Eman El-Sheikh

Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud

Applications with Service-oriented Enterprise Architectures in the Cloud are emerging and will shape future trends in technology and communication. The development of such applications integrates Enterprise Architecture and Management with Architectures for Services & Cloud Computing, Web Services, Semantics and Knowledge-based Systems, Big Data Management, among other Architecture Frameworks and Software Engineering Methods. In the present work in progress research, we explore Service-oriented Enterprise Architectures and application systems in the context of Big Data applications in cloud settings. Using a Big Data scenario, we investigate the integration of Services and Cloud Computing architectures with new capabilities of Enterprise Architectures and Management. The underlying architecture reference model can be used to support semantic analysis and program comprehension of service-oriented Big Data Applications. Enterprise Services Computing is the current trend for powerful large-scale information systems, which increasingly converge with Cloud Computing environments. In this paper we combine architectures for services with cloud computing. We propose a new integration model for service-oriented Enterprise Architectures on basis of ESARC - Enterprise Services Architecture Reference Cube, which is our previous developed service-oriented enterprise architecture classification framework, with MFESA - Method Framework for Engineering System Architectures - for the design of service-oriented enterprise architectures, and the systematic development, diagnostics and optimization of architecture artifacts of service-oriented cloud-based enterprise systems for Big Data applications.

A Framework for Developing Intelligent Tutoring Systems Incorporating Reusability

The need for effective tutoring and training is mounting, especially in industry and engineering fields, which demand the learning of complex tasks and knowledge. Intelligent tutoring systems are being employed for this purpose, thus creating a need for cost-effective means of developing tutoring systems. We discuss a novel approach to developing an Intelligent Tutoring System shell that can generate tutoring systems for a wide range of domains. Our focus is to develop an ITS shell framework for the class of Generic Task expert systems. We describe the development of an ITS for an existing expert system, which serves as an evaluation test-bed for our approach.

Adaptable Enterprise Architectures for Software Evolution of SmartLife Ecosystems

SmartLife ecosystems are emerging as intelligent user-centered systems that will shape future trends in technology and communication. Biological metaphors of living adaptable ecosystems provide the logical foundation for self-optimizing and self-healing run-time environments for intelligent adaptable business services and related information systems with service-oriented enterprise architectures. The present research in progress work investigates mechanisms for adaptable enterprise architectures for the development of service-oriented ecosystems with integrated technologies like Semantic Technologies, Web Services, Cloud Computing and Big Data Management. With a large and diverse set of ecosystem services with different owners, our scenario of service-based SmartLife ecosystems can pose challenges in their development, and more importantly, for maintenance and software evolution. Our research explores the use of knowledge modeling using ontologies and flexible metamodels for adaptable enterprise architectures to support program comprehension for software engineers during maintenance and evolution tasks of service-based applications. Our previous reference enterprise architecture model ESARC -- Enterprise Services Architecture Reference Cube -- and the Open Group SOA Ontology was extended to support agile semantic analysis, program comprehension and software evolution for a SmartLife applications scenario. The Semantic Browser is a semantic search tool that was developed to provide knowledge-enhanced investigation capabilities for service-oriented applications and their architectures.

Understanding Interoperable Systems: Challenges for the Maintenance of SOA Applications

Software interoperability is a pressing need to allow governments and businesses to function efficiently. The most commonly recommended technology for interoperability is Services Oriented Architecture (SOA) implemented using web services. Several authors have argued that SOA systems may be particularly challenging to maintain, largely due to difficulties in program comprehension. Program comprehension for SOA could be aided by appropriate software tools to provide information to SOA maintainers. However, there is little experience regarding the questions that SOA maintainers will need to ask. This paper describes use of a prototype SOA search tool in an informal requirements elicitation study to gather feedback from practicing programmers about what SOA maintainers will want to know. Several specific information needs were identified, including the need for a compact way of representing data types used in services, and the need for ontology support to help understand the many different elements and attributes in web services descriptions.

Human-robot team navigation in visually complex environments

Current fully autonomous robots are unable to navigate effectively in visually complex environments due to limitations in sensing and cognition. Full teleoperation using current interfaces is difficult and the operator often makes navigation mistakes due to lack of operating environment information and a limited field of view. We present a novel method for combining the sensing and cognition of a robot with that of a human. Our collaborative approach is different from most in that we address bi-directional considerations. It provides the human a mechanism to supplement the robots capabilities in a new and unique way and provides novel forms of feedback from the robot to enhance the humans understanding of the current state of the system and its intentions.

Generating Intelligent Tutoring Systems from Reusable Components and Knowledge-Based Systems

This research addresses the need for easier, more cost-effective means of developing intelligent tutoring systems (ITSs). A novel and advantageous solution to this problem is the development of a task-specific ITS shell that can generate tutoring systems for different domains within a given class of tasks. Task-specific authoring shells offer an appropriate knowledge representation method to build knowledgeable tutors as well as flexibility for generating ITSs for different domains. In this paper, we describe the development of an architecture that can generate intelligent tutoring systems for different domains by interfacing with existing generic task-based expert systems, and reusing the other tutoring components. The architecture was used to generate an ITS for the domain of composite materials fabrication using an existing expert system.

Towards intelligent search support for web services evolution identifying the right abstractions

Services Oriented Architecture (SOA) is becoming a popular style for building complex systems-of-systems that allow businesses to work together across organizational boundaries. However concerns have been raised about the comprehensibility and maintainability of SOA composite applications. Integrating and deploying SOA applications requires artifacts in a variety of web-based languages (WSDL, XSD, BPEL, etc.) often produced by code-generation tools. It becomes difficult for a human to discover and understand the dependencies between these artifacts in an existing system. In this paper, we describe ongoing research on using search techniques to facilitate SOA maintenance by allowing users to query collections of artifacts making up a SOA composite application. The main focus in this paper is a case study using our prototype search tool SOAMiner to identify a set of abstractions that extract useful and critical information for maintainers, thereby bridging the heterogeneity of SOA artifacts while opportunistically exploiting their structure. Results of the study indicate that the highest priority abstractions for SOA are datatype summaries, service invocation (calling) relationships, and data usage relationships.

Application of Structural Case-Based Reasoning to Activity Recognition in Smart Home Environments

Improvements in sensor technology and small, handheld wireless communication devices provide new opportunities for smart home applications to support independent living for elder care. However, with addition of new sensing technology in the smart home, intelligent methods are needed that process data collected by the sensors to recognize activities for monitoring the well-being of the homes inhabitants. To address this challenge, we designed a smart home system with a multi-agent middle layer to study case-based reasoning methods and constraint satisfaction for activity recognition. The study includes the development of an ontology encoded in RDF to match features of cases and their constraints against observed events in the home. Initial results show that activity recognition can be done successfully using the proposed methods.

Discovering effective connectivity among brain regions from functional MRI data

Functional magnetic resonance imaging (fMRI) data have been used for identifying brain regions that activate when a subject is presented a stimulus or performs a task. Beyond identifying which regions of the brain are active during a task, it is also of interest to discover causal relationships among activity in those regions, that is, which regions of the brain influence, which other regions of the brain during a task. Two algorithms for causal discovery were applied to fMRI data, the greedy equivalence search (GES) algorithm and the independent multiple-sample greedy equivalence search (iMAGES). GES applies to individual datasets, and iMAGES to multiple datasets. We consider the stability of the GES results across subjects and experimental repetitions with the same subject. We find that some iMAGES connections agree with previous knowledge of the functional roles of the brain regions. The strengths and limitations of the research work and opportunities for future work are also discussed.

A Knowledge-Based System Approach for Extracting Abstractions from Service Oriented Architecture Artifacts

Rule-based methods have traditionally been applied to develop knowledge-based systems that replicate expert performance on a deep but narrow problem domain. Knowledge engineers capture expert knowledge and encode it as a set of rules for automating the expert’s reasoning process to solve problems in a variety of domains. We describe the development of a knowledge-based system approach to enhance program comprehension of Service Oriented Architecture (SOA) software. Our approach uses rule-based methods to automate the analysis of the set of artifacts involved in building and deploying a SOA composite application. The rules codify expert knowledge to abstract information from these artifacts to facilitate program comprehension and thus assist Software Engineers as they perform system maintenance activities. A main advantage of the knowledge-based approach is its adaptability to the heterogeneous and dynamically evolving nature of SOA environments.