Mohammed Eunus Ali

Group trip planning queries in spatial databases

In this paper we discuss a new type of query in Spatial Databases, called the Trip Planning Query (TPQ). Given a set of points of interest P in space, where each point belongs to a specific category, a starting point S and a destination E, TPQ retrieves the best trip that starts at S, passes through at least one point from each category, and ends at E. For example, a driver traveling from Boston to Providence might want to stop to a gas station, a bank and a post office on his way, and the goal is to provide him with the best possible route (in terms of distance, traffic, road conditions, etc.). The difficulty of this query lies in the existence of multiple choices per category. In this paper, we study fast approximation algorithms for TPQ in a metric space. We provide a number of approximation algorithms with approximation ratios that depend on either the number of categories, the maximum number of points per category or both. Therefore, for different instances of the problem, we can choose the algorithm with the best approximation ratio, since they all run in polynomial time. Furthermore, we use some of the proposed algorithms to derive efficient heuristics for large datasets stored in external memory. Finally, we give an experimental evaluation of the proposed algorithms using both synthetic and real datasets.

Efficient Computation of Trips with Friends and Families

A group of friends located at their working places may want to plan a trip to visit a shopping center, have dinner at a restaurant, watch a movie at a theater, and then finally return to their homes with the minimum total trip distance. For a group of spatially dispersed users a group trip planning (GTP) query returns points of interests (POIs) of different types such as a shopping center, a restaurant and a movie theater that minimize the aggregate trip distance for the group. The aggregate trip distance could be the sum or maximum of the trip distances of all users in the group, where the users travel from their source locations via the jointly visited POIs to their individual destinations. In this paper, we develop both optimal and approximation algorithms for GTP queries for both Euclidean space and road networks. Processing GTP queries in real time is a computational challenge as trips involve POIs of multiple types and computation of aggregate trip distances. We develop novel techniques to refine the POI search space for a GTP query based on geometric properties of ellipses, which in turn significantly reduces the number of aggregate trip distance computations. An extensive set of experiments on a real and synthetic datasets shows that our approach outperforms the most competitive approach on an average by three orders of magnitude in terms of processing time.

A motion-aware approach for efficient evaluation of continuous queries on 3D object databases

With recent advances in mobile computing technologies, mobile devices can render 3D objects realistically. Users of these devices such as tourists, mixed-reality gamers, and rescue officers, often need real-time retrieval of 3D objects over wireless networks. Due to bandwidth and latency restrictions in mobile settings, efficient continuous retrieval of 3D objects is a major challenge. In this paper, we present a motion-aware approach to this problem in a client-server model. Specifically, we propose: (i) representing 3D objects in multiple resolutions through wavelets to facilitate motion-aware incremental retrieval, (ii) motion-aware buffer management schemes for both client and server, (iii) an efficient index structure for 3D objects represented by wavelets, and (iv) techniques for processing group queries exploiting group motion behavior of clients. The results of our extensive experimental study demonstrate the effectiveness of our solution.

Protecting privacy for group nearest neighbor queries with crowdsourced data and computing

User privacy in location-based services (LBSs) has become an important research area. We introduce a new direction to protect user privacy that evaluates LBSs with crowdsourced data and computation and eliminates the role of a location-based service provider. We focus on the group nearest neighbor (GNN) query that allows a group to meet at their nearest point of interest such as a restaurant that minimizes the total or maximum distance of the group. We develop a crowdsource-based approach, called PrivateMeetUp, to evaluate GNN queries in a privacy preserving manner and implement a working prototype of PrivateMeetUp.

Efficient Computation of Group Optimal Sequenced Routes in Road Networks

The proliferation of location-based social networks allows people to access location-based services as a group. We address Group Optimal Sequenced Route (GOSR) queries that enable a group to plan a trip with a minimum aggregate trip distance. The trip starts from the source locations of the group members, goes via a predefined sequence of different point of interests (POIs) such as a restaurant, shopping center and movie theater, and ends at the destination locations of the group members. The aggregate trip distance can be the total or the maximum trip distance of the group members. We introduce a novel approach to efficiently compute group optimal sequenced routes in road networks. We exploit elliptical properties to refine the POI search space and develop efficient algorithms for GOSR queries. Experiments show that our approach outperforms a naive approach significantly in terms of processing time and I/Os.

User Interaction Based Community Detection in Online Social Networks

Discovering meaningful communities based on the interactions of different people in online social networks (OSNs) is an active research topic in recent years. However, existing interaction based community detection techniques either rely on the content analysis or only consider underlying structure of the social network graph, while identifying communities in OSNs. As a result, these approaches fail to identify active communities, i.e., communities based on actual interactions rather than mere friendship. To alleviate the limitations of existing approaches, we propose a novel solution of community detection in OSNs. The key idea of our approach comes from the following observations: (i) the degree of interaction between each pair of users can widely vary, which we term as the strength of ties, and (ii) for each pair of users, the interactions with mutual friends, which we term the group behavior, play an important role to determine their belongingness to the same community. Based on these two observations, we propose an efficient solution to detect communities in OSNs. The detailed experimental study shows that our proposed algorithm significantly outperforms state-of-the-art techniques for both real and synthetic datasets

SafeStreet: empowering women against street harassment using a privacy-aware location based application

Sexual harassment of women in public places (e.g., foot-paths, buses, and shopping malls) of major cities in developing countries is a growing concern. These harassments can happen in various forms ranging from commenting, catcalling, and staring to touching and groping, to attacking and raping. Though, the most severe form of harassments such as attacking and raping get some attention from the society, NGOs and law-enforcement agencies, unfortunately, other forms of harassments that are more widespread in public places remain largely un-attended or ignored in our conservative society. However, these harassments are more common and can have various negative psychological impacts on women that include a persistent feeling of insecurity, loss of self-esteem, restricted participation in daily life activities in public places. In this paper, we propose a crowd-powered privacy-aware location based mobile application, SafeStreet, that empowers women in public places against sexual harassments. SafeStreet allows a women to privately capture and share her own experiences in the street. SafeStreet enables a women to find a safe path, i.e., the path to a destination that has less harassment hazard, at any point of time.

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.

Visibility Color Map for a Fixed or Moving Target in Spatial Databases

The widespread availability of 3D city models enables us to answer a wide range of spatial visibility queries in the presence of obstacles (e.g., buildings). Example queries include “what is the best position for placing a billboard in a city?” or “which hotel gives the best view of the city skyline?”. These queries require computing and differentiating the visibility of a target object from each viewpoint of the surrounding spe. A recent approach models the visibility of a fixed target object from the surrounding area with a visibility color map (VCM), where each point in the space is assigned a color value denoting the visibility measure of the target. In the proposed VCM, a viewpoint is simply discarded (i.e., considered as non-visible) if an obstacle even slightly blocks the view of the target from the viewpoint, which restricts its applicability for a wide range of applications. To alleviate this limitation, in this paper, we propose a scalable, efficient and comprehensive solution to construct a VCM for a fixed target that considers the partial visibility of the target from viewpoints. More importantly, our proposed data structures for the fixed target support incremental updates of the VCM if the target moves to near-by positions. Our experimental results show that our approach is orders of magnitude faster than the straightforward approach.

Load balancing for moving object management in a P2P network

Online games and location-based services now form the potential application domains for the P2P paradigm. In P2P systems, balancing the workload is essential for overall performance. However, existing load balancing techniques for P2P systems were designed for stationary data. They can produce undesirable workload allocations for moving objects that is continuously updated. In this paper, we propose a novel load balancing technique for moving object management using a P2P network. Our technique considers the mobility of moving objects and uses an accurate cost model to optimize the performance in the management network, in particular for handling location updates in tandem with query processing. In a comprehensive set of experiments, we show that our load balancing technique gives constantly better update and query performance results than existing load balancing techniques.