Johann Gamper

A Review of Intelligent CALL Systems

Computer-assisted language learning is a research field which explores the use of computational methods and techniques as well as new media for language learning and teaching. Recently, an increasing number of language learning systems began to adopt Artificial Intelligence techniques. This paper provides an overview about intelligent computer-assisted language learning. The most advanced systems have been investigated and classified along five dimension: supported languages, Artificial Intelligence techniques, language skills, language elements, and availability. The paper concludes with a discussion about outstanding problems that need further research in order to exploit the full potential of intelligent technologies in language learning envoronments.

The pq -gram distance between ordered labeled trees

When integrating data from autonomous sources, exact matches of data items that represent the same real-world object often fail due to a lack of common keys. Yet in many cases structural information is available and can be used to match such data. Typically the matching must be approximate since the representations in the sources differ. We propose pq-grams to approximately match hierarchical data from autonomous sources and define the pq-gram distance between ordered labeled trees as an effective and efficient approximation of the fanout weighted tree edit distance. We prove that the pq-gram distance is a lower bound of the fanout weighted tree edit distance and give a normalization of the pq-gram distance for which the triangle inequality holds. Experiments on synthetic and real-world data (residential addresses and XML) confirm the scalability of our approach and show the effectiveness of pq-grams.

Abstract temporal diagnosis in medical domains.

Most current model-based diagnosis formalisms and algorithms are defined only for static systems, which is often inadequate for medical reasoning. In this paper we describe a model-based framework plus algorithms for diagnosing time-dependent systems where we can define qualitative temporal scenarios. Complex temporal behavior is described within a logical framework extended by qualitative temporal constraints. Abstract observations aggregate from observations at time points to assumptions over time intervals. These concepts provide a very natural representation and make diagnosis independent of the number of actual observations and the temporal resolution. The concept of abstract temporal diagnosis captures in a natural way the kind of indefinite temporal knowledge which is frequently available in medical diagnoses. We use viral hepatitis B (including a set of real hepatitis B data) to illustrate and evaluate our framework. The comparison of our results with the results of HEPAXPERT-I is promising. The diagnosis computed in our system is often more precise than the diagnosis in HEPAXPERT-I and we detect inconsistent data sequences which cannot be detected in the latter system.

Multi-dimensional Aggregation for Temporal Data

Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms.

Supporting teachers as content authors in intelligent educational systems

Intelligent Educational Systems (IESs) need large amounts of educational content that is typically not provided by the creators of these systems. In this paper we discuss a new approach for authoring practical IESs where core authoring is done by professional design teams, while the educational content is mainly developed by teachers who use the system in their classes. The major bottleneck of this approach is the lack of intelligent authoring support tools that allow regular teachers to author intelligent content that an IES needs in order to perform its functions. As a contribution to solving this problem, we present our recent work on authoring support for an adaptive vocabulary acquisition system, ELDIT. The paper describes the ELDIT system, the needs and challenges of language content authoring by teachers, and the two authoring support components that we have developed for two essential kinds of language learning content: illustrative examples and educational texts.

Approximate Joins for Data-Centric XML

In data integration applications, a join matches elements that are common to two data sources. Often, however, elements are represented slightly different in each source, so an approximate join must be used. For XML data, most approximate join strategies are based on some ordered tree matching technique. But in data-centric XML the order is irrelevant: two elements should match even if their subelement order varies. In this paper we give a solution for the approximate join of unordered trees. Our solution is based on windowed pq-grams. We develop an efficient technique to systematically generate windowed pq-grams in a three-step process: sorting the unordered tree, extending the sorted tree with dummy nodes, and computing the windowed pq-grams on the extended tree. The windowed pq-gram distance between two sorted trees approximates the tree edit distance between the respective unordered trees. The approximate join algorithm based on windowed pq-grams is implemented as an equality join on strings which avoids the costly computation of the distance between every pair of input trees. Our experiments with synthetic and real world data confirm the analytic results and suggest that our technique is both useful and scalable.

Temporal alignment

In order to process interval timestamped data, the sequenced semantics has been proposed. This paper presents a relational algebra solution that provides native support for the three properties of the sequenced semantics: snapshot reducibility, extended snapshot reducibility, and change preservation. We introduce two temporal primitives, temporal splitter and temporal aligner, and define rules that use these primitives to reduce the operators of a temporal algebra to their nontemporal counterparts. Our solution supports the three properties of the sequenced semantics through interval adjustment and timestamp propagation. We have implemented the temporal primitives and reduction rules in the kernel of PostgreSQL to get native database support for processing interval timestamped data. The support is comprehensive and includes outer joins, antijoins, and aggregations with predicates and functions over the time intervals of argument relations. The implementation and empirical evaluation confirms effectiveness and scalability of our solution that leverages existing database query optimization techniques.

Efficient event pattern matching with match windows

In event pattern matching a sequence of input events is matched against a complex query pattern that specifies constraints on extent, order, values, and quantification of matching events. In this paper we propose a general pattern matching strategy that consists of a pre-processing step and a pattern matching step. Instead of eagerly matching incoming events, the pre-processing step buffers events in a match window to apply different pruning techniques (filtering, partitioning, and testing for necessary match conditions). In the second step, an event pattern matching algorithm, A, is called only for match windows that satisfy the necessary match conditions. This two-phase strategy with a lazy call of the matching algorithm significantly reduces the number of events that need to be processed by A as well as the number of calls to A. This is important since pattern matching algorithms tend to be expensive in terms of runtime and memory complexity, whereas the pre-processing can be done very efficiently. We conduct extensive experiments using real-world data with pattern matching algorithms for, respectively, automata and join trees. The experimental results confirm the effectiveness of our strategy for both types of pattern matching algorithms.

Computing isochrones in multi-modal, schedule-based transport networks

Isochrones are defined as the set of all points from which a specific point of interest is reachable within a given time span. This demo paper presents a solution to compute isochrones in multi-modal, schedule-based transport networks. The system is implemented in Java on top of the Oracle Spatial Network Model and is deployed at the Municipality of Bolzano-Bozen as a planning instrument.

The Role of Web Services in Digital Government

Since a few years digital government is becoming an active research area with lots of promises to revolutionise government and its interaction with citizens and businesses. A crucial point for the success of e-government is the integration and sharing of services and information provided by different authorities. We argue that Web services are a promising technology to solve this problem.