The Database Architectures Research Group at CWI

The Database research group at CWI was established in 1985. It has steadily grown from two PhD students to a group of 17 people ultimo 2011. The group is supported by a scientific programmer and a system engineer to keep our machines running. In this short note, we look back at our past and highlight the multitude of topics being addressed

A survey on tree matching and XML retrieval

International audienceWith the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Semantics and efficient evaluation of partial tree-pattern queries on XML

Current applications export and exchange XML data on the web. Usually, XML data are queried using keyword queries or using the standard structured query language XQuery the core of which consists of the navigational query language XPath. In this context, one major challenge is the querying of the data when the structure of the data sources is complex or not fully known to the user. Another challenge is the integration of multiple data sources that export data with structural differences and irregularities. In this dissertation, a query language for XML called Partial Tree-Pattern Query (PTPQ) language is considered. PTPQs generalize and strictly contain Tree-Pattern Queries (TPQs) and can express a broad structural fragment of XPath. Because of their expressive power and flexibility, they are useful for querying XML documents the structure of which is complex or not fully known to the user, and for integrating XML data sources with different structures. The dissertation focuses on three issues. The first one is the design of efficient non-main-memory evaluation methods for PTPQs. The second one is the assignment of semantics to PTPQs so that they return meaningful answers. The third one is the development of techniques for answering TPQs using materialized views. Non-main-memory XML query evaluation can be done in two modes (which also define two evaluation models). In the first mode, data is preprocessed and indexes, called inverted lists, are built for it. In the second mode, data are unindexed and arrives continuously in the form of a stream. Existing algorithms cannot be used directly or indirectly to efficiently compute PTPQs in either mode. Initially, the problem of efficiently evaluating partial path queries in the inverted lists model has been addressed. Partial path queries form a subclass of PTPQs which is not contained in the class of TPQs. Three novel algorithms for evaluating partial path queries including a holistic one have been designed. The analytical and experimental results show that the holistic algorithm outperforms the other two. These results have been extended into holistic and non-holistic approaches for PTPQs in the inverted lists model. The experiments show again the superiority of the holistic approach. The dissertation has also addressed the problem of evaluating PTPQs in the streaming model, and two original efficient streaming algorithms for PTPQs have been designed. Compared to the only known streaming algorithm that supports an extension of TPQs, the experimental results show that the proposed algorithms perform better by orders of magnitude while consuming a much smaller fraction of memory space. An original approach for assigning semantics to PTPQs has also been devised. The novel semantics seamlessly applies to keyword queries and to queries with structural restrictions. In contrast to previous approaches that operate locally on data, the proposed approach operates globally on structural summaries of data to extract tree patterns. Compared to previous approaches, an experimental evaluation shows that our approach has a perfect recall both for XML documents with complete and with incomplete data. It also shows better precision compared to approaches with similar recall. Finally, the dissertation has addressed the problem of answering XML queries using exclusively materialized views. An original approach for materializing views in the context of the inverted lists model has been suggested. Necessary and sufficient conditions have been provided for tree-pattern query answerability in terms of view-to-query homomorphisms. A time and space efficient algorithm was designed for deciding query answerability and a technique for computing queries over view materializations using stack- based holistic algorithms was developed. Further, optimizations were developed which (a) minimize the storage space and avoid redundancy by materializing views as bitmaps, and (b) optimize the evaluation of the queries over the views by applying bitwise operations on view materializations. The experimental results show that the proposed approach obtains largely higher hit rates than previous approaches, speeds up significantly the evaluation of queries without using views, and scales very smoothly in terms of storage space and computational overhead

State Management for Efficient Event Pattern Detection

Event Stream Processing (ESP) Systeme überwachen kontinuierliche Datenströme, um benutzerdefinierte Queries auszuwerten. Die Herausforderung besteht darin, dass die Queryverarbeitung zustandsbehaftet ist und die Anzahl von Teilübereinstimmungen mit der Größe der verarbeiteten Events exponentiell anwächst. Die Dynamik von Streams und die Notwendigkeit, entfernte Daten zu integrieren, erschweren die Zustandsverwaltung. Erstens liefern heterogene Eventquellen Streams mit unvorhersehbaren Eingaberaten und Queryselektivitäten. Während Spitzenzeiten ist eine erschöpfende Verarbeitung unmöglich, und die Systeme müssen auf eine Best-Effort-Verarbeitung zurückgreifen. Zweitens erfordern Queries möglicherweise externe Daten, um ein bestimmtes Event für eine Query auszuwählen. Solche Abhängigkeiten sind problematisch: Das Abrufen der Daten unterbricht die Stream-Verarbeitung. Ohne eine Eventauswahl auf Grundlage externer Daten wird das Wachstum von Teilübereinstimmungen verstärkt. In dieser Dissertation stelle ich Strategien für optimiertes Zustandsmanagement von ESP Systemen vor. Zuerst ermögliche ich eine Best-Effort-Verarbeitung mittels Load Shedding. Dabei werden sowohl Eingabeeevents als auch Teilübereinstimmungen systematisch verworfen, um eine Latenzschwelle mit minimalem Qualitätsverlust zu garantieren. Zweitens integriere ich externe Daten, indem ich das Abrufen dieser von der Verwendung in der Queryverarbeitung entkoppele. Mit einem effizienten Caching-Mechanismus vermeide ich Unterbrechungen durch Übertragungslatenzen. Dazu werden externe Daten basierend auf ihrer erwarteten Verwendung vorab abgerufen und mittels Lazy Evaluation bei der Eventauswahl berücksichtigt. Dabei wird ein Kostenmodell verwendet, um zu bestimmen, wann welche externen Daten abgerufen und wie lange sie im Cache aufbewahrt werden sollen. Ich habe die Effektivität und Effizienz der vorgeschlagenen Strategien anhand von synthetischen und realen Daten ausgewertet und unter Beweis gestellt.Event stream processing systems continuously evaluate queries over event streams to detect user-specified patterns with low latency. However, the challenge is that query processing is stateful and it maintains partial matches that grow exponentially in the size of processed events. State management is complicated by the dynamicity of streams and the need to integrate remote data. First, heterogeneous event sources yield dynamic streams with unpredictable input rates, data distributions, and query selectivities. During peak times, exhaustive processing is unreasonable, and systems shall resort to best-effort processing. Second, queries may require remote data to select a specific event for a pattern. Such dependencies are problematic: Fetching the remote data interrupts the stream processing. Yet, without event selection based on remote data, the growth of partial matches is amplified. In this dissertation, I present strategies for optimised state management in event pattern detection. First, I enable best-effort processing with load shedding that discards both input events and partial matches. I carefully select the shedding elements to satisfy a latency bound while striving for a minimal loss in result quality. Second, to efficiently integrate remote data, I decouple the fetching of remote data from its use in query evaluation by a caching mechanism. To this end, I hide the transmission latency by prefetching remote data based on anticipated use and by lazy evaluation that postpones the event selection based on remote data to avoid interruptions. A cost model is used to determine when to fetch which remote data items and how long to keep them in the cache. I evaluated the above techniques with queries over synthetic and real-world data. I show that the load shedding technique significantly improves the recall of pattern detection over baseline approaches, while the technique for remote data integration significantly reduces the pattern detection latency

Matching Subsequences in Trees

Given two rooted, labeled trees $P$ and $T$ the tree path subsequence problem is to determine which paths in $P$ are subsequences of which paths in $T$. Here a path begins at the root and ends at a leaf. In this paper we propose this problem as a useful query primitive for XML data, and provide new algorithms improving the previously best known time and space bounds.Comment: Minor correction of typos, et

Towards Efficient Locality Aware Parallel Data Stream Processing

Abstract: Parallel data processing and parallel streaming systems become quite popular. They are employed in various domains such as real-time signal processing, OLAP database systems, or high performance data extraction. One of the key components of these systems is the task scheduler which plans and executes tasks spawned by the application on available CPU cores. The multiprocessor systems and CPU architecture of the day become quite complex, which makes the task scheduling a challenging problem. In this paper, we propose a novel task scheduling strategy for parallel data stream systems, that reflects many technical issues of the current hardware. In addition, we have implemented a NUMA aware memory allocator that improves data locality in NUMA systems. The proposed task scheduler combined with the new memory allocator achieve up to 3× speed up on a NUMA system and up to 10% speed up on an older SMP system with respect to the unoptimized versions of the scheduler and allocator. Many of the ideas implemented in our parallel framework may be adopted for task scheduling in other domains that focus on different priorities or employ additional constraints

On Teaching XQuery to Digital Humanists

Abstract XQuery provides an excellent means for teaching programming to digital humanists because it works seamlessly with their existing XML data, has an elegant and simple core with a well-structured standard library, and can be used in conjunction with XML databases to develop end-to-end web applications. However, current teaching materials for XQuery do not address the needs of digital humanists, presupposing implicit knowledge of programming concepts that they frequently lack. Based on experience teaching XQuery to digital humanists (including alt-ac professionals, archivists, faculty members, graduate students, and librarians) in three distinct settings: a weekly training session for librarians, a graduate seminar on digital humanities, and a two week NEHsupported Institute for Advanced Topics in Digital Humanities, I suggest how the XML community might develop resources to widen the appeal and accessibility of XQuery

Static code analysis of data-driven applications through common lingua and the Semantic Web technologies

Web applications have become increasingly popular due to their potential for businesses' high revenue gain through global reach. Along with these opportunities, also come challenges in terms of Web application security. The increased rise in the number of datadriven applications has also seen an increased rise in their systematic attacks. Cyberattacks exploit Web application vulnerabilities. Attack trends show a major increase in Web application vulnerabilities caused by improper implementation of information-flow control methods and they account for more than 50% of all Web application vulnerabilities found in the year 2013. Static code analysis using methods of information-flow control is a widely acknowledged technique to secure Web applications. Whilst this technique has been found to be both very effective and efficient in finding Web application vulnerabilities, specific tools are highly dependent on the programming language. This thesis leverages Semantic Web technologies in order to offer a common language through source code represented using the Resource Description Framework format, whereby reasoning can be applied to securely test Web applications. In this thesis, we present a framework that extracts source code facts from various programming languages at a variable-level of granularity using Abstract Syntax Trees (ASTs) generated using language grammars and the ANTLR parser generator. The methodology for detecting Web application vulnerabilities implements three phases: entry points identification, tracing information-flow and vulnerability detection using the Jena framework inference mechanism and rules describing patterns of source code. The approach discussed in this thesis is found to be effective and practical in finding Web application vulnerabilities with the limitation that it can only detect patterns that are used as training data or very similar patterns. False positives are caused by limitations of the language grammar, but they do not affect the accuracy of the security vulnerability detection method in identifying the correct Web application vulnerability.Doctor of Philosoph