Carl Howard Mooney

Sequential pattern mining -- approaches and algorithms

Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.

Linear temporal sequences and their interpretation using midpoint relationships

The temporal interval relationships formalized by Allen, and later extended to accommodate semiintervals by Freksa, have been widely utilized in both data modeling and artificial intelligence research to facilitate reasoning between the relative temporal ordering of events. In practice, however, some modifications to the relationships are necessary when linear temporal sequences are provided, when event times are aggregated, or when data is supplied to a granularity which is larger than required. This paper discusses these modifications and outlines a solution to this problem which accommodates any available knowledge of interval midpoints.

A multi-level framework for the analysis of sequential data

Traditionally text mining has had a strong link with information retrieval and classification and has largely aimed to classify documents according to embedded knowledge. Association rule mining and sequence mining, on the other hand, have had a different goal; one of eliciting relationships within or about the data being mined. Recently there has been research conducted using sequence mining techniques on digital document collections by treating the text as sequential data. In this paper we propose a multi-level framework that is applicable to text analysis and that improves the knowledge discovery process by finding additional or hitherto unknown relationships within the data being mined. We believe that this can lead to the detection or fine tuning of the context of documents under consideration and may lead to a more informed classification of those documents. Moreover, since we use a semantic map at varying stages in the framework, we are able to impose a greater degree of focus and therefore a greater transitivity of semantic relatedness that facilitates the improvement in the knowledge discovery process.

Mining itemsets - an approach to longitudinal and incremental association rule mining

With the improvements in data warehousing and database technology, the amount of data being collected has grown at a remarkable rate. The field of data mining, which enables the extraction of interesting or relevant information from such data, has also grown at a similar rate. Methods now exist that enable the extraction of rules ffom varied data sources ffom which users are able to draw inferences about the underlying data. This paper surveys and extends a new area of data mining that has recently emerged – Rule Mining. Rule mining uses the results of previous mining sessions as input to a second mining process that produces rules with very different semantics which can be used to extend the inferences made from the underlying data. The paper fwst presents an overview of the work in this area. We then present an efficient method of longitudinal association rule mining that uses previously constructed frequent itemsets (rather than either the (larger) source datasets or the (larger) resultant rulesets) within a rule production engine. We also show how this method can be used for longitudinal association rule mining.

Using balanced time perspective to explain well-being and planning in retirement

The notion of whether people focus on the past, present or future, and how it shapes their behavior is known as Time Perspective. Fundamental to the work of two of its earliest proponents, Zimbardo and Boyd (2008), was the concept of balanced time perspective and its relationship to wellness. A person with balanced time perspective can be expected to have a flexible temporal focus of mostly positive orientations (past-positive, present-hedonistic, and future) and much less negative orientations (past-negative and present-fatalistic). This study measured deviation from balanced time perspective (DBTP: Zhang et al., 2013) in a sample of 243 mature adults aged 45 to 91 years and explored relationships to Retirement Planning, Depression, Anxiety, Stress, Positive Mood, and Negative Mood. Results indicate that DBTP accounts for unexplained variance in the outcome measures even after controlling for demographic variables. DBTP was negatively related to Retirement Planning and Positive Mood and positively related to Depression, Anxiety, Stress, and Negative Mood. Theoretical and practical implications regarding balanced time perspective are discussed.

Mobile learning in Thai public university: Opportunities and barriers

The use of Internet-enabled mobile devices in learning (mobile learning or m-learning) offers students the opportunity to learn anytime and anywhere. However, our preliminary survey conducted at Rajamangala University of Technology Rattanakosin, Thailand has shown that there are both social and technological issues to consider when developing mobile learning system in Thai public universities. This paper reviews mobile learning content authoring tools, and presents the current picture of mobile learning in a small-sized Thai public university. The research intent is to provide a guideline for future development of a framework that is suitable for universities in developing countries.

Mobile Learning for Teachers Training in Indonesia: The Potential of Mobile Phones as the Device

This paper presents a review on mobile phones, from an Indonesian perspective to investigate the prospect of using mobile phones for mobile learning based training. The review revealed the potential of mobile phones, compared to other mobile devices, to be used as the primary tool in a mobile learning system. In Indonesia, mobile phones are used extensively, and the price for devices and services are affordable for Indonesians. The broad coverage area of the mobile network was also a reason to choose mobile phones as the preferred mobile learning device. Keywords— mobile learning; mobile phones; device