Search

In the frame work of Collaborative Research Center (CRC) 653, magnetic data storage on surfaces of components has been developed. The principle is based on magnetic data storage of hard disk drives. Rigid magnetic writing heads successfully developed so far still have limited capabilities; they cannot be adapted to rough or curved surfaces and cannot withstand high mechanical shock. To overcome such drawbacks, a new design of a writing head based on a flexible substrate is proposed. This publication focuses on an investigation of a material which serves as a required substrate for this application. Baseline examinations for asserting compatibility of the flexible substrate to production processes for microelectromechanical systems (MEMS) are presented. Successfully fabricated micro structures using simple fabrication techniques based on photolithography and thin film processes are demonstrated as well. Obtained results through this work indicate that the polyimide film, namely Kapton film, is suitable as the flexible substrate for the flexible magnetic writing / reading system.

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11 GB. The performance section also provides an extensive comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met.

We outline an efficient ACID-compliant mechanism for structural inserts and deletes in relational XML document storage that uses a region based pre/size/level encoding (equivalent to the pre/post encoding). Updates to such node-numbering schemes are considered prohibitive (i.e. physical cost linear to document size), because structural updates cause shifts in all pre-numbers after the update point, and require updates of the size of all ancestors, such that the root of the tree becomes a locking bottleneck. We show how such locking can be avoided by updating the size of ancestors using delta-increments, which are transaction-commutative operations. We also reduce the physical cost to the minimum (i.e. linear to update volume) by carefully exploiting the virtual column feature of MonetDB to store pre numbers (virtual columns are never materialized, and thus need not be updated). In our evaluation, we show the overhead of the update-feature in MonetDB/XQuery in terms of added XMark evaluation cost to stay within an acceptable limit (<30% on average).

Using a relational DBMS as back-end engine for an XQuery processing system leverages relational query optimization and scalable query processing strategies provided by mature DBMS engines in the XML domain. Though a lot of theoretical work has been done in this area and various solutions have been proposed, no complete systems have been made available so far to give the practical evidence that this is a viable approach. In this paper, we describe the ourely relational XQuery processor Pathfinder that has been built on top of the extensible RDBMS MonetDB. Performance results indicate that the system is capable of evaluating XQuery queries efficiently, even if the input XML documents become huge. We additionally present further contributions such as loop-lifted staircase join, techniques to derive order properties and to reduce sorting effort in the generated relational algebra plans, as well as methods for optimizing XQuery joins, which, taken together, enabled us to reach our performance and scalability goals

Various techniques have been proposed for efficient evaluation of XPath expressions, where the XPath location steps are rooted in a single sequence of context nodes. Among these techniques, the staircase join allows to evaluate XPath location steps along arbitrary axes in at most one scan over the XML document, exploiting the XPath accelerator encoding (aka. pre/post encoding).In XQuery, however, embedded XPath sub-expressions occur in arbitrarily nested for-loops. Thus, they are rooted in multiple sequences of context nodes (one per iteration). Consequently, the previously proposed algorithms need to be applied repeatedly, requiring multiple scans over the XML document encoding.In this work, we present loop-lifted staircase join, an extension of the staircase join that allows to efficiently evaluate XPath sub-expressions in arbitrarily nested XQuery iteration scopes with only a single scan over the document. We implemented the loop-lifted staircase join in MonetDB/XQuery, that uses the XQuery-to-Relational Algebra compiler Pathfinder on top of the extensible RDBMS MonetDB. Performance results indicate that the proposed technique allows to build a system that is capable of efficiently evaluating XQuery queries including embedded XPath expressions, obtaining interactive query execution times for all XMark queries even on multi-gigabyte XML documents