Knowledge discovery in data streams

Knowing what to do with the massive amount of data collected has always been an ongoing issue for many organizations. While data mining has been touted to be the solution, it has failed to deliver the impact despite its successes in many areas. One reason is that data mining algorithms were not designed for the real world, i.e., they usually assume a static view of the data and a stable execution environment where resources are abundant. The reality however is that data are constantly changing and the execution environment is dynamic. Hence, it becomes difficult for data mining to truly deliver timely and relevant results. Recently, the processing of stream data has received many attention. What is interesting is that the methodology to design stream-based algorithms may well be the solution to the above problem. In this entry, we discuss this issue and present an overview of recent works

Using iTextbook as an alternate frontend to learning management systems

Learning management systems (LMS) have been a popular tool for delivery of learning content as well as the management of learners and courses. In recent years, the ubiquity of mobile devices such as smartphones and tablets has seen the increased popularity of using them to consume eBooks. While the LMS is popular among administrators, accessing content on mobile devices appear to be the preference of our learners. Furthermore, there are reports on a number of shortcomings with learners using the LMS, e.g., the experience of using LMSes on mobile devices falling short and learners are less engaged interacting with the LMS than with their mobile devices, etc. In this paper, we investigate the idea of using eBooks as an alternative frontend for learners to interact with the LMS. A proof of concept eBook was developed for a data management course to showcase how content on the LMS can be deployed via the eBook interface while connecting our learners to the LMS for learning management. We find that this approach delivers a rich and immersive experience to our learners, as they would expect from their devices. The outcomes also gave us food for thought regarding how LMSes may evolve in the future

2Loud? Monitoring traffic noise with mobile phones

The World Health Organization has recently focused attention on guidelines for night noise in urban areas, based on significant medical evidence of the adverse impacts of exposure to excessive traffic noise on health, especially caused by sleep disturbance. This includes serious illnesses, such as hypertension, arteriosclerosis and myocardial infarction. 2Loud? is a research project with the aim of developing and testing a mobile phone application to allow a community to monitor traffic noise in their environment, with focus on the night period and indoor measurement. Individuals, using mobile phones, provide data on characteristics of their dwellings and systematically record the level of noise inside their homes overnight. The records from multiple individuals are sent to a server, integrated into indicators and shared through mapping. The 2Loud? application is not designed to replace existing scientific measurements, but to add information which is currently not available. Noise measurements to assist the planning and management of traffic noise are normally carried out by designated technicians, using sophisticated equipment, and following specific guidelines for outdoors locations. This process provides very accurate records, however, for being a time consuming and expensive system, it results in a limited number of locations being surveyed and long time between updates. Moreover, scientific noise measurements do not survey inside dwellings. In this paper we present and discuss the participatory process proposed, and currently under implementation and test, to characterize the levels of exposure to traffic noise of residents living in the vicinity of highways in the City of Boroondara (Victoria, Australia) using the 2Loud? application

Business 2.0 : a novel model for delivery of business services

Web 2.0, regardless of the exact definition, has proven to bring about significant changes to the way the Internet was used. Evident by key innovations such as Wikipedia, FaceBook, YouTube, and Blog sites, these community-based Website in which contents are generated and consumed by the same group of users are changing the way businesses operate. Advertisements are no longer dasiaforcedpsila upon the viewers but are instead dasiaintelligentlypsila targeted based on the contents of interest. In this paper, we investigate the concept of Web 2.0 in the context of business entities. We asked if Web 2.0 concepts could potentially lead to a change of paradigm or the way businesses operate today. We conclude with a discussion of a Web 2.0 application we recently developed that we think is an indication that businesses will ultimately be affected by these community-based technologies; thus bringing about Business 2.0 - a paradigm for businesses to cooperate with one another to deliver improved products and services to their own customers.<br /

Trading in open marketplace using trust and risk

In this paper, we propose buying and selling models for agents to trade in the open multi-agent marketplace. Unlike auctions, we take into account of the fact that agents trading in such open environments has to maximize their profits and at the same time, protect themselves from fraud and deception. We attempt to address this issue by incorporating the element of trust and risk management into our proposed buying and selling model. During buying, agents learn to select their partners based on the trustworthiness of the potential partner as well as its personal risk attitude. During selling, agents learn to increase the chances of winning a deal by adjusting their profit rate, which is a measure that considers both trust and risk. The novelty of this proposal is that it ensures agents continuing to seek maximum expected utility in a dynamic trading environment. Our experimental results confirm the feasibility of our approach. <br /

Novel applications and research problems for sensor-clouds

Recent developments in sensor networks and cloud computing saw the emergence of a new platform called sensor-clouds. While the proposition of such a platform is to virtualise the management of physical sensor devices, we foresee novel applications being created based on a new class of social sensors. Social sensors are effectively a human-device combination that sends torrents of data as a result of social interactions. The data generated appear in different formats such as photographs, videos, or short texts, etc. Unlike other sensor devices, social sensors operate on the control of individuals via their mobile devices like smart phones, tablets or laptops. Further, they do not generate data at a constant rate or format like other sensors do. Instead, data from social sensors are spurious and varied, often in response to social events, or a news announcement of interests to the public. This collective presence of social data creates opportunities for novel applications never experienced before. This paper discusses three such applications utilising social sensors within a sensor-cloud environment. Consequently, the associated research problems are also presented.<br /

Agents and stream data mining: a new perspective

Many organizations struggle with the massive amount of data they collect. Today, data does more than serve as the ingredients for churning out statistical reports. They help support efficient operations in many organizations, and to some extent, data provide the competitive intelligence organizations need to survive in today\u27s economy. Data mining can\u27t always deliver timely and relevant results because data are constantly changing. However, stream-data processing might be more effective, judging by the Matrix project.<br /