Evaluating linear XPath expressions by pattern-matching automata

Abstract: We consider the problem of efficiently evaluating a large number of XPath expressions, especially in the case when they define subscriber profiles for filtering of XML documents. For each document in an XML document stream, the task is to determine those profiles that match the document. In this article we present a new general method for filtering with profiles expressed by linear XPath expressions with child operators (/), descendant operators (//), and wildcards ( * ). This new filtering algorithm is based on a backtracking deterministic finite automaton derived from the classic Aho-Corasick pattern-matching automaton. This automaton has a size linear in the sum of the sizes of the XPath filters, and the worst-case time bound of the algorithm is much less than the time bound of the simulation of linear-size nondeterministic automata. Our new algorithm has a predecessor that can handle child and descendant operators but not wildcards, and has been shown to be extremely efficient when a documenttype definition (DTD) has been used to prune out all the wildcards and most of the descendant operators. But in some cases, such as when the DTD is highly recursive, it may not be possible to prune out all wildcards without producing a too large set of filters. Then it is important to have the full generality of an evaluation algorithm, as presented in this article, that can also handle wildcards

Algorithms for XML filtering

In a publish-subscribe system based on XML filtering, the subscriber profiles are usually specified by filters written in the XPath language. The system processes the stream of XML documents and delivers to subscribers a notification or the content of those documents that match the filters. The number of interested subscribers and their stored profiles can be very large, thousands or even millions. In this case, the scalability of the system is critical. In this thesis, we develop several algorithms for XML filtering with linear XPath expressions. The algorithms are based on a backtracking Aho-Corasick pattern-matching automaton (PMA) built from "keywords" extracted from the filters, where a keyword is a maximal substring consisting only of XML element names. The output function of the PMA indicates which keyword occurrences of which filter are recognized at a given state. Our best results have been obtained by using a dynamically changing output function, which is dynamically updated during the processing of the input document. We have conducted an extensive performance study in which we compared our filtering algorithms with YFilter and the lazy DFA, two well-known automata-based filtering methods. With a non-recursive XML data set, PMA-based filtering is tens of times more efficient than YFilter and also significantly more efficient than the lazy DFA. With a slightly recursive data set PMA-based filtering has the same performance as the lazy DFA and it is significantly more efficient than YFilter. We have also developed an optimization method called filter pruning. This method improves the performance of filtering by utilizing knowledge about the XML document type definition (DTD) to simplify the filters. The optimization algorithm takes as input a DTD and a set of linear XPath filters and produces a set of pruned linear XPath filters that contain as few wildcards and descendant operators as possible. With a non-recursive data set and with a slightly recursive data set the filter-pruning method yielded a tenfold increase in the filtering speed of the PMA-based algorithms and a hundredfold increase with YFilter and the lazy DFA. Filter pruning can also increase the filtering speed in the case of highly recursive data sets

Evaluation of XPath Queries against XML Streams

XML is nowadays the de facto standard for electronic data interchange on the Web. Available XML data ranges from small Web pages to ever-growing repositories of, e.g., biological and astronomical data, and even to rapidly changing and possibly unbounded streams, as used in Web data integration and publish-subscribe systems. Animated by the ubiquity of XML data, the basic task of XML querying is becoming of great theoretical and practical importance. The last years witnessed efforts as well from practitioners, as also from theoreticians towards defining an appropriate XML query language. At the core of this common effort has been identified a navigational approach for information localization in XML data, comprised in a practical and simple query language called XPath. This work brings together the two aforementioned ``worlds'', i.e., the XPath query evaluation and the XML data streams, and shows as well theoretical as also practical relevance of this fusion. Its relevance can not be subsumed by traditional database management systems, because the latter are not designed for rapid and continuous loading of individual data items, and do not directly support the continuous queries that are typical for stream applications. The first central contribution of this work consists in the definition and the theoretical investigation of three term rewriting systems to rewrite queries with reverse predicates, like parent or ancestor, into equivalent forward queries, i.e., queries without reverse predicates. Our rewriting approach is vital to the evaluation of queries with reverse predicates against unbounded XML streams, because neither the storage of past fragments of the stream, nor several stream traversals, as required by the evaluation of reverse predicates, are affordable. Beyond their declared main purpose of providing equivalences between queries with reverse predicates and forward queries, the applications of our rewriting systems shed light on other query language properties, like the expressivity of some of its fragments, the query minimization, or even the complexity of query evaluation. For example, using these systems, one can rewrite any graph query into an equivalent forward forest query. The second main contribution consists in a streamed and progressive evaluation strategy of forward queries against XML streams. The evaluation is specified using compositions of so-called stream processing functions, and is implemented using networks of deterministic pushdown transducers. The complexity of this evaluation strategy is polynomial in both the query and the data sizes for forward forest queries and even for a large fragment of graph queries. The third central contribution consists in two real monitoring applications that use directly the results of this work: the monitoring of processes running on UNIX computers, and a system for providing graphically real-time traffic and travel information, as broadcasted within ubiquitous radio signals

Ressourcen Optimierung von SOA-Technologien in eingebetteten Netzwerken

Embedded networks are fundamental infrastructures of many different kinds of domains, such as home or industrial automation, the automotive industry, and future smart grids. Yet they can be very heterogeneous, containing wired and wireless nodes with different kinds of resources and service capabilities, such as sensing, acting, and processing. Driven by new opportunities and business models, embedded networks will play an ever more important role in the future, interconnecting more and more devices, even from other network domains. Realizing applications for such types of networks, however, is a highly challenging task, since various aspects have to be considered, including communication between a diverse assortment of resource-constrained nodes, such as microcontrollers, as well as flexible node infrastructure. Service Oriented Architecture (SOA) with Web services would perfectly meet these unique characteristics of embedded networks and ease the development of applications. Standardized Web services, however, are based on plain-text XML, which is not suitable for microcontroller-based devices with their very limited resources due to XML's verbosity, its memory and bandwidth usage, as well as its associated significant processing overhead. This thesis presents methods and strategies for realizing efficient XML-based Web service communication in embedded networks by means of binary XML using EXI format. We present a code generation approach to create optimized and dedicated service applications in resource-constrained embedded networks. In so doing, we demonstrate how EXI grammar can be optimally constructed and applied to the Web service and service requester context. In addition, so as to realize an optimized service interaction in embedded networks, we design and develop an optimized filter-enabled service data dissemination that takes into account the individual resource capabilities of the nodes and the connection quality within embedded networks. We show different approaches for efficiently evaluating binary XML data and applying it to resource constrained devices, such as microcontrollers. Furthermore, we will present the effectful placement of binary XML filters in embedded networks with the aim of reducing both, the computational load of constrained nodes and the network traffic. Various evaluation results of V2G applications prove the efficiency of our approach as compared to existing solutions and they also prove the seamless and successful applicability of SOA-based technologies in the microcontroller-based environment

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Algorithms for XML stream processing : massive data, external memory and scalable performance

Many modern applications require processing of massive streams of XML data, creating difficult technical challenges. Among these, there is the design and implementation of applications to optimize the processing of XPath queries and to provide an accurate cost estimation for these queries processed on a massive steam of XML data. In this thesis, we propose a novel performance prediction model which a priori estimates the cost (in terms of space used and time spent) for any structural query belonging to Forward XPath. In doing so, we perform an experimental study to confirm the linear relationship between stream-processing and data-access resources. Therefore, we introduce a mathematical model (linear regression functions) to predict the cost for a given XPath query. Moreover, we introduce a new selectivity estimation technique. It consists of two elements. The first one is the path tree structure synopsis: a concise, accurate, and convenient summary of the structure of an XML document. The second one is the selectivity estimation algorithm: an efficient streamquerying algorithm to traverse the path tree synopsis for estimating the values of cost-parameters. Those parameters are used by the mathematical model to determine the cost of a given XPath query. We compare the performance of our model with existing approaches. Furthermore, we present a use case for an online stream-querying system. The system uses our performance predicate model to estimate the cost for a given XPath query in terms of time/memory. Moreover, it provides an accurate answer for the query's sender. This use case illustrates the practical advantages of performance management with our techniques.Plusieurs applications modernes nécessitent un traitement de flux massifs de données XML, cela crée de défis techniques. Parmi ces derniers, il y a la conception et la mise en ouvre d'outils pour optimiser le traitement des requêtes XPath et fournir une estimation précise des coûts de ces requêtes traitées sur un flux massif de données XML. Dans cette thèse, nous proposons un nouveau modèle de prédiction de performance qui estime a priori le coût (en termes d'espace utilisé et de temps écoulé) pour les requêtes structurelles de Forward XPath. Ce faisant, nous réalisons une étude expérimentale pour confirmer la relation linéaire entre le traitement de flux, et les ressources d'accès aux données. Par conséquent, nous présentons un modèle mathématique (fonctions de régression linéaire) pour prévoir le coût d'une requête XPath donnée. En outre, nous présentons une technique nouvelle d'estimation de sélectivité. Elle se compose de deux éléments. Le premier est le résumé path tree: une présentation concise et précise de la structure d'un document XML. Le second est l'algorithme d'estimation de sélectivité: un algorithme efficace de flux pour traverser le synopsis path tree pour estimer les valeurs des paramètres de coût. Ces paramètres sont utilisés par le modèle mathématique pour déterminer le coût d'une requête XPath donnée. Nous comparons les performances de notre modèle avec les approches existantes. De plus, nous présentons un cas d'utilisation d'un système en ligne appelé "online stream-querying system". Le système utilise notre modèle de prédiction de performance pour estimer le coût (en termes de temps / mémoire) d'une requête XPath donnée. En outre, il fournit une réponse précise à l'auteur de la requête. Ce cas d'utilisation illustre les avantages pratiques de gestion de performance avec nos technique

Scalable structural index construction for json analytics

JavaScript Object Notation ( JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices, can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison – a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine

HIGH PERFORMANCE XPATH EVALUATION IN XML STREAMS

This thesis presents methods for efficiently evaluating structural queries over tree-structured data streams. A data stream usually consists of a sequence of items that arrive in an order determined by the source. An application that uses such data cannot revisit an earlier item in the stream unless it buffers the item itself. Naive buffering methods are not practical due to the high throughput and indefinite length of data streams. Compared with the flat, relational-like data model for data streams that has received recent attention, processing a tree-structured XML data stream poses additional challenges, since a data item cannot, in general, be interpreted without taking structural information into account. In this thesis, we focus on the evaluation of XPath queries on streaming XML. As a W3C standard, XPath has become a core XML technology not only as a standalone query language but also as the foundation of XQuery and XSLT. Features such as subqueries and reverse axes make XPath a powerful query language but they also complicate XPath query processing. We present our work on XSQ, a streaming XPath query engine. Our methods are based on a novel segment-based evaluation scheme. XSQ uses very little memory and is able to process unbounded and unsegmented streaming data because it does not build a DOM tree in memory. It also provides high throughput by only processing the relevant portions of the data and low response time by returning results as early as possible. XSQ is the first streaming system to support complex XPath features such as multiple predicates, closure axes, aggregations, reverse axes, and subqueries. We also describe our work on XPaSS, an XPath-based publish-subscribe system that simultaneously evaluates a large number of XPath queries over XML streams. Unlike other similar systems that filter pre-segmented documents as results, XPaSS returns only the precisely delineated data specified by a user query. It uses a segment-sharing scheme instead of prefix- and suffix-sharing that are commonly used. In our experiments, XPaSS supports up to one million XPath subscriptions using a modest PC-class server, with a throughput comparable to that of the simpler filtering systems

From Relations to XML: Cleaning, Integrating and Securing Data

While relational databases are still the preferred approach for storing data, XML is emerging as the primary standard for representing and exchanging data. Consequently, it has been increasingly important to provide a uniform XML interface to various data sources— integration; and critical to protect sensitive and confidential information in XML data — access control. Moreover, it is preferable to first detect and repair the inconsistencies in the data to avoid the propagation of errors to other data processing steps. In response to these challenges, this thesis presents an integrated framework for cleaning, integrating and securing data. The framework contains three parts. First, the data cleaning sub-framework makes use of a new class of constraints specially designed for improving data quality, referred to as conditional functional dependencies (CFDs), to detect and remove inconsistencies in relational data. Both batch and incremental techniques are developed for detecting CFD violations by SQL efficiently and repairing them based on a cost model. The cleaned relational data, together with other non-XML data, is then converted to XML format by using widely deployed XML publishing facilities. Second, the data integration sub-framework uses a novel formalism, XML integration grammars (XIGs), to integrate multi-source XML data which is either native or published from traditional databases. XIGs automatically support conformance to a target DTD, and allow one to build a large, complex integration via composition of component XIGs. To efficiently materialize the integrated data, algorithms are developed for merging XML queries in XIGs and for scheduling them. Third, to protect sensitive information in the integrated XML data, the data security sub-framework allows users to access the data only through authorized views. User queries posed on these views need to be rewritten into equivalent queries on the underlying document to avoid the prohibitive cost of materializing and maintaining large number of views. Two algorithms are proposed to support virtual XML views: a rewriting algorithm that characterizes the rewritten queries as a new form of automata and an evaluation algorithm to execute the automata-represented queries. They allow the security sub-framework to answer queries on views in linear time. Using both relational and XML technologies, this framework provides a uniform approach to clean, integrate and secure data. The algorithms and techniques in the framework have been implemented and the experimental study verifies their effectiveness and efficiency