Big data analytics recommender system for housing health and safety

The dissertation analyses issues arising in the management of safe and healthy housing. The research focuses on the management of safe and healthy housing in micro, meso and macro environment in order to keep it safe and healthy, according to the needs of stakeholder groups. The aim of the dissertation is to develop an integrated big data analytics recommender system for housing health and safety that will allow to manage safe and healthy house for the stakeholder groups.

The following objectives have been set for this thesis: to analyse the most advanced global experience which allows to identify the methods for the purpose to analyse integrated big data for safe and healthy housing and assess the reliabi-lity of research; based on the literature analysis, to develop an integrated big data analytics recommender system for housing health and safety.

The dissertation contains an introduction, three chapters, general conclusions, a list of references, a list of the author’s scientific publications by the author on the topic of the dissertation and 14 annexes.

The introduction states the problem, points out what makes this research relevant, describes the research object, states the research aim and objectives, and presents the research methodology, original contribution, practical impact of the research findings and defended propositions. It concludes with an overview of the author’s dissertation-related articles and conference presentations and an outline of the structure of the dissertation.

Chapter 1 presents the analysis of the theoretical review. The important role played by the key concepts and decision making in public policy implementation is pointed out, the importance of big data analytics and recommender systems is highlighted, assessment methods and systems are analysed, and the life cycle of housing and its assessment are overviewed. The chapter ends with conclusions and a revised set of research objectives. Chapter 2 introduces sets of criteria, ana-lyses the methods used in the dissertation to build the integrated system, and pre-sents a conceptual big data assessment model for safe and healthy housing. Chap-ter 3 describes the integrated big data analytics recommender system for housing health and safety and presents several cases of this system put to practice.

In total, nine articles related to this dissertation were published and four pre-sentations related to the dissertation were given at conferences in Lithuania and abroad.

Electronic version of the book:

DOI: https://doi.org/10.20334/2018-033-M