A Case Study: A Model-Based Approach to Retrofit a Network Fault Management System with Self-Healing Functionality
Adding self-healing capabilities to network management systems holds great promise for delivering important goals, such as QoS, while simultaneously lowering capital expenditure, operation cost, and maintenance cost. In this paper, we present a model-based approach to add self-healing capabilities to a fault management system for cellular networks. We propose a generic modeling framework to categorize software failures and specify their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting functionality. While self- monitoring oversees the environmental conditions and system behavior, self-healing is accomplished by instrumenting the system with self-adjusting operations. We include a case study on a prototype intelligent network fault management system to illustrate this approach by showing how these autonomic capabilities can be added and deployed. Specifically, these autonomic capabilities are derived from self-model specifications, and are used to mitigate the risk of specified failures and maintain the health of the system in response to different types of faults encountered.

title: A Context-Aware Policy Model to Support Autonomic Networking
abstract: Autonomic networking requires that the services and resources offered by the network at any specific time adapt to evolving changes in user needs, business goals and environmental conditions. This paper describes a rich and extensible context-aware policy model as part of the FOCALE autonomic network architecture. This context-aware policy model is built explicitly to support context-awareness that in turn enables the control loops in FOCALE to be adaptive.

title: Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
abstract: Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.

title: A model-based approach to adding autonomic capabilities to network fault management system
abstract: Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.

title: Model-Driven Adaptive Self-healing for Autonomic Computing
abstract: Self-healing is a vital property that an autonomic system must possess in order to provide robust performance and survivability. The promise of self-healing depends on other properties that the system should provide, which include self-monitoring and self-configuring. Autonomic systems further require self-healing behavior to adapt to changes in user needs, business goals, and environmental conditions such that self-healing decisions are made dynamically and adaptively according to the system context. In this paper, we propose a model-driven approach that leverages modeling techniques, reliability engineering methodologies, and aspect-oriented development to realize an adaptive self- healing paradigm for autonomic computing.

title: Enhancing Software Product Line Maintenance with Source Code Mining
abstract: Large-scale reuse and accelerated software development have been some of the key attractions behind software product lines. Various strategies and processes have been developed to facilitate product line development, maintenance, and evolution. However, experiences with software product lines also showed that it is a rather challenging task to maintain software product lines and families over a long period of time. The time and effort needed to manage and maintain product lines increase and quality degrades as product lines evolve. Without proper methods and tools to support the evolution, the cost can outweigh the benefits. 
 
This paper describes an approach to simplifying the maintenance of software product lines and improving software quality by integrating traditional software maintenance practices with pattern-based source code mining for defect detection and correction. Our case studies were performed in an industrial setting where the evolution of multiple mobile phone models of a product line was investigated.

title: A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
abstract: In this paper, we propose a unified topic modeling approach and its integration into the random walk framework for academic search. Specifically, we present a topic model for simultaneously modeling papers, authors, and publication venues. We combine the proposed topic model into the random walk framework. Experimental results show that our proposed approach for academic search significantly outperforms the baseline methods of using BM25 and language model, and those of using the existing topic models (including pLSI, LDA, and the AT model).

title: CACS: A Novel Classification Algorithm Based on Concept Similarity
abstract: This paper proposes a novel algorithm of classification based on the similarities among data attributes. This method assumes data attributes of dataset as basic vectors of m dimensions, and each tuple of dataset as a sum vector of all the attribute-vectors. Based on transcendental concept similarity information among attributes, this paper suggests a novel distance algorithm to compute the similarity distance of each pairs of attribute-vectors. In the method, the computing of correlation is turned to attribute-vectors and formulas of their projections on each other, and the correlation among any two tuples of dataset can be worked out by computing these vectors and formulas. Based on the correlation computing method, this paper proposes a novel classification algorithm. Extensive experiments prove the efficiency of the algorithm.

title: Discover gene specific local co-regulations from time-course gene expression data
abstract: Discovering gene co-regulatory relationships is one of most important research in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest called target gene, identify the condition subsets where strong gene co-regulations of the target gene are observed and its co-regulated genes in these condition subsets. The co-regulations are local in the sense that they occur in some subsets of full experimental conditions. The study on this problem can contribute to better understanding and characterizing the target gene during the biological activity involved. In this paper, we propose an innovative method for finding gene specific co-regulations using genetic algorithm GA. A sliding window is used to delimit the allowed length of conditions in which gene co-regulations occur and an ad hoc GA, called the progressive GA, is performed in each window position to find those condition subsets having high fitness. It is called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in its preceding window position, enabling the GA to achieve a better accuracy than the non-progressive algorithm. kNN Lookup Table is utilized to substantially speed up fitness evaluation in the GA. Experimental results with a real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific co-regulations.

title: The Intelligent Reconfigurable Measuring Node Based on Wireless Access Network
abstract: An instance of measuring node of service life measuring and control system of household appliances using FPGA and nRF2401 is present. FPGA is the core of the system which is embedded with E-MAC IP core to realize the remote monitor system replying on the TCP/IP communication protocol, and nRF2401 is wireless periphery that realizes the wireless data acquisition. How to design and implement the hardware and software on the platform of FPGA is expatiated, including constructing the hardware system in the FPGA, designing the software and analyzing the wireless module. The system uses wireless network technology and LabView software, connecting the traditional monitoring system with Internet together, and realizing automatization and informatization of household appliances measuring and control system. Result shows that it’ s a highly integrated and reconfigurable system with great flexibility.

title: Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
abstract: In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features) in which the given points are outliers, which are called their outlying subspaces. Since the state-of-the-art outlier detection techniques fail to handle this new problem, we propose a novel detection algorithm, called High-Dimension Outlying subspace Detection (HighDOD), to detect the outlying subspaces of high-dimensional data efficiently. The intuitive idea of HighDOD is that we measure the OD of the point using the sum of distances between this point and itsknearest neighbors. Two heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top–down, bottom–up and random search methods, and the existing outlier detection methods cannot fulfill this new task effectively.