Research strengths within the RMIT Centre for Information Discovery include data integration, data quality (lineage), data visualization, and data exploration, with expertise in spatial data, high dimensional data, and multimedia data. The increasing volume of data, such as from GPS and sensor devices, enables us to answer many real-life queries for different applications. Examples of this include facility location selection (finding a suitable location of a facility or a store for business and marketing, identifying an optimal location such that the facility is close to the maximum number of its customers) and trajectory travel patterns analysis (multi-range queries that find trajectories passing through a set of given spatio-temporal ranges). In high dimensional data analysis, real world objects can be represented by features of different aspects, it is essential to identify the similarities, differences and relevance between objects in terms of these features, including through techniques such as clustering, multi-objective optimisation, and influence set identification. Multimedia data analysis aims to identify the similarity or relevance of media data effectively and efficiently for different applications. It covers the research on several typical topics: digital copy detection, anomaly detection, media recommendation, media compression and summarization.
Data mining, or knowledge discovery in data, aims to infer hidden knowledge, patterns and insights from data for various applications. Data is usually in high volumes and can be in formats such as transactions, natural language texts, and networks of linked data items. The knowledge mined from data can be patterns describing human behaviours and activities, or predictive models that classify a credit card application as low risk or high risk, or classify a review as fraudulent or genuine. Data mining technologies can include efficient algorithms searching for knowledge patterns, or machine learning models for predictive analytics. The RMIT Centre for Information Discovery has expertise in several areas: (1) Designing efficient data mining algorithms for knowledge patterns from large volumes of data on big data processing platforms. (2) Sentiment analysis and opinion mining, including opinion spam detection, opinion summarization. (3) Fraud detection and anomaly detection, especially for financial applications. (4) Social media and social network data mining, including user profiling, sentiment analysis and information credibility analysis. (4) Recommender systems, including complex applications such as recommending itineraries for travel or amusement parks, contextual recommendation for personal assistants and dynamic and personalised scenarios such as recommending publications to read and study. (5) Biomedical text mining.
Information Retrieval (IR) systems retrieve information relevant to a user’s information need. While this sounds a simple process (just find documents containing query words), the volume of documents matching virtually any query is often so large that an IR system must attempt to infer what the user is seeking in order to locate relevant documents. Outstanding IR systems need to be fast, intuitive to use, and accurate. The technology underpinning IR systems can be found in search systems (e.g. web search engines such as Google and Bing), recommender systems (Netflix, Amazon), and many other tools to retrieve information. The RMIT Centre for Information Discovery has expertise in designing, evaluating, and improving complex, multi-stage retrieval in a wide variety of application areas, such as web, legal, medical, genomic, product, and job-based search systems. Particular areas of focus include improving the efficiency and scalability search engines, improving the effectiveness of retrieval systems through learning-to-rank and other state-of-the-art ranking models, modelling and understanding user behaviour, and evaluating the quality of the search results returned.
Statistics has long been a cornerstone of data modelling and data analytics. The RMIT Centre for Information Discovery provides expertise in all facets of data analysis, including data visualisation, machine learning, deep learning, Bayesian statistics, biostatistics, statistical inference, design and analysis of experiments, forecasting, and optimisation. Past projects include work with medical statistics, spatial statistics, cybersecurity, epidemiological systems, ecology environment and climate change, financial models, social networks, and recommendation systems. CID members have expertise with the ability to explore deep theoretical statistical questions to more practical but challenging studies on how best to improve business productivity and profits.
Spatial computing focuses on unique challenges posed and opportunities offered by computing with spatiotemporal information. Spatial information is connected with locations, regions, movement, events, and relationships in geographic space. Most information relates to geographic space in some way (for example, with studies showing between 60-80% of Wikipedia articles have some spatial reference). Whether this information is quantitative, such as coordinates and geometry, or qualitative, such as spatial relations or networks, making sense of spatial data underpins many of the most impactful applications in computing and information discovery today. The RMIT Centre for Information Discovery includes expertise from across the whole breadth of spatial computing, from the foundations of spatial algorithms, spatial databases, and spatial visualisation, through to applications of big spatial data to emergency response, transportation and indoor tracking, environmental monitoring, and enabling tomorrow’s smart urban infrastructure.
Ubiquitous computing (or pervasive computing) research and systems is concerned with making computational capability available in everyday objects and activities. Our focus is on 1) making computation human centred to enable meaningful and seamless human interactions with computing devices and everyday objects and 2) improving the efficiency and usability of smart devices and environments to be more aware, predictive, and proactive to user situations and needs. The RMIT Centre for Information Discovery is at the forefront of fundamental and practical time-series and spatio-temporal data mining and machine learning techniques for high-dimensional multivariate data which can be applied on multiple types of time-series, nominal, textual, and graph data from sensors, smartphones, wireless infrastructures (Wi-Fi, bluetooth, RF sensors), UAV / drones, Internet of Things, location-based social networks, and moving objects (vehicles, airplanes). Our research is grounded in practical problems and we consider user needs and business objectives to ensure our research is aligned with real-world contexts. As examples, our work has been applied in the domain of smart cities, smart buildings, smart parking, intelligent transportation systems, and intelligent assistants.