Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis

In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.FJVA and AP were partially supported by EUFP7 project LiMo- SINe (contract288024) for this research. CPM was partially supported by the Spanish Ministry of Economics and Competitiveness projects TEC2014-61729-EXP and TEC2014-53390-P

An intelligent teaching system for database modeling.

Database (DB) modelling, like other analysis and design tasks, can only be learnt through extensive practice. Conventionally, DB modelling is taught in a classroom environment where the instructor demonstrates the task using typical examples and students practise modelling in labs or tutorials. Although one-to-one human tutoring is the most effective mode of teaching, there will never be sufficient resources to provide individualised attention to each and every student. However, Intelligent Teaching Systems (ITS) offer bright prospects to fulfilling the goal of providing individualised pedagogical sessions to all students. Studies have shown that ITSs with problem-solving environments are ideal tools for enhancing learning in domains where extensive practice is essential. This thesis describes the design, implementation and evaluation of an ITS named KERMIT, developed for the popular database modelling technique, Entity Relationship (ER) modelling. KERMIT, the Knowledge-based Entity Relationship Modelling Intelligent Tutor, is developed as a problem-solving environment in which students can practice their ER modelling skills with the individualised assistance of the system. KERMIT presents a description of a scenario for which the student models a database using ER modelling constructs. The student can ask for guidance from the system during any stage of the problem solving process, and KERMIT evaluates the solution and presents feedback on its errors. The system adapts to each individual student by providing individualised hint messages and selecting new problems that best suit the student. The effectiveness of KERMIT was tested by three evaluations. The first was a think-aloud study to gain first-hand experience of the student's perception of the system. The second study, conducted as a classroom experiment, yielded some positive results, considering the time limitations and the instabilities of the system. The third evaluation, a similar classroom experiment, clearly demonstrated the effectiveness of KERMIT as a teaching system. Students were divided into an experimental group that interacted with KERMIT and a control group that used a conventional drawing tool to practice ER modelling. Both group's learning was monitored by pre- and post-tests, and a questionnaire recorded their perception of the system. The results of the study showed that students using KERMIT showed a significantly higher gain in their post-test. Their responses to the questionnaire reaffirmed their positive perception of KERMIT. The usefulness of feedback from the system and the amount learnt from the system was also on a significantly higher scale. Their free-form comments were also very positive

Formalism for a multiresolution time series database model

We formalise a specialised database management system model for time series using a multiresolution approach. These special purpose database systems store time series lossy compressed in a space-bounded storage. Time series can be stored at multiple resolutions, using distinct attribute aggregations and keeping its temporal attribute managed in a consistent way. The model exhibits a generic approach that facilitates its customisation to suit better the actual application requirements in a given context. The elements, the meaning of which depends on a real application, are of generic nature. Furthermore, we consider some specific time series properties that are a challenge in the multiresolution approach. We also describe a reference implementation of the model and introduce a use case based on real data.Peer ReviewedPostprint (author's final draft

Parametric Rough Sets with Application to Granular Association Rule Mining

Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable

Spatial Data Warehouse Modelling

is concerned with multidimensional data models for spatial data warehouses. It first draws a picture of the research area, and then introduces a novel spatial multidimensional data model for spatial objects with geometry: the Multigranular Spatial Data warehouse (MuSD). The main novelty of the model is the representation of spatial measures at multiple levels of geometric granularit