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

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

A Multilevel Secure Relational Database Model with key-polyinstantiation

In multilevel security there is a hierarchy of users or user-levels, in which each user has its own version of information. Most of the existing multilevel secure (MLS) data models support u-polyinstantiation. The only model that supports key-polyinstantiation was proposed by Gadia et al[GS1998, JS1990, CG1995], but work on it remains incomplete. It is important for a model to support key-polyinstantiation because in the real world it is often the case that an object varies in its key value(s) (such as name, SSN, identification number etc.) when it occurs in the beliefs of different users. Thus having a unique key across beliefs limits our ability to accurately model the real world. Our work focuses on the relational database model, supports key-polyinstantiation and has semantics defined in an SQL-like format since most database users are experienced in using SQL and hence such semantics are intuitive and easy to understand

A Multilevel Secure Relational Database Model with key-polyinstantiation

The problem of recognizing motifs from biological data has been well-studied and numerous algorithms, both exact and approximate, have been proposed to address the underlying issue. We strongly believe that open availability and ease of accessibility of quality implementations for such algorithms are critical to the research community, in order to directly reproduce and utilize the results from other studies, so as not to reinvent the wheel. Moreover, it is also important for the implementation to be as generic as possible so that any researcher can to extend it with minimal effort to test a newly implemented algorithmic extension or heuristic. With this motivation, we choose to focus an existing algorithm, PatternBranching and, to a lesser degree, Yang2004. We analyze these approaches for minor heuristical changes & speed-ups by adjusting certain thresholds, and finally, implement the variant in high-level language (Java) using thought through programming practices and generic, extensible interfaces. We also analyze the performance of PatternBranching using a synthetically generated test-suite for a variety of sequence lengths and report the results. Code from this project will be made freely available online to the research community

Making the most of clade selection

Clade selection is unpopular with philosophers who otherwise accept multilevel selection theory. Clades cannot reproduce, and reproduction is widely thought necessary for evolution by natural selection, especially of complex adaptations. Using microbial evolutionary processes as heuristics, I argue contrariwise, that (1) clade growth (proliferation of contained species) substitutes for clade reproduction in the evolution of complex adaptation, (2) clade-level properties favoring persistence – species richness, dispersal, divergence, and possibly intraclade cooperation – are not collapsible into species-level traits, (3) such properties can be maintained by selection on clades, and (4) clade selection extends the explanatory power of the theory of evolution

The Complete MLSK Model—incorporation of lattice operations and XML implementation

Many multilevel security relational models have been proposed and different models offer different advantages. In this paper, we adapt and refine some of the best ideas from these models and add new ones of own to extend our Multilevel Security with Key-polyinstantiation (MLSK) relational model. MLSK now supports relational algebra and user lattice manipulations while ensuring that the soundness, completeness and security that it originally guaranteed are not compromised. We also implement MLSK in a non-relational scenario, thereby demonstrating the extensibility of the model to other environments

The Complete MLSK Model - incorporation of lattice operations and XML implementation

Many multilevel security relational models have been proposed and different models offer different advantages. In this paper, we adapt and refine some of the best ideas from these models and add new ones of own to extend our Multilevel Security with Key-polyinstantiation (MLSK) relational model. MLSK now supports relational algebra and user lattice manipulations while ensuring that the soundness, completeness and security that it originally guaranteed are not compromised. We also implement MLSK in a non-relational scenario, thereby demonstrating the extensibility of the model to other environments

Software modelling languages: A wish list

© 2015 IEEE. Contemporary software engineering modelling tends to rely on general-purpose languages, such as the Unified Modeling Language. However, such languages are practice-based and seldom underpinned with a solid theory-be it mathematical, ontological or concomitant with language use. The future of software modelling deserves research to evaluate whether a language base that is compatible with these various elements as well as being philosophically coherent offers practical advantages to software developers

The Economic Determinants of American Professional Sports Franchise Valuations

This thesis seeks to analyze the impact of regional identities on American professional sports team valuations. Regional identities are classified as any name of a team that is not tied directly to the city that they reside in. For example, the Carolina Panthers have a regional identity because they are not based out of “Carolina”, they are based out of Charlotte, North Carolina. Another example would be the Arizona Cardinals, whose name encompasses the whole state of Arizona rather than Phoenix, the city they are based out of. The leagues that will be involved in this study are the National Football League (NFL), Major League Baseball (MLB), National Hockey League (NHL), Major League Soccer (MLS), and National Basketball Association (NBA). In these five leagues, teams that reside outside of the United States will not be included due to insufficient statistics about their localities. From this study, the goal is to see if the results differ from a similar study previously done with teams in the 1990s from the MLB, NFL, NBA, and NHL. As the MLS continues to add more teams as the years go by, this is a good opportunity to look at how these teams should be named from a valuation perspective. To answer that question, panel data regression will be using statistical software R, with a different regression being done on each of the five leagues