Access Rights Inheritance in Information Systems Controlled by Metadata

All information systems have to be protected. As the number of information objects and the number of users increase the task of information system’s protection becomes more difficult. One of the most difficult problems is access rights assignment. This paper describes the graph model of access rights inheritance. This model takes into account relations and dependences between different objects and between different users. The model can be implemented in the information systems controlled by the metadata, describing information objects and connections between them, such as the systems based on CASE-technology METAS.

Automated Problem Domain Cognition Process in Information Systems Design

An automated cognitive approach for the design of Information Systems is presented. It is supposed to be used at the very beginning of the design process, between the stages of requirements determination and analysis, including the stage of analysis. In the context of the approach used either UML or ERD notations may be used for model representation. The approach provides the opportunity of using natural language text documents as a source of knowledge for automated problem domain model generation. It also simplifies the process of modelling by assisting the human user during the whole period of working upon the model (using UML or ERD notations).

The Involvement of Institute for Information Technologies in Text Processing

The activities of the Institute of Information Technologies in the area of automatic text processing are outlined. Major problems related to different steps of processing are pointed out together with the shortcomings of the existing solutions.

Large scale data processing using MapReduce

As massive data sets become increasingly available, people are facing the problem of how to effectively process and understand these data. Traditional sequential computing models are giving way to parallel and distributed computing models, such as MapReduce, both due to the large size of the data sets and their high dimensionality. This dissertation, as in the same direction of other researches that are based on MapReduce, tries to develop effective techniques and applications using MapReduce that can help people solve large-scale problems. Three different problems are tackled in the dissertation. The first one deals with processing terabytes of raster data in a spatial data management system. Aerial imagery files are broken into tiles to enable data parallel computation. The second and third problems deal with dimension reduction techniques that can be used to handle data sets of high dimensionality. Three variants of the nonnegative matrix factorization technique are scaled up to factorize matrices of dimensions in the order of millions in MapReduce based on different matrix multiplication implementations. Two algorithms, which compute CANDECOMP/PARAFAC and Tucker tensor decompositions respectively, are parallelized in MapReduce based on carefully partitioning the data and arranging the computation to maximize data locality and parallelism.

Hospitality Information Systems: Intuitive, Object-Oriented, and Wireless Technology

Automated information system design and implementation is one of the fastest changing aspects of the hospitality industry. During the past several years nothing has increased the professionalism or improved the productivity within the industry more than the application of computer technology. Intuitive software applications, deemed the first step toward making computers more people-literate, object-oriented programming, intended to more accurately model reality, and wireless communications are expected to play a significant role in future technological advancement.

Towards a model for exploring the relationship between managerial decision problems and decision support opportunities

The organisational decision making environment is complex, and decision makers must deal with uncertainty and ambiguity on a continuous basis. Managing and handling decision problems and implementing a solution, requires an understanding of the complexity of the decision domain to the point where the problem and its complexity, as well as the requirements for supporting decision makers, can be described. Research in the Decision Support Systems domain has been extensive over the last thirty years with an emphasis on the development of further technology and better applications on the one hand, and on the other hand, a social approach focusing on understanding what decision making is about and how developers and users should interact. This research project considers a combined approach that endeavours to understand the thinking behind managers’ decision making, as well as their informational and decisional guidance and decision support requirements. This research utilises a cognitive framework, developed in 1985 by Humphreys and Berkeley that juxtaposes the mental processes and ideas of decision problem definition and problem solution that are developed in tandem through cognitive refinement of the problem, based on the analysis and judgement of the decision maker. The framework facilitates the separation of what is essentially a continuous process, into five distinct levels of abstraction of manager’s thinking, and suggests a structure for the underlying cognitive activities. Alter (2004) argues that decision support provides a richer basis than decision support systems, in both practice and research. The constituent literature on decision support, especially in regard to modern high profile systems, including Business Intelligence and Business analytics, can give the impression that all ‘smart’ organisations utilise decision support and data analytics capabilities for all of their key decision making activities. However this empirical investigation indicates a very different reality.

The effects of centrally acting ACE inhibitors on the rate of cognitive and functional decline in dementia: a KDD approach

Alzheimer’s Disease and other dementias are one of the most challenging illnesses confronting countries with ageing populations. Treatment options for dementia are limited, and the costs are significant. There is a growing need to develop new treatments for dementia, especially for the elderly. There is also growing evidence that centrally acting angiotensin converting enzyme (ACE) inhibitors, which cross the blood-brain barrier, are associated with a reduced rate of cognitive and functional decline in dementia, especially in Alzheimer’s disease (AD). The aim of this research is to investigate the effects of centrally acting ACE inhibitors (CACE-Is) on the rate of cognitive and functional decline in dementia, using a three phased KDD process. KDD, as a scientific way to process and analysis clinical data, is used to find useful insights from a variety of clinical databases. The data used are from three clinic databases: Geriatric Assessment Tool (GAT), the Doxycycline and Rifampin for Alzheimer’s Disease (DARAD), and the Qmci validation databases, which were derived from several different geriatric clinics in Canada. This research involves patients diagnosed with AD, vascular or mixed dementia only. Patients were included if baseline and end-point (at least six months apart) Standardised Mini-Mental State Examination (SMMSE), Quick Mild Cognitive Impairment (Qmci) or Activities Daily Living (ADL) scores were available. Basically, the rates of change are compared between patients taking CACE-Is, and those not currently treated with CACE-Is. The results suggest that there is a statistically significant difference in the rate of decline in cognitive and functional scores between CACE-I and NoCACE-I patients. This research also validates that the Qmci, a new short assessment test, has potential to replace the current popular screening tests for cognition in the clinic and clinical trials.

The framework for REVIEWS: an exploration into design principles for an electronic medical early warning system observation chart

The observation chart is for many health professionals (HPs) the primary source of objective information relating to the health of a patient. Information Systems (IS) research has demonstrated the positive impact of good interface design on decision making and it is logical that good observation chart design can positively impact healthcare decision making. Despite the potential for good observation chart design, there is a paucity of observation chart design literature, with the primary source of literature leveraging Human Computer Interaction (HCI) literature to design better charts. While this approach has been successful, this design approach introduces a gap between understanding of the tasks performed by HPs when using charts and the design features implemented in the chart. Good IS allow for the collection and manipulation of data so that it can be presented in a timely manner that support specific tasks. Good interface design should therefore consider the specific tasks being performed prior to designing the interface. This research adopts a Design Science Research (DSR) approach to formalise a framework of design principles that incorporates knowledge of the tasks performed by HPs when using observation charts and knowledge pertaining to visual representations of data and semiology of graphics. This research is presented in three phases, the initial two phases seek to discover and formalise design knowledge embedded in two situated observation charts: the paper-based NEWS chart developed by the Health Service Executive in Ireland and the electronically generated eNEWS chart developed by the Health Information Systems Research Centre in University College Cork. A comparative evaluation of each chart is also presented in the respective phases. Throughout each of these phases, tentative versions of a design framework for electronic vital sign observation charts are presented, with each subsequent iteration of the framework (versions Alpha, Beta, V0.1 and V1.0) representing a refinement of the design knowledge. The design framework will be named the framework for the Retrospective Evaluation of Vital Sign Information from Early Warning Systems (REVIEWS). Phase 3 of the research presents the deductive process for designing and implementing V0.1 of the framework, with evaluation of the instantiation allowing for the final iteration V1.0 of the framework. This study makes a number of contributions to academic research. First the research demonstrates that the cognitive tasks performed by nurses during clinical reasoning can be supported through good observation chart design. Secondly the research establishes the utility of electronic vital sign observation charts in terms of supporting the cognitive tasks performed by nurses during clinical reasoning. Third the framework for REVIEWS represents a comprehensive set of design principles which if applied to chart design will improve the usefulness of the chart in terms of supporting clinical reasoning. Fourth the electronic observation chart that emerges from this research is demonstrated to be significantly more useful than previously designed charts and represents a significant contribution to practice. Finally the research presents a research design that employs a combination of inductive and deductive design activities to iterate on the design of situated artefacts.

Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.

Data Science Seminar: Generic Big Data Processing for Advancing Situation Awareness and Decision-Support

The generation of heterogeneous big data sources with ever increasing volumes, velocities and veracities over the he last few years has inspired the data science and research community to address the challenge of extracting knowledge form big data. Such a wealth of generated data across the board can be intelligently exploited to advance our knowledge about our environment, public health, critical infrastructure and security. In recent years we have developed generic approaches to process such big data at multiple levels for advancing decision-support. It specifically concerns data processing with semantic harmonisation, low level fusion, analytics, knowledge modelling with high level fusion and reasoning. Such approaches will be introduced and presented in context of the TRIDEC project results on critical oil and gas industry drilling operations and also the ongoing large eVacuate project on critical crowd behaviour detection in confined spaces.

How can geographical information systems and spatial analysis inform a response to prescription opioid misuse? a discussion in the context of existing literature

HEALTHCARE INFORMATION SYSTEMS INTEGRATION USING SOA

The growing pressure to increase the quality of health services, as well as reducing costs, has caused healthcare organizations to increase the use of Information and Communication Technologies (ICT) through the development and adoption of Healthcare Information Systems (HIS). However, the need for exchange of information between HIS and between organizations has also increased, resulting in the problem of interoperability. This problem is considered complex, but the use of Service Oriented Architecture (SOA) appears as a good way to address this issue. This paper presents a systematic review, performed in order to find out how and in which contexts SOA is being used to ensure the interoperability of HIS.

Cálculo de caudales de diseño al cierre de la cuenca del Río Frío en el Departamento de Cundinamarca

90 p.

DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment.

Applicability of an assessment model for healthcare information systems in a public hospital

Assessment processes are essential to guarantee quality and continuous improvement of software in healthcare, as they measure software attributes in their lifecycle, verify the degree of alignment between the software and its objectives and identify unpredicted events. This article analyses the use of an assessment model based on software metrics for three healthcare information systems from a public hospital that provides secondary and tertiary care in the region of Ribeirão Preto. Compliance with the metrics was investigated using questionnaires in guided interviews of the system analysts responsible for the applications. The outcomes indicate that most of the procedures specified in the model can be adopted to assess the systems that serves the organization, particularly in the attributes of compatibility, reliability, safety, portability and usability.