Bilal Gonen

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.

User characterization for online social networks

Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender and age; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots versus humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights into researchers and better services to online users.

Probabilistic Trans-Algorithmic search for automated network management and configuration

Online configuration of large-scale systems such as networks require parameter optimization to be done within a limited amount of time. This time limit is even more pressing when configuration is needed as a recovery response to a failure in the system. To quickly configure such systems in an online manner, we propose a Probabilistic Trans-Algorithmic Search (PTAS) framework which leverages multiple optimization search algorithms in an iterative manner. Essentially, PTAS applies a search algorithm to find out how to best distribute available experiment budget among multiple optimization search algorithms. Specifically, PTAS allocates experiment budget to each available search algorithm and observes each algorithms performance on the system-at-hand. PTAS then probabilistically reallocates the experiment budget for the next round proportional to an algorithms performance. This “roulette wheel” approach probabilistically favors the more successful algorithm in the next round. Following each round, the PTAS framework “transfers” the best found result(s) among the individual algorithms, making our framework “trans-algorithmic”. PTAS thus aims to systematize how to “search for the best search”. We show the performance of PTAS on well-known benchmark objective functions including scenarios where the objective function changes in the middle of the optimization process. To illustrate applicability of our framework to automated network management, we apply PTAS on the problem of optimizing link weights of an intra-domain routing protocol on a topology obtained from Rocketfuel dataset.

Event driven energy depth and channel aware routing for underwater acoustic sensor networks

Employment of multiple routing parameters such as energy, propagation time, link quality, hop count and queue size in an integrated manner.The cluster heads are selected based on the speed of data transmission that vary with the depth of node placement, temperature, and salinity of water environment.A cross layer approach is adopted to estimate the link quality.To avoid the void conditions and improve the scalability, 3D deterministic deployment scheme is considered.Adoption of agent based routing scheme with flexible and customized services. The objective of the paper is to develop an agent based routing scheme with following features. (1) Increased Lifetime of network, (2) Reduced Latency, (3) Void Avoidance, (4) Reliable neighbor discovery, and (5) Connectivity enhancement during link breakdown. In order to realize the objectives, we define an agent based routing scheme that has intelligence embedded in it to take autonomous decisions and act intelligently.Display Omitted An energy efficient channel aware and depth based scalable and multipath agent based routing protocol is proposed in this paper. We consider a scenario consisting of underwater sensor nodes, autonomous underwater vehicles (AUV), underwater gateways, and surface gateways deployed at the territory to monitor the ocean environment (temperature, salinity and pressure) and detect the intruder (moving or static object). Dynamic clustering process is initiated on occurrence of an event; sensor nodes in the event affected area facilitate collection and aggregation of data by the cluster head. The cluster head initiates routing scheme by using mobile agent and its clones to discover multiple paths to a surface gateway based on parameters such as node energy, hop count, propagation delay and channel quality. In order to improve network connectivity and reliability in case of network partitions, an agent based dynamic AUV traversal algorithm is proposed to reorient direction of AUV movement.

Approaches to the Evolution of SOA Systems

The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.

Efficient camera selection for maximized target coverage in underwater acoustic sensor networks

In addition to sensors, cameras have started to be deployed in underwater acoustic sensor networks (UWASNs) for improved monitoring. However, since cameras already consume a lot of energy, they are kept in sleep mode most of the time and only activated when sensors detect a target. Due to random deployment and lack of cameras, there may not be any cameras within the vicinity of a detected target. A possible solution to this problem is to relocate remote cameras via vertical movements to certain locations to capture the target. In this paper, we propose a distributed camera selection and relocation scheme in UWASNs to maximize the coverage of the detected targets with the least vertical movement of cameras. The problem is modeled as a weighted set covering problem and solved using a greedy heuristic. The performance of the proposed approach is assessed through extensive simulations under a variety of conditions.

Knowledge modeling in the health care domain to support software development & maintenance

This article contains a description of a knowledge elicitation effort and representation pertaining to the modeling of conceptual knowledge in the health care field. The project has the goal of building a conceptual model of data in the Military Health System Data Repository, a large DoD/VA aggregation of databases that can be used in the implementation of software. The goal is to create a just-in-time conceptual model of the data to facilitate software development and foster software developer understanding of the domain.

Knowledge Modeling in the Health Care Domain: Capturing Semantics to Bridge the Gap Between Complex Data Models and Object Models

This article contains a description of a knowledge elicitation effort and representation pertaining to the modeling of conceptual knowledge in the health care field. The project has the goal of building a conceptual model of data in the Military Health System Data Repository, a large DoD/VA aggregation of databases that can be used in the implementation of software. The goal is to create a just-in-time conceptual model of the data to facilitate software development and foster software developer understanding of the domain.

Maintaining SOA Systems of the Future

Many future Services Oriented Architecture (SOA) systems may be pervasive SmartLife applications that provide real-time support for users in everyday tasks and situations. Development of such applications will be challenging, but in this position paper we argue that their ongoing maintenance may be even more so. Ontological modelling of the application may help to ease this burden, but maintainers need to understand a system at many levels, from a broad architectural perspective down to the internals of deployed components. Thus we will need consistent models that span the range of views, from business processes through system architecture to maintainable code. We provide an initial example of such a modelling approach and illustrate its application in a semantic browser to aid in software maintenance tasks.

Improving music artist recommendations through analysis of influences

To improve the quality of search results in huge digital music databases, we developed a simple algorithm based on artist influences and complex network theory that produces interesting and novel results. Traditionally, music recommendation engines use audio feature similarity to suggest new music based on a given artist. We propose a search that takes influences into account provides a richer result set than one based on audio features alone. We constructed an artist influence network using the Rovi dataset and studied it using complex network theory. Analysis revealed many complex network phenomena which we used to tune the search algorithm. Finally, we consider the difficulty of qualitatively rating our results and the need for a tool to exercise the algorithm.