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-theart with a number of new technical contributions, such as looplifted 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. 1.

In previous papers [SC05, SBC+07], some of us predicted the end of “one size fits all ” as a commercial relational DBMS paradigm. These papers presented reasons and experimental evidence that showed that the major RDBMS vendors can be outperformed by 1-2 orders of magnitude by specialized engines in the data warehouse, stream processing, text, and scientific database markets. Assuming that specialized engines dominate these markets over time, the current relational DBMS code lines will be left with the business data processing (OLTP) market and hybrid markets where more than one kind of capability is required. In this paper we show that current RDBMSs can be beaten by nearly two orders of magnitude in the OLTP market as well. The experimental evidence comes from comparing a new OLTP prototype, H-Store, which we have built at M.I.T. to a popular RDBMS on the standard transactional benchmark, TPC-C. We conclude that the current RDBMS code lines, while attempting to be a “one size fits all ” solution, in fact, excel at nothing. Hence, they are 25 year old legacy code lines that should be retired in favor of a collection of “from scratch ” specialized engines. The DBMS vendors (and the research community) should start with a clean sheet of paper and design systems for tomorrow’s requirements, not continue to push code lines and architectures designed for yesterday’s needs. 1.

This article is a proposal for a database index structure, the XPath accelerator, that has been specifically designed to support the evaluation of XPath path expressions. As such, the index is capable to support all XPath axes (including ancestor, following, preceding-sibling, descendant-or-self, etc.). This feature lets the index stand out among related work on XML indexing structures which had a focus on the child and descendant axes only. The index has been designed with a close eye on the XPath semantics as well as the desire to engineer its internals so that it can be supported well by existing relational database query processing technology: the index (a) permits set-oriented (or, rather, sequence-oriented) path evaluation, and (b) can be implemented and queried using well-established relational index structures, notably B-trees and R-trees

CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics. CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below are the names of the clusters and in parentheses their acronyms.

Annotations play a central role in the curation of scientific databases. Despite their importance, data formats and schemas are not designed to manage the increasing variety of annotations. Moreover, DBMS’s often lack support for storing and querying annotations. Furthermore, annotations and data are only loosely coupled. This paper introduces an annotation-oriented data model for the manipulation and querying of both data and annotations. In particular, the model allows for the specification of annotations on sets of values and for effectively querying the information on their association. We use the concept of block to represent an annotated set of values. Different colors applied to the blocks represent different annotations. We introduce a color query language for our model and prove it to be both complete (it can express all possible queries over the class of annotated databases), and minimal (all the algebra operators are primitive). We present MONDRIAN, a prototype implementation of our annotation mechanism, and we conduct experiments that investigate the set of parameters which influence the evaluation cost for color queries. 1.

This paper introduces the PF/Tijah system, a text search system that is integrated with an XML/XQuery database management system. We present examples of its use, we explain some of the system internals, and discuss plans for future work. PF/Tijah is part of the open source release of MonetDB/XQuery.

This paper analyzes the performance of concurrent (index) scan operations in both record (NSM/PAX) and column (DSM) disk storage models and shows that existing scheduling policies do not fully exploit data-sharing opportunities and therefore result in poor disk bandwidth utilization. We propose the Cooperative Scans framework that enhances performance in such scenarios by improving data-sharing between concurrent scans. It performs dynamic scheduling of queries and their data requests, taking into account the current system situation. We first present results on top of an NSM/PAX storage layout, showing that it achieves significant performance improvements over traditional policies in terms of both the number of I/Os and overall execution time, as well as latency of individual queries. We provide benchmarks with varying system parameters, data sizes and query loads to confirm the improvement occurs in a wide range of scenarios. Then we extend our proposal to a more complicated DSM scenario, discussing numerous problems related to the two-dimensional nature of disk scheduling in column stores. 1.

In the implementation of hosted business services, multiple tenants are often consolidated into the same database to reduce total cost of ownership. Common practice is to map multiple single-tenant logical schemas in the application to one multi-tenant physical schema in the database. Such mappings are challenging to create because enterprise applications allow tenants to extend the base schema, e.g., for vertical industries or geographic regions. Assuming the workload stays within bounds, the fundamental limitation on scalability for this approach is the number of tables the database can handle. To get good consolidation, certain tables must be shared among tenants and certain tables must be mapped into fixed generic structures such as Universal and Pivot Tables, which can degrade performance. This paper describes a new schema-mapping technique for multi-tenancy called Chunk Folding, where the logical tables are vertically partitioned into chunks that are folded together into different physical multi-tenant tables and joined as needed. The database’s “meta-data budget ” is divided between application-specific conventional tables and a large fixed set of generic structures called Chunk Tables. Good performance is obtained by mapping the most heavily-utilized parts of the logical schemas into the conventional tables and the remaining parts into Chunk Tables that match their structure as closely as possible. We present the results of several experiments designed to measure the efficacy of Chunk Folding and describe the multi-tenant database testbed in which these experiments were performed.

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Abstract. A new generation of applications running on a network of nodes, that share data on an ad-hoc basis, will benefit from data management services including powerful querying facilities. In this paper, we introduce the goals, assumptions and architecture of AmbientDB, a new peer-to-peer (P2P) DBMS prototype developed at CWI. Our focus is on the query processing facilities of AmbientDB, that are based on a three-level translation of a global query algebra into multiwave stream processing plans, distributed over an ad-hoc P2P network. We illustrate the usefulness of our system by outlining how it eases construction of a music player that generates intelligent playlists with collaborative filtering over distributed music logs. Finally, we show how the use of Distributed Hash Tables (DHT) at the basis of AmbientDB allows applications like the P2P music player to scale to large amounts of nodes. 1