Factors influencing the adoption of mobile services consumers' preferences using analytic hierarchy process

The rapid and widespread development of innovations in mobile services is changing societies and improving lives around the world. Due to lagging adoption, many of these new innovations have yet failed to generate revenue that was expected by mobile network operators, application and content developers. There are several factors which are affecting the service adoption by consumers. This paper aims to provide practitioners and academics, an insight on what consumers' preferences are by using an Analytic Hierarchy Approach (AHP). The objective of this paper is to identify factors influencing the adoption of the mobile services. In this study we have considered Payment Mode, Functionality, Added Value and PQCP (perceived quality, cost and performance) as the main service adoption factors. The survey results indicate that Functionality is the most important influencing factor for the respondents, followed by Added Value, PQCP and Payment Mode.Adoption,AHP,Mobile Value Services,Consumer's Preferences

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

An Evaluation and Selection of 3G Mobile Value-Added Service

As the wireless communication and mobile phone market develop rapidly, telecommunication dealers provide diverse mobile value-added services for consumers to choose from. However, which mobile value-added services are those consumers need have become a worthy issue for discussion. In this empirical study, cluster analyses and analytic hierarchy processes are used to investigate and understand the need for cognition in the young users (20-29 years old). The selected subjects’ preferences for services, like mobile communication service, mobile entertainment service, mobile information service and mobile transaction service are evaluated. By surveying the subjects’ need for recognition, cluster analysis can further be used to cluster diverse mobile value-added services. Furthermore, by means of the Analytic Hierarchy Process (AHP), services that subjects pay more attention to can be sifted out for the further development of service functions. The results of analysis indicate that the mobile value-added services young users pay most attention to are: wireless emergency services in the communications category, mobile mapping in the information category, mobile taxi services in the communication category, contact list in the communication category and short messaging service in the communications category

Optimal Bundle of Multimedia Services in Emerging Mobile Markets

Although various emerging technologies have been launched, they present limitations as far as offering full-scale ubiquitous services independently is concerned. In view of this fact, service providers are likely to provide bundled services among possible combinations of services. Indeed, making a timely decision regarding the value maximization of bundled service is directly related to service providers' future growth and success in the turbulent market environment. This paper aims to find the optimal service bundle among five emerging mobile services: T-DMB, S-DMB, WiBro, HSDPA, and Telematics. Considering what kinds of service features among the five emerging services offer differentiation to customers, we examine four attributes (TV, voice, portable wireless internet, and location-based services) using conjoint analysis to distinguish the service features. Our results show that TV service is the most favored among the attributes, followed by voice service in second position, and the internet and location-based service in third and fourth place respectively. Our result implies that mobile operators would be better off bundling HSDPA and S-DMB first, and then adding other services later, while fixed operators would be better off bundling WiBro and S-DMB first and other services later.telecommunications and broadcasting convergence; emerging service; 4G Technology; T-DMB; S-DMB; WiBro; HSDPA; telematics; customer preference

Assessment of socio-techno-economic factors affecting the market adoption and evolution of 5G networks: Evidence from the 5G-PPP CHARISMA project

5G networks are rapidly becoming the means to accommodate the complex demands of vertical sectors. The European project CHARISMA is aiming to develop a hierarchical, distributed-intelligence 5G architecture, offering low latency, security, and open access as features intrinsic to its design. Finding its place in such a complex landscape consisting of heterogeneous technologies and devices, requires the designers of the CHARISMA and other similar 5G architectures, as well as other related market actors to take into account the multiple technical, economic and social aspects that will affect the deployment and the rate of adoption of 5G networks by the general public. In this paper, a roadmapping activity identifying the key technological and socio-economic issues is performed, so as to help ensure a smooth transition from the legacy to future 5G networks. Based on the fuzzy Analytical Hierarchy Process (AHP) method, a survey of pairwise comparisons has been conducted within the CHARISMA project by 5G technology and deployment experts, with several critical aspects identified and prioritized. The conclusions drawn are expected to be a valuable tool for decision and policy makers as well as for stakeholders

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Classification techniques are becoming essential in the financial world for reducing risks and possible disasters. Managers are interested in not only high accuracy, but in interpretability and transparency as well. It is widely accepted now that the comprehension of how inputs and outputs are related to each other is crucial for taking operative and strategic decisions. Furthermore, inputs are often affected by contextual factors and characterized by a high level of uncertainty. In addition, financial data are usually highly skewed toward the majority class. With the aim of achieving high accuracies, preserving the interpretability, and managing uncertain and unbalanced data, this paper presents a novel method to deal with financial data classification by adopting type-2 fuzzy rule-based classifiers (FRBCs) generated from data by a multiobjective evolutionary algorithm (MOEA). The classifiers employ an approach, denoted as scaled dominance, for defining rule weights in such a way to help minority classes to be correctly classified. In particular, we have extended PAES-RCS, an MOEA-based approach to learn concurrently the rule and data bases of FRBCs, for managing both interval type-2 fuzzy sets and unbalanced datasets. To the best of our knowledge, this is the first work that generates type-2 FRBCs by concurrently maximizing accuracy and minimizing the number of rules and the rule length with the objective of producing interpretable models of real-world skewed and incomplete financial datasets. The rule bases are generated by exploiting a rule and condition selection (RCS) approach, which selects a reduced number of rules from a heuristically generated rule base and a reduced number of conditions for each selected rule during the evolutionary process. The weight associated with each rule is scaled by the scaled dominance approach on the fuzzy frequency of the output class, in order to give a higher weight to the minority class. As regards the data base learning, the membership function parameters of the interval type-2 fuzzy sets used in the rules are learned concurrently to the application of RCS. Unbalanced datasets are managed by using, in addition to complexity, selectivity and specificity as objectives of the MOEA rather than only the classification rate. We tested our approach, named IT2-PAES-RCS, on 11 financial datasets and compared our results with the ones obtained by the original PAES-RCS with three objectives and with and without scaled dominance, the FRBCs, fuzzy association rule-based classification model for high-dimensional dataset (FARC-HD) and fuzzy unordered rules induction algorithm (FURIA), the classical C4.5 decision tree algorithm, and its cost-sensitive version. Using nonparametric statistical tests, we will show that IT2-PAES-RCS generates FRBCs with, on average, accuracy statistically comparable with and complexity lower than the ones generated by the two versions of the original PAES-RCS. Further, the FRBCs generated by FARC-HD and FURIA and the decision trees computed by C4.5 and its cost-sensitive version, despite the highest complexity, result to be less accurate than the FRBCs generated by IT2-PAES-RCS. Finally, we will highlight how these FRBCs are easily interpretable by showing and discussing one of them

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Efficient Multi-way Theta-Join Processing Using MapReduce

Multi-way Theta-join queries are powerful in describing complex relations and therefore widely employed in real practices. However, existing solutions from traditional distributed and parallel databases for multi-way Theta-join queries cannot be easily extended to fit a shared-nothing distributed computing paradigm, which is proven to be able to support OLAP applications over immense data volumes. In this work, we study the problem of efficient processing of multi-way Theta-join queries using MapReduce from a cost-effective perspective. Although there have been some works using the (key,value) pair-based programming model to support join operations, efficient processing of multi-way Theta-join queries has never been fully explored. The substantial challenge lies in, given a number of processing units (that can run Map or Reduce tasks), mapping a multi-way Theta-join query to a number of MapReduce jobs and having them executed in a well scheduled sequence, such that the total processing time span is minimized. Our solution mainly includes two parts: 1) cost metrics for both single MapReduce job and a number of MapReduce jobs executed in a certain order; 2) the efficient execution of a chain-typed Theta-join with only one MapReduce job. Comparing with the query evaluation strategy proposed in [23] and the widely adopted Pig Latin and Hive SQL solutions, our method achieves significant improvement of the join processing efficiency.Comment: VLDB201