to thank the members of the ARW Organising Committee for their advice and assis- tance. I would also like to thank the organisers of TABLEAUX/FroCoS 2019, and Andrei Popescu, the TABLEAUX Conference Chair, in particular, for the enormous work related to the organisation of this affiliation. I would also like to thank Natalia Yerashenia for helping in preparing these proceedings. London Alexander Bolotov September 201

A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID

An important trend in modern medicine is towards individualisation of healthcare to tailor care to the needs of the individual. This makes it possible, for example, to personalise diagnosis and treatment to improve outcome. However, the benefits of this can only be fully realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data, analysis algorithms, knowledge and expertise). Potentially, grid can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. The integration of grid and the new concept of bioprofile represents a new topic in the healthgrid for individualisation of healthcare. A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's current and past bio-history, biopatterns and prognosis. It combines not just data, but also analysis and predictions of future or likely susceptibility to disease, such as brain diseases and cancer. The creation and use of bioprofile require the support of a number of healthcare and ICT technologies and techniques, such as medical imaging and electrophysiology and related facilities, analysis tools, data storage and computation clusters. The need to share clinical data, storage and computation resources between different bioprofile centres creates not only local problems, but also global problems. Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the use and management of heterogeneous IT resources at different bioprofile centres. Grid as an emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including discovery, access, monitoring and allocation of distributed bioprofile databases, computation resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the grid and bioprofile technologies together in order to offer an advanced distributed bioprofile environment to support individualized healthcare remains. The aim of this project is to develop a framework for one of the key meta-level bioprofile applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles). Analysis makes it possible, for example, to extract markers from data for diagnosis and to assess individual's health status. The framework provides a basis for a "grid-based" solution to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions of the thesis are fourfold: A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture is fundamental to the development of any ICT systems. The architecture creates a meaiis for categorisation, determination and organisation of core grid components to support the development and use of grid for bioprofile analysis; B. A service model for bioprofile analysis over grid. The service model proposes a service design principle, a service architecture for bioprofile analysis over grid, and a distributed EEG analysis service model. The service design principle addresses the main service design considerations behind the service model, in the aspects of usability, flexibility, extensibility, reusability, etc. The service architecture identifies the main categories of services and outlines an approach in organising services to realise certain functionalities required by distributed bioprofile analysis applications. The EEG analysis service model demonstrates the utilisation and development of services to enable bioprofile analysis over grid; C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing grid middleware tools. They provide essential experimental platforms for research in bioprofiling over grid. The work here demonstrates how resources, grid middleware and services can be utilised, organised and implemented to support distributed EEG analysis for early detection of dementia. The distributed Electroencephalography (EEG) analysis environment can be used to support a variety of research activities in EEG analysis; D. A scheme for organising multiple (heterogeneous) descriptions of individual grid entities for knowledge representation of grid. The scheme solves the compatibility and adaptability problems in managing heterogeneous descriptions (i.e. descriptions using different languages and schemas/ontologies) for collaborated representation of a grid environment in different scales. It underpins the concept of bioprofile analysis over grid in the aspect of knowledge-based global coordination between components of bioprofile analysis over grid

Providing packages of relevant ATM information: An ontology-based approach

ATM information providers publish reports and notifications of different types using standardized information exchange models. For a typical information user, e.g., an aircraft pilot, only a fraction of the published information is relevant for a particular task. Filtering out irrelevant information from different information sources is in itself a challenging task, yet it is only a first step in providing relevant information, the challenges concerning maintenance, auditability, availability, integration, comprehensibility, and traceability. This paper presents the Semantic Container approach, which employs ontology-based faceted information filtering and allows for the packaging of filtered information and associated metadata in semantic containers, thus facilitating reuse of filtered information at different levels. The paper formally defines an abstract model of ontology-based information filtering and the structure of semantic containers, their composition, versioning, discovery, and replicated physical allocation. The paper further discusses different usage scenarios, the role of semantic containers in SWIM, an architecture for a semantic container management system, as well as a proof-of-concept prototype. Finally the paper discusses a blockchain-based notary service to realize tamper-proof version histories for semantic containers.acceptedVersio