Approximating the Minimum Chain Completion problem

A bipartite graph G=(U,V,E) is a chain graph [M. Yannakakis, Computing the minimum fill-in is NP-complete, SIAM J. Algebraic Discrete Methods 2 (1) (1981) 77–79] if there is a bijection such that Γ(π(1))Γ(π(2))Γ(π(|U|)), where Γ is a function that maps a node to its neighbors.We give approximation algorithms for two variants of the Minimum Chain Completion problem, where we are given a bipartite graph G(U,V,E), and the goal is find the minimum set of edges F that need to be added to G such that the bipartite graph G^′=(U,V,E^′) (E^′=EF) is a chain graph.     

Video data storage policies: an access frequency based approach

Video applications are characterized by their increased requirements for huge storage spaces and timing synchronization. Video data storage is a critical issue due to the so-called I/O bottleneck problem in relation to the quality of service while accessing video applications. The main contribution of the paper is that it considers video data dependencies, access frequencies and timing constraints in order to introduce a video data representation model which guides the storage policies. Two video data representation levels are considered to capture the frequencies of accesses at external (video objects) and internal (video clips) levels. A simulation model has been developed in order to evaluate the placement strategies. Video data placement is performed on a tertiary storage subsystem by both constructive and iterative improvement policies. Iterative improvement placement has been proven to outperform the other video data placement approaches.     

Detecting anomalous access patterns in relational databases

A considerable effort has been recently devoted to the development of Database Management Systems (DBMS) which guarantee high assurance and security. An important component of any strong security solution is represented by Intrusion Detection (ID) techniques, able to detect anomalous behavior of applications and users. To date, however, there have been few ID mechanisms proposed which are specifically tailored to function within the DBMS. In this paper, we propose such a mechanism. Our approach is based on mining SQL queries stored in database audit log files. The result of the mining process is used to form profiles that can model normal database access behavior and identify intruders. We consider two different scenarios while addressing the problem. In the first case, we assume that the database has a Role Based Access Control (RBAC) model in place. Under a RBAC system permissions are associated with roles, grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals while holding a specific role, behave differently than expected. An important advantage of providing an ID technique specifically tailored to RBAC databases is that it can help in protecting against insider threats. Furthermore, the existence of roles makes our approach usable even for databases with large user population. In the second scenario, we assume that there are no roles associated with users of the database. In this case, we look directly at the behavior of the users. We employ clustering algorithms to form concise profiles representing normal user behavior. For detection, we either use these clustered profiles as the roles or employ outlier detection techniques to identify behavior that deviates from the profiles. Our preliminary experimental evaluation on both real and synthetic database traces shows that our methods work well in practical situations. This material is based upon work supported by the National Science Foundation under Grant No. 0430274 and the sponsors of CERIAS.