Model-Based Mitigation of Availability Risks

The assessment and mitigation of risks related to the availability of the IT infrastructure is becoming increasingly important in modern organizations. Unfortunately, present standards for Risk Assessment and Mitigation show limitations when evaluating and mitigating availability risks. This is due to the fact that they do not fully consider the dependencies between the constituents of an IT infrastructure that are paramount in large enterprises. These dependencies make the technical problem of assessing availability issues very challenging. In this paper we define a method and a tool for carrying out a Risk Mitigation activity which allows to assess the global impact of a set of risks and to choose the best set of countermeasures to cope with them. To this end, the presence of a tool is necessary due to the high complexity of the assessment problem. Our approach can be integrated in present Risk Management methodologies (e.g. COBIT) to provide a more precise Risk Mitigation activity. We substantiate the viability of this approach by showing that most of the input required by the tool is available as part of a standard business continuity plan, and/or by performing a common tool-assisted Risk Management

The Minnesota Supportive Housing and Managed Care Pilot: Evaluation Summary

Summarizes an evaluation of a pilot program to end homelessness for those with complex needs by providing housing and intensive supports. Examines outcomes including housing stability, mental health, and alcohol and/or drug use, as well as service costs

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

Jefferson Digital Commons quarterly report: April-June 2019

This quarterly report includes: Articles CREATE Day Presentations Dissertations From the Archives Grand Rounds and Lectures House Staff Quality Improvement and Patient Safety Posters JCIPE Student Hotspotting Posters Journals and Newsletters MPH Capstone Presentations Posters Sigma Xi Research Day What People are Saying About the Jefferson Digital Common

Towards Optimal IT Availability Planning: Methods and Tools

The availability of an organisation’s IT infrastructure is of vital importance for supporting business activities. IT outages are a cause of competitive liability, chipping away at a company financial performance and reputation. To achieve the maximum possible IT availability within the available budget, organisations need to carry out a set of analysis activities to prioritise efforts and take decisions based on the business needs. This set of analysis activities is called IT availability planning. Most (large) organisations address IT availability planning from one or more of the three main angles: information risk management, business continuity and service level management. Information risk management consists of identifying, analysing, evaluating and mitigating the risks that can affect the information processed by an organisation and the information-processing (IT) systems. Business continuity consists of creating a logistic plan, called business continuity plan, which contains the procedures and all the useful information needed to recover an organisations’ critical processes after major disruption. Service level management mainly consists of organising, documenting and ensuring a certain quality level (e.g. the availability level) for the services offered by IT systems to the business units of an organisation. There exist several standard documents that provide the guidelines to set up the processes of risk, business continuity and service level management. However, to be as generally applicable as possible, these standards do not include implementation details. Consequently, to do IT availability planning each organisation needs to develop the concrete techniques that suit its needs. To be of practical use, these techniques must be accurate enough to deal with the increasing complexity of IT infrastructures, but remain feasible within the budget available to organisations. As we argue in this dissertation, basic approaches currently adopted by organisations are feasible but often lack of accuracy. In this thesis we propose a graph-based framework for modelling the availability dependencies of the components of an IT infrastructure and we develop techniques based on this framework to support availability planning. In more detail we present: 1. the Time Dependency model, which is meant to support IT managers in the selection of a cost-optimal set of countermeasures to mitigate availability-related IT risks; 2. the Qualitative Time Dependency model, which is meant to be used to systematically assess availability-related IT risks in combination with existing risk assessment methods; 3. the Time Dependency and Recovery model, which provides a tool for IT managers to set or validate the recovery time objectives on the components of an IT architecture, which are then used to create the IT-related part of a business continuity plan; 4. A2THOS, to verify if availability SLAs, regulating the provisioning of IT services between business units of the same organisation, can be respected when the implementation of these services is partially outsourced to external companies, and to choose outsourcing offers accordingly. We run case studies with the data of a primary insurance company and a large multinational company to test the proposed techniques. The results indicate that organisations such as insurance or manufacturing companies, which use IT to support their business can benefit from the optimisation of the availability of their IT infrastructure: it is possible to develop techniques that support IT availability planning while guaranteeing feasibility within budget. The framework we propose shows that the structure of the IT architecture can be practically employed with such techniques to increase their accuracy over current practice

Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.Comment: 7 pages, 7 figure

Juvenile Probation Officers Call for a New Response to Teen Drug and Alcohol Use and Dependency

Shares lessons learned from RWJF's Reclaiming Futures initiative from a juvenile justice practitioner's perspective. Discusses the need to reform the system's treatment services, the challenges faced at the ten project sites, and recommendations

Dynamic microsimulation of health care demand, health care finance and the economic impact of health behaviours: survey and review

This paper reviews the issues to be faced in attempting to create a microsimulation of health care demand, health care finance and the economic impact of health behaviour. These issues identified via an in-depth review of seven dynamic microsimulation models, selected from an initial set of 27 models in order to highlight the main differences in approaches and modelling options currently adopted. After presenting a brief description of each of the seven selected models, the main modelling approaches are summarized and critically appraised using five main distinguishing criteria. These criteria are the use of alignment techniques, model complexity (as reflected in the range of variables used), theoretical foundations, type of starting population, and the extent and detail of financial issues covered. Building upon this appraisal, the paper goes on to show how the ‘12 SAGE lessons’ apply in the field of health care microsimulation. The trade-off between complexity and predictive power is shown to be key. Finally an appendix summarises the main features of all 27 of the dynamic microsimulation models originally surveyed.health care, microsimulation