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- Jerrold R. Griggs, Daniel J. Kleitman, Aditya Shastri
- Journal of Graph Theory
- 1989

We consider the problem of finding a spanning tree that maximizes the number of leaves (MaxLeaf). We provide a 3/2-approximation algorithm for this problem when restricted to cubic graphs, improving on the previous 5/3-approximation for this class. To obtain this approximation we define a graph parameter x(G), and construct a tree with at least (n−x(G)+4)/3… (More)

- Tao Jiang, Dhruv Mubayi, Aditya Shastri, Douglas B. West
- SIAM J. Discrete Math.
- 1999

The edge-bandwidth of a graph is the minimum, over all labelings of the edges with distinct integers, of the maximum diierence between labels of two incident edges. We prove that edge-bandwidth is at least as large as bandwidth for every graph, with equality for certain caterpillars. We obtain sharp or nearly-sharp bounds on the change in edge-bandwidth… (More)

- G. W. Peck, Aditya Shastri
- Discrete Mathematics
- 1992

- Aditya Shastri
- COCOON
- 1995

Business Data always growth from kilo byte, mega byte, giga byte, tera byte, peta byte, and so far. There is no way to avoid this increasing rate of data till business still running. Because of this issue, database tuning be critical part of a information system. Tuning a database in a cost-effective manner is a growing challenge. The total cost of… (More)

In present scenario the manual work (Done by Human) cost more to an organization than the automatic work (Done by Machine)and the ratio is increasing day by day as per the tremendous increment in Machine (Hardware + Software) Intelligence. We are moving towards the world where the Machines will be able to perform better than today by their own intelligence.… (More)

- Aditya Shastri
- Discrete Mathematics
- 1990

- Aditya Shastri
- Discrete Applied Mathematics
- 1998

- Tao Jiang, Dhruv Mubayi, Aditya Shastri, Douglas B. West
- SIAM J. Discrete Math.
- 2000

Modern data analysis applications are driven by the Network databases. They are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. There are three major challenges in working with network databases: interoperability due to heterogeneous data… (More)