Hoda M. O. Mokhtar

An extended k-means technique for clustering moving objects

This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft.

A Flexible Tool for Web Service Selection in Service Oriented Architecture

Web Services are emerging technologies that enable application to application communication and reuse of services over Web. Semantic Web improves the quality of existing tasks, including Web services discovery, invocation, composition, monitoring, and recovery through describing Web services capabilities and content in a computer interpretable language. To provide most of the requested Web services, a Web service matchmaker is usually required. Web service matchmaking is the process of finding an appropriate provider for a requester through a middle agent. To provide the right service for the right user request, Quality of service (QoS)-based Web service selection is widely used. Employing QoS in Web service selection helps to satisfy user requirements through discovering the best service(s) in terms of the required QoS. Inspired by the mode of the Internet Web search engine, like Yahoo, Google, in this paper we provide a QoS-based service selection algorithm that is able to identify the best candidate semantic Web service(s) given the description of the requested service(s) and QoS criteria of user requirements. In addition, our proposed approach proposes a ranking method for those services. We also show how we employ data warehousing techniques to model the service selection problem. The proposed algorithm integrates traditional match making mechanism with data warehousing techniques. This integration of methodologies enables us to employ the historical preference of the user to provide better selection in future searches. The main result of the paper is a generic framework that is implemented to demonstrate the feasibility of the proposed algorithm for QoS-based Web application. Our presented experimental results show that the algorithm indeed performs well and increases the system reliability.

Directional Skyline Queries

Continuous monitoring of queries over moving objects has become an important topic as it supports a wide range of useful mobile applications. A continuous skyline query involves both static and dynamic dimensions. In the dynamic dimension, the data object not only has a distance from the query object, but it also has a direction with respect to the query object motion. In this paper, we propose a direction-oriented continuous skyline query algorithm to compute the skyline objects with respect to the current position of the user. The goal of the proposed algorithm is to help the user to retrieve the best objects that satisfy his/her constraints and fall either in any direction around the query object, or is aligned along the object’s direction of motion. We also create a pre-computed skyline data set that facilitates skyline update, and enhances query running time and performance. Finally, we present experimental results to demonstrate the performance and efficiency of our proposed algorithms.

A Keystroke Dynamics Based Approach for Continuous Authentication

In this paper, we present the application of keystroke dynamics for continuous user authentication in desktop platform. We show the differences between static and continuous systems based on keystroke dynamics in terms of creating the template and authentication phase. The key factor in the continuous authentication system is monitoring the genuineness of the user during the whole session, and not only at log-in. Moreover, we propose a general approach for continuous authentication based on keystroke dynamics. In our experiments, we use the email application as a case study to present the effectiveness and efficiency of our proposed approach. Our main conclusion is that using keystroke dynamics can serve as a feasible and acceptable measure for continuous user authentication. The investigations have shown that it is feasible to authenticate users based on keystroke dynamics for continuous authentication systems.

Multi-level continuous skyline queries (MCSQ)

Most of the current work on skyline queries mainly dealt with querying static query points over static data sets. With the advances in wireless communication, mobile computing, and positioning technologies, it has become possible to obtain and manage (model, index, query, etc.) the trajectories of moving objects in real life, and consequently the need for continuous skyline query processing has become more and more pressing. In this paper, we address the problem of efficiently maintaining continuous skyline queries which contain both static and dynamic attributes. We present a Multi-level Continuous Skyline Query (MCSQ) algorithm, which basically creates a pre-computed skyline data set, facilitates skyline update, and enhances query running time and performance. Our algorithm in brief proceeds as follows: First, we distinguish the data points that are permanently in the skyline and use them to derive a search bound. Second, we establish a pre-computed data set for dynamic skyline that depends on the number of skyline levels (M) which is later used to update the first (initial) skyline points. Finally, every time the skyline needs to be updated we use the pre-computed data sets of skyline to update the previous skyline set and consequently updating first skyline. Finally, we present experimental results to demonstrate the performance and efficiency of our algorithm.

QUESTO: A Query Language for Uncertain and Exact Spatio-temporal Objects

Querying moving objects is a crucial ingredient in many applications involving moving objects. Being able to query the location of an object is important to achieve services like E-911, m-commerce, fleet management, etc. However, moving objects have inherent uncertainty in their location information. Obtaining the exact location of an object at every time instant is infeasible. In this paper we aim towards designing an algebraic query language that can query and reason about trajectory properties of moving objects both with precise and uncertain trajectories.

ICF based automation system for spinal cord injuries rehabilitation

Disabilities, specially the ones caused by injuries of spinal cord, affect both peoples behavior and participation in daily activities. Therefore, developing automated systems that monitor and assess the progress of Spinal Cord Injury (SCI) patients represents an accelerated need. In this paper we utilize the framework of the International Classification for Functioning, disability and health (ICF) in order to propose an automatic system for SCI patients progress monitoring and rehabilitation. The ICF is a reference classification system that aims at improving integration of health information and ensuring the collection of accurate health data. The proposed system is divided into three phases; namely (1) ICF code construction, (2) progress monitoring, and (3) progress assessment phases. Comparing the manual expert-based progress assessment to experimental results obtained by the proposed system in this article, an efficiency of 100% has been achieved by the three previously stated phases of the proposed system.

Processing universal quantification queries using MapReduce

Universal quantification queries are an interesting type of queries that are used in many applications. Although, universal queries have gained their importance in querying traditional databases that are usually implemented on a single machine; nowadays, the rapid growth in information and the extremely fast increase in the number of Web users have driven the need to migrate to new processing environments that are capable to access, process, store, and maintain huge amounts of valuable data. Thus, the use of cloud emerged as a solution for several big data problems. In this paper, we present a number of computing techniques for processing universal quantification queries on large datasets using the popular MapReduce framework. In addition, we present experimental results that show the speed-up and scale-out properties of our proposed algorithms.

Dynamic k-means: a clustering technique for moving object trajectories

k-means clustering algorithm is a famous clustering algorithm applied in many applications. However, traditional k-means algorithm assumes that the initial number of centroids is known in advance. This dependence on the number of clusters and the initial choice of the centroids affect both the performance and accuracy of the algorithm. To overcome this problem, in this paper, we propose a heuristic that dynamically calculates k based on the movement patterns in the trajectory dataset and optimally initialises the k centroids. We basically consider distinct similar moving patterns as an initialisation for the number of clusters (k). In addition, we design a scalable tool for mining moving object data through (an architecture composed of) a rich set of cluster refinement modules that operate on top of the moving object database enabling users to analyse trajectory data from different perspectives. We validate our approaches experimentally on both real and synthetic data and test the performance and accuracy of our techniques.

Enhanced Authentication Mechanisms for Desktop Platform and Smart Phones

With hundreds of millions using computers and mobile devices all over the globe, these devices have an established position in modern society. Nevertheless, most of these devices use weak authentication techniques with passwords and PINs which can be easily hacked. Thus, stronger identification is needed to ensure data security and privacy. In this paper, we will explain the employment of biometrics to computer and mobile platforms. In addition, the possibility of using keystroke and mouse dynamics for computer authentication is being checked. Finally, we propose an authentication scheme for smart phones that shows positive results.