Weltbauen durch Großtechnik, Atlantropa - ein Architektentraum der zwanziger bis fünfziger Jahre

Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universität zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien

The anisotropic quantum antiferromagnet on the Sierpinski gasket: Ground state and thermodynamics

We investigate an antiferromagnetic s=1/2 quantum spin system with anisotropic spin exchange on a fractal lattice, the Sierpinski gasket. We introduce a novel approximative numerical method, the configuration selective diagonalization (CSD) and apply this method to the Sierpinski gasket with N=42. Using this and other methods we calculate ground state energies, spin gap, spin-spin correlations and specific heat data and conclude that the s=1/2 quantum antiferromagnet on the Sierpinski gasket shows a disordered magnetic ground state with a very short correlation length of about 1 and an, albeit very small, spin gap. This conclusion holds for Heisenberg as well a for XY exchange.Comment: LaTeX: 16 pages, 9 figures, 1 tabl

The position and morphology of honeycombs in normal skeletal muscle fibres of the healthy frog

Honeycombs are regularly arranged networks of tubules in continuity with the T-system of the skeletal muscle fibre. Their occurrence is usually described in pathologically modified or cultivated muscle fibres. Here we describe the occurrence of honeycombs in macroscopic normal muscle fibres of healthy frogs. We characterize their light- and electronmicroscopic features and represent their relationships to motor end plates, fibre nuclei and myofibrils. In these normal muscle fibres of healthy frogs, the honeycombs are connected to subsynaptic folds and the T-system. Their regional occurrence is discussed with respect to regional differences in the regulation of the membrane metabolism. Since we can demonstrate them in healthy animals, we do not see any basis why their occurrence should be related to pathological modifications of the muscle fibr

Flexible Relational Data Model: A Common Ground for Schema-Flexible Database Systems

An increasing number of application fields represent dynamic and open discourses characterized by high mutability, variety, and pluralism in data. Data in dynamic and open discourses typically exhibits an irregular schema. Such data cannot be directly represented in the traditional relational data model. Mapping strategies allow representation but increase development and maintenance costs. Likewise, NoSQL systems offer the required schema flexibility but introduce new costs by not being directly compatible with relational systems that still dominate enterprise information systems. With the Flexible Relational Data Model (FRDM) we propose a third way. It allows the direct representation of data with irregular schemas. It combines tuple-oriented data representation with relation-oriented data processing. So that, FRDM is still relational, in contrast to other flexible data models currently in vogue. It can directly represent relational data and builds on the powerful, well-known, and proven set of relational operations for data retrieval and manipulation. In addition to FRDM, we present the flexible constraint framework FRDM-C. It explicitly allows restricting the flexibility of FRDM when and where needed. All this makes FRDM backward compatible to traditional relational applications and simplifies the interoperability with existing pure relational databases

Is induced abortion a risk factor in subsequent pregnancy?

Objective: To determine whether a history of terminations of pregnancy influences subsequent pregnancies in terms of pregnancy risks, prematurity and neonatal biometrics. Patients and methods: Based on the perinatal statistics of eight German federal states, data of 247,593 primiparous women with singleton pregnancies born between 1998 and 2000 were analyzed. The control group consisted of primiparous women without previous induced abortions. Maternal age was adjusted for. Results: There was an overall trend towards an increased rate of preterm delivery at <= 36 weeks' gestation and early preterm delivery at <= 31 weeks' gestation in women who had previous pregnancy terminations. For the cohort of 28-30 years, the observed rates of prematurity in women with one and with >= 2 previous induced abortions were 7.8% and 8.5%, respectively, compared to 6.5% in the control population (P=0.015). Preceding terminations of pregnancy did not alter the rate of small-for-gestational-age newborns. Psychosocial stress and symptoms associated with prematurity such as cervical incompetence and vaginal bleeding before and after 28 weeks of gestation occurred more frequently in women with previous induced abortion compared to the control group (P<0.0001). Conclusion: The rate of preterm births increases with the number of preceding abortions. Similarly, symptoms associated with prematurity are more common. The rate of small-for-gestational-age newborns was not affected by preceding terminations of pregnancy

Online horizontal partitioning of heterogeneous data

In an increasing number of use cases, databases face the challenge of managing heterogeneous data. Heterogeneous data is characterized by a quickly evolving variety of entities without a common set of attributes. These entities do not show enough regularity to be captured in a traditional database schema. A common solution is to centralize the diverse entities in a universal table. Usually, this leads to a very sparse table. Although today’s techniques allow efficient storage of sparse universal tables, query efficiency is still a problem. Queries that address only a subset of attributes have to read the whole universal table includingmany irrelevant entities. Asolution is to use a partitioning of the table, which allows pruning partitions of irrelevant entities before they are touched. Creating and maintaining such a partitioning manually is very laborious or even infeasible, due to the enormous complexity. Thus an autonomous solution is desirable. In this article, we define the Online Partitioning Problem for heterogeneous data. We sketch how an optimal solution for this problem can be determined based on hypergraph partitioning. Although it leads to the optimal partitioning, the hypergraph approach is inappropriate for an implementation in a database system. We present Cinderella, an autonomous online algorithm for horizontal partitioning of heterogeneous entities in universal tables. Cinderella is designed to keep its overhead low by operating online; it incrementally assigns entities to partition while they are touched anyway duringmodifications. This enables a reasonable physical database design at runtime instead of static modeling

Poster session: Constrained dynamic physical database design

Physical design has always been an important part of database administration. Today's commercial database management systems offer physical design tools, which recommend a physical design for a given workload. However, these tools work only with static workloads and ignore the fact that workloads, and physical designs, may change over time. Research has now begun to focus on dynamic physical design, which can account for time-varying workloads. In this paper, we consider a dynamic but constrained approach to physical design. The goal is to recommend dynamic physical designs that reflect major workload trends but that are not tailored too closely to the details of the input workloads. To achieve this, we constrain the number of changes that are permitted in the recommended design. In this paper we present our definition of the constrained dynamic physical design problem and discuss several techniques for solving it

SMIX Live - A Self-Managing Index Infrastructure for Dynamic Workloads

As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in lot of indexed but never queried data and prohibitively high memory and maintenance costs. In our demonstration, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity. In the demonstration, we visualize performance and system measures for different scenarios and allow the user to interactively change several system parameters

SMIX: Self-managing indexes for dynamic workloads

As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in many indexed but never queried records and prohibitively high storage and maintenance costs. In this paper, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity