Data assurance in opaque computations

The chess endgame is increasingly being seen through the lens of, and therefore effectively defined by, a data ‘model’ of itself. It is vital that such models are clearly faithful to the reality they purport to represent. This paper examines that issue and systems engineering responses to it, using the chess endgame as the exemplar scenario. A structured survey has been carried out of the intrinsic challenges and complexity of creating endgame data by reviewing the past pattern of errors during work in progress, surfacing in publications and occurring after the data was generated. Specific measures are proposed to counter observed classes of error-risk, including a preliminary survey of techniques for using state-of-the-art verification tools to generate EGTs that are correct by construction. The approach may be applied generically beyond the game domain.

Drivers of fund performance: a panel data analysis

The principle aim of this research is to elucidate the factors driving the total rate of return of non-listed funds using a panel data analytical framework. In line with previous results, we find that core funds exhibit lower yet more stable returns than value-added and, in particular, opportunistic funds, both cross-sectionally and over time. After taking into account overall market exposure, as measured by weighted market returns, the excess returns of value-added and opportunity funds are likely to stem from: high leverage, high exposure to development, active asset management and investment in specialized property sectors. A random effects estimation of the panel data model largely confirms the findings obtained from the fixed effects model. Again, the country and sector property effect shows the strongest significance in explaining total returns. The stock market variable is negative which hints at switching effects between competing asset classes. For opportunity funds, on average, the returns attributable to gearing are three times higher than those for value added funds and over five times higher than for core funds. Overall, there is relatively strong evidence indicating that country and sector allocation, style, gearing and fund size combinations impact on the performance of unlisted real estate funds.

Annotating climate data with commentary: the CHARMe project

The CHARMe project enables the annotation of climate data with key pieces of supporting information that we term “commentary”. Commentary reflects the experience that has built up in the user community, and can help new or less-expert users (such as consultants, SMEs, experts in other fields) to understand and interpret complex data. In the context of global climate services, the CHARMe system will record, retain and disseminate this commentary on climate datasets, and provide a means for feeding back this experience to the data providers. Based on novel linked data techniques and standards, the project has developed a core system, data model and suite of open-source tools to enable this information to be shared, discovered and exploited by the community.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Information access law and disclosing of information by joint-stock companies in Brazil: open data and Extensible Business Reporting Language (XBRL) standard

The strategic management of information plays a fundamental role in the organizational management process since the decision-making process depend on the need for survival in a highly competitive market. Companies are constantly concerned about information transparency and good practices of corporate governance (CG) which, in turn, directs relations between the controlling power of the company and investors. In this context, this article presents the relationship between the disclosing of information of joint-stock companies by means of using XBRL, the open data model adopted by the Brazilian government, a model that boosted the publication of Information Access Law (Lei de Acesso à Informação), nº 12,527 of 18 November 2011. Information access should be permeated by a mediation policy in order to subsidize the knowledge construction and decision-making of investors. The XBRL is the main model for the publishing of financial information. The use of XBRL by means of new semantic standard created for Linked Data, strengthens the information dissemination, as well as creates analysis mechanisms and cross-referencing of data with different open databases available on the Internet, providing added value to the data/information accessed by civil society.

Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

Background: Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods: Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results: The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e. g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions: A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.

A model and an algebra for semi-structured and full-text queries

The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

Locally Efficient Estimation in Censored Data Models: Theory and Examples

In many applications the observed data can be viewed as a censored high dimensional full data random variable X. By the curve of dimensionality it is typically not possible to construct estimators that are asymptotically efficient at every probability distribution in a semiparametric censored data model of such a high dimensional censored data structure. We provide a general method for construction of one-step estimators that are efficient at a chosen submodel of the full-data model, are still well behaved off this submodel and can be chosen to always improve on a given initial estimator. These one-step estimators rely on good estimators of the censoring mechanism and thus will require a parametric or semiparametric model for the censoring mechanism. We present a general theorem that provides a template for proving the desired asymptotic results. We illustrate the general one-step estimation methods by constructing locally efficient one-step estimators of marginal distributions and regression parameters with right-censored data, current status data and bivariate right-censored data, in all models allowing the presence of time-dependent covariates. The conditions of the asymptotics theorem are rigorously verified in one of the examples and the key condition of the general theorem is verified for all examples.

Integration of QUAL2Kw and ArcGIS for Silver Bow Creek, Montana

The purpose of this project is to take preliminary steps towards the development of a QUAL2Kw model for Silver Bow Creek, MT. These preliminary steps include initial research and familiarization with QUAL2Kw, use of ArcGIS to fill in geospatial data gaps, and integration of QUAL2Kw and ArcGIS. The integration involves improvement of the QUAL2Kw model output through adding functionality to the model itself, and development of a QUAL2Kw specific tool in ArcGIS. These improvements are designed to help expedite and simplify the viewing of QUAL2Kw output data spatially in ArcGIS as opposed to graphically within QUAL2Kw. These improvements will allow users to quickly and easily view the many output parameters of each model run geographically within ArcGIS. This will make locating potential problem areas or “hot spots” much quicker and easier than interpreting the QUAL2Kw output data from a graph alone. The added functionality of QUAL2KW was achieved through the development of an excel Macro, and the tool in ArcGIS was developed using python scripting and the model builder feature in ArcGIS.

Integrating structured and unstructured data in a business-intelligence-system

The recent liberalization of the German energy market has forced the energy industry to develop and install new information systems to support agents on the energy trading floors in their analytical tasks. Besides classical approaches of building a data warehouse giving insight into the time series to understand market and pricing mechanisms, it is crucial to provide a variety of external data from the web. Weather information as well as political news or market rumors are relevant to give the appropriate interpretation to the variables of a volatile energy market. Starting from a multidimensional data model and a collection of buy and sell transactions a data warehouse is built that gives analytical support to the agents. Following the idea of web farming we harvest the web, match the external information sources after a filtering and evaluation process to the data warehouse objects, and present this qualified information on a user interface where market values are correlated with those external sources over the time axis.

Capacity building in the use of geospatial tools for natural resource management in Tajikistan: Final Report

The Swiss Swiss Consultant Trust Fund (CTF) support covered the period from July to December 2007 and comprised four main tasks: (1) Analysis of historic land degradation trends in the four watersheds of Zerafshan, Surkhob, Toirsu, and Vanj; (2) Translation of standard CDE GIS training materials into Russian and Tajik to enable local government staff and other specialists to use geospatial data and tools; (3) Demonstration of geospatial tools that show land degradation trends associated with land use and vegetative cover data in the project areas, (4) Preliminary training of government staff in using appropriate data, including existing information, global datasets, inexpensive satellite imagery and other datasets and webbased visualization tools like spatial data viewers, etc. The project allowed building of local awareness of, and skills in, up-to-date, inexpensive, easy-to-use GIS technologies, data sources, and applications relevant to natural resource management and especially to sustainable land management. In addition to supporting the implementation of the World Bank technical assistance activity to build capacity in the use of geospatial tools for natural resource management, the Swiss CTF support also aimed at complementing the Bank supervision work on the ongoing Community Agriculture and Watershed Management Project (CAWMP).

Modelling zero-inflated count data when exposure varies: with an application to tumor counts)

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a modified zero-inflated count data model where the probability of an extra zero is derived from an underlying duration model with Weibull hazard rate. The new model is compared to the standard Poisson model with logit zero inflation in an application to the effect of treatment with thiotepa on the number of new bladder tumors.

Capacity Building in the Use of Geospatial Tools for Natural Resource Management in Tajikistan: Inception report

The sustainable management of natural resources is a key issue for sustainable development of a poor, mountainous country such as Tajikistan. In order to strengthen its agricultural and infrastructural development efforts and alleviate poverty in rural areas, spatial information and analysis are of crucial importance to improve priority setting and decision making efficiency. However, poor access to geospatial data and tools, and limited capacity in their use has greatly constrained the ability of governmental institutions to effectively assess, plan, and monitor natural resources management. The Centre for Development and Environment (CDE) has thus been mandated by the World Bank Group to provide adequate technical support to the Community Agriculture and Watershed Management Project (CAWMP). This support consists of a spatial database on soil degradation trends in 4 watersheds, capacity development in and awareness creation about geographic information technology and a spatial data exchange hub for natural resources management in Tajikistan. CDE’s support has started in July 2007 and will last until December 2007 with a possible extension in 2008.

Economies of Scale and Cost Efficiencies: A Panel-Data Stochastic-Frontier Analysis of Real Estate Investment Trusts

This paper extends the existing research on real estate investment trust (REIT) operating efficiencies. We estimate a stochastic-frontier panel-data model specifying a translog cost function, covering 1995 to 2003. The results disagree with previous research in that we find little evidence of scale economies and some evidence of scale diseconomies. Moreover, we also generally find smaller inefficiencies than those shown by other REIT studies. Contrary to previous research, the results also show that self-management of a REIT associates with more inefficiency when we measure output with assets. When we use revenue to measure output, selfmanagement associates with less inefficiency. Also contrary with previous research, higher leverage associates with more efficiency. The results further suggest that inefficiency increases over time in three of our four specifications.

Geospatial analysis of groundwater depth for manual well drilling suitability in the Zinder Region, Niger

Manual and low-tech well drilling techniques have potential to assist in reaching the United Nations' millennium development goal for water in sub-Saharan Africa. This study used publicly available geospatial data in a regression tree analysis to predict groundwater depth in the Zinder region of Niger to identify suitable areas for manual well drilling. Regression trees were developed and tested on a database for 3681 wells in the Zinder region. A tree with 17 terminal leaves provided a range of ground water depth estimates that were appropriate for manual drilling, though much of the tree's complexity was associated with depths that were beyond manual methods. A natural log transformation of groundwater depth was tested to see if rescaling dataset variance would result in finer distinctions for regions of shallow groundwater. The RMSE for a log-transformed tree with only 10 terminal leaves was almost half that of the untransformed 17 leaf tree for groundwater depths less than 10 m. This analysis indicated important groundwater relationships for commonly available maps of geology, soils, elevation, and enhanced vegetation index from the MODIS satellite imaging system.