Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data

The meteoric rise in the popularity of public engagement platforms such as social media, customer review websites, and public input solicitation efforts strives for establishing an inclusive environment for the public to share their thoughts, ideas, opinions, and experiences. Many decisions made at a personal, local, or national scale are often fueled by data generated by the public. As such, inclusive collection, analysis, sensemaking, and utilization of pubic-generated data are crucial to support the exercise of successful decision-making processes. However, people often struggle to engage, participate, and share their opinions due to inaccessibility, the rigidity of traditional public engagement methods, and the lack of options to provide opinions while avoiding potential confrontations. Concurrently, data analysts and decision-makers grapple with the challenges of analyzing, sensemaking, and making informed decisions based on public-generated data, which includes high dimensionality, ambiguity present in human language, and a lack of tools and techniques catered to their needs. Novel technological interventions are therefore necessary to enable the public to share their input without barriers and allow decision-makers to capture, forage, peruse, and sublimate public-generated data into concrete and actionable insights. The goal of this dissertation is to demonstrate how human-centered approaches involve the stakeholders in the design, development, and evaluation of tools and techniques that can lead to inclusive, effective, and efficient approaches to public-generated data collection and analysis to support informed decision-making. To that end, in this dissertation, I first addressed the challenges of empowering the public to share their opinions by exploring two major opinion-sharing avenues --- social media and public consultation. To learn more about people\u27s social media experiences and challenges, I built two technology probes and conducted a qualitative exploratory study with 16 participants. This study is followed up by exploring the challenges of inclusive participation during public consultations such as town halls. Based on a formative study with 66 participants and 20 organizers, I designed and developed CommunityClick to enable reticent share their opinions silently and anonymously during town halls. Equipped with the knowledge and experiences from these works, I designed, developed, and evaluated technologies and methods to facilitate and accelerate informed data-driven decision-making based on increased public-generated data. Based on interviews with 14 analysts and decision-makers in the civic domain, I built a visual analytics system CommunityClick that can facilitate public input analysis by surfacing hidden insights, people\u27s reflections, and priorities. Leveraging the lessons learned during this work, I created a visual text analytics system that supports serendipitous discovery and balanced analysis of textual data to help make informed decisions. In this work, I contribute an understanding of how people collect and analyze public-generated data to fuel their decisions when they have increased exposure to alternative avenues for opinion-sharing. Through a series of human-centered studies, I highlight the challenges that inhibit inclusivity in opinion sharing and shortcomings of existing methods that prevent decision-makers to account for comprehensive public input that includes marginalized or unpopular opinions. To address these challenges, I designed, developed, and evaluated a collection of interactive systems including CommunityClick, CommunityPulse, and Serendyze. Through a rigorous set of evaluation strategies which include creativity sessions, controlled lab studies, in-the-wild deployment, and field experiments, I involved stakeholders to assess the effectiveness and utility of the built systems. Through the empirical evidence from these studies, I demonstrate how alternative designs for social media could enhance people\u27s social media experiences and enable them to make new connections with others to share opinions. In addition, I show how CommunityClick can be utilized to enable reticent attendees during public consultation to share their opinions while avoiding unwanted confrontation and allowing organizers to capture and account for silent feedback. I highlight how CommunityPulse allowed analysts and decision-makers to examine public input from multiple angles for an accelerated analysis and more informed decision-making. Furthermore, I demonstrate how supporting serendipitous discovery and balanced analysis using Serendyze can lead to more informed data-driven decision-making. I conclude the dissertation with a discussion on future avenues to expand this research including the facilitation of multi-user collaborative analysis, integration of multi-modal signals in the analysis of public-generated data, and potential adoption strategies for decision-support systems designed for inclusive collection and analysis of public-generated data

Topic-based analysis for technology intelligence

University of Technology Sydney. Faculty of Engineering and Information Technology.Since the past several decades, scientific literature, patents and other semi-structured technology indicators have been generating and accumulating at a very rapid rate. Their growth provides a wealth of information regarding technology development in both the public and private domain. However, it has also caused increasingly severe information overload problems whereby researchers, analysts and decision makers are not able to read, summarize and understand massive technical documents and records manually. The concept and tools of technology intelligence aims to handle this issue. In the current technology intelligence research, one of the big challenges is that, the frameworks and applications of existing technology intelligence conducted semantic content analysis and temporal trend estimation separately, lacking a comprehensive perspective on trend analysis of the detailed content within an area. In addition, existing research of technology intelligence is mainly constructed on the fundamentals of semantic properties of the semi-structured technology indicators; however, single keywords and their ranking alone, are too general or ambiguous to represent complex concepts and their corresponding temporal patterns. Thirdly, systematic post-processing, forecasting and evaluation on both content analysis and trend identification outputs are still in great demand, for diverse and flexible technological decision support and opportunity discovery. This research aims to handle these three challenges in both theoretical and practical aspects. It first quantitatively defines and presents temporal characteristics and semantic properties of typical semi-structured technology indicators. Then this thesis proposes a framework of topic-based technology intelligence, with three main functionalities, including data-driven trend identification, topic discovery and comprehensive topic evaluation, to synthetically process and analyse technological publication count sequence, textual data and metadata of target technology indicators. To achieve the three functionalities, this research proposes an empirical technology trend analysis method to extract temporal trend turning points and trend segments, which help with producing a more reasonable time-based measure; a topic-based technological forecasting method to first discover and characterize the semantic knowledge underlying in massive textual data of technology indicators, meanwhile estimating the future trends of the discovered topics; a comprehensive topic evaluation method that links metadata and discovered topics, to provide integrated landscape and technological insight in depth. In order to demonstrate the proposed topic-based technology intelligence framework and all the related methods, this research presents case studies with both patents and scientific literature. Experimental results on Australian patents, United States patents and scientific papers from Web of Science database, showed that the proposed framework and methods are well-suited in dealing with semi-structured technology indicators analysis, and can provide valuable topic-based knowledge to facilitate further technological decision making or opportunity discovery with good performance

ECOSENSUS: developing collaborative learning systems for stakeholding development in environmental planning

ECOSENSUS *(Electronic/Ecological Collaborative Sensemaking Support System) investigates the socio-technological issues around developing collaboration tools for participatory environmental decision making amongst (a) marginalised natural resource users, (b) professional 'experts' from different countries, and (c) key decision makers associated with managing ecosystems. An integral activity is the production of open content learning resources to support stakeholders in facilitating distributed environmental decision making. This involves the integrated use of three open source software tools: Moodle (online course management), Compendium (dialogue mapping) and uDig (user friendly desktop/internet GIS). In the first ECOSENSUS-1 phase, the pilot collaborative effort has been focused on supporting stakeholders in developing adaptive management plans for the Rupununi Wetlands in southern Guyana, a region rich in flora and fauna but also under intense pressure to expand the exploitation of its natural resources, including timber, gold, and commercially viable fish species. Results of the ECOSENSUS-1 are briefly described along with some preliminary notes on the current ECOSENUS-2 phase of associated research in Guyana supported by an additional grant from DEFRA. The paper prompts questions on how ECOSENSUS can feed into wider open source course development using the LabSpace on the OpenLearn project

Integrating information and knowledge for enterprise innovation

It has widely been accepted that enterprise integration, can be a source of socio-technical and cultural problems within organisations wishing to provide a focussed end-to-end business service. This can cause possible “straitjacketing” of business process architectures, thus suppressing responsive business re-engineering and competitive advantage for some companies. Accordingly, the current typology and emergent forms of Enterprise Resource Planning (ERP) and Enterprise Application Integration (EAI) technologies are set in the context of understanding information and knowledge integration philosophies. As such, key influences and trends in emerging IS integration choices, for end-to-end, cost-effective and flexible knowledge integration, are examined. As touch points across and outside organisations proliferate, via work-flow and relationship management-driven value innovation, aspects of knowledge refinement and knowledge integration pose challenges to maximising the potential of innovation and sustainable success, within enterprises. This is in terms of the increasing propensity for data fragmentation and the lack of effective information management, in the light of information overload. Furthermore, the nature of IS mediation which is inherent within decision making and workflow-based business processes, provides the basis for evaluation of the effects of information and knowledge integration. Hence, the authors propose a conceptual, holistic evaluation framework which encompasses these ideas. It is thus argued that such trends, and their implications regarding enterprise IS integration to engender sustainable competitive advantage, require fundamental re-thinking

Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface

DATA WAREHOUSE AND BUSINESS INTELLIGENCE STRATEGIES AND TRENDS

In recent decades following the evolution of information technology, decision support systems have played an important role by presenting the necessary information resulted from the operational systems processes. By continuing improvement of the methods as well as the contribution of technological advance the applicability of decision support systems is now generalized and has reached the status of complex systems of business intelligence. Business Intelligence is about creating intelligence about a business based on a cyclic flow which consists of capturing, analyzing, planning and implementation resulting in streamlining the organization.Decisions, DSS, Data Driven, Business Intelligence, Data Warehouse