A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

The Consistency of Relational Database and Object-Relational Database in GIS Applications

This final year project involves a research and a simple Geographical Information System (GIS) application that will show GIS, spatial data, spatial database management, which focused on relational and object-relational database management system. The main objectives of this project is to study and gain deeper understanding on the implementation of the two types of databases in GIS applications, to compare the level of performance between the databases in a GIS application and to determine the most suggested database to be implemented in a GIS application. The scope of the study will focus on integrating a GIS application that implements Malacca spatial database with two different database management system, namely relational database and objectrelational database system. The performance of each database system will be identified and compared. Rapid Development environment methodology will be utilized in the research on the performance of relational and object-relational databases in GIS applications and also in the development of an application that will implement the database with GIS applications. This methodology basically involved overlapping Planning, Analysis, Design and Implementation phases. Database development design process involved conceptual, logical and physical design stages. This report also includes discussions on the consistency of relational database as well as of the objectrelational database in GIS applications. This report suggests GIS application developer to choose object-relational database in order to manage both spatial and attributes data for the applications efficiently. Furthermore, the result from the object-relational database will be more consistent and reliable compared to a relational database and the performance is better. Recommendations for continuing this project are to compare and determine the level of consistency between relational database and object-relational database in World Wide Web environment or with multi-user accessing the database concurrently in order to study on the effects to the level of consistency and also to develop a map query interface

Environmental information systems : the development and implementation of the Lake Rukwa Basin integrated project environmental information system (LRBIP-EIS) database, Tanzania

Bibliography: leaves 91-97.The quest for sustenance inevitably forces mankind to exploit natural resources found within their environs. In many cases, the exploitation results in massive environmental degradation that disrupts the ecosystem and causes loss of bio-diversity. There is generally a lack of information systems to monitor and provide quantitative information on the state of the affected environment. Decision-makers usually fail to make informed decisions with regard to conservation strategies. The need to provide decision-makers with quantitative environmental information formed the basis of this thesis. An integrated environmental information system (EIS) database was developed according to the Software Development Methodology for three of the identified environmental sectors. This involved detailed user needs assessment to identify the information requirements (both spatial and textual) for each sector. The results were used to design separate data models that were later merged to create an integrated data model for the database application. A fisheries application prototype was developed to implement the proposed database design. The prototype has three major components. The Geographic Information System (GIS) handles the spatial data such as rivers, settlements, roads, and lakes. A relational database management system (RDBMS) was used to store and maintain the non-spatial data such as fisherman ' s personal details and fish catch data. Customized graphical user interfaces were designed to handle the data visualization and restricted access to the GIS and RDBMS environments

Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg