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Although the data modeling field has made progress in proposing and evaluating methods that address novice designer difficulties as exemplified in studies and surveys such as (Antony, Batra, & Santhanam, 2005; Bock & Ryan, 1993; Chan, Siau, & Wei, 1998; Khatri, Vessey, Ramesh, Clay, & Park, 2006; Masri, Parker, & Gemino, 2008; Topi & Ramesh, 2002), there is surprisingly little empirical research on techniques that facilitate cognitive processes dealing with modeling data events, which account for a substantial portion of the massive amounts of data generated in diverse applications such as social networks, health information systems, and website navigation tracking. Thus, in addition to possessing domain knowledge (Storey, 1992), modelers should have the ability to visualize how the model will support business events and the processes that they trigger (Evermann, 2005). Several authors have proposed the use of cognitive research as a reference discipline for information modeling and method engineering (Browne & Ramesh, 2002; Pitts & Browne, 2007; Siau, 1999). Using the Cognitive Load Theory (CLT) (Sweller, 2010), this study reports findings from an experiment comparing a top-down with a bottom-up technique that can address the difficulties encountered by novices when modeling events using the entity-relationship model.
Previous research has established that novice designers have little trouble recognizing and representing state-based entities, which correspond to discrete real-world actors, subjects, and objects such as employee and department (Batra, Hoffer, & Bostrom, 1990; Bock & Ryan, 1993; Palvia, King, Xia, & Palvia, 2010). Modeling events, however, may be difficult for novice designers given that an event generally occurs at the junction of several entities, and may be confused as one or more relationships when it should actually be modeled as an entity with its own identifier. Only state-based associations should be modeled as relationships, whereas the event itself in the event-based associations should be modeled as an entity.
Conceptual data modeling using the ER model (P. P. Chen, 1976) is widely acknowledged as one of the most important aspects of database development (Storey, 1991). Although the ER model is generally labeled as a conceptual model (Batini, Ceri, & Navathe, 1992), it has a strong influence on the logical representation (Teorey, Yang, & Fry, 1986), which is almost always based on the relational model (Codd, 1970; Teorey, 1999) for transactional databases. Data modeling methods generally represent entities, attributes, and the degree and cardinality of relationships in the ER diagram (Hoffer, Ramesh, & Topi, 2010; Milton & Kazmierczak, 2004; Teorey, 1999), which can be translated into the relational representation using straightforward rules (An, Hu, & Song, 2010; Ram, 1995; Teorey et al., 1986).
Existing techniques based on the ER-based model typically rely on examples that show exercises and resulting solutions but rarely explain the problem-solving process; in fact, according to Scheer (1998), many publications merely interpret preexisting entity relationship models. Furthermore, many experimental studies that evaluate the effectiveness of the ER model, such as those by (Batra et al., 1990; Bock & Ryan, 1993), provide somewhat model-ready user requirements as exercises for testing. When model-ready user requirements are not provided, research shows that literal translation employed by novice designers can result in data modeling errors (Batra & Antony, 1994).