Logical Foundations of Multilevel Databases

International audienceIn this paper, we propose a formal model for multilevel databases. This model aims at being a generic model, that is it can be interpreted for any kind of database (relational, object-oriented...). Our model has three layers. The first layer corresponds to a model for a non-protected database. The second layer corresponds to a model for a multilevel database. In this second layer, we propose a list of theorems that must be respected in order to build a secure multilevel database. We also propose a new solution to manage cover stories without using the ambiguous technique of polyinstantiation. The third layer corresponds to a model for a MultiView database, that is, a database that provides at each security level a consistent view of the multilevel database. Finally, as an illustration, we interpret our 3-layer model in the case of an object-oriented database

The Usefulness of Multilevel Hash Tables with Multiple Hash Functions in Large Databases

In this work, attempt is made to select three good hash functions which uniformly distribute hash values that permute their internal states and allow the input bits to generate different output bits. These functions are used in different levels of hash tables that are coded in Java Programming Language and a quite number of data records serve as primary data for testing the performances. The result shows that the two-level hash tables with three different hash functions give a superior performance over one-level hash table with two hash functions or zero-level hash table with one function in term of reducing the conflict keys and quick look-up for a particular element. The result assists to reduce the complexity of join operation in query language from O(n2) to O(1) by placing larger query result, if any, in multilevel hash tables with multiple hash functions and generate shorter query result

FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Applicability of Temporal Data Models to Query Multilevel Security Databases: A Case Study

In a multilevel security database there are multiple beliefs about a given real world object. The ability of a database model to accommodate multiple beliefs is termed polyinstantiation in the multilevel security literature. In this paper we remark that in an abstract sense polyinstantiation is a priori present in all models for temporal and spatial databases. In particular we investigate the applicability of the parametric model for temporal data to query multilevel security data and, as a case study, compare it to a model for multilevel security given by Winslett, Smith, and Qian