Madhushi Bandara

University of Technology Sydney | UTS · Faculty of Engineering and Information Technology

Data Analytics Solution (DAS) engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective of this paper is to review existing techniques that l...

Enterprises today are presented with a plethora of data, tools and analytics techniques, but lack systems which help analysts to navigate these resources and identify best fitting solutions for their analytics problems. To support enterprise-level data analytics research, this paper presents Research Variable Ontology (RVO), an ontology designed to...

Communication and mobility between different regions of a country are a reflection of social, cultural and economic linkages. We leverage an anonymized Call Detail Record (CDR) dataset for Sri Lanka to extract human calling and travel patterns between different regions in the country at high spatiotemporal resolutions. These datasets naturally lend...

Comprehensively describing data analytics requirements is becoming an integral part of developing enterprise information systems. It is a challenging task for analysts to completely elicit all requirements shared by the organization’s decision makers. With a multitude of data available from e-commerce sites, social media and data warehouses selecti...

Events at public beaches are one of the most popular recreational activities of local communities and international visitors in all places around the world. Amongst others, the beach safety management in protected areas needs support for continuous analysis and decision making on incidents at the beach areas. There is a lack of available standard m...

The outbreak of the SARS-CoV-2 pandemic of the new COVID-19 disease (COVID-19 for short) demands empowering existing medical, economic, and social emergency backend systems with data analytics capabilities. An impediment in taking advantages of data analytics in these systems is the lack of a unified framework or reference model. Ontologies are hig...

A meaningful understanding of clinical protocols and patient pathways helps improve healthcare outcomes. Electronic health records (EHR) reflect real-world treatment behaviours that are used to enhance healthcare management but present challenges; protocols and pathways are often loosely defined and with elements frequently not recorded in EHRs, co...

An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best combination to use when solving the problem at hand. Often, such knowledge is not explicitly captur...

Many business processes present in modern enterprises are loosely defined, highly interactive, involve frequent human interventions and coupled with a multitude of abstract entities defined within an enterprise architecture. Further, they demand agility and responsiveness to address the frequently changing business requirements. Traditional busines...

Organisations today collect vast amounts of data which can be used to provide valuable data-driven insights for their business. However, heterogenous data sources, numerous analytical techniques and various data processing tools create difficulties for data scientists in organisations to navigate these resources and use them effectively to unravel...

As regulatory reporting involves loosely defined processes, it is a considerable challenge for data scientists and academics to extract instances of such processes from event records and analyse their characteristics e.g. whether they satisfy certain process compliance requirements. This paper proposes a software framework based on a semantic data...

This paper focuses on the use of knowledge management techniques to help organisations tap into the power of statistical learning when conducting analytics. Its main contribution is in the use of an ontology development process to derive the essential concepts required for an ontology to represent variables of interest and their interrelationships...

As big data analytics is adapted across multitude of domains and applications there is a need for new platforms and architectures that support analytic solution engineering as a lean and iterative process. In this paper we discuss how different software development processes can be adapted to data analytic process engineering, incorporating service...

Many business processes present in modern enterprises are loosely defined, highly interactive, involve frequent human interventions. They are coupled with a multitude of abstract entities defined within an enterprise architecture. Further, they demand agility and responsiveness to address the frequently changing business requirements. Traditional p...

With the growth of data available for analysis, people in many sectors are looking for tools to assist them in collating and visualising patterns in that data. We have developed an event based visualisation system which provides an interactive interface for experts to filter and analyse data. We show that by thinking in terms of events, event hiera...

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focu...

The learning objectives, learning activities and assessment are very much interrelated. Assessment helps to evaluate students learning achievement. Poorly designed assessments usually fail to examine the achievement of intended learning outcome of a course. There are different taxonomies that have been developed to identify the level of the assessm...

We expect software systems to be dependable and sufficiently responsive to the inevitable changes regularly happen in their operational environments. This can be a challenging task to achieve when systems are in enterprise scale and large enough to cater for multiple complex business processes. One approach to address this is by incorporating suita...

With the advent of high volume data streams, we have seen the need for real time analytic techniques like Complex Event Processing. This paper extends a Complex Event Processing Engine to support real time identification of technical chart patterns from streaming data. Technical chart patterns are known interesting recurring patterns on time series...

With the advent of large high volume data, we have seen need for real time analytic techniques like Complex Event Processing. This paper extends a Complex Event Processing Engine to support real time identification of technical chart patterns from streaming data. Technical chart patterns are known interesting recurring patterns on time series data,...