Special Tracks

In addition to our regular academic track AusDM 2018 invites papers in the following special categories. Papers accepted from each of these tracks will be presented together in a session. These special tracks are:

Papers submitted in these tracks will be reviewed in the same process as those submitted to the regular academic track. All accepted papers will be published in the same proceedings.

Image Data Mining

One picture is worth more than thousands of words. Image data potentially contains a wealth of information and widely exists in diverse fields. Therefore, developing data mining technologies to extract useful patterns and knowledge from image data is of great importance and significance. In the current age of big data, digital images and videos are flying through cyberspace. Large-scale image/video analysis and security/privacy have been attracting an increasing attention. But due to the unstructured nature of image data, analyzing and interpreting it for object detection and recognition and behaviour analysis from the multi-discipline perspective remains a challenging problem in the field of computer vision and data mining.

This special track aims to provide a platform for researchers to discuss and showcase the distinctive theoretical concepts, new mining models and cutting-edge mining technology related to image and video data.

Potential topics include, but are not limited to:

  • Image Feature extraction and classification
  • Object detection and recognition from images/videos
  • Image retrieval and knowledge discovery
  • Information hiding, digital watermarking and steganography
  • Recent machine learning applications in image data mining

Identification Through Data Mining

Identification is a fundamental task in various disciplines such as computer vision, cybersecurity, digital forensics, and biometrics. A substantial amount of research efforts has been devoted to developing identification approaches applied in the above-mentioned fields. Meanwhile, the data generated in different knowledge areas has explosively increased in the past few decades. The ever-increasing amount and variety of data open new opportunities in boosting the performance of identification, but the large-scale size of data also presents new challenges for identification systems. Utilizing data mining techniques to gain useful insights from massive data becomes crucial for many identification tasks.

The aim of this special track is to showcase the recent advances in identification systems driven by data mining techniques. An important focus is on identification methods capable of working with large-scale data acquired in camera surveillance network and social media. We solicit high-quality original research papers that advance the development of identification systems through data mining techniques. Submitted papers should not be previously published or be under consideration for publication elsewhere.

  • Biometric identification on large-scale databases
  • Human characteristics prediction and inference from social media data
  • People identification/re-identification in camera surveillance networks
  • Anomaly detection and identification in camera surveillance network
  • Intrusion detection and identification in computer networks
  • Provenance-oriented identification/clustering in large-scale databases and social media
  • Preference identification for recommending purpose

Mobile and Sensor Network Data Mining

There has recently been a considerable amount of research work on using data compression techniques to minimise the volume of transmitted traffic, and consequently assist in reducing power consumption levels in Wireless Sensor Networks. Data management and processing for wireless sensor networks have become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. The main aim of deploying the WSNs-based applications is to make the real-time decision which has been proved to be very challenging due to the highly resource-constrained computing, communicating capacities, and the huge volume of fast-changed data generated by WSNs. This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large and continuously arriving data from WSNs. Traditional data mining techniques are not directly applicable to WSNs due to the nature of sensor data, their special characteristics, and limitations of the WSNs.

This track aims to showcase the recent advances in data mining techniques in mobile and sensor networks. We solicit high-quality original research papers that advance the development of identification systems through data mining techniques. Submitted papers should not be previously published or under consideration for publication elsewhere.

Potential topics include, but are not limited to:

  • Data mining techniques in mobile and sensor networks
  • Data stream processing in mobile and sensor networks
  • Data fusion techniques in mobile and sensor networks
  • Social data mining through distributed mobile sensing
  • Application of data mining techniques in mobile and sensor networks
  • Distributed data mining techniques for mobile and sensor networks
  • Mobile and sensor network data analytics
  • Challenges for data mining in distributed mobile and sensor networks

Statistics in Data Science

Intensive-data-driven research is empowering theoretical breakthroughs and high-tech innovations, enabling new methodologies in academic discovery, and offering new sustainable means to solve significant societal and economic challenges and understand the world. Data science is a very fast growing research domain which embedded the combination of computational (i.e. computer-intensive) and inferential (i.e. statistics-oriented) thinking, with the notion of Microdata to Big Data. Since the core theories in computer science and statistical science were developed separately, there is an oil and water problem to be surmounted in data science.

For an example, the basic statistical theory does not have a place for runtime and other computational resources while core computer science theory does not have a place for statistical risk and inferential resources. As well, the most appealing challenge in Microdata is the simulation of detailed population characteristics, while in Big Data is the potential of personalised attributes. This session welcomes the full range of submissions, ranging from methodological contributions to case studies involving statistics based modellings or decisions making process (e.g., Bayesian thinking, spatial statistics, machine learning methods, modern statistics, or novel modelling techniques) in data science.

We ask that each submission include a statement about possible implications for resolving some computational and inferential challenges in data science with special focus to data mining, combining data, computation, and inferences – that is – as wide as from computing statistics or running machine learning algorithms to estimating reliability measures including standard errors and confidence intervals on their outputs in any fields.