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**11 - 13**of**13**### BODHI: A Database Engine for . . .

, 2006

"... Biodiversity research generates and uses a variety of data spanning across diverse do-mains, including taxonomy, geo-spatial and genetic domains, which vary greatly in their structural features and complexities, query processing costs and storage volumes. In this thesis, we present BODHI, a database ..."

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Biodiversity research generates and uses a variety of data spanning across diverse do-mains, including taxonomy, geo-spatial and genetic domains, which vary greatly in their structural features and complexities, query processing costs and storage volumes. In this thesis, we present BODHI, a database engine that seamlessly integrates these diverse types of data, spanning the range from molecular to organism-level information. BODHI is a native object-oriented database system built around a publically available micro-kernel and extensible query processor, and offers a functionally comprehensive query interface. The server is partitioned into three service modules: object, spatial and sequence, each handling the associated data domain and providing appropriate storage, modeling inter-faces, and evaluation algorithms for predicates over the corresponding data types. To accelerate query response times, a variety of specialized access structures are included for each domain. Our experiments with complex cross-domain queries over a representative

### Clustering in Trees: Optimizing . . .

- JOURNAL OF GRAPH ALGORITHMS AND APPLICATIONS
, 2000

"... This paper considers partitioning the vertices of an n-vertex tree into p disjoint sets C1,C 2,...,C p , called clusters so that the number of vertices in a cluster and the number of subtrees in a cluster are minimized. For this NP-hard problem we present greedy heuristics which di#er in (i) how su ..."

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This paper considers partitioning the vertices of an n-vertex tree into p disjoint sets C1,C 2,...,C p , called clusters so that the number of vertices in a cluster and the number of subtrees in a cluster are minimized. For this NP-hard problem we present greedy heuristics which di#er in (i) how subtrees are identified (using either a best-fit, good-fit, or first-fit selection criteria), (ii) whether clusters are filled one at a time or simultaneously, and (iii) how much cluster sizes can di#er from the ideal size of c vertices per cluster, n = cp. The last criteria is controlled by a constant #,0# #<1, such that cluster C i satisfies (1 - # 2 )c #|C i |#c(1+#), 1 # i # p. For algorithms resulting from combinations of these criteria we develop worst-case bounds on the number of subtrees in a cluster in terms of c, #, and the maximum degree of a vertex. We present experimental results which give insight into how parameters c, #, and the maximum degree of a vertex impact the...

### Exact and Approximation Algortihms for Clustering

, 1997

"... In this paper we present a n O(k1�1=d) time algorithm for solving the k-center problem in R d, under L1 and L2 metrics. The algorithm extends to other metrics, and can be used to solve the discrete k-center problem, as well. We also describe a simple (1 +)-approximation algorithm for the k-center pr ..."

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In this paper we present a n O(k1�1=d) time algorithm for solving the k-center problem in R d, under L1 and L2 metrics. The algorithm extends to other metrics, and can be used to solve the discrete k-center problem, as well. We also describe a simple (1 +)-approximation algorithm for the k-center problem, with running time O(n log k) + (k = ) O(k1�1=d). Finally, we present a n O(k1�1=d) time algorithm for solving the L-capacitated k-center problem, provided that L = (n=k 1�1=d) or L = O(1). We conclude with a simple approximation algorithm for the L-capacitated k-center problem.