Interface between astrophysical datasets and distributed database management systems (DAVID)

This is a status report on the progress of the DAVID (Distributed Access View Integrated Database Management System) project being carried out at Louisiana State University, Baton Rouge, Louisiana. The objective is to implement an interface between Astrophysical datasets and DAVID. Discussed are design details and implementation specifics between DAVID and astrophysical datasets

Gorenstein algebras and Hochschild cohomology

For homomorphism K-->S of commutative rings, where K is Gorenstein and S is essentially of finite type and flat as a K-module, the property that all non-trivial fiber rings of K-->S are Gorenstein is characterized in terms of properties of the cohomology modules Ext_n^{S\otimes_KS}S{S\otimes_KS}.Comment: This is the published version, except for updates to references and bibliography. Sections 3, 4 and 8 have been removed from the preceding version, arXiv:0704.3761v2. Substantial generalizations of results in those sections are proved in our paper with Joseph Lipman and Suresh Nayak, arXiv:0904.400

Degree Ranking Using Local Information

Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only $1\%$ samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erd\H{o}s-R\'{e}nyi model. Results show that the proposed methods can be efficiently used for random networks as well