An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

Female frequent internet gamblers: a qualitative study of their gambling, its impact and their views on treatment and policy

Aims: to study the gambling history and stories of participants, motivations, impact and helpseeking. Method: Details were advertised on websites and newspapers. 30 frequent gamblers were interviewed over the telephone for approximately one hour. Verbatim transcriptions were analysed using NVIVO and grounded theory. Results/conclusions: Not all women had gambled before. However, internet accessibility meant prolonged periods were spent gambling to the neglect of other life areas. Some were originally motivated by excitement but others gambled to escape from current difficulties. Depression, anxiety, panic attacks and suicide ideation were common. The women were ambivalent towards their gambling and receiving help.