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We consider the issue of query and data propagation in the context of geosen-sor networks over geo-aware sensors. In such networks, techniques for efficient propagation of queries and data play a significant role in reducing energy consumption. Georouting is a new technique for the broadcasting of localized data and queries in geo-aware sensor networks; it… (More)

— In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM (Transaction Mapping) algorithm from hereon. In this algorithm, transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space and the counting of itemsets is performed by… (More)

This paper reflects our experience with CQA/CDB, a prototype rational linear constraint database. First, we show that the standard semantics of constraint databases lead to an anomaly when queried in the presence of missing attributes. In CQA/CDB, this anomaly is avoided by enriching the CDB relational schema, resulting in heteroge-nous databases. Then, we… (More)

In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is N P-hard. Numerous approximation algorithms have been proposed for this problem. In this paper, we proposed three constant approximation algorithms for k-means clustering. The first algorithm runs in time O((k) k nd), where k is the number of… (More)

In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM algorithm from hereon. In this algorithm, we employ the vertical representation of a database. Transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space thus reducing the number… (More)

In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is NP-hard. Numerous approximation algorithms have been proposed for this problem. In this paper, we propose three constant approximation algorithms for k-means clustering. The first algorithm runs in time O((k) k nd), where k is the number of… (More)