A prototype system for the digital lodgement of spatial data

This dissertation develops the model of a prototype system for the digital lodgement of spatial data sets with statutory bodies responsible for the registration and approval of land related actions under the Torrens Title system. Spatial data pertain to the location of geographical entities together with their spatial dimensions and are classified as point, line, area or surface. This dissertation deals with a sub-set of spatial data, land boundary data that result from the activities performed by surveying and mapping organisations for the development of land parcels. The prototype system has been developed, utilising an event-driven paradigm for the user-interface, to exploit the potential of digital spatial data being generated from the utilisation of electronic techniques. The system provides for the creation of a digital model of the cadastral network and dependent data sets for an area of interest from hard copy records. This initial model is calibrated on registered control and updated by field survey to produce an amended model. The field-calibrated model then is electronically validated to ensure it complies with standards of format and content. The prototype system was designed specifically to create a database of land boundary data for subsequent retrieval by land professionals for surveying, mapping and related activities. Data extracted from this database are utilised for subsequent field survey operations without the need to create an initial digital model of an area of interest. Statistical reporting of differences resulting when subsequent initial and calibrated models are compared, replaces the traditional checking operations of spatial data performed by a land registry office. Digital lodgement of survey data is fundamental to the creation of the database of accurate land boundary data. This creation of the database is fundamental also to the efficient integration of accurate spatial data about land being generated by modem technology such as global positioning systems, and remote sensing and imaging, with land boundary information and other information held in Government databases. The prototype system developed provides for the delivery of accurate, digital land boundary data for the land registration process to ensure the continued maintenance of the integrity of the cadastre. Such data should meet also the more general and encompassing requirements of, and prove to be of tangible, longer term benefit to the developing, electronic land information industry.

Exploring location-dependency in process modeling

Purpose – Context-awareness has emerged as an important principle in the design of flexible business processes. The goal of the research is to develop an approach to extend context-aware business process modeling toward location-awareness. The purpose of this paper is to identify and conceptualize location-dependencies in process modeling. Design/methodology/approach – This paper uses a pattern-based approach to identify location-dependency in process models. The authors design specifications for these patterns. The authors present illustrative examples and evaluate the identified patterns through a literature review of published process cases. Findings – This paper introduces location-awareness as a new perspective to extend context-awareness in BPM research, by introducing relevant location concepts such as location-awareness and location-dependencies. The authors identify five basic location-dependent control-flow patterns that can be captured in process models. And the authors identify location-dependencies in several existing case studies of business processes. Research limitations/implications – The authors focus exclusively on the control-flow perspective of process models. Further work needs to extend the research to address location-dependencies in process data or resources. Further empirical work is needed to explore determinants and consequences of the modeling of location-dependencies. Originality/value – As existing literature mostly focusses on the broad context of business process, location in process modeling still is treated as “second class citizen” in theory and in practice. This paper discusses the vital role of location-dependencies within business processes. The proposed five basic location-dependent control-flow patterns are novel and useful to explain location-dependency in business process models. They provide a conceptual basis for further exploration of location-awareness in the management of business processes.

FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential data-analytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improves the application end-to-end transfer performance.

Temperature dependent vibrational lifetimes in supercritical fluids near the critical point

Vibrational relaxation measurements on the CO asymmetric stretching mode (similar to 1980 cm(-1)) of tungsten hexacarbonyl (W(CO)(6)) as a function of temperature at constant density in several supercritical solvents in the vicinity of the critical point are presented. In supercritical ethane, at the critical density, there is a region above the critical temperature (Tc) in which the lifetime increases with increasing temperature. When the temperature is raised sufficiently (similar to T-c + 70 degrees C), the lifetime decreases with further increase in temperature. A recent hydrodynamic/thermodynamic theory of vibrational relaxation in supercritical fluids reproduces this behavior semiquantitatively. The temperature dependent data for fixed densities somewhat above and below the critical density is in better agreement with the theory. In fluoroform solvent at the critical density, the vibrational lifetime also initially increases with increasing temperature. However, in supercritical CO2 at the critical density, the temperature dependent vibrational lifetime decreases approximately linearly with temperature beginning almost immediately above T-c. The theory does not reproduce this behavior. A comparison between the absolute lifetimes in the three solvents and the temperature trends is made.

Weighted rank regression for clustered data analysis

We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.

Energy-dependent in-situ small-angle x-ray scattering study of nano-ceramics

R Winter, D Le Messurier, CM Martin; Cryst Rev 12 (2006) 3 Sponsorship: EPSRC, CCLRC, Pilkington

"One of our hosts in another country": Challenges of data geolocation in cloud storage

Physical location of data in cloud storage is an increasingly urgent problem. In a short time, it has evolved from the concern of a few regulated businesses to an important consideration for many cloud storage users. One of the characteristics of cloud storage is fluid transfer of data both within and among the data centres of a cloud provider. However, this has weakened the guarantees with respect to control over data replicas, protection of data in transit and physical location of data. This paper addresses the lack of reliable solutions for data placement control in cloud storage systems. We analyse the currently available solutions and identify their shortcomings. Furthermore, we describe a high-level architecture for a trusted, geolocation-based mechanism for data placement control in distributed cloud storage systems, which are the basis of an on-going work to define the detailed protocol and a prototype of such a solution. This mechanism aims to provide granular control over the capabilities of tenants to access data placed on geographically dispersed storage units comprising the cloud storage.

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk.

Social networks: evolving graphs with memory dependent edges

The plethora, and mass take up, of digital communication tech- nologies has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the ex- istence or otherwise of certain infinite products and series involving age dependent model parameters. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Evaluation of tropospheric and ionospheric effects on the geographic localization of data collection platforms

The Brazilian National Institute for Space Research (INPE) is operating the Brazilian Environmental Data Collection System that currently amounts to a user community of around 100 organizations and more than 700 data collection platforms installed in Brazil. This system uses the SCD-1, SCD-2, and CBERS-2 low Earth orbit satellites to accomplish the data collection services. The main system applications are hydrology, meteorology, oceanography, water quality, and others. One of the functionalities offered by this system is the geographic localization of the data collection platforms by using Doppler shifts and a batch estimator based on least-squares technique. There is a growing demand to improve the quality of the geographical location of data collection platforms for animal tracking. This work presents an evaluation of the ionospheric and tropospheric effects on the Brazilian Environmental Data Collection System transmitter geographic location. Some models of the ionosphere and troposphere are presented to simulate their impacts and to evaluate performance of the platform location algorithm. The results of the Doppler shift measurements, using the SCD-2 satellite and the data collection platform (DCP) located in Cuiabá town, are presented and discussed.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

Missing Links in the Proposed EU Data Protection Regulation and Cloud Computing Scenarios: A Brief Overview

Applying location-focused data protection law within the context of a location-agnostic cloud computing framework is fraught with difficulties. While the Proposed EU Data Protection Regulation has introduced a lot of changes to the current data protection framework, the complexities of data processing in the cloud involve various layers and intermediaries of actors that have not been properly addressed. This leaves some gaps in the regulation when analyzed in cloud scenarios. This paper gives a brief overview of the relevant provisions of the regulation that will have an impact on cloud transactions and addresses the missing links. It is hoped that these loopholes will be reconsidered before the final version of the law is passed in order to avoid unintended consequences.

MethDB—a public database for DNA methylation data

Methylation of cytosine in the 5 position of the pyrimidine ring is a major modification of the DNA in most organisms. In eukaryotes, the distribution and number of 5-methylcytosines (5mC) along the DNA is heritable but can also change with the developmental state of the cell and as a response to modifications of the environment. While DNA methylation probably has a number of functions, scientific interest has recently focused on the gene silencing effect methylation can have in eukaryotic cells. In particular, the discovery of changes in the methylation level during cancer development has increased the interest in this field. In the past, a vast amount of data has been generated with different levels of resolution ranging from 5mC content of total DNA to the methylation status of single nucleotides. We present here a database for DNA methylation data that attempts to unify these results in a common resource. The database is accessible via WWW (http://www.methdb.de). It stores information about the origin of the investigated sample and the experimental procedure, and contains the DNA methylation data. Query masks allow for searching for 5mC content, species, tissue, gene, sex, phenotype, sequence ID and DNA type. The output lists all available information including the relative gene expression level. DNA methylation patterns and methylation profiles are shown both as a graphical representation and as G/A/T/C/5mC-sequences or tables with sequence positions and methylation levels, respectively.

Designing for Location-Dependence

This paper proposes a methodological framework for use in designing location-dependent games or experiences. An increasing interest in this genre has given rise to a number of common issues surrounding the design and development of these types of experience. Specifically, the treatment of and approach to the use of location gives rise to particular design considerations. The framework proposed here aims to highlight these and provide designers with tools for use in addressing these.

Time-dependent evolution of tissue markers by MALDI-MS imaging

We have used MALDI-MS imaging (MALDI-MSI) to monitor the time dependent appearance and loss of signals when tissue slices are brought rapidly to room temperature for short to medium periods of time. Sections from mouse brain were cut in a cryostat microtome, placed on a MALDI target and allowed to warm to room temperature for 30 s to 3 h. Sections were then refrozen, fixed by ethanol treatment and analysed by MALDI-MSI. The intensity of a range of markers were seen to vary across the time course, both increasing and decreasing, with the intensity of some markers changing significantly within 30 s and markers also showed tissue location specific evolution. The markers resulting from this autolysis were compared directly to those that evolved in a comparable 16 h on-tissue trypsin digest, and the markers that evolved in the two studies were seen to be substantially different. These changes offer an important additional level of location-dependent information for mapping changes and seeking disease-dependent biomarkers in the tissue. They also indicate that considerable care is required to allow comparison of biomarkers between MALDI-MSI experiments and also has implications for the standard practice of thaw-mounting multiple tissue sections onto MALDI-MS targets.