A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Verifying Strong Eventual Consistency in Distributed Systems

Data replication is used in distributed systems to maintain up-to-date copies of shared data across multiple computers in a network. However, despite decades of research, algorithms for achieving consistency in replicated systems are still poorly understood. Indeed, many published algorithms have later been shown to be incorrect, even some that were accompanied by supposed mechanised proofs of correctness. In this work, we focus on the correctness of Conflict-free Replicated Data Types (CRDTs), a class of algorithm that provides strong eventual consistency guarantees for replicated data. We develop a modular and reusable framework in the Isabelle/HOL interactive proof assistant for verifying the correctness of CRDT algorithms. We avoid correctness issues that have dogged previous mechanised proofs in this area by including a network model in our formalisation, and proving that our theorems hold in all possible network behaviours. Our axiomatic network model is a standard abstraction that accurately reflects the behaviour of real-world computer networks. Moreover, we identify an abstract convergence theorem, a property of order relations, which provides a formal definition of strong eventual consistency. We then obtain the first machine-checked correctness theorems for three concrete CRDTs: the Replicated Growable Array, the Observed-Remove Set, and an Increment-Decrement Counter. We find that our framework is highly reusable, developing proofs of correctness for the latter two CRDTs in a few hours and with relatively little CRDT-specific code

Supporting awareness in heterogeneous collaboration environments

Rapid technological advancements have made it possible for humans to collaborate as never before. However demands of group work necessitate distributed collaboration in very heterogeneous environments. Heterogeneity as in various applications, platforms, hardware and communication infrastructure. User mobility, lack of availability and cost often make imposing a common collaboration environment infeasible. Awareness is essential for successful collaboration. Awareness is a key design criterion in groupware but often collaboration occurs with applications not designed to support useful awareness. This dissertation deals with the issue of effective group awareness support in heterogeneous environments.;Awareness propagation is effective if the appropriate amount of information, relevant to the user\u27s sphere of activity is delivered in a timely, unobtrusive fashion. Thus issues such as information overload, and distraction have to be addressed. Furthermore ability to establish the appropriate balance between awareness and privacy is essential. Enhanced forms of awareness such as intersubjectivity and historical awareness are often invaluable. Heterogeneous environments significantly impact the above quality factors impeding effective awareness propagation. Users are unable to tailor the quality of awareness received.;Heterogeneity issues that affect awareness quality are identified. An awareness framework is proposed that binds various sources of awareness information. However for effective awareness support, physical integration must be augmented by information integration. As a solution, an awareness model is proposed. Specification of the awareness model and framework\u27s architecture and features is the key contribution. The proposed model has been validated through simulations of realistic collaboration involving human participation. Scenarios created, have tested the model\u27s usefulness in enhancing the quality of group work by propagating effective awareness among users. To accomplish the same, an Awareness Simulator application has been created. In the validation process, efforts made to create an experimental methodology revealed some techniques related to awareness evaluation in CSCW, which are proposed. Various issues required to successfully engineer such awareness frameworks are identified and their impact on requirements such as security and performance, discussed. With various standards and technologies that can be harnessed to create awareness frameworks, there is great promise that barriers in heterogeneous collaboration environments can be overcome

Securely sharing dynamic medical information in e-health

This thesis has introduced an infrastructure to share dynamic medical data between mixed health care providers in a secure way, which could benefit the health care system as a whole. The study results of the universally data sharing into a varied patient information system prototypes

Designing Digital Work

Combining theory, methodology and tools, this open access book illustrates how to guide innovation in today’s digitized business environment. Highlighting the importance of human knowledge and experience in implementing business processes, the authors take a conceptual perspective to explore the challenges and issues currently facing organizations. Subsequent chapters put these concepts into practice, discussing instruments that can be used to support the articulation and alignment of knowledge within work processes. A timely and comprehensive set of tools and case studies, this book is essential reading for those researching innovation and digitization, organization and business strategy

Factors impacting on tacit knowledge transfer within Scrum software development teams

Over time, there has been a high failure rate of information systems development (ISD) projects, although Agile development has brought recent improvements. Knowledge management is also known to be one of the critical factors to Agile and project success; however, there are some knowledge transfer studies in Agile development. Therefore, the purpose of this research is to present a theoretical model examining what makes individuals successful at transferring knowledge in teams using Scrum, Agile’s most popular methodology. Twelve semi-structured interviews were conducted at two Scrum companies in Cape Town. Participants interviewed ranged from project managers and Scrum masters to software developers, business analyst and testers. The interviews were all transcribed, then analysed using thematic analysis. The findings produced new relationships between characteristics already well known to impact knowledge transfer as well as newly defined characteristics impacting knowledge transfer in Scrum teams: empathy and articulation skills of the source. The results have shown that the recipient should perceive the person wanting to transfer knowledge as having these characteristics to enable successful knowledge transfer: empathy, motivation, capability, credibility, articulate and ability to communicate enough. The contribution of this study to practice is a list of attributes for HR managers to help improve the knowledge transfer of Scrum team members. The contribution to Scrum research is a new theoretical model that suggests which characteristics a person needs to transfer knowledge successfully in Scrum teams, adapted from Joshi, Sarker and Sarker’s (2007) knowledge transfer model. This model can also be extended in the future by looking more deeply into the new relationships between constructs, such as how motivation together with capability of the source affect knowledge transfer in Scrum teams

A customized semantic service retrieval methodology for the digital ecosystems environment

With the emergence of the Web and its pervasive intrusion on individuals, organizations, businesses etc., people now realize that they are living in a digital environment analogous to the ecological ecosystem. Consequently, no individual or organization can ignore the huge impact of the Web on social well-being, growth and prosperity, or the changes that it has brought about to the world economy, transforming it from a self-contained, isolated, and static environment to an open, connected, dynamic environment. Recently, the European Union initiated a research vision in relation to this ubiquitous digital environment, known as Digital (Business) Ecosystems. In the Digital Ecosystems environment, there exist ubiquitous and heterogeneous species, and ubiquitous, heterogeneous, context-dependent and dynamic services provided or requested by species. Nevertheless, existing commercial search engines lack sufficient semantic supports, which cannot be employed to disambiguate user queries and cannot provide trustworthy and reliable service retrieval. Furthermore, current semantic service retrieval research focuses on service retrieval in the Web service field, which cannot provide requested service retrieval functions that take into account the features of Digital Ecosystem services. Hence, in this thesis, we propose a customized semantic service retrieval methodology, enabling trustworthy and reliable service retrieval in the Digital Ecosystems environment, by considering the heterogeneous, context-dependent and dynamic nature of services and the heterogeneous and dynamic nature of service providers and service requesters in Digital Ecosystems.The customized semantic service retrieval methodology comprises: 1) a service information discovery, annotation and classification methodology; 2) a service retrieval methodology; 3) a service concept recommendation methodology; 4) a quality of service (QoS) evaluation and service ranking methodology; and 5) a service domain knowledge updating, and service-provider-based Service Description Entity (SDE) metadata publishing, maintenance and classification methodology.The service information discovery, annotation and classification methodology is designed for discovering ubiquitous service information from the Web, annotating the discovered service information with ontology mark-up languages, and classifying the annotated service information by means of specific service domain knowledge, taking into account the heterogeneous and context-dependent nature of Digital Ecosystem services and the heterogeneous nature of service providers. The methodology is realized by the prototype of a Semantic Crawler, the aim of which is to discover service advertisements and service provider profiles from webpages, and annotating the information with service domain ontologies.The service retrieval methodology enables service requesters to precisely retrieve the annotated service information, taking into account the heterogeneous nature of Digital Ecosystem service requesters. The methodology is presented by the prototype of a Service Search Engine. Since service requesters can be divided according to the group which has relevant knowledge with regard to their service requests, and the group which does not have relevant knowledge with regard to their service requests, we respectively provide two different service retrieval modules. The module for the first group enables service requesters to directly retrieve service information by querying its attributes. The module for the second group enables service requesters to interact with the search engine to denote their queries by means of service domain knowledge, and then retrieve service information based on the denoted queries.The service concept recommendation methodology concerns the issue of incomplete or incorrect queries. The methodology enables the search engine to recommend relevant concepts to service requesters, once they find that the service concepts eventually selected cannot be used to denote their service requests. We premise that there is some extent of overlap between the selected concepts and the concepts denoting service requests, as a result of the impact of service requesters’ understandings of service requests on the selected concepts by a series of human-computer interactions. Therefore, a semantic similarity model is designed that seeks semantically similar concepts based on selected concepts.The QoS evaluation and service ranking methodology is proposed to allow service requesters to evaluate the trustworthiness of a service advertisement and rank retrieved service advertisements based on their QoS values, taking into account the contextdependent nature of services in Digital Ecosystems. The core of this methodology is an extended CCCI (Correlation of Interaction, Correlation of Criterion, Clarity of Criterion, and Importance of Criterion) metrics, which allows a service requester to evaluate the performance of a service provider in a service transaction based on QoS evaluation criteria in a specific service domain. The evaluation result is then incorporated with the previous results to produce the eventual QoS value of the service advertisement in a service domain. Service requesters can rank service advertisements by considering their QoS values under each criterion in a service domain.The methodology for service domain knowledge updating, service-provider-based SDE metadata publishing, maintenance, and classification is initiated to allow: 1) knowledge users to update service domain ontologies employed in the service retrieval methodology, taking into account the dynamic nature of services in Digital Ecosystems; and 2) service providers to update their service profiles and manually annotate their published service advertisements by means of service domain knowledge, taking into account the dynamic nature of service providers in Digital Ecosystems. The methodology for service domain knowledge updating is realized by a voting system for any proposals for changes in service domain knowledge, and by assigning different weights to the votes of domain experts and normal users.In order to validate the customized semantic service retrieval methodology, we build a prototype – a Customized Semantic Service Search Engine. Based on the prototype, we test the mathematical algorithms involved in the methodology by a simulation approach and validate the proposed functions of the methodology by a functional testing approach