Hans-Joachim Lenz

The Influence of Incentives and Culture on Knowledge Sharing

A major challenge in knowledge management involves motivating people to share their knowledge with others. In many modern knowledge-oriented companies, this challenge is addressed by incentive systems that are linked to the knowledge-sharing activities of employees. The design of appropriate incentive systems is becoming more difficult for companies distributed internationally which operate in a variety of countries and cultures. In this study, we will analyze the combined influence of incentives and culture on knowledge sharing in large organizations.

The R/sub a/*-tree: an improved R*-tree with materialized data for supporting range queries on OLAP-data

OLAP applications make use of fast indexes and materialization of data. Most research treats just one topic. Either the materialized values or the design of index structures are considered. The paper examines a possible combination of both techniques. The R* tree is taken as an example of a multidimensional index structure. Aggregated data is stored in the inner nodes of the index structure in addition to the references to the successor nodes. We describe how this mechanism works in detail and present results of performance evaluation.

OLAP databases and aggregation functions

Aggregation functions are a class of generic functions which must be usable in any database application. We characterize the case where the aggregation functions can be correctly applied on macrodata (data cube) which are computed on the microdata.

Learning, networks and statistics

Neural Nets: S. Raudys: Overtraining in Single-Layer Perceptrons H. Ritter: Neural Networks for Rapid Learning in Computer Vision and Robotics.- Statistics and Networks: R.A. Muller: Adaptive Market Simulation and Risk Assessment E. Rodel: Processing of Prior-Information in Statistics by Projections on Convex Cones.- Classification and Data Mining: H.H. Bock: Simultaneous Visualization and Clustering Methods as an Alternative to Kohonen Maps H. Hellendoorn: Data analysis in Industry - A Practical Guideline F. Klawonn, R. Kruse, H. Timm: Fuzzy Shell Cluster Analysis F. Wysotzki, W. Muller, B. Schulmeister: Automatic Construction of Decision Trees and Neural Nets for Classification Using Statistical Considerations Y. Kodratoff: From the Art of KDD to the Science of KDD.- Machine Learning: I. Bratko: Machine Learning: Between Accuracy and Interpretability N. Lavrac, D. Gamberger, P. Turney: Preprocessing by a Cost-Sensitive Literal Reduction Algorithm: REDUCE L. Saitta: A General Framework for Supporting Relational Concept Learning R. Trappl, J. Furnkranz, J. Petrak, J. Bercovitch: Machine Learning and Case-Based Reasoning: Their Potential Role in Preventing the Outbreak of Wars or in Ending Them.

PISA: Performance models for Index Structures with and without Aggregated data

Different models to estimate the performance of tree-based index structures exist. Materialized aggregates in the inner nodes of such index structures are used to speed up range queries on aggregates. This is achieved by avoiding traversing the index tree down to the leaves through aggregates precomputed in the inner nodes. None of the existing models deals with these aggregated data. In this paper, we extend the existing models to take account of aggregated data. Our main contribution is a new generic performance model to estimate the Performance of Index Structures with and without Aggregated data (PISA) that covers both aspects. In addition, the PISA model is adaptable to the distribution of the data and to the location of the query boxes (e.g. uniform, normal and skewed distributions). Experiments confirm that, in most cases, the PISA model is more accurate than other models.

Preferences and Similarities

The fields of similarity and preference are still broadening due to the exploration of new fields of application. This is caused by the strong impact of vagueness, imprecision, uncertainty and dominance on human and agent information, communication, planning, decision, action, and control as well as by the technical progress of the information technology itself. The topics treated in this book are of interest to computer scientists, statisticians, operations researchers, experts in AI, cognitive psychologists and economists.

Dissemination of multidimensional data using broadcast clusters

The multidimensional modeling of data is steadily gaining popularity, finding adoption not only for business but for scientific applications as well. Data Warehousing is the most prominent example of multidimensional data usage. In parallel, wireless networks, with their rapid growth, already play a fundamental role in facilitating time critical decision-making. Nevertheless, their inherent shortcomings, but also those of the mobile devices operating within their proximity, introduce additional complexity. Access time and energy consumption become, among others, factors that should be taken into consideration. This paper deals with the efficient dissemination of multidimensional data into wireless networks. In this context, a new family of scheduling algorithms, which simultaneously exploits various characteristics both of OLAP data and wireless networks, is introduced. These algorithms clearly outperform existing proposals, on all counts: average access time, energy consumption and network utilization.

The conceptual schema and external schemata of metadatabases

We consider statistical databases. They consist of three layers which refer to micro-, macro- and metadata. Metadata are viewed as data about micro- and macrodata. Metadata have non-standard characteristics, i.e. they are not fixed formatted, are highly cross-referenced and are extremely heterogenous. This is especially true for the underlying data structures and the set of operators needed for data definition, manipulation and control. Moreover, data integrity and confidentiality need special attention. We focus on the conceptual design of metadata and consider different user views in some detail.<<ETX>>

OLAP schemata for correct applications

OLAP applications are currently widely used in business applications. These applications are implicitly defined on top of OLTP systems. The applications make use of aggregation functions and data combinations. A number of paradoxes is observed if arbitrary aggregation functions and combinations are used. We develop a theory of aggregation functions, OLTP-OLAP transformations, and of the data cube. Based on these investigations we derive an architecture for OLTP-OLAP applications that supports sound and correct querying: OLTP-OLAP specification frames. The specification frame of OLTP-OLAP schemata specifically emphasises soundness of all operations involved by built-in guards. Or to turn it around, we make provision that an innocent user does not start non-sense operations. This specification frame is based on OLTP schemata, OLTP-OLAP transformations, and a rigid theory of OLAP schemata and functions.

A Procedure to Estimate Relations in a Balanced Scorecard

A Balanced Scorecard is more than a business model because it moves performance measurement to performance management. It consists of performance indicators which are inter-related. Some relations are hard to find, like soft skills. We propose a procedure to fully specify these relations. Three types of relationships are considered. For the function types inverse functions exist. Each equation can be solved uniquely for variables at the right hand side. By generating noisy data in a Monte Carlo simulation, we can specify function type and estimate the related parameters. An example illustrates our procedure and the corresponding results.