Our Seminars in 2017

Multi-objective search-based approach to estimate issue resolution time

Remote Healthcare: Using Technology to Facilitate Quality Healthcare for Citizens

Insights into the data management at Geoscience Australia

Presenter(s): Alexis Harper – PhD Candidate, Decision Systems Lab at UOW
Date:  April 7, 2017 Time: 12:30pm onwards
Venue: 6.105 – Smart Building

Abstract: Geoscience Australia is Australia’s pre-eminent public sector geoscience organisation. They are the nation’s trusted advisor on the geology and geography of Australia. They apply science and technology to describe and understand the Earth for the benefit of Australia. With large volumes of data including images, data sets, documents and findings from not just Australian’s Land and water but also its international interests, managing such data is a huge undertaking. An often overlooked aspect of such a tasks is managing the accessibility, presentation and provenance of such a large volume of data. From insights and experiences gains from working at Geoscience Australia, we present a interesting look into the processes and managements of these activities.

A body of knowledge for enterprise analytics

Evaluation of Big Data Health Analytics on Earth and in Space

Presenter(s): Jonah Glass – Masters candidate, University of Ontario Institute of Technology
Date: March 9, 2017 Time: 4pm onwards

Venue: 6.105 – Smart Building

Abstract: While physiological data can be displayed on monitors, the storage and analysis capabilities are extremely limited. With millions of readings being collected daily at high frequencies, the Artemis platform was developed to perform health analytics utilizing streaming physiological data. The Artemis platform has been utilized in neonatal intensive care units in North America and in China as a way to develop algorithms that assist in the identification of infection and other health complications. Additionally, this big data analytics platform has been used to support health analytics in space, and in tactical operations. To evaluate the implementation of a system like Artemis, research is being performed to determine a proper framework and key evaluation metrics using an ontological approach. This presentation will introduce Artemis and the companions Artemis in Space and Athena Big Data analytics research projects. Our new research for standardised ontologically based frameworks for evaluation will also be discussed.

Rethinking BPM Concepts for Cases and Decisions

 Prof. Matthias Weske – Hasso Plattner Institute for IT Systems, University of Potsdam, Germany
Date: March 14, 2017
Time:  12:30 – 1:30pm
Venue: 6.105 – Smart Building ]

Abstract: While research in business process management has mainly focused on modelling and enactment of structured processes, recently novel aspects are addressed, among which case management and decision management play key roles. Case management aims at supporting business processes with a flexible structure. For decades, flexibility has been a recurring topic, mainly driven by changing process models at runtime and migrating processes instances accordingly. Case management, on the contrary, aims at providing models with more flexibility in the first place, thus rendering unnecessary any dynamic changes. After introducing the main aspects of case management, the talk will introduce a novel approach to case management, based on combining process fragments at runtime. The conceptual results are prototypically implemented in the Chimera platform. Behavioural analysis of process models has been another key area of research in business process management. With the recent rise of decision management and its strong ties to process management, behavioural analysis need to be reconsidered. With decision soundness, a novel correctness criterion is introduced, which takes into account decision logic when analysing behavioural properties of business processes.

Bio: Professor Dr. Mathias Weske is chair of the business process technology research group at Hasso Plattner Institute of IT Systems Engineering at the University of Potsdam, Germany. The research group aims at addressing real-world problems in business process management with formal approaches and engineering useful prototypes. His research focuses on process oriented information systems, decision management, and event handling. The research group has a track record in engineered prototypes with a significant impact on research and practice, including projects like Oryx and jBPT. In 2009 he co-founded Berlin-based software company Signavio. Dr. Weske is author of the first textbook on business process management and he held the first massive open online course on the topic in 2013. With Matthias Kunze, in 2016 he published a textbook on behavioural models. He on the Editorial Board of Springer’s Distributed and Parallel Databases journal, Springer’s Distributed Computing journal and a founding member of the steering committee of the BPM conference series.

In memory of Kenneth Arrow: Is democracy impossible (and what that might mean for computer scientists)?

 Prof. Aditya Ghose –  UOW
Date:  March 2, 2017
Time: 4pm onwards
Venue: 6.105 – Smart Building

Abstract: American economist and Nobel Laureate Kenneth Arrow passed away on Feb. 21st, 2017. Kenneth Arrow is best known for his contributions to
social choice theory in the form of his celebrated Impossibility Theorem (he is also known for his work on the General Equilibrium Theory). Many have read his Impossibility Theorem to suggest that truly fair social decision mechanisms (such as elections) are impossible. In this talk, I will briefly outline the Impossibility Theorem, and then show some instance of where these results have profoundly impacted computer science research (including our own work in the Decision Systems Lab).

A search-based approach to estimate issue resolution time

Wisam Al-Zubaidi – Decision Systems Lab at UOW
Date:  February 23, 2017
Time: 4pm onwards
Venue: 6.105 – Smart Building

Abstract: Resolving issues is central to modern agile software development where a software is developed and evolved incrementally through series of issue resolutions. An issue could represent a requirement for a new functionality, a report of a software bug or even a description of a project task. Knowing when an issue will be resolved is thus important to different stakeholders including end-users, bug reporters, bug triagers, developers and project managers. This paper presents a novel search-based approach to estimate the resolution time of issues.
Using genetic programming, we iteratively generate candidate estimate models and search for a robust model in estimating issue resolution time.We evaluate both single-objective and multiobjective search approaches using 5,301 issues we collected from four Apache Hadoop projects. The results demonstrate that our search-based approach outperforms both the baseline and stateof- the-art statistically significantly (p < 0:001).

Context-Aware Recommendation of Task Allocations in Service Systems

Renuka Sindhgatta – IBM Research and Decision Systems Lab at UOW
Date:  February 16, 2017
Time: 4pm onwards
Venue: 6.105 – Smart Building

Abstract: In a service system comprising of knowledge intensive tasks, a pull-based allocation strategy (where knowledge workers decide on tasks to commit to, as opposed to having these commitments decided for them) can often be quite effective. Such a scenario is characterized by different types of tasks and workers with varying efficiencies. As workers and tasks change with time, a key challenge faced by knowledge workers is in deciding the most suitable tasks to commit to. Organizational roles of workers provide them the privilege of working on the tasks that the role is authorized to perform, but the suitability of a worker to perform a task varies because workers could have varying operational performance on different types of tasks. Past allocations, when correlated with execution histories annotated with quality of service (or performance) measures, can provide insights on the suitability of a task for a worker. It has been recognized that the effectiveness of a resource in performing a task often depends on the context in which the task is executed. In this work, we present a context-aware collaborative filtering recommender system that predicts a worker’s suitability for a task, in different contexts or situations. The context-aware recommender uses information on the performance of similar resources in similar contexts to predict a resource’s suitability for a task. Experiments performed on real-world execution logs demonstrate the effectiveness of the proposed approach.