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.

Reward management: a critical text

This, the second edition, adopts a critical and theoretical perspective on remuneration policy and practices in the UK, from the decline of collective bargaining to the rise of more individualistic systems based on employee performance. It tackles the conceptual issues missing from existing texts in the field of HRM by critically examining the latest academic literature on the topic. [Taken from publisher's product description].