TRIIBE paper “Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces” published in EPJ Data Science (OA)

Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. In our new study published in EPj Data Science, we explore associations based on a combined understanding of customer Cyber (online), Physical (Where?), and Social behaviour. We combine the results of a traditional questionnaire with large-scale WiFi access logs which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources and provide strong support for demographic studies based on large-scale logs data capture.

This is the most recent publication from the fruitful ARC LP TRIIBE collaboration, with colleagues Yongli Ren, Flora Salim, Jeff Chan and Mark Sanderson (all RMIT).

Cite as:

Ren, Y., M. Tomko, F. D. Salim, J. Chan and M. Sanderson (2018). “Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces.” EPJ Data Science 7(1): 1-21. DOI: 10.1140/epjds/s13688-017-0128-2

The paper can be freely accessed here:


Ivan’s first paper “Finding equivalent keys in OpenStreetMap” accepted to GeoAI’17

It is a special feeling when your PhD student publishes their first paper! Congratulations to Ivan Majic (that’s Magic with a “J“) for the first workshop paper – a great start for the PhD, keep on the good work!

Reference: Majic, I., Winter, S., & Tomko, M. (2017). Finding equivalent keys in OpenStreetMap: semantic similarity computation based on extensional definitions. In GeoAl’17: 1st ACM SIGSPATIAL Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, . Los Angeles Area, CA, USA: ACM. (pre-print)

TRIIBE paper “A Location-Query-Browse Graph for Contextual Recommendation” accepted to TKDE

I am excited that one of our main papers from TRIIBE (lead author Yongli Ren, RMIT) has been accepted to TKDE.”

Working with large-scale WiFi sensing data is never easy, and with the bulk the amount of noise increases as well. Here we present a method that clearly demonstrates the interplay, and the benefits of combining the physical location, as well as the search and browsing histories of indoor visitors (in an aggregate, privacy aware way) to improve contextual recommendations in indoor spaces.

Ren, Y., M. Tomko, F. Salim, J. Chan, C. L. A. Clarke and M. Sanderson (accepted, 2017). “A Location-Query-Browse Graph for Contextual Recommendation.” IEEE Transactions on Knowledge and Data Engineering. DOI:10.1109/TKDE.2017.2766059 (pre-print, publisher link)


P.S.> apologies for the huge delay in posting. Many more news are overdue.

PHD scholarships: Making Computers Understand Place

Your PhD research will contribute to a project at the forefront of fundamental AI and spatial information research: Making computers understand common language about place. This project, funded by the Australian Research Council (2017-2019), will deliver the computational methods to capture, model, process, and interact on human place knowledge, which conceptualizes places and their relations, instead of using coordinates and maps. The outcomes will enable human-machine interaction that is capable of responding to human intuition.

The project engages in research in artificial intelligence, language technologies, and spatial information science – you will work in an interdisciplinary environment. The project is also geographically spread between The University of Melbourne, the Australian National University in Canberra, and the University of California, Santa Barbara. The scholarships, however, are allocated to the two Australian universities, which are Australia’s leading research universities.

We seek applicants covering one of the following tasks:

1. Capture (Melbourne: led by Tim Baldwin). This task addresses the extraction of place knowledge in conversational context from large-scale text corpora such as social media and web sources, or crowd-sourced from mobile apps.

2. Modelling (Melbourne: led by Stephan Winter). This task addresses the representation of extracted place knowledge in place graphs, especially the representation of context with places and their relationships.

3. Reasoning (Canberra: led by Jochen Renz). This task addresses context-sensitive spatial reasoning beyond maintaining a locally consistent database. We expect to make progress on this long avoided challenge by considering the spatial context derived from the integration of place knowledge and maps.

4. User Interaction (Melbourne: led by Martin Tomko). This task addresses the user interaction with a place graph, enabling users to query or modify stored data by developing methods answering user queries taking into account the context of the querying user.

Selection Criteria

* Masters or Honours Degree in Computer Science, Geodesy/Geomatics/Geoinformatics, or another relevant discipline;

* A weighted average mark at least satisfying the entry requirements of the PhD programs at the respective universities;

* Demonstrated ability to perform independent research (e.g., by a research component in the master degree);

* Excellent written and verbal communication skills to technical and non-technical audiences (e.g., demonstrated in the written application and interview);

* Software development skills;

* Ability to work in (at least one of) spatial data handling and analysis, database systems, machine learning, natural language understanding, and artificial intelligence;

* Ability to work co-operatively in a multi-disciplinary environment and team.


Scholarships are for three years, with the expectation to complete a PhD in this time-frame. The universities have excellent environments and frameworks to support PhD research. Also, successful candidates will be provided a tuition fee waiver. Scholarships are sufficient to cover living costs in Melbourne or Canberra. The project also provides funds for conference travels commensurate with opportunity.

How to apply

Send your letter of application (addressing the selection criteria, and identifying the particular task of interest), a CV, and a transcript of your last degree per email to the project leader, Prof Stephan Winter (

The application deadline is 20 March 2017, for a start later in 2017. Selection is pending admission at the respective university and visa processes.

Vacancy: Research Fellow in Autonomous Spatial Data Integration

All #AI #ML #DB talent out there!

A Research Fellow position is available within a project funded by the Australian Research Council, “Self-Healing Maps”. The project aims to expand our ability to automatically integrate real-time data in map databases of high integrity, enabling adaptive and autonomous protection of maps from the insertions of erroneous or malicious data, and the detection and correction of inconsistencies.

You will work within under the guidance of Dr Martin Tomko and other chief investigators across all aspects of Geomatics and spatial information. You will be in charge of conducting research and coordinating the efforts leading to a new generation of mechanisms supporting spatial data integration. You will devise novel methods enabling the autonomous and evolving discovery and rectification of errors, discrepancies and inconsistencies in spatial databases. You will apply your outstanding computational skills to demonstrate the validity of your theoretical contributions through the implementation of proof-of-concept software.

You will conduct collaborative and independent research, leading to the preparation and publication of research outcomes in conferences and journals. You will be located in the Department of Infrastructure Engineering in the Melbourne School of Engineering.


For more information, contact Dr Martin Tomko (

“Ripe for the Picking? Dataset Maturity Assessment based on Temporal Dynamics of Feature Definitions”

Our paper that has been recently accepted for publication in the International Journal of Geographical Information Science investigates a thus-far unexplored aspect of spatial data of particular relevance to VGI usability (OSM as a case) – the differences between (geometric) feature definitions within a feature class. I am particularly pleased by this paper as it is the outcome of a short, but intense and very satisfying collaboration with Stephen Maguire, a Masters of IT (Spatial) student here at the University of Melbourne. Congratulations Stephen!


From the abstract:

Map databases traditionally capture snapshot representations of the world following strict data collection and representation guidelines. The content of these map databases is often assessed using data quality metrics focusing on accuracy, completeness and consistency. The success of volunteered geographic information, supporting evolving representations of the world based on fluid guidelines, has rendered these measures insufficient. In this paper, we address the need to capture the variability in quality of a map database. We propose a new spatial data quality measure — dataset maturity — enabling assessment of the database based on temporal trends in feature definitions, specifically geometry type definitions. The proposed measure can be (1) efficiently used to identify feature definition patterns reflecting community consensus that could be formalised in community guidelines; and (2) deployed to identify regions that would benefit from increased editorial activity to achieve greater map homogeneity. We demonstrate the measure based on the content of the OpenStreetMap database in four regions of the world, and show how the proposed dataset maturity measure captures a distinct quality of the datasets, distinct to data completeness and consistency.

Read the full thing in pre-print, supplementary materials here.

Maguire, S., & Tomko, M. (accepted 2017). Ripe for the Picking? Dataset Maturity Assessment based on Temporal Dynamics of Feature Definitions. International Journal of Geographical Information Science. doi:10.1080/13658816.2017.1287370

Our paper on D-Log accepted to Pervasive and Mobile Computing

A new output from our TRIIBE project is now available: “D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate“, by our TRIIBE team (Yongli Ren, Flora Salim, Martin Tomko, Brian Bai, Jeffrey Chan, Kyle Qin and Mark Sanderson) is now online here. It presents a way to post-process large amounts of single AP+RSSI fixations to better estimate the approximate location of users in an indoor environment. I will publish a pre-print version soon here.