Workshop on Data and Text Mining in Biomedical informatics (DTMBio 2017)

DTMBio 2017 organizers are pleased to announce that the 11th DTMBio will be held in conjunction with CIKM, one of the largest data and text mining conferences. While CIKM presents the state-of-the-art research in informatics with the primary focus on data and text mining, the main focus of DTMBio is on biomedical and healthcare informatics. DTMBio delegates will bring forth interesting applications of up-to-date informatics in the context of biomedical research.

Organizers: Doheon Lee, KAIST, KOREA | Mark Stevenson, University of Sheffield, United Kingdom


Workshop on Interpretable Data Mining – Bridging the Gap between Shallow and Deep Models (IDM 2017)

Intelligent systems built upon complex machine learning and data mining models (e.g., deep neural networks) have shown superior performances on various real-world applications. However, their effectiveness is limited by the difficulty in interpreting the resultant prediction mechanisms or how the results are obtained. In contrast, the results of many simple or shallow models, such as rule-based or tree-based methods, are explainable but not sufficiently accurate. Model interpretability enables the systems to be clearly understood, properly trusted, effectively managed and widely adopted by end users.

Interpretations are necessary in applications such as medical diagnosis, fraud detection and object recognition where valid reasons would be significantly helpful, if not necessary, before taking actions based on predictions. This workshop is about interpreting the prediction mechanisms or results of the complex computational models for data mining by taking advantage of simple models which are easier to understand. We wish to exchange ideas on recent approaches to the challenges of model interpretability, identify emerging fields of applications for such techniques, and provide opportunities for relevant interdisciplinary research or projects.

Organizers: Xia “Ben” Hu, Texas A&M University | Shuiwang Ji, Washington State University

Workshop on Social Media Analytics for Smart Cities (SMASC 2017)

In an increasingly digital urban setting, Connected & Concerned Citizens typically give voice to their opinions on various civic topics online over the social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is an essential need for developing smart cities. The very nature of the data namely its heterogeneity and dynamism, the lack of large annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics.

We are interested in fostering cross collaboration between researchers on information retrieval, social media analytics, linguistics, social scientists, and civic authorities, to develop scalable and practical solutions to the real life problems of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications of smart cities.

Organizers: Manjira Sinha, Conduent Labs India | Alessandro Bozzon, Delft University of Technology | Sandya Mannarswamy, Conduent Labs India | Xiangnan He, National University of Singapore | Pradeep K. Murukannaiah, Rochester Instittute of Technology | Tridib Mukerjee, Conduent Labs India


Workshop on Data and Algorithm Bias (DAB 2017)

More and more, we as members of society are becoming subject to socio-economic and political decisions made using statistical models trained on enormous amounts of cross-referenced data. This data may originate from many different sources, including governments (e.g. census data), industry (e.g. telephone or credit card transactions) and even ourselves (e.g. our use of online social networks).

However, even the cleanest of datasets, those generated with the utmost care, using careful phrasing of survey questions and careful sampling, may contain bias. Data sets often reflect historical bias of gender, age or ethnicity that can be extremely subtle and deep-rooted. In addition, these "small”, subtle biases can be further amplified algorithmically into full-blown discriminatory profiling of certain groups. It is therefore imperative to study scientifically the causes and effects of bias in the era of big data and propose palliative measures.

The aim of this workshop is to gather researchers in industry and academia working on algorithmic and data bias in all areas of society: health care, finance, education and other that can help To design discrimination-free algorithms and fairness-aware data mining.

Organizers: Ricardo Baeza-Yates, NTENT, USA; U Pompeu Fabra, Spain & University of Chile | Loreto Bravo, Data Science Institute, Universidad del Desarrollo, and Telefónica R&D, Chile | Ciro Cattuto, ISI Foundation Torino, Italy | Leo Ferres, Data Science Institute, Universidad del Desarrollo, and Telefónica R&D, Chile | Jeanna Matthews, Clarkson University, USA | Daniela Paolotti, ISI Foundation Torino, Italy


Workshop on Big Data Analytics for Enhancing Public Transport (BigTransport17)

Public transport is a critical component of smart city. In a dense urban city, public transport system is the preferred means to move people around. As the most sustainable and scalable solution, public transport now needs innovation to respond to new challenges brought by improving commuting experience.

These challenges include:
  • Increased expectation of service quality, comfort and efficiency from commuters;
  • Influx of new commuters working or visiting cities;
  • Imbalanced supply and demand; and
  • Last mile commuting gaps.

Meanwhile, public commuters today generate massive amount of data traces. These include: (i) sensor, image, and video data collected by the existing public transport infrastructure; (ii) train and bus trips recorded by electronic farecard systems; (iii) taxi bookings and taxi trips recorded by mobile apps; (iv) bicycle rental and biking trips recorded by bike sharing apps; and (v) social media posts on public transport events.

These rich data traces offer new opportunities for research in information retrieval, database, data mining and machine learning to enhance commuting experience enhancement, namely:
  • Identifying areas for public transport service improvement;
  • Discovering regular travel patterns of commuters;
  • Modelling and monitoring of commuting experience;
  • Personalizing public transport services to improve individual commuting experience; and
  • Engaging commuters in crowdsourcing resources to address unmet demand

Organizers: Baihua Zheng, Singapore Management University | Chih-Chieh Hung, Tamkang University, Taiwan | Wang-Chien Lee, Penn State University, USA | Ee-Peng Lim, Singapore Management University