Clustering of Bootstrap for Web Service Discovery

Web services are accessed using URLs in a distributed environment. WS WSDL document URLs are manually tabulated and clustered which increases the cost and timing for the developer. This paper introduces a new Clustering of URLs (CU) framework for clustering of bootstrap for web service discovery and clustering them in various domains using transfer, filter, spell check and domain set methods. These methods set them under the specific domain or general category. The CU framework is implemented with a sample URLs. The result shows the efficiency of the clustering of WSDL URLs

WEB ADMINISTRATION SUGGESTION BY MEANS OF MISUSING AREA AND QOS DATA

Web administrations are incorporated programming segments for the backing of interoperable machine-to-machine association over a system. Web administrations have been broadly utilized for building administration situated applications in both industry and the educated community in late years. The quantity of freely accessible Web administrations is consistently expanding on the Internet. Be that as it may, this multiplication makes it hard for a client to choose an appropriate Web administration among a lot of administration competitors. An improper administration determination may bring about numerous issues (e.g., illsuited execution) to the subsequent applications. In this paper, we propose a novel community separating based Web administration recommender framework to help clients select administrations with ideal Quality-of-Service (QoS) execution. Our recommender framework utilizes the area data and QoS qualities to bunch clients and administrations, and makes customized administration proposal for clients in view of the grouping results. Contrasted and existing administration suggestion techniques, our methodology accomplishes impressive change on the proposal precision. Extensive tests are led including more than 1.5 million QoS records of true Web administrations to exhibit the adequacy of our methodology

Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network

Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Personal Web API Recommendation Using Network-based Inference

Abstract. In this paper, we evaluate a generic network-based inference algorithm for Web API recommendation. Based on experimental data collected from the Programmable Web repository, we construct two tripartite networks: one where the nodes are Web APIs, users and mashups, and another where the nodes are Web APIs, users and tags. Experimental results show that the network-based inference algorithm yields higher precision, ranking quality and personalization score when applied to the second network. This approach also outperforms three existing methods: a global ranking method, a collaborative filtering method and the Programmable Web recommendation tool

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy