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.

Indiana University-Purdue University Indianapolis (IUPUI) / Query segmentation module is an integral part of Natural Language Processing which analyzes users' query and divides them into separate phrases. Published works on the query segmentation focus on the web search using Google n-gram frequencies corpus or text retrieval from relational databases. However, this module is also useful in the domain of E-Commerce for product search. In this thesis, we will discuss query segmentation in the context of the E-Commerce area. We propose a hybrid unsupervised segmentation methodology which is based on prefix tree, mutual information and relative frequency count to compute the score of query pairs and involve Wikipedia for new words recognition. Furthermore, we use two unique E-Commerce evaluation methods to quantify the accuracy of our query segmentation method.