Ramzi A. Haraty

Regression testing of database applications

Database applications features such as SQL, exception programming, integrity constraints, and table triggers pose some difficulties for maintenance activities, especially for regression testing that follows modifications to database applications. In this work, we address these difficulties and propose a two-phase regression testing methodology. In Phase 1, we explore control flow and data flow analysis issues of database applications. Then, we propose an impact analysis technique that is based on dependencies that exist among the components of database applications. This analysis leads to selecting test cases from the initial test suite for regression testing the modified application. In Phase 2, further reduction in the regression test cases is performed by using reduction algorithms. We present two such algorithms. Finally, a maintenance environment for database applications is described. Our experience with the environment prototype shows promising results.

An auto-indexing method for Arabic text

This work addresses the information retrieval problem of auto-indexing Arabic documents. Auto-indexing a text document refers to automatically extracting words that are suitable for building an index for the document. In this paper, we propose an auto-indexing method for Arabic text documents. This method is mainly based on morphological analysis and on a technique for assigning weights to words. The morphological analysis uses a number of grammatical rules to extract stem words that become candidate index words. The weight assignment technique computes weights for these words relative to the container document. The weight is based on how spread is the word in a document and not only on its rate of occurrence. The candidate index words are then sorted in descending order by weight so that information retrievers can select the more important index words. We empirically verify the usefulness of our method using several examples. For these examples, we obtained an average recall of 46% and an average precision of 64%.

Secure transaction management and query processing in multilevel secure database systems

In a multilevel secure database system, every data item is assigned a classification level and each user that accesses the data has a clearance level. Users can read data items that exist at a lower level and write at their own level. In such systems multilevel databases may be partitioned and stored as single-level databases. To construct a multilevel relation, repeated joins of different single level base relations are taken, thus resulting in delayed query response time. However, trying t o accelerate transactions might establish covert channels which can send high level sensitive information to low level users. This paper describes secure algorithms for both concurrency control and query processing in such systems and shows how these two techniques could be integrated together to give best performance. The data structures, needed in these algorithms, are based on bit vector techniques developed in [13], and [14]. Our method accelerates both read-only (queries) and read-write transactions in a secure and correct manner.

An enhanced k -means clustering algorithm for pattern discovery in healthcare data

The huge amounts of data generated by media sensors in health monitoring systems, by medical diagnosis that produce media (audio, video, image, and text) content, and from health service providers are too complex and voluminous to be processed and analyzed by traditional methods. Data mining approaches offer the methodology and technology to transform these heterogeneous data into meaningful information for decision making. This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. The proposed algorithm, which we call G-means, utilizes a greedy approach to produce the preliminary centroids and then takes k or lesser passes over the dataset to adjust these center points. Our experimental results, which were used in an increasing manner on the same dataset, show that G-means outperforms k-means in terms of entropy and F-scores. The experiments also yield better results for G-means in terms of the coefficient of variance and the execution time.

The TOR Data Communication System

Since the day the Internet became a common and reliable mechanism for communication and data transfer, security officers and enthusiasts rallied to enforce security standards on data transported over the globe. Whenever a user tries communicating with another recipient on the Internet, vital information is sent over different networks until the information is dropped, intercepted, or normally reaches the recipient. Critical information traversing networks is usually encrypted. In order to conceal the senders identity, different implementations have proven successful ¿ one of which is the invention of anonymous networks. This paper thoroughly investigates one of the most common and existing techniques used during data communication for avoiding traffic analysis as well as assuring data integrity ¿ the onion router (TOR). The paper also scrupulously presents the benefits and drawbacks of TOR.

C2 secure database management systems—a comparative study

In this paper we discuss the architecture, security policy, and protection mechanisms of four National Security Agency C2 certified database management systems. We compare their techniques used for protecting the database against users who are not authorized to access a part of the database or the whole database.

Semantics-based approach for detecting flaws, conflicts and redundancies in XACML policies

Display Omitted We provide policy analysis scheme to detect access contradictions among web services.We propose semantic-based policy analysis through deductive logic and inference rules.We present flaw, conflict and redundancy detection algorithms for XACML policy analysis.We show through experiments that SBA-XACML provides efficient detection mechanisms. XACML (eXtensible Access Control Markup Language) policies, which are widely adopted for defining and controlling dynamic access among Web/cloud services, are becoming more complex in order to handle the significant growth in communication and cooperation between individuals and composed services. However, the large size and complexity of these policies raise many concerns related to their correctness in terms of flaws, conflicts and redundancies presence. This paper addresses this problem through introducing a novel set and semantics based scheme that provides accurate and efficient analysis of XACML policies. First, our approach resolves the complexity of policies by elaborating an intermediate set-based representation to which the elements of XACML are automatically converted. Second, it allows to detect flaws, conflicts and redundancies between rules by offering new mechanisms to analyze the meaning of policy rules through semantics verification by inference rule structure and deductive logic. All the approach components and algorithms realizing the proposed analysis semantics have been implemented in one development framework. Experiments carried out on synthetic and real-life XACML policies explore the relevance of our analysis algorithms with acceptable overhead. Please visit http://www.azzammourad.org/#projects to download the framework.

Neuro-classification for handwritten Arabic text

Summary form only given. The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English preprinted or handwritten characters space. Thus the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The process starts by using a neuro-conventional algorithm, and a neural network for segmenting binarized connected blocks of characters; then, a heuristic algorithm extracts features from these characters and feeds them into two neural networks for classification purpose. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters.

Software metrics for small database applications

Known software metrics for estimating complexity and effort are usually based on lines of code or the programs flowgraph. Such metrics are suitable for large-scale procedural or object-oriented software applications. In this work, we propose a new complexity metric, called DataBase Points (DBP), that is suitable for small-scale relational database business applications developed in the MS-ACCESS (ACCESS is a trademark of Microsoft Corporation) or similar environments. DBP is constructed from components that are derived from typical ACCESS design. Further, DBP is used to estimate the effort needed to develop such software. The results of applying this new metric to a number of applications show that it is promising and that it captures the complexity features of small database applications.

Advances in industrial engineering and operations research

A Comprehensive Movement Compatibility Study for Hong Kong Chinese.- A Study of Comparative Design Satisfaction Between Culture and Modern Bamboo Chair.- Factors Influencing Symbol-Training Effectiveness.- Multiple-Colony Ant Algorithm with Forward-Backward Scheduling Approach for Job-Shop Scheduling Problem.- Proposal of New Paradigm for Hand and Foot Controls in the Context of Spatial Compatibility Effect.- Development of a Mathematical Model for Process with S-Type Quality Characteristics to a Quality Selection Problem.- Temporal Aggregation and the Production Smoothing Model: Evidence from Electronic Parts and Components Manufacturing in Taiwan.- Simulations of Gear Shaving and the Tooth Contact Analysis.- On Aggregative Methods of Supplier Assessment.- Human Factors and Ergonomics for Nondestructive Testing.- A Novel Matrix Approach to Determine Makespan for Zero-Wait Batch Processes.- Interactive Meta-Goal Programming: A Decision Analysis Approach for Collaborative Manufacturing.- Nonlinear Programming Based on Particle Swarm Optimization.- A Heuristic for the Capacitated Single Allocation Hub Location Problem.- Multimodal Transport: A Framework for Analysis.- Fractional Matchings of Graphs.- Correlation Functions for Dynamic Load Balancing of Cycle Shops.- Neural Network-Based Integral Sliding Mode Control for Nonlinear Uncertain Systems.- Decentralized Neuro-Fuzzy Control of a Class of Nonlinear Systems.- A New Training Algorithm of Adaptive Fuzzy Control for Chaotic Dynamic Systems.- General-Purpose Simulation Management for Satellite Navigation Signal Simulation.- Multilayered Quality-of-Service Architecture with Cross-layer Coordination for Teleoperation System.- Improvement of State Estimation for Systems with Chaotic Noise.- Combined Sensitivity-Complementary Sensitivity Min-Max Approach for Load Disturbance-Setpoint Trade-off Design.- Nonlinear Adaptive Sliding Mode Control for a Rotary Inverted Pendulum.- Robust Load Frequency Sliding Mode Control Based on Uncertainty and Disturbance Estimator.- Robust Intelligent Motion Control for Linear Piezoelectric Ceramic Motor System Using Self-constructing Neural Network.- Development of Hybrid Magnetic Bearings System for Axial-Flow Blood Pump.- Critical Angle for Optimal Correlation Assignment to Control Memory and Computational Load Requirements in a Densely Populated Target Environment.- High-Precision Finite Difference Method Calculations of Electrostatic Potential.- Newton-Tau Method.- Reconfigurable Hardware Implementation of the Successive Overrelaxation Method.- Tabu Search Algorithm Based on Strategic Oscillation for Nonlinear Minimum Spanning Tree Problems.- Customization of Visual Lobe Measurement System for Testing the Effects of Foveal Load.