Employing a Multilevel Secure Approach in CRM Systems

This research shows how Multilevel Secure (MLS) data models can be used in a Customer Relationship Management (CRM) context. MLS models were originally developed as database models for the management of information in environments characterized by a strict hierarchy of security levels, such as military institutions and government security agencies. Improvements in evolving database technologies have made MLS data modeling practical as well as theoretically appealing. This paper illustrates how an MLS model can be used as a part of the technology for coordinating business-customer interactions with the objective of building long-term customer loyalty. Several examples are used to show how organizing a database management system based on MLS principles can be used to help businesses provide consistent and appropriate content to various customers and partners. Improvements in flexibility and cost of applications, as well as opportunities for new CRM strategies, are discussed as potential benefits of integrating MLS and CRM technology

Multilevel Hashing based Access Control for Authentication and Security in Relational Database Management System

The relational database is very widely used in these days and so as the relational database security is very vital to protect from different kinds of threats and attacks. The security of relational database is very important because now these days all data and information are stored in database by some form of database objects including user’s personal information like credit/debit card details, username, passwords etc. as well as confidential data of business organization and companies. Many researches are done in order to protect the relational database from these vulnerabilities but the methodologies of relational database security are not able to protect the relational database from all security issues. Authentication of users is an important issue in the database that is needed to be addressed most because it can give full access to the database objects. In this paper we present various security issues that can cause degradation in relational database security and we also identify various problems in the current database security policies. In this paper we propose the use of Multilevel Hashing based Access control mechanism for authentication and security in Relational database

Variation in compulsory psychiatric inpatient admission in England:a cross-sectional, multilevel analysis

Background: Rates of compulsory admission have increased in England in recent decades, and this trend is accelerating. Studying variation in rates between people and places can help identify modifiable causes. Objectives: To quantify and model variances in the rate of compulsory admission in England at different spatial levels and to assess the extent to which this was explained by characteristics of people and places. Design: Cross-sectional analysis using multilevel statistical modelling. Setting: England, including 98% of Census lower layer super output areas (LSOAs), 95% of primary care trusts (PCTs), 93% of general practices and all 69 NHS providers of specialist mental health services. Participants: 1,287,730 patients. Main outcome measure: The study outcome was compulsory admission, defined as time spent in an inpatient mental illness bed subject to the Mental Health Act (2007) in 2010/11. We excluded patients detained under sections applying to emergency assessment only (including those in places of safety), guardianship or supervision of community treatment. The control group comprised all other users of specialist mental health services during the same period. Data sources: The Mental Health Minimum Data Set (MHMDS). Data on explanatory variables, characterising each of the spatial levels in the data set, were obtained from a wide range of sources, and were linked using MHMDS identifiers. Results: A total of 3.5% of patients had at least one compulsory admission in 2010/11. Of (unexplained) variance in the null model, 84.5% occurred between individuals. Statistically significant variance occurred between LSOAs [6.7%, 95% confidence interval (CI) 6.2% to 7.2%] and provider trusts (6.9%, 95% CI 4.3% to 9.5%). Variances at these higher levels remained statistically significant even after adjusting for a large number of explanatory variables, which together explained only 10.2% of variance in the study outcome. The number of provider trusts whose observed rate of compulsory admission differed from the model average to a statistically significant extent fell from 45 in the null model to 20 in the fully adjusted model. We found statistically significant associations between compulsory admission and age, gender, ethnicity, local area deprivation and ethnic density. There was a small but statistically significant association between (higher) bed occupancy and compulsory admission, but this was subsequently confounded by other covariates. Adjusting for PCT investment in mental health services did not improve model fit in the fully adjusted models. Conclusions: This was the largest study of compulsory admissions in England. While 85% of the variance in this outcome occurred between individuals, statistically significant variance (around 7% each) occurred between places (LSOAs) and provider trusts. This higher-level variance in compulsory admission remained largely unchanged even after adjusting for a large number of explanatory variables. We were constrained by data available to us, and therefore our results must be interpreted with caution. We were also unable to consider many hypotheses suggested by the service users, carers and professionals who we consulted. There is an imperative to develop and evaluate interventions to reduce compulsory admission rates. This requires further research to extend our understanding of the reasons why these rates remain so high. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Implementing Belief-Consistent Multilevel Secure Relational Data Model: Issues and Solutions

This paper summarizes our efforts in implementing a working multi-level secure database prototype. We have chosen Belief-Consistent Multilevel Secure Relational Data Model (BCMLS) as a basis for our prototype because of its comprehensive semantics for interpreting all stored information. While semantically superior to other models, this model has not been implemented as a working system before. Our prototype, which was created on an Informix database server with a PHP web client, enables insertion, deletion and update of multi-level data while addressing the underlying model complexities through a number of original solutions