An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Case-based reasoning using expert systems to determine electricity reduction in residential buildings

Case-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013info:eu-repo/semantics/publishedVersio

New product development resource forecasting

Forecasting resource requirements for new product development (NPD) projects is essential for both strategic and tactical planning. Sophisticated, elegant planning tools to present data and inform decision-making do exist. However, in NPD, such tools run on unreliable, estimation-based resource information derived through undefined processes. This paper establishes that existing methods do not provide transparent, consistent, timely or accurate resource planning information, highlighting the need for a new approach to resource forecasting, specifically in the field of NPD. The gap between the practical issues and available methods highlights the possibility of developing a novel design of experiments approach to create resource forecasting models

Modeling A Green Decision Support System for Data Center Sustainability

The objective of this dissertation is developing more energy efficient data centers while focusing on the environment as well as meeting the increasing computing needs. Reliability of data centers will be the number one priority for management; however, the focus will be to implement a design by incorporating free cooling, applying thermal profiling, utilizing data mining, and continuing virtualization to create more efficient green data centers that are good for the environment. Since the fall of 2009, electrical consumption patterns were measured in the main data center for the servers and the air-conditioners at Montclair State University (MSU) to quantify the carbon footprint and the electrical costs. An important outcome of this work is to build a Decision Support System (DSS) for green computing in data centers. A DSS is a computer based application to assist in providing solutions with respect to decision-making to multifaceted problems. In summary, building on our measurements, the objective is to design a DSS for data centers to enhance energy efficiency, reduce the carbon footprint, and promote sustainability science across disciplines

An intelligent framework for dynamic web services composition in the semantic web

As Web services are being increasingly adopted as the distributed computing technology of choice to securely publish application services beyond the firewall, the importance of composing them to create new, value-added service, is increasing. Thus far, the most successful practical approach to Web services composition, largely endorsed by the industry falls under the static composition category where the service selection and flow management are done a priori and manually. The second approach to web-services composition aspires to achieve more dynamic composition by semantically describing the process model of Web services and thus making it comprehensible to reasoning engines or software agents. The practical implementation of the dynamic composition approach is still in its infancy and many complex problems need to be resolved before it can be adopted outside the research communities. The investigation of automatic discovery and composition of Web services in this thesis resulted in the development of the eXtended Semantic Case Based Reasoner (XSCBR), which utilizes semantic web and AI methodology of Case Based Reasoning (CBR). Our framework uses OWL semantic descriptions extensively for implementing both the matchmaking profiles of the Web services and the components of the CBR engine. In this research, we have introduced the concept of runtime behaviour of services and consideration of that in Web services selection. The runtime behaviour of a service is a result of service execution and how the service will behave under different circumstances, which is difficult to presume prior to service execution. Moreover, we demonstrate that the accuracy of automatic matchmaking of Web services can be further improved by taking into account the adequacy of past matchmaking experiences for the requested task. Our XSCBR framework allows annotating such runtime experiences in terms of storing execution values of non-functional Web services parameters such as availability and response time into a case library. The XSCBR algorithm for matchmaking and discovery considers such stored Web services execution experiences to determine the adequacy of services for a particular task. We further extended our fundamental discovery and matchmaking algorithm to cater for web services composition. An intensive knowledge-based substitution approach was proposed to adapt the candidate service experiences to the requested solution before suggesting more complex and computationally taxing AI-based planning-based transformations. The inconsistency problem that occurs while adapting existing service composition solutions is addressed with a novel methodology based on Constraint Satisfaction Problem (CSP). From the outset, we adopted a pragmatic approach that focused on delivering an automated Web services discovery and composition solution with the minimum possible involvement of all composition participants: the service provider, the requestor and the service composer. The qualitative evaluation of the framework and the composition tools, together with the performance study of the XSCBR framework has verified that we were successful in achieving our goal

Information feedback to local communities of HDSS sites using infographics: the case of niakhar HDSS site

A research report submitted to the Faculty of Health Science in partial fulfillment of the requirements for the degree of Master of Science (MSc) in Epidemiology - Research Data Management June 2017Health and Demographic Surveillance System (HDSS) sites are institutions that primarily collect periodic demographic and health related data within a defined geographical area. The data are then analysed and the research findings are published in peer-reviewed journals or presented at conferences and workshops. However, In some sites there are processes for disseminating the research findings to the participating communities. The dissemination of research findings using infographics can enable health care service providers and community decision makers to make effective use and incorporate such findings into their strategies, policies and planning to improve heath outcome of the population. In this project, we have implemented a visualization web platform that can be used by researchers, community decision makers and public health policy-makers to better identify trends associated with the research findings. We implemented this platform on top of the core HDSS dataset. An Extract Transform and Load (ETL) process feeds processed data to our layer which then provides the requisite utility tools for visualization. We have provided a framework that allows other cohort studies that utilize the HDSS core dataset as baseline data to be plugged in for additional infographics displays. We tested and prototyped our tool using Niakhar HDSS Site core HDSS datasets. The project also provides some generic guidelines that allow this tool to be used in other HDSS sites.MT201

A process-based model of network capability development by a start-up firm

Start-up firms are notoriously resource and time poor. One way of addressing these deficits is to develop strategic capability to access, activate and co-shape resources with other firms in the start-up's network. The capability literature assumes such a development is inevitable, provided a start-up survives. But developing network capability depends on the managers of other firms, the deepening managerial understanding of business relationships, and the ability of the start-up managers to adjust to and understand interdependence in networks. We present a processual model of how managerial understanding of network capability develops, comprising of three parts each building on the earlier: (i) in relationships, (ii) through relationships and (iii) in the network. The model was inductively developed from a longitudinal study of a start-up firm. Also, two sensemaking processes were found to predominate â problem solving and social-cognitive processes. Our model highlights the role of the start-up manager in sensemaking with managers across a number of firms to resolve commercial problems. Thus, the independence many start-up managers seek must turn towards interdependence. Second, managers' temporal horizons and the specific temporal profile of events and activities inside the involved business relationships are important in understanding and developing, with other firms, network capability

Derivation of a methodology to compare C,B and R detection capability in urban events

Many comparisons have been made between Chemical detectors (C), between Biological (B) detectors, and between Radiological detectors (R), providing insights to the best C, B and R equipment for a given purpose. However, no comparison has been made between C, B and R systems to appraise how C, B and R detectors perform against each other and where capability gaps lie. The dissertation generates a method to achieve an inter-comparison between C, B and R detection capabilities and identifies where to invest resources to achieve a more effective overall CBR detection architecture. The inter-comparison methodology is based on an operational analysis tool (SMARTS). The overall CBR detection architecture is illustrated through detect to warn and detect to treat mechanisms across the timeline of a realistic scenario. The scenario has been created to be non-prejudicial to C, B or R incidents, deconstructed into four frames to accommodate SMARTS. The most suitable deconstruction is into early warning, personnel security screening, initial response and definitive identification frames. The most suitable detector Key Performance Characteristics (KPCs) are identified for each frame. SMARTS is performed by analysing the current performance of the C, B and R detection systems drawn from the literature and the target requirements determined by defensible logic. The desire to improve each capability from its current state to target requirement is subjectively determined by the author. A sensitivity analysis is applied to mitigate the effect of a limited pool of opinion. Applying the methodology to published CBR detection capability data and the author’s appraisal of the target requirement reveals that B detection requires the greatest development and R the least, and that detection in the security screening and initial response frames falls short of capability compared to early warning and definitive identification frames. Selectivity is a challenge across a broad range of frames and agents. This work provides a methodology that is modular and transparent so that it can be repopulated should new data or alternative perception arises