Akram Salah

Runtime Monitoring of SOA Applications: Importance, Implementations and Challenges

This paper presents the usages, current status and challenges that face monitoring real runtime SOA applications from both research and industry points of view. SOA application monitoring can be done for collecting statistics, guaranteeing quality of service, generating test cases or other purposes. The key challenges that face SOA monitoring are (1) monitoring overhead and performance degradation, (2) the diversity of supported formats and protocols, which is further complicated by the growth in the number of integrated applications that requires complex logic to be able to monitor individual paths across multiple services, and (3) the distribution of services which is further complicated by deployment in the cloud. We cover academic perspectives that are typically proposals for models or architectures for SOA middleware for monitoring. And we cover as well real monitoring techniques supported in SOA frameworks provided by software vendors.

Enhanced Customer Churn Prediction using Social Network Analysis

There were 6.8 billion estimates for mobile subscriptions worldwide by end of 2013 [11]. As the mobile market gets saturated, it becomes harder for telecom providers to acquire new customers, and makes it essential for them to retain their own. Due to the high competition between different telecom providers and the ability of customers to move from one provider to another, all telecom service providers suffer from customer churn. As a result, churn prediction has become one of the main telecom challenges. The primary goal of churn prediction is to predict a list of potential churners, so that telecom providers can start targeting them by retention campaigns. This work describes work in progress in which we model churn as a dyadic social behavior, where customer churn propagates in the telecom network over strong social ties. We propose a novel method for measuring social tie strength between telecom customers. We then, incorporate strong social ties in an influence propagation model, and apply a machine-learning based prediction model that combines both churn social influence and other traditional churn factors. The goals of our proposed model is to enhance churn prediction by modeling churn as a dyadic phenomena, provide an enhanced evaluation for the social tie strength based on customers social interactions, and to study the effect of strong social ties on churn propagation over mobile telecom networks.

A reduction methodology for a differential diagnosis expert system

Abstract The simple production rule representation is generalized by adding programs to a management system that manipulate rules in a rule-based system. By adapting this methodology, a single generalized rule can represent a group of simple ones. Then programs are employed to satisfy the general rule in a partial way while recursively reducing a decision problem into smaller ones of the same nature until a decision is made. It is shown that the reduction method is more efficient than the simple rule approach and that it minimizes the number of rules used to express a problem. The concept of using a management program to manipulate a set of rules is emphasized through solving a problem in a differential diagnosis expert system. A comparison between the number of rules employed to express a problem is made to show advantages of the reduction methodology over the simple rule representation.

Multiobjective Optimization using Genetic Programming: Reducing Selection Pressure by Approximate Dominance.

Multi-objective optimization is currently an active area of research, due to the difficulty of obtaining diverse and high-quality solutions quickly. Focusing on the diversity or quality aspect means deterioration of the other, while optimizing both results in impractically long computational times. This gives rise to approximate measures, which relax the constraints and manage to obtain good-enough results in suitable running times. One such measure, epsilon-dominance, relaxes the criteria by which a solution dominates another. Combining this measure with genetic programming, an evolutionary algorithm that is flexible and can solve sophisticated problems, makes it potentially useful in solving difficult optimization problems. Preliminary results on small problems prove the efficacy of the method and suggest its potential on problems with more objectives.

Semantic Web Service discovery: A systematic survey

Semantically annotating Web services is gaining more attention as an important aspect to support the automatic matchmaking of Web services. Hence, in this paper, we follow a systematic survey related to the Semantic Web Services discovery to answer a research questions about how retrieve the semantic Web services that match with a user query. We examined and analyzed 19 papers from the literature, and summarized the results.

Approximate Dominance for Many-Objective Genetic Programming

In recent years, many-objective optimization has become a popular research topic, after it was noted that algorithms that excelled in solving problems with two objectives were not suitable for problems with more than three objectives. In these more difficult problems, selection pressure towards the Pareto front deteriorates, leading to most solutions becoming non-dominated to each other, which makes selection very difficult. To overcome this, approximate measures, for example epsilon-dominance, relax the competition criteria between solutions and make it easier to eliminate worse solutions that would otherwise be non-dominated. In this paper, epsilon dominance is combined with genetic programming to solve a many-objective optimization problem for the first time. Results show that this combination is promising.

Semantic Cloud Community Framework for Services Provision

Cloud computing represents an evolution paradigm that enables information technology (IT) capabilities to be delivered “as a service”. In the last decade number of cloud-based services has grown intensely and rapidly. The diversification of cloud service providers has generated the diversification of their offers. Therefore, end-users face a huge challenge while choosing the appropriate cloud provider. Furthermore, the battle for dominance between the big vendors, like Amazon, Google and Salesforce, makes them reluctant to agree on widely accepted standards promoting their own incompatible formats, thus increases the lock-in effect and affects the competition. Interoperability is the missing element that will recover this situation and allows switch between cloud providers whenever needed without setting data and applications at risk. In this paper, we present an approach that will help strengthen semantic and technical interoperability of services. The approach presents a Cloud Community that acts as a broker to mediate between service providers and service consumers based on web ontology language OWLS. This concept would enable end users to select the right services and compose services across multiple Clouds. It would, also, to provide cloud arbitration services that allow users to shift and to choose between existing platforms.

Poster: Web Service based Communication for Devices with Limited Resources

Mobile devices, tablets, wireless sensors, become more common every day. The generated data of those devices are huge but only used in platforms in which those generated data are hosted on servers in traditional client-server architecture. However, allowing these devices to inter-operate and to access those generated data on a peer-to-peer basis and perform service tasks is still in research.The Service Oriented Architecture (SOA) could benefit from resources provided by the connected embedded devices. And one implemented techniques of SOA is Web Services. Applying Web Services on resource-constrained devices often need more resources and computing power than available. This is where Device Profile for Web Service (DPWS) comes into play.The Objective of this project is to create a web service platform on mobile and embedded devices and to let other devices discover and interact with the created services, rather than creating those services in remote server and accessing them, which consumes time and traffic

ACUAI framework for automatic composition of web services using gaming AI

In a domain SOA environment, multiple services exist to help in executing the users requests but manually handling each request is time wasting and very tiring thus the idea of automatic composition of web services emerged. This paper describes the ACUAI framework which is an AI automatic composer for web services with learning capabilities, since games have the most developed dynamic system adaptation to user action this framework uses two AI techniques which are used in games development they are decision trees and the finite state machines, the user request will be handled in the same way games react to user input then a decision is taken based on current state of the orchestration and then the algorithm changes the state of the orchestration according to the last decision taken to be able to guide the orchestration towards the goal and get a successful composition then each successful composition and its template request are saved to be used later on in enhancing the execution time.

Extracting Hidden Information and Conclusions in Software Testing Via Distributed Relational Visual Mining

Visual Mining is typically concerned with the visualization of data and its representation to facilitate the mining aiming at extracting interesting and hidden information. It can also mean the visualization of the results of the mining process with the purpose of deepening the understanding of such results and maximizing its exploitation. However, in global systems and global economies, the targeted knowledge of interest may not be embedded in one database or data system. Instead, it may be hidden, not in the data sets, but in the relations between seemingly unrelated data systems. We introduce this problem and the concept of Distributed Relational Visual Mining and its potential for information and knowledge discovery from distributed seemingly disconnected systems. With potential applications in many areas, we introduce a case study applying the proposed technique in the area of Software Development in general and Software testing in Particular.