Fast Algorithms Using Minimal Data Structures for Common Topological Relationships in Large, Irregularly-spaced Topographic Data Sets

Digital terrain models (DTM) typically contain large numbers of postings, from hundreds of thousands to billions. Many algorithms that run on DTMs require topological knowledge of the postings, such as finding nearest neighbors, finding the posting closest to a chosen location, etc. If the postings are arranged irregu- larly, topological information is costly to compute and to store. This paper offers a practical approach to organizing and searching irregularly-space data sets by presenting a collection of efficient algorithms (O(N),O(lgN)) that compute important topological relationships with only a simple supporting data structure. These relationships include finding the postings within a window, locating the posting nearest a point of interest, finding the neighborhood of postings nearest a point of interest, and ordering the neighborhood counter-clockwise. These algorithms depend only on two sorted arrays of two-element tuples, holding a planimetric coordinate and an integer identification number indicating which posting the coordinate belongs to. There is one array for each planimetric coordinate (eastings and northings). These two arrays cost minimal overhead to create and store but permit the data to remain arranged irregularly.

Detecting common scientific workflow fragments using templates and execution provenance

Provenance plays a major role when understanding and reusing the methods applied in a scientic experiment, as it provides a record of inputs, the processes carried out and the use and generation of intermediate and nal results. In the specic case of in-silico scientic experiments, a large variety of scientic workflow systems (e.g., Wings, Taverna, Galaxy, Vistrails) have been created to support scientists. All of these systems produce some sort of provenance about the executions of the workflows that encode scientic experiments. However, provenance is normally recorded at a very low level of detail, which complicates the understanding of what happened during execution. In this paper we propose an approach to automatically obtain abstractions from low-level provenance data by finding common workflow fragments on workflow execution provenance and relating them to templates. We have tested our approach with a dataset of workflows published by the Wings workflow system. Our results show that by using these kinds of abstractions we can highlight the most common abstract methods used in the executions of a repository, relating different runs and workflow templates with each other.

Comparing Common Data Environment Platforms for Student Collaborative Working: A Case Study from Ulster University

PAS1192-2 (2013) outlines the “fundamental principles of Level 2 information modeling”, one of these principles is the use of what is commonly referred to as a Common Data Environment (CDE). A CDE could be described as an internet-enabled cloudhosting platform, accessible to all construction team members to access shared project information. For the construction sector to achieve increased productivity goals, the next generation of industry professionals will need to be educated in a way that provides them with an appreciation of Building Information Modelling (BIM) working methods, at all levels, including an understanding of how data in a CDE should be structured, managed, shared and published. This presents a challenge for educational institutions in terms of providing a CDE that addresses the requirements set out in PAS1192-2, and mirrors organisational and professional working practices without causing confusion due to over complexity. This paper presents the findings of a two-year study undertaken at Ulster University comparing the use of a leading industry CDE platform with one derived from the in-house Virtual Learning Environment (VLE), for the delivery of a student BIM project. The research methodology employed was a qualitative case study analysis, focusing on observations from the academics involved and feedback from students. The results of the study show advantages for both CDE platforms depending on the learning outcomes required.

Does the Common Agricultural Policy Reduce Farm Labour Migration? Panel data analysis across EU regions. Factor Markets Working Paper No. 28, July 2012

This paper deals with the determinants of labour out-migration from agriculture across 149 EU regions over the 1990–2008 period. The central aim is to shed light on the role played by payments from the common agricultural policy (CAP) on this important adjustment process. Using static and dynamic panel data estimators, we show that standard neoclassical drivers, like relative income and the relative labour share, represent significant determinants of the intersectoral migration of agricultural labour. Overall, CAP payments contributed significantly to job creation in agriculture, although the magnitude of the economic effect was rather moderate. We also find that pillar I subsidies exerted an effect approximately two times greater than that of pillar II payments.

Instructions to accountants relating to manner of recording data in connection with financial history of common carriers /

Signed: D.E. Brown, Supervisor of Accounts.

Core data elements and common definitions for employment and training programs : report to Congress /

Includes bibliographical references.

The design and implementation of a repository for the management of spatial data integrity constraints

This paper reviews the key features of an environment to support domain users in spatial information system (SIS) development. It presents a full design and prototype implementation of a repository system for the storage and management of metadata, focusing on a subset of spatial data integrity constraint classes. The system is designed to support spatial system development and customization by users within the domain that the system will operate.