A.P. de Vries

Fuzzy Logic Instructional Models: The Dynamic Construction of Programming Assignments in CASCO

This chapter introduces Fuzzy Logic Instructional Models (FLIM’s) as a promising approach to model knowledge of instruction. FLIM’s are applied in CASCO, an ITS for the dynamic construction of assignments to practice introductory programming. CASCO uses the Completion Strategy as a training strategy and generates so-called completion assignments, which consist of a problem description together with a solution (i.e., a program) that may be either complete, incomplete, or absent, explanations, questions, and instructional tasks. The learner has to complete increasingly larger parts of the given program as programming experience develops. This chapter offers a description of the Completion Strategy, an overview of CASCO’s architecture, and an in-depth description of the FLIM’s that govern the dynamic construction of assignments.

Exact matching in image databases

We believe there is a niche for exact sub-image searching in image databases. When only a sub-image is searched for, traditional image searching approaches fail because the sub-image features usually represent only a small fraction of the global image features. Said differently: it is difficult to find the sub-image in the global features. We present a new technique to find sub-images in a (possibly large) image database. The users articulate their interest in a sub-image, i.e. an object within an image, in a so-called precise multi-spot query. We search our spatially oriented color-based image database image-by-image for matches with this multi-spot query.

Moa and the Multi-model Architecture: A New Perspective on NF2

Advanced non-traditional application domains such as geographic information systems and digital library systems demand advanced data management support. In an effort to cope with this demand, we present the concept of a novel multi-model DBMS architecture which provides evaluation of queries on complexly structured data without sacrificing efficiency. A vital role in this architecture is played by the Moa language featuring a nested relational data model based on XNF2, in which we placed renewed interest. Furthermore, extensibility in Moa avoids optimization obstacles due to black-box treatment of ADTs. The combination of a mapping of queries on complexly structured data to an efficient physical algebra expression via a nested relational algebra, extensibility open to optimization, and the consequently better integration of domain-specific algorithms, makes that the Moa system can efficiently and effectively handle complex queries from non-traditional application domains.