A k nearest neighbor query q retrieves k objects that lie closest to the query point q among a given set of objects P. With the availability of inexpensive location aware mobile devices, the continuous monitoring of such queries has gained lot of attention and many methods have been proposed for continuously monitoring the kNNs in highly dynamic environment. Multiple continuous queries require real-time results and both the objects and queries issue frequent location updates. Most popular spatial index, R-tree, is not suitable for continuous monitoring of these queries due to its inefficiency in handling frequent updates. Recently, the interest of database community has been shifting towards using grid-based index for continuous queries due to its simplicity and efficient update handling. For kNN queries, the order in which cells of the grid are accessed is very important. In this research, we present two efficient and effective grid access methods, CircularTrip and ArcTrip, that ensure that the number of cells visited for any continuous kNN query is minimum. Our extensive experimental study demonstrates that CircularTrip-based continuous kNN algorithm outperforms existing approaches in terms of both efficiency and space requirement. Moreover, we show that CircularTrip and ArcTrip can be used for many other variants of nearest neighbor queries like constrained nearest neighbor queries, farthest neighbor queries and (k + m)-NN queries. All the algorithms presented for these queries preserve the properties that they visit minimum number of cells for each query and the space requirement is low. Our proposed techniques are flexible and efficient and can be used to answer any query that is hybrid of above mentioned queries. For example, our algorithms can easily be used to efficiently monitor a (k + m) farthest neighbor query in a constrained region with the flexibility that the spatial conditions that constrain the region can be changed by the user at any time.

Honey bees, Apis mellifera, play an important role in many ecosystems, pollinating a wide variety of native, agricultural, and exotic plants. The recent decline in the number of feral and managed honey bee colonies in North America, as well as the arrival of Africanized honey bees, have caused concern about adequate pollination for agricultural crops and natural plant communities. However, little is known about feral colonies, and the feral population is the source for Africanized honey bees as they spread and infiltrate managed populations. The goal of my dissertation was to examine the ecology of feral honey bee colonies, adding the spatial context necessary to understand the population ecology and patterns of resource use by feral honey bees on the Welder Wildlife Refuge. I defined the functional heterogeneity of feral honey bee habitat by identifying the suitability of different habitats for feral colonies based on the distribution and abundance of important resources (cavities, nectar, and pollen). I evaluated the distribution and abundance of feral colonies by examining nest site characteristics, population trends, and spatial and temporal patterns in cavity use. Lastly, I examined resource use by evaluating patterns in pollen collection and identifying where and when honey bees searched for resources. Overall, the Welder Wildlife Refuge provided excellent habitat for feral honey bees, supporting a high density of feral colonies. The dense live oak habitat was the best overall source for cavities, nectar, and pollen. Nectar and pollen were abundant throughout the year, with the exception of December and January, when a large number of honey bees searched for resources. Cavities did not appear to vary in their suitability for feral colonies based on measured structural and environmental attributes, since no cavity attributes were correlated with indices of cavity quality. However, the cavity quality indices varied between cavities, suggesting some cavities were more suitable for feral honey bees than others. Colonies were aggregated within the study area, probably due to the distribution of resources. The invasion of Africanized honey bees appeared to fragment the existing European population, with Africanized colonies aggregated in distribution and European colonies random in distribution.

This study aims to investigate the settlement patterns of Altinova in the Early Bronze Age and its reflection to social and cultural phenomena. Altinova, which is the most arable plain in Eastern Anatolia, is situated in the borders of Elazig province. The region in the Early Bronze Age was the conjunction and interaction area for two main cultural complexes in the Near East, which were Syro-Mesopotamia and Transcaucasia, with a strong local character. The effect of the foreign and local cultural interactions to the settlement patterns of Altinova in the Early Bronze Age and its reflection in the socio-economic structures have been discussed in the social perspective. In addition, the settlement distribution and its system were analyzed through the quantitative methods, that were gravity model, rank-size analysis, and nearest neighbor analysis. The results of these quantitative analyses with the archaeological data have been discussed in the social and theoretical context.

A k nearest neighbor query q retrieves k objects that lie closest to the query point q among a given set of objects P. With the availability of inexpensive location aware mobile devices, the continuous monitoring of such queries has gained lot of attention and many methods have been proposed for continuously monitoring the kNNs in highly dynamic environment. Multiple continuous queries require real-time results and both the objects and queries issue frequent location updates. Most popular spatial index, R-tree, is not suitable for continuous monitoring of these queries due to its inefficiency in handling frequent updates. Recently, the interest of database community has been shifting towards using grid-based index for continuous queries due to its simplicity and efficient update handling. For kNN queries, the order in which cells of the grid are accessed is very important. In this research, we present two efficient and effective grid access methods, CircularTrip and ArcTrip, that ensure that the number of cells visited for any continuous kNN query is minimum. Our extensive experimental study demonstrates that CircularTrip-based continuous kNN algorithm outperforms existing approaches in terms of both efficiency and space requirement. Moreover, we show that CircularTrip and ArcTrip can be used for many other variants of nearest neighbor queries like constrained nearest neighbor queries, farthest neighbor queries and (k + m)-NN queries. All the algorithms presented for these queries preserve the properties that they visit minimum number of cells for each query and the space requirement is low. Our proposed techniques are flexible and efficient and can be used to answer any query that is hybrid of above mentioned queries. For example, our algorithms can easily be used to efficiently monitor a (k + m) farthest neighbor query in a constrained region with the flexibility that the spatial conditions that constrain the region can be changed by the user at any time.