Karl Anton Froeschl

Statistical composites: a transformation-bound representation of statistical datasets

Statistical data processing makes use of data matrices and tables as primary structures for data representation. Embedding these structures into processing-relevant context information gives rise to enhanced data structures linking data and metadata. The paper describes a framework for statistical data processing utilising metadata computationally.

Informed Decision Making in Tourism Management Closing the Information Circuit

Tourist information systems (TIS) play an increasingly important role in the promotion of tourism destinations. Currently, this vivid discussion about TIS puts heavy emphasis on the distribution and selling aspect of tourism management. However, the importance and opportunities of effectively utilizing the back-flow information — which, in fact, can also be seen as an immediate indicator of a destination’s business performance — obtained from TIS as a by-product for business planning and decision making processes has not yet been recognized properly. Our contribution outlines an operative information management framework aiming at a deep integration of tourism marketing and distribution activities with performance monitoring and decision making processes, thus closing the current gap in the — electronic — tourism information management loop by adding an upstream information flow from markets to decision makers.

WAMASTEX — Heuristic Guidance for Statistical Analysis

The current state and the direction of further development of the WAMASTEX system are described. The main portion of the paper discusses the empirical assessment of several decision heuristics Wamastex’s internal workings are based upon.

Training on the project: a quantifying approach to competence development

In the project ‘Competence-Driven Project Portfolio Analysis’ (CDPPA), an integrated system for supporting R&D project selection, staff assignment and activity scheduling with special consideration of the strategic development of competencies has been designed and implemented prototypically. The system has been field-tested at the Electronic Commerce Competence Center (EC3), a public–private partnership R&D enterprise. Experiences from this trial application are summarised and discussed, particularly concerning data collection and competence measurement, the benefits and limits of the chosen multi-criteria decision analysis approach, the evaluation of introduced changes to the decision-making processes, and the transparency of the formal planning model and its components.

A metadata approach to statistical query processing

Concerning the task of integrating census and survey data from different sources as it is carried out by supranational statistical agencies, a formal metadata approach is investigated which supports data integration and table processing simultaneously. To this end, a metadata model is devised such that statistical query processing is accomplished by means of symbolic reasoning on machine-readable, operative metadata. As in databases, statistical queries are stated as formal expressions specifying declaratively what the intended output is; the operations necessary to retrieve appropriate available source data and to aggregate source data into the requested macrodata are derived mechanically. Using simple mathematics, this paper focuses particularly on the metadata model devised to harmonize semantically related data sources as well as the table model providing the principal data structure of the proposed system. Only an outline of the general design of a statistical information system based on the proposed metadata model is given and the state of development is summarized briefly.

Semantic Metadata: Query Processing and Data Aggregation

This paper outlines an approach to model semantic meta data such that statistical query processing is supported effectively. The model comprises both types of queries: checking the availability of data, and deriving formally specified target tables of aggregate data.

Dynamic Population Segmentation in Online Market Monitoring

The objective of the SEMAMO (Semantic Market Monitoring) project is to make use of the increasingly growing information available at Web-based sales and marketing channels for market research, using semi-automatic analysis driven by application domain models. The assumptions are that (i) the Web may serve as a representative “picture” of reality, (ii) the respective online channels map salient market developments, and (iii) all of this accurately and in a timely manner. Limited server requests and market specific access structures of Web portals inhibit both full scans of sampling populations and random selection of sampled offers. Further, product feature categories entail multiple classifications within offer clusters (e.g., geography in tourism). Therefore, SEMAMO proposes an adaptive sampling strategy dealing simultaneously with (i) the dynamics of the population frame, (ii) price dynamics, and (iii) multiple (fuzzy) classifications of offered products. The paper discusses a heuristic method of dynamically segmenting monitored offer populations to stratify online data harvesting depending on both observed price changes and information relevance, and outlines the mechanics of harvest schedule derivation.

A formal model evaluation approach to the analysis of treatment effects in paired sample data

Abstract The choice of an appropriate statistical model is critical for sound statistical inference and data interpretation but causes serious difficulties in practice very often, especially for statistically less experienced and trained researchers. In particular, practitioners may not be aware at all of the potentially malicious effects of non-sampling sources of error. In this paper, the frequently encountered problem of comparing means of paired samples is taken as a starting point to devise a formal strategy which supports a researcher in the assessment of standard model assumptions. The general principles as well as the computational details of this strategy are presented.

Adaptive change estimation in the context of online market monitoring

In the Internet-based economy, the (relative) transparency of e-markets and increasing online market dynamics call for more responsive and encompassing approaches towards the monitoring of markets and competitors. Accordingly, this paper proposes to observe continuously a preselected set of e-commerce Web channels, or online portals, to gather a comprehensive as possible picture of market dynamics. In so doing, a historical market data repository is accumulated based on an adaptive scheme of harvesting Web data online in order to provide dynamic information about both market structure and prices. A description of the proposed estimator for online data sampling based on observed (price) change frequencies is given. Numerical simulations highlight the virtues of the proposed adaptive estimator compared to established Web page change frequency estimators, even more so in case of considering constraints on (observation) resources. As an example, the methodology is applied to the online hotel room booking market.

Statistical structures for analyzing time-dependent observations

In order to utilize statistical knowledge in a more or less automated way an appropriate symbolic data structure for capturing this knowledge is necessary. We describe such a data structure for the analysis of time dependent observations. Main emphasis is laid on the analysis of the trend component of observed processes (i.e. regression analysis). This data dependent knowledge is used later on in the phases of determination and execution of actual analyses. Furthermore, the procedural mechanism necessary for the specification of analyses is outlined.