On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.

Handling and communicating uncertainty in chained geospatial web services

Recent developments in service-oriented and distributed computing have created exciting opportunities for the integration of models in service chains to create the Model Web. This offers the potential for orchestrating web data and processing services, in complex chains; a flexible approach which exploits the increased access to products and tools, and the scalability offered by the Web. However, the uncertainty inherent in data and models must be quantified and communicated in an interoperable way, in order for its effects to be effectively assessed as errors propagate through complex automated model chains. We describe a proposed set of tools for handling, characterizing and communicating uncertainty in this context, and show how they can be used to 'uncertainty- enable' Web Services in a model chain. An example implementation is presented, which combines environmental and publicly-contributed data to produce estimates of sea-level air pressure, with estimates of uncertainty which incorporate the effects of model approximation as well as the uncertainty inherent in the observational and derived data.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

Services for Satellite Data Processing

Data processing services for Meteosat geostationary satellite are presented. Implemented services correspond to the different levels of remote-sensing data processing, including noise reduction at preprocessing level, cloud mask extraction at low-level and fractal dimension estimation at high-level. Cloud mask obtained as a result of Markovian segmentation of infrared data. To overcome high computation complexity of Markovian segmentation parallel algorithm is developed. Fractal dimension of Meteosat data estimated using fractional Brownian motion models.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.

Drug prescribing patterns in the General Medical Services Scheme in Ireland and projected costs to 2026

The primary objective is to investigate the main factors contributing to GMS expenditure on pharmaceutical prescribing and projecting this expenditure to 2026. This study is located in the area of pharmacoeconomic cost containment and projections literature. The thesis has five main aims: 1. To determine the main factors contributing to GMS expenditure on pharmaceutical prescribing. 2. To develop a model to project GMS prescribing expenditure in five year intervals to 2026, using 2006 Central Statistics Office (CSO) Census data and 2007 Health Service Executive{Primary Care Reimbursement Service (HSE{PCRS) sample data. 3. To develop a model to project GMS prescribing expenditure in five year intervals to 2026, using 2012 HSE{PCRS population data, incorporating cost containment measures, and 2011 CSO Census data. 4. To investigate the impact of demographic factors and the pharmacology of drugs (Anatomical Therapeutic Chemical (ATC)) on GMS expenditure. 5. To explore the consequences of GMS policy changes on prescribing expenditure and behaviour between 2008 and 2014. The thesis is centered around three published articles and is located between the end of a booming Irish economy in 2007, a recession from 2008{2013, to the beginning of a recovery in 2014. The literature identified a number of factors influencing pharmaceutical expenditure, including population growth, population aging, changes in drug utilisation and drug therapies, age, gender and location. The literature identified the methods previously used in predictive modelling and consequently, the Monte Carlo Simulation (MCS) model was used to simulate projected expenditures to 2026. Also, the literature guided the use of Ordinary Least Squares (OLS) regression in determining demographic and pharmacology factors influencing prescribing expenditure. The study commences against a backdrop of growing GMS prescribing costs, which has risen from e250 million in 1998 to over e1 billion by 2007. Using a sample 2007 HSE{PCRS prescribing data (n=192,000) and CSO population data from 2008, (Conway et al., 2014) estimated GMS prescribing expenditure could rise to e2 billion by2026. The cogency of these findings was impacted by the global economic crisis of 2008, which resulted in a sharp contraction in the Irish economy, mounting fiscal deficits resulting in Ireland's entry to a bailout programme. The sustainability of funding community drug schemes, such as the GMS, came under the spotlight of the EU, IMF, ECB (Trioka), who set stringent targets for reducing drug costs, as conditions of the bailout programme. Cost containment measures included: the introduction of income eligibility limits for GP visit cards and medical cards for those aged 70 and over, introduction of co{payments for prescription items, reductions in wholesale mark{up and pharmacy dispensing fees. Projections for GMS expenditure were reevaluated using 2012 HSE{PCRS prescribing population data and CSO population data based on Census 2011. Taking into account both cost containment measures and revised population predictions, GMS expenditure is estimated to increase by 64%, from e1.1 billion in 2016 to e1.8 billion by 2026, (ConwayLenihan and Woods, 2015). In the final paper, a cross{sectional study was carried out on HSE{PCRS population prescribing database (n=1.63 million claimants) to investigate the impact of demographic factors, and the pharmacology of the drugs, on GMS prescribing expenditure. Those aged over 75 (ẞ = 1:195) and cardiovascular prescribing (ẞ = 1:193) were the greatest contributors to annual GMS prescribing costs. Respiratory drugs (Montelukast) recorded the highest proportion and expenditure for GMS claimants under the age of 15. Drugs prescribed for the nervous system (Escitalopram, Olanzapine and Pregabalin) were highest for those between 16 and 64 years with cardiovascular drugs (Statins) were highest for those aged over 65. Females are more expensive than males and are prescribed more items across the four ATC groups, except among children under 11, (ConwayLenihan et al., 2016). This research indicates that growth in the proportion of the elderly claimants and associated levels of cardiovascular prescribing, particularly for statins, will present difficulties for Ireland in terms of cost containment. Whilst policies aimed at cost containment (co{payment charges, generic substitution, reference pricing, adjustments to GMS eligibility) can be used to curtail expenditure, health promotional programs and educational interventions should be given equal emphasis. Also policies intended to affect physicians prescribing behaviour include guidelines, information (about price and less expensive alternatives) and feedback, and the use of budgetary restrictions could yield savings.

CTAMS GeoMedia User Tool Documentation and Technical GIS Concepts, 1998

A user manual for the GeoMedia software program, along with some discussion of digital orthophotos.

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.

KE & Science Europe briefing paper: Funding Research Data Management and related infrastructures

Responsible Research Data Management (RDM) is a pillar of quality research. In practice good RDM requires the support of a well-functioning Research Data Infrastructure (RDI). One of the challenges the research community is facing is how to fund the management of research data and the required infrastructure. Knowledge Exchange and Science Europe have both defined activities to explore how RDM/RDI are, or can be, funded. Independently they each planned to survey users and providers of data services and on becoming aware of the similar objectives and approaches, the Science Europe Working Group on Research Data and the Knowledge Exchange Research Data expert group joined forces and devised a joint activity to to inform the discussion on the funding of RDM/RDI in Europe.

Active Data Collection Techniques to Understand Online Scammers and Cybercriminals

Nigerian scam, also known as advance fee fraud or 419 scam, is a prevalent form of online fraudulent activity that causes financial loss to individuals and businesses. Nigerian scam has evolved from simple non-targeted email messages to more sophisticated scams targeted at users of classifieds, dating and other websites. Even though such scams are observed and reported by users frequently, the community’s understanding of Nigerian scams is limited since the scammers operate “underground”. To better understand the underground Nigerian scam ecosystem and seek effective methods to deter Nigerian scam and cybercrime in general, we conduct a series of active and passive measurement studies. Relying upon the analysis and insight gained from the measurement studies, we make four contributions: (1) we analyze the taxonomy of Nigerian scam and derive long-term trends in scams; (2) we provide an insight on Nigerian scam and cybercrime ecosystems and their underground operation; (3) we propose a payment intervention as a potential deterrent to cybercrime operation in general and evaluate its effectiveness; and (4) we offer active and passive measurement tools and techniques that enable in-depth analysis of cybercrime ecosystems and deterrence on them. We first created and analyze a repository of more than two hundred thousand user-reported scam emails, stretching from 2006 to 2014, from four major scam reporting websites. We select ten most commonly observed scam categories and tag 2,000 scam emails randomly selected from our repository. Based upon the manually tagged dataset, we train a machine learning classifier and cluster all scam emails in the repository. From the clustering result, we find a strong and sustained upward trend for targeted scams and downward trend for non-targeted scams. We then focus on two types of targeted scams: sales scams and rental scams targeted users on Craigslist. We built an automated scam data collection system and gathered large-scale sales scam emails. Using the system we posted honeypot ads on Craigslist and conversed automatically with the scammers. Through the email conversation, the system obtained additional confirmation of likely scam activities and collected additional information such as IP addresses and shipping addresses. Our analysis revealed that around 10 groups were responsible for nearly half of the over 13,000 total scam attempts we received. These groups used IP addresses and shipping addresses in both Nigeria and the U.S. We also crawled rental ads on Craigslist, identified rental scam ads amongst the large number of benign ads and conversed with the potential scammers. Through in-depth analysis of the rental scams, we found seven major scam campaigns employing various operations and monetization methods. We also found that unlike sales scammers, most rental scammers were in the U.S. The large-scale scam data and in-depth analysis provide useful insights on how to design effective deterrence techniques against cybercrime in general. We study underground DDoS-for-hire services, also known as booters, and measure the effectiveness of undermining a payment system of DDoS Services. Our analysis shows that the payment intervention can have the desired effect of limiting cybercriminals’ ability and increasing the risk of accepting payments.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.