Conversational Data Exploration: A Game-Changer for Designing Data Science Pipelines

This paper proposes a conversational approach implemented by the system Chatin for driving an intuitive data exploration experience. Our work aims to unlock the full potential of data analytics and artificial intelligence with a new generation of data science solutions. Chatin is a cutting-edge tool that democratises access to AI-driven solutions, empowering non-technical users from various disciplines to explore data and extract knowledge from it

Conversational Sensing

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects

A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration

This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data. The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the ‘Best Student Paper Award’)

Retrieving information from heterogeneous freight data sources to answer natural language queries

textThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to user’s query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a user’s question is proposed.Civil, Architectural, and Environmental Engineerin

Trading Consequences: A Case Study of Combining Text Mining and Visualization to Facilitate Document Exploration

Large-scale digitization efforts and the availability of computational methods, including text mining and information visualization, have enabled new approaches to historical research. However, we lack case studies of how these methods can be applied in practice and what their potential impact may be. Trading Consequences is an interdisciplinary research project between environmental historians, computational linguists and visualization specialists. It combines text mining and information visualization alongside traditional research methods in environmental history to explore commodity trade in the nineteenth century from a global perspective. Along with a unique data corpus, this project developed three visual interfaces to enable the exploration and analysis of four historical document collections, consisting of approximately 200,000 documents and 11 million pages related to commodity trading. In this paper we discuss the potential and limitations of our approach based on feedback from historians we elicited over the course of this project. Informing the design of such tools in the larger context of digital humanities projects, our findings show that visualization-based interfaces are a valuable starting point to large-scale explorations in historical research. Besides providing multiple visual perspectives on the document collection to highlight general patterns, it is important to provide a context in which these patterns occur and offer analytical tools for more in-depth investigations.PostprintPeer reviewe

Interactive maps: What we know and what we need to know

This article provides a review of the current state of science regarding cartographic interaction a complement to the traditional focus within cartography on cartographic representation. Cartographic interaction is defined as the dialog between a human and map mediated through a computing device and is essential to the research into interactive cartography geovisualization and geovisual analytics. The review is structured around six fundamental questions facing a science of cartographic interaction: (1) what is cartographic interaction (e.g. digital versus analog interactions interaction versus interfaces stages of interaction interactive maps versus mapping systems versus map mash-ups); (2) why provide cartographic interaction (e.g. visual thinking geographic insight the stages of science the cartographic problematic); (3) when should cartographic interaction be provided (e.g. static versus interactive maps interface complexity the productivity paradox flexibility versus constraint work versus enabling interactions); (4) who should be provided with cartographic interaction (e.g. user-centered design user ability expertise and motivation adaptive cartography and geocollaboration); (5) where should cartographic interaction be provided (e.g. input capabilities bandwidth and processing power display capabilities mobile mapping and location-based services); and (6) how should cartographic interaction be provided (e.g. interaction primitives objective-based versus operator-based versus operand-based taxonomies interface styles interface design)? The article concludes with a summary of research questions facing cartographic interaction and offers an outlook for cartography as a field of study moving forward

Data Commons

Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be joined easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work