Influencing collaboration to enhance knowledge work through serendipity: user-study and design considerations

We all were strangers to someone at some point and that is the starting point to analyze unexpected encounters. The busy pace of life has alienated people from each other, hence, this created an opportunity for technology to support social experiences. Meeting new people that one would not normally encounter in the vicinity or in the regular social sphere would expand the opportunities for establishing connections. Connections that go beyond establishing friendship bonds, but finding collaborators for the development of projects. This thesis was developed in order to understand the concept of serendipity in the context of computational systems and how it can be used to facilitate encounters among knowledge workers. The analysis of this thesis is conceived within the borders of Human-Technology Interaction, using psychological and sociality approaches from a technological perspective that allows a better understanding of the people’s needs when developing tools to support social interactions. The theoretical chapters start analyzing the phenomenon of serendipity from different perspectives, along with concepts about knowledge work and matchmaking. In order to understand the phenomenon of serendipity, the term is defined from social perspectives to psychological ones. The purpose of this is to set the basic premises of the study and introduce how serendipity is approached in terms of computational systems and knowledge work. Then, it analyzes matchmaking and grouping by presenting knowledge networks, social matchmaking with professional purposes and context awareness. The user study is carried out by a set of interviews to participants in Demola (an ecosystem that joins students with projects from companies), followed by a comparison of different tools that already exist that help matchmaking. The purpose of the user study was to analyze manual matchmaking among strangers. It analyzes participants’ experiences when working with strangers to carry out different innovation projects. It also intends to determine the expectations when forming a group. Added to that, the head of Demola Tampere was interviewed to understand the manual matching participants process. The final chapter presents a set of considerations when designing for serendipity to enhance knowledge work. The conceptualization of serendipity and the user study are the basis for establishing a set of guidelines in design. Which intend to enhance matchmaking in knowledge workers by analyzing weak ties as a way of serendipity. This study emphasizes on the goals and expectations of the users when finding a professional partner. Based on the user study, a model is presented which shows a possible structure for matchmaking

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data are usually multi-variant streams that are heterogeneous, sporadic, multi-modal, and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types, and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery, and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised, which hinders their scalability. The primary objective of this article is to provide a holistic overview of the state-of-the-art on indexing, discovery, and ranking of IoT data. The article aims to pave the way for researchers to design, develop, implement, and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services

Probabilistic Matchmaking Methods for Automated Service Discovery

Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalised discounted cumulative gain values

Probabilistic Matchmaking Methods for Automated Service Discovery

Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalised discounted cumulative gain values