Pathfinder: relational XQuery over multi-gigabyte XML inputs in interactive time

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 goal

Spinning Relations: High-Speed Networks for Distributed Join Processing

By leveraging modern networking hardware (RDMA-enabled network cards), we can shift priorities in distributed database processing significantly. Complex and sophisticated mechanisms to avoid network traffic can be replaced by a scheme that takes advantag

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Pathfinder: relational XQuery over multi-gigabyte XML inputs in interactive time

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 goal

Loop-lifted staircase join: from XPath to XQuery

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 document

Quantitative assessment of the power loss of silicon PV modules by IR thermography and its dependence on data‐filtering criteria

Reliability and quality control of photovoltaic (PV) plants increases in importance as the relevance of PV for the worldwide renewable energy production grows. In this study, a new method is presented which allows for a quantitative assessment of silicon PV module performance solely by relying on the cell temperatures measured via thermography (IR). A module temperature and power key figure are formulated and found to correlate very well with a linear relationship. The dependence of the deduced correlation's precision on measurement conditions is calculated and discussed. It facilitates decision‐making because optimal measurement conditions usually occur only very rarely, such that a compromise between data quality and measurement frequency has to be found. The power loss correlation presented in this paper may be used as part of a maintenance routine in order to ensure the best possible long‐term performance of the PV plant over its lifetime. The practical application in the field is outlined and explained

A spinning join that does not get dizzy

As network infrastructures with 10 Gb/s bandwidth and beyond have become pervasive and as cost advantages of large commodity-machine clusters continue to increase, research and industry strive to exploit the available processing performance for large-scale database processing tasks. In this work we look at the use of high-speed networks for distributed join processing. We propose Data Roundabout as alight weight transport layer that uses Remote Dir