Modern technology is ubiquitous i.e., “always there” – available to us when we want it and engaging us even when we don’t actively seek it. This constantly available ubiquitous technology influences people’s perception of time. This conceptual paper explores how ubiquitous technology creates a new time vision we call ubichronic time. We argue ubichronic time is qualitatively different from existing time visions and highlight the new values and behaviours associated with it. Specifically, people who have an ubichronic time vision perform disparate activities that span short durations across the day, find specific tiny units of time valuable and cram many repeated activities into a day. We also argue ubichronic time will have profound implications on the way we work and as such propose new concepts and research directions on how the way we work needs to adapt at the individual, team, and organisational levels.

As the World Wide Web grows, the number and variety of online deceptive attacks likewise increases. Extant research examines online deception from an information processing perspective. However, users’ ability to process information is partly based on their information seeking modes. Information seeking has not been well studied in the security domain. Accordingly, this study explores the effect of users’ information seeking modes on their deception detection behavior. Specifically, we propose that human information needs and the framing of important information such as warnings significantly impact users’ vulnerability to online deception. Results suggest that users are more vulnerable to deception when they are actively seeking information compared with when seeking information passively and that warning frames have a positive effect on users’ attitude toward dealing with online deception. The findings also suggest that users’ attitudes and behaviors are not aligned.

In certain situations, syntactically valid, but incorrect, data entered into a database can result in near-immediate, catastrophic financial losses for an organization. Examples include: omitting zeros in prices of goods on e-commerce sites; and financial fraud where data is directly entered into databases, bypassing application-level financial checks. Such “dangerous data” can, and should, be detected, because it deviates substantially from the statistical properties of existing data. Detection of this kind of problem requires comparing individual data items to a large amount of existing data in the database at run-time. Furthermore, the identification of errors is probabilistic, rather than deterministic, in nature. This research proposes part-whole validation as an approach to addressing the dangerous data situation. Part-whole validation addresses fundamental issues in database management, for example, integrity maintenance. Illustrative and representative examples are first defined, and analyzed. Then, an architecture for part-whole validation is presented and implemented in a prototype to illustrate the feasibility of the research.

Substantial work in knowledge engineering has focused on eliciting knowledge and representing it in a computational form. However, before elicited knowledge can be represented, it must be integrated and transformed so the knowledge engineer can understand it. This research identifies the need to separate knowledge representation into human comprehension and computational reasoning and shows that this will lead to better knowledge representation. Modeling of human comprehension is called conceptual knowledge representation. The Conceptual Knowledge Representation Scheme is developed and validated by conducting a combined qualitative/quantitative repeated-measures experiment comparing the Conceptual Knowledge Representation Scheme to two computation-oriented ones. The results demonstrate that the Conceptual Knowledge Representation Scheme better facilitates human comprehension than existing representation schemes. Four principles of the Conceptual Knowledge Representation Scheme emerge that help to attain effective knowledge representation. These are: (1) a focus on human comprehension only, (2) design around natural language, (3) addition of constructs common in the domain, and (4) constructs for representing abstract versions of detailed concepts.

While much research has been devoted to data modeling, little attention has been paid to developing constructs for modeling micro-objects, i.e. constructs for modeling low dimension objects such as the attribute Birth_Date and the data type Boolean. Most data models consider attributes as functions of macro-objects (i.e. objects formed from constructs such as entity sets, relations, and object sets). For example, the attribute Birth_Date is often modeled as a function of the entity set Person. This research proposes a Conceptual Micro-Object Model (CMoM), which considers the attribute as the foundation of data modeling. Other constructs such as the Conceptual Data Type Primitive (CDTP) and Attribute Group (AG) are developed to model the constituent components of attributes and objects that can be formed from attributes respectively. CMoM is useful for modeling intricate micro-objects such as dates. It also allows one to identify and resolve redundancy between conceptual macro-objects such as those modeled as entity sets and relations. For example, redundancies between attributes in different relations can be detected. Finally, it facilitates a formal foundation for defining object-oriented concepts such as inheritance, aggregation, and encapsulation.