Farhana Murtaza Choudhury

Maximizing bichromatic reverse spatial and textual k nearest neighbor queries

The problem of maximizing bichromatic reverse k nearest neighbor queries (BRkNN) has been extensively studied in spatial databases. In this work, we present a related query for spatial-textual databases that finds an optimal location, and a set of keywords that maximizes the size of bichromatic reverse spatial textual k nearest neighbors (MaxBRSTkNN). Such a query has many practical applications including social media advertisements where a limited number of relevant advertisements are displayed to each user. The problem is to find the location and the text contents to include in an advertisement so that it will be displayed to the maximum number of users. The increasing availability of spatial-textual collections allows us to answer these queries for both spatial proximity and textual similarity. This paper is the first to consider the MaxBRSTkNN query. We show that the problem is NP-hard and present both approximate and exact solutions.

Maximum visibility queries in spatial databases

Many real-world problems, such as placement of surveillance cameras and pricing of hotel rooms with a view, require the ability to determine the visibility of a given target object from different locations. Advances in large-scale 3D modeling (e.g., 3D virtual cities) provide us with data that can be used to solve these problems with high accuracy. In this paper, we investigate the problem of finding the location which provides the best view of a target object with visual obstacles in 2D or 3D space, for example, finding the location that provides the best view of fireworks in a city with tall buildings. To solve this problem, we first define the quality measure of a view (i.e., visibility measure) as the visible angular size of the target object. Then, we propose a new query type called the k-Maximum Visibility (kMV) query, which finds k locations from a set of locations that maximize the visibility of the target object. Our objective in this paper is to design a query solution which is capable of handling large-scale city models. This objective precludes the use of approaches that rely on constructing a visibility graph of the entire data space. As a result, we propose three approaches that incrementally consider relevant obstacles in order to determine the visibility of a target object from a given set of locations. These approaches differ in the order of obstacle retrieval, namely: query centric distance based, query centric visible region based, and target centric distance based approaches. We have conducted an extensive experimental study on real 2D and 3D datasets to demonstrate the efficiency and effectiveness of our solutions.

Efficient Location-Aware Web Search

Mobile search is quickly becoming the most common mode of search on the internet. This shift is driving changes in user behaviour, and search engine behaviour. Just over half of all search queries from mobile devices have local intent, making location-aware search an increasingly important problem. In this work, we compare the efficiency and effectiveness of two general types of geographical search queries, range queries and k nearest neighbor queries, for common web search tasks. We test state-of-the-art spatial-textual indexing and search algorithms for both query types on two large datasets. Finally, we present a rank-safe dynamic pruning algorithm that is simple to implement and use with current inverted indexing techniques. Our algorithm is more efficient than the tightly coupled best-in-breed hybrid indexing algorithms that are commonly used for top-k spatial textual queries, and more likely to find relevant documents than techniques derived from range queries.

Batch processing of Top-k Spatial-textual Queries

Top-k spatial-textual queries have received significant attention in the research community. Several techniques to efficiently process this class of queries are now widely used in a variety of applications. However, the problem of how best to process multiple queries efficiently is not well understood. Applications relying on processing continuous streams of queries, and offline pre-processing of other queries could benefit from solutions to this problem. In this work, we study practical solutions to efficiently process a set of top-k spatial-textual queries. We propose an efficient best-first algorithm for the batch processing of top-k spatial-textual queries that promotes shared processing and reduced I/O in each query batch. By grouping similar queries and processing them simultaneously, we are able to demonstrate significant performance gains using publicly available datasets.

Finding the optimal location and keywords in obstructed and unobstructed space

The problem of optimal location selection based on reverse k nearest neighbor (R

Continuous maximum visibility query for a moving target

k

MaxBRkNN Queries for Streaming Geo-Data

kNN) queries has been extensively studied in spatial databases. In this work, we present a related query, denoted as a Maximized Bichromatic Reverse Spatial Textual k Nearest Neighbor (MaxST) query, that finds an optimal location and a set of keywords for an object so that the object is a