Acyclic join queries can be evaluated instance-optimally using Yannakakis' algorithm, which avoids needlessly large intermediate results through semi-join passes. Recent work proposes to address the significant hidden constant factors arising from a naive implementation of Yannakakis by decomposing the hash join operator into two suboperators, called Lookup and Expand. We present a novel method for integrating Lookup and Expand plans in interpreted environments, like column stores, formalizing them using Nested Semijoin Algebra (NSA) and implementing them through a shredding approach. We characterize the class of NSA expressions that can be evaluated instance-optimally as those that are 2-phase: no 'shrinking' operator is applied after an unnest (i.e., expand). We introduce Shredded Yannakakis (SYA), an evaluation algorithm for acyclic joins that, starting from a binary join plan, transforms it into a 2-phase NSA plan, and then evaluates it through the shredding technique. We show that SYA is provably robust (i.e., never produces large intermediate results) and without regret (i.e., is never worse than the binary join plan under a suitable cost model) on the class of well-behaved binary join plans. Our experiments on a suite of 1,849 queries show that SYA improves performance for 85.3% of the queries with speedups up to 62.5x, while remaining competitive on the other queries.

We propose a theory that can determine the lowest isolation level that can be allocated to each transaction program in an application in a mixed-isolation-level setting, to guarantee that all executions will be serializable and thus preserve all integrity constraints, even those that are not explicitly declared. This extends prior work applied to completely known transactions, to deal with the realistic situation where transactions are generated by running programs with parameters that are not known in advance. Using our theory, we propose an optimization method that allows for high throughput while ensuring that all executions are serializable. Our method is based on searching for application code modifications that are semantics-preserving while improving the isolation level allocation. We illustrate our approach to the SmallBank benchmark.

A serializable concurrency control mechanism ensures consistency for OLTP systems at the expense of a reduced transaction throughput. A DBMS, therefore usually offers the possibility to allocate lower isolation levels for some transactions when it is safe to do so. However, such trading of consistency for efficiency does not come with any safety guarantees. In this article, we study the mixed robustness problem which asks whether, for a given set of transactions and a given allocation of isolation levels, every possible interleaved execution of those transactions that is allowed under the provided allocation is always serializable. That is, whether the given allocation is indeed safe. While robustness has already been studied in the literature for the homogeneous setting where all transactions are allocated the same isolation level, the heterogeneous setting that we consider in this article, despite its practical relevance, has largely been ignored. We focus on multiversion concurrency control and consider the isolation levels that are available in PostgreSQL and Oracle: read committed (RC), snapshot isolation (SI), and serializable snapshot isolation (SSI). We show that the mixed robustness problem can be decided in polynomial time. In addition, we provide a polynomial time algorithm for computing the optimal robust allocation for a given set of transactions, prioritizing lower over higher isolation levels. The present results therefore establish the groundwork to automate isolation level allocation within existing databases supporting multiversion concurrency control.

Approximate functional dependencies (AFDs) are functional dependencies (FDs) that “almost” hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is unclear which measure is preferable when one needs to discover FDs in real-world data, i.e., data that only approximately satisfies the FD. In response, this paper formally and qualitatively compares AFD measures. We obtain a formal comparison through a novel presentation of measures in terms of Shannon and logical entropy. Qualitatively, we perform a sensitivity analysis w.r.t. structural properties of input relations and quantitatively study the effectiveness of AFD measures for ranking AFDs on real world data. Based on this analysis, we give clear recommendations for the AFD measures to use in practice.

A DBMS allows trading consistency for efficiency through the allocation of isolation levels that are strictly weaker than serializability. The robustness problem asks whether, for a given set of transactions and a given allocation of isolation levels, every possible interleaved execution of those transactions that is allowed under the provided allocation, is always safe. In the literature, safe is interpreted as conflict-serializable (to which we refer here as conflict-robustness). In this paper, we study the view-robustness problem, interpreting safe as view-serializable. View-serializability is a more permissive notion that allows for a greater number of schedules to be serializable and aligns more closely with the intuitive understanding of what it means for a database to be consistent. However, view-serializability is more complex to analyze (e.g., conflict-serializability can be decided in polynomial time whereas deciding view-serializability is NP-complete). While conflict-robustness implies view-robustness, the converse does not hold in general. In this paper, we provide a sufficient condition for isolation levels guaranteeing that conflict- and view-robustness coincide and show that this condition is satisfied by the isolation levels occurring in Postgres and Oracle: read committed (RC), snapshot isolation (SI) and serializable snapshot isolation (SSI). It hence follows that for these systems, widening from conflict- to view-serializability does not allow for more sets of transactions to become robust. Interestingly, the complexity of deciding serializability within these isolation levels is still quite different. Indeed, deciding conflict-serializability for schedules allowed under RC and SI remains in polynomial time, while we show that deciding view-serializability within these isolation levels remains NP-complete.

Citations for the The ACM PODS Alberto O. Mendelzon Test-of-Time Award 2023

A serializable concurrency control mechanism ensures consistency for OLTP systems at the expense of a reduced transaction throughput. A DBMS therefore usually offers the possibility to allocate lower isolation levels for some transactions when it is safe to do so. However, such trading of consistency for efficiency does not come with any safety guarantees. In this paper, we study the mixed robustness problem which asks whether, for a given set of transactions and a given allocation of isolation levels, every possible interleaved execution of those transactions that is allowed under the provided allocation is always serializable. That is, whether the given allocation is indeed safe. While robustness has already been studied in the literature for the homogeneous setting where all transactions are allocated the same isolation level, the heterogeneous setting that we consider in this paper, despite its practical relevance, has largely been ignored. We focus on multiversion concurrency control and consider the isolation levels that are available in Postgres and Oracle: read committed (RC), snapshot isolation (SI) and serializable snapshot isolation (SSI). We show that the mixed robustness problem can be decided in polynomial time. In addition, we provide a polynomial time algorithm for computing the optimal robust allocation for a given set of transactions, prioritizing lower over higher isolation levels. The present results therefore establish the groundwork to automate isolation level allocation within existing databases supporting multiversion concurrency control.

Data ingestion validation, the task of certifying the quality of continuously collected data, is crucial to ensure trustworthiness of analytics insights. A widely used approach for validating data quality is to specify, either manually or automatically, so-called data unit tests that check whether data quality metrics lie within expected bounds. We employ conditional unit tests based on conditional metrics (CMs) that compute data quality signals over specific parts of the ingestion data and therefore allow for a fine-grained detection of errors. A violated conditional unit test specifies a set of erroneous tuples in a natural way: the subrelation that its CM refers to. Unfortunately, the downside of their fine-grained nature is that violating unit tests are often correlated: a single error in an ingestion batch may cause multiple tests (each referring to different parts of the batch) to fail. The key challenge is therefore to untangle this correlation and filter out the most relevant violated conditional unit tests, i.e., tests that identify a core set of erroneous tuples and act as an explanation for the errors. We present CM-Explorer, a system that supports data stewards in quickly finding the most relevant violated conditional unit tests. The system consists of three components: (1) a graph explorer for visualizing the correlation structure of the violated unit tests; (2) a relation explorer for browsing the tuples selected by conditional unit tests; and, (3) a history explorer to get insight why conditional unit tests are violated. In this paper, we discuss these components and present the different scenarios that we make available for the demonstration.

The popular isolation level Multiversion Read Committed (RC) trades some of the strong guarantees of serializability for increased transaction throughput. Sometimes, transaction workloads can be safely executed under RC obtaining serializability at the lower cost of RC. Such workloads are said to be robust against RC. Previous work has yielded a tractable procedure for deciding robustness against RC for workloads generated by transaction programs modeled as transaction templates. An important insight of that work is that, by more accurately modeling transaction programs, we are able to recognize larger sets of workloads as robust. In this work, we increase the modeling power of transaction templates by extending them with functional constraints, which are useful for capturing data dependencies like foreign keys. We show that the incorporation of functional constraints can identify more workloads as robust that otherwise would not be. Even though we establish that the robustness problem becomes undecidable in its most general form, we show that various restrictions on functional constraints lead to decidable and even tractable fragments that can be used to model and test for robustness against RC for realistic scenarios.

Transaction processing is a central part of most database applications. While serializability remains the gold standard for desirable transactional semantics, many database systems offer improved transaction throughput at the expense of introducing potential anomalies through the choice of a lower isolation level. Transactions are often not arbitrary but are constrained by a set of transaction programs defined at the application level (as is the case for TPC-C for instance), implying that not every potential anomaly can effectively be realized. The question central to this paper is the following: when - within the context of specific transaction programs - do isolation levels weaker than serializability, provide the same guarantees as serializability? We refer to the latter as the robustness problem. This paper surveys recent results on robustness testing against (multiversion) read committed focusing on complete rather than sufficient conditions. We show how to lift robustness testing to transaction templates as well as to programs to increase practical applicability. We discuss open questions and highlight promising directions for future research.

While serializability always guarantees application correctness, lower isolation levels can be chosen to improve transaction throughput at the risk of introducing certain anomalies. A set of transactions is robust against a given isolation level if every possible interleaving of the transactions under the specified isolation level is serializable. Robustness therefore always guarantees application correctness with the performance benefit of the lower isolation level. While the robustness problem has received considerable attention in the literature, only sufficient conditions have been obtained. The most notable exception is the seminal work by Fekete where he obtained a characterization for deciding robustness against SNAPSHOT ISOLATION. In this article, we address the robustness problem for the lower SQL isolation levels READ UNCOMMITTED and READ COMMITTED, which are defined in terms of the forbidden dirty write and dirty read patterns. The first main contribution of this article is that we characterize robustness against both isolation levels in terms of the absence of counter-example schedules of a specific form (split and multi-split schedules) and by the absence of cycles in interference graphs that satisfy various properties. A critical difference with Fekete’s work, is that the properties of cycles obtained in this article have to take the relative ordering of operations within transactions into account as READ UNCOMMITTED and READ COMMITTED do not satisfy the atomic visibility requirement. A particular consequence is that the latter renders the robustness problem against READ COMMITTED coNP-complete. The second main contribution of this article is the coNP-hardness proof. For READ UNCOMMITTED, we obtain LOGSPACE-completeness.

The isolation level Multiversion Read Committed (RC), offered by many database systems, is known to trade consistency for increased transaction throughput. Sometimes, transaction workloads can be safely executed under RC obtaining the perfect isolation of serializability at the lower cost of RC. To identify such cases, we introduce an expressive model of transaction programs to better reason about the serializability of transactional workloads. We develop tractable algorithms to decide whether any possible schedule of a workload executed under RC is serializable (referred to as the robustness problem). Our approach yields robust subsets that are larger than those identified by previous methods. We provide experimental evidence that workloads that are robust against RC can be evaluated faster under RC compared to stronger isolation levels. We discuss techniques for making workloads robust against RC by promoting selective read operations to updates. Depending on the scenario, the performance improvements can be considerable. Robustness testing and safely executing transactions under the lower isolation level RC can therefore provide a direct way to increase transaction throughput without changing DBMS internals.

This paper introduces a declarative framework to specify and reason about distributions of data over computing nodes in a distributed setting. More specifically, it proposes distribution constraints which are tuple and equality generating dependencies (tgds and egds) extended with node variables ranging over computing nodes. In particular, they can express co-partitioning constraints and constraints about range-based data distributions by using comparison atoms. The main technical contribution is the study of the implication problem of distribution constraints. While implication is undecidable in general, relevant fragments of so-called data-full constraints are exhibited for which the corresponding implication problems are complete for EXPTIME, PSPACE and NP. These results yield bounds on deciding parallel-correctness for conjunctive queries in the presence of distribution constraints.

While serializability always guarantees application correctness, lower isolation levels can be chosen to improve transaction throughput at the risk of introducing certain anomalies. A set of transactions is robust against a given isolation level if every possible interleaving of the transactions under the specified isolation level is serializable. Robustness therefore always guarantees application correctness with the performance benefit of the lower isolation level. While the robustness problem has received considerable attention in the literature, only sufficient conditions have been obtained. The most notable exception is the seminal work by Fekete where he obtained a characterization for deciding robustness against SNAPSHOT ISOLATION. In this paper, we address the robustness problem for the lower SQL isolation levels READ UNCOMMITTED and READ COMMITTED which are defined in terms of the forbidden dirty write and dirty read patterns. The first main contribution of this paper is that we characterize robustness against both isolation levels in terms of the absence of counter example schedules of a specific form (split and multi-split schedules) and by the absence of cycles in interference graphs that satisfy various properties. A critical difference with Fekete's work, is that the properties of cycles obtained in this paper have to take the relative ordering of operations within transactions into account as READ UNCOMMITTED and READ COMMITTED do not satisfy the atomic visibility requirement. A particular consequence is that the latter renders the robustness problem against READ COMMITTED coNP-complete. The second main contribution of this paper is the coNP-hardness proof. For READ UNCOMMITTED, we obtain LOGSPACE-completeness.

Recently, Ketsman et al. started the investigation of the parallel evaluation of recursive queries in the Massively Parallel Communication (MPC) model. Among other things, it was shown that parallel-correctness and parallel-boundedness for general Datalog programs is undecidable, by a reduction from the undecidable containment problem for Datalog. Furthermore, economic policies were introduced as a means to specify data distribution in a recursive setting. In this paper, we extend the latter framework to account for more general distributed evaluation strategies in terms of communication policies. We then show that the undecidability of parallel-correctness runs deeper: it already holds for fragments of Datalog, e.g., monadic and frontier-guarded Datalog, with a decidable containment problem, under relatively simple evaluation strategies. These simple evaluation strategies are defined w.r.t. data-moving distribution constraints. We then investigate restrictions of economic policies that yield decidability. In particular, we show that parallel-correctness is 2EXPTIME-complete for monadic and frontier-guarded Datalog under hash-based economic policies. Next, we consider restrictions of data-moving constraints and show that parallel-correctness and parallel-boundedness are 2EXPTIME-complete for frontier-guarded Datalog. Interestingly, distributed evaluation no longer preserves the usual containment relationships between fragments of Datalog. Indeed, not every monadic Datalog program is equivalent to a frontier-guarded one in the distributed setting. We illustrate the latter by considering two alternative settings where in one of these parallel-correctness is decidable for frontier-guarded Datalog but undecidable for monadic Datalog.

Programs for extracting structured information from text, namely information extractors, often operate separately on document segments obtained from a generic splitting operation such as sentences, paragraphs, k-grams, HTTP requests, and so on. An automated detection of this behavior of extractors, which we refer to as split-correctness, would allow text analysis systems to devise query plans with parallel evaluation on segments for accelerating the processing of large documents. Other applications include the incremental evaluation on dynamic content, where re-evaluation of information extractors can be restricted to revised segments, and debugging, where developers of information extractors are informed about potential boundary crossing of different semantic components. We propose a new formal framework for split-correctness within the formalism of document spanners. Our preliminary analysis studies the complexity of split-correctness over regular spanners. We also discuss different variants of split-correctness, for instance, in the presence of black-box extractors with "split constraints".

SCULPT is a simple schema language inspired by the recent working effort towards a recommendation by the World Wide Web Consortium (W3C) for tabular data and metadata on the Web. In its core, a SCULPT schema consists of a set of rules where left-hand sides select sets of regions in the tabular data and the right-hand sides describe the contents of these regions. A document (divided in cells by row- and column-delimiters) then satisfies a schema if it satisfies every rule. In this paper, we study the satisfiability problem for SCULPT schemas. As SCULPT describes grid-like structures, satisfiability obviously becomes undecidable rather quickly even for very restricted schemas. We define a schema language called L-SCULPT (Lego SCULPT) that restricts the walking power of SCULPT by selecting rectangular shaped areas and only considers tables for which selected regions do not intersect. Depending on the axes used by L-SCULPT, we show that satisfiability is PTIME-complete or undecidable. One of the tractable fragments is practically useful as it extends the structural core of the current W3C proposal for schemas over tabular data. We therefore see how the navigational power of the W3C proposal can be extended while still retaining tractable satisfiability tests.

Single-round multiway join algorithms first reshuffle data over many servers and then evaluate the query at hand in a parallel and communication-free way. A key question is whether a given distribution policy for the reshuffle is adequate for computing a given query. This property is referred to as parallel-correctness. Another key problem is to detect whether the data reshuffle step can be avoided when evaluating subsequent queries. The latter problem is referred to as transfer of parallel-correctness. This paper extends the study of parallel-correctness and transfer of parallel-correctness of conjunctive queries to incorporate bag semantics. We provide semantical characterizations for both problems, obtain complexity bounds and discuss the relationship with their set semantics counterparts. Finally, we revisit both problems under a modified distribution model that takes advantage of a linear order on compute nodes and obtain tight complexity bounds.

While the migration from DTD to XML Schema was driven by a need for increased expressivity and flexibility, the latter was also significantly more complex to use and understand. Whereas DTDs are characterized by their simplicity, XML Schema Documents are notoriously difficult. In this article, we introduce the XML specification language BonXai, which incorporates many features of XML Schema but is arguably almost as easy to use as DTDs. In brief, the latter is achieved by sacrificing the explicit use of types in favor of simple patterns expressing contexts for elements. The goal of BonXai is not to replace XML Schema but rather to provide a simpler alternative for users who want to go beyond the expressiveness and features of DTD but do not need the explicit use of types. Furthermore, XML Schema processing tools can be used as a back-end for BonXai, since BonXai can be automatically converted into XML Schema. A particularly strong point of BonXai is its solid foundation rooted in a decade of theoretical work around pattern-based schemas. We present a formal model for a core fragment of BonXai and the translation algorithms to and from a core fragment of XML Schema. We prove that BonXai and XML Schema can be converted back-and-forth on the level of tree languages and we formally study the size trade-offs between the two languages.

Modern data management systems extensively use parallelism to speed up query processing over massive volumes of data. This trend has inspired a rich line of research on how to formally reason about the parallel complexity of join computation. In this paper, we go beyond joins and study the parallel evaluation of recursive queries. We introduce a novel framework to reason about multi-round evaluation of Datalog programs, which combines implicit predicate restriction with distribution policies to allow expressing a combination of data-parallel and query-parallel evaluation strategies. Using our framework, we reason about key properties of distributed Datalog evaluation, including parallel-correctness of the evaluation strategy, disjointness of the computation effort, and bounds on the number of communication rounds.

Database research has witnessed a renewed interest for data processing in distributed and parallel settings. While distributed and parallel data management systems have been around for quite some time, it is the rise of cloud computing and the advent of Big Data that presents the community with new challenges. This paper highlights recent research concerning the logical foundations of massively parallel and distributed systems. The first part of the paper concerns massively parallel systems where computation proceeds in a number of synchronized rounds. Here, the focus is on evaluation algorithms for conjunctive queries as well as on reasoning about correctness and optimization of such algorithms. The second part of the paper addresses a distributed asynchronous setting where eventual consistency comes into play. Here, the focus is on coordination-free computation and its relationship to logical monotonicity and Datalog programs.

While services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries. In this work, we provide algorithms for parallel evaluation of SGF queries in MapReduce that optimize total time, while retaining low net time. Not only can SGF queries specify all semi-join reducers, but also more expressive queries involving disjunction and negation. Since SGF queries can be seen as Boolean combinations of (potentially nested) semi-joins, we introduce a novel multi-semi-join (MSJ) MapReduce operator that enables the evaluation of a set of semi-joins in one job. We use this operator to obtain parallel query plans for SGF queries that outvalue sequential plans w.r.t. net time and provide additional optimizations aimed at minimizing total time without severely affecting net time. Even though the latter optimizations are NP-hard, we present effective greedy algorithms. Our experiments, conducted using our own implementation Gumbo on top of Hadoop, confirm the usefulness of parallel query plans, and the effectiveness and scalability of our optimizations, all with a significant improvement over Pig and Hive.

The CALM-conjecture, first stated by Hellerstein [2010] and proved in its revised form by Ameloot et al. [2013] within the framework of relational transducer networks, asserts that a query has a coordination-free execution strategy if and only if the query is monotone. Zinn et al. [2012] extended the framework of relational transducer networks to allow for specific data distribution strategies and showed that the nonmonotone win-move query is coordination-free for domain-guided data distributions. In this article, we extend the story by equating increasingly larger classes of coordination-free computations with increasingly weaker forms of monotonicity and present explicit Datalog variants that capture each of these classes. One such fragment is based on stratified Datalog where rules are required to be connected with the exception of the last stratum. In addition, we characterize coordination-freeness as those computations that do not require knowledge about all other nodes in the network, and therefore, can not globally coordinate. The results in this article can be interpreted as a more fine-grained answer to the CALM-conjecture.

We present Gumbo, a system for the efficient evaluation of guarded fragment queries on top of Hadoop and Spark. A key asset of Gumbo is the reduced number of jobs in comparison with recent systems such as Pig, Hive or Shark. For unnested guarded fragment queries, Gumbo even provides a constant bound on the number of jobs independent of the size of the query. In the demo, we will address the following features of Gumbo: ease-of-use, query plan construction and visualisation, and query execution.

We investigate the class D of queries that distribute over components. These are the queries that can be evaluated by taking the union of the query results over the connected components of the database instance. We show that it is undecidable whether a (positive) Datalog program distributes over components. Additionally, we show that connected Datalog with Negation (the fragment of Datalog with Negation where all rules are connected) provides an effective syntax for Datalog with Negation programs that distribute over components under the stratified as well as under the well-founded semantics. As a corollary, we obtain a simple proof for one of the main results in previous work [Zinn, Green, and Ludäscher, ICDT2012], namely, that the classic win-move query is in F_2 (a particular class of coordination-free queries).

In a distributed context where data is dispersed over many computing nodes, monotone queries can be evaluated in an eventually consistent and coordination-free manner through a simple but naive broadcasting strategy which makes all data available on every computing node. In this paper, we investigate more economical broadcasting strategies for full conjunctive queries without self-joins that only transmit a part of the local data necessary to evaluate the query at hand. We consider oblivious broadcasting strategies which determine which local facts to broadcast independent of the data at other computing nodes. We introduce the notion of broadcast dependency set (BDS) as a sound and complete formalism to represent locally optimal oblivious broadcasting functions. We provide algorithms to construct a BDS for a given conjunctive query and study the complexity of various decision problems related to these algorithms.

We introduce three formal models of distributed systems for query evaluation on massive databases: Distributed Streaming with Register Automata (DSAs), Distributed Streaming with Register Transducers (DSTs), and Distributed Streaming with Register Transducers and Joins (DSTJs). These models are based on the key-value paradigm where the input is transformed into a dataset of key-value pairs, and on each key a local computation is performed on the values associated with that key resulting in another set of key-value pairs. Computation proceeds in a constant number of rounds, where the result of the last round is the input to the next round, and transformation to key-value pairs is required to be generic. The difference between the three models is in the local computation part. In DSAs it is limited to making one pass over its input using a register automaton, while in DSTs it can make two passes: in the first pass it uses a finite-state automaton and in the second it uses a register transducer. The third model DSTJs is an extension of DSTs, where local computations are capable of constructing the Cartesian product of two sets. We obtain the following results: (1) DSAs can evaluate first-order queries over bounded degree databases; (2) DSTs can evaluate semijoin algebra queries over arbitrary databases; (3) DSTJs can evaluate the whole relational algebra over arbitrary databases; (4) DSTJs are strictly stronger than DSTs, which in turn, are strictly stronger than DSAs; (5) within DSAs, DSTs and DSTJs there is a strict hierarchy w.r.t. the number of rounds.

No abstract available.

A dominant cost for query evaluation in modern massively distributed systems is the number of communication rounds. For this reason, there is a growing interest in single-round multiway join algorithms where data is first reshuffled over many servers and then evaluated in a parallel but communication-free way. The reshuffling itself is specified as a distribution policy. We introduce a correctness condition, called parallel-correctness, for the evaluation of queries w.r.t. a distribution policy. We study the complexity of parallel-correctness for conjunctive queries as well as transferability of parallel-correctness between queries. We also investigate the complexity of transferability for certain families of distribution policies, including, for instance, the Hypercube distribution.

While the migration from DTD to XML Schema was driven by a need for increased expressivity and flexibility, the latter was also significantly more complex to use and understand. Whereas DTDs are characterized by their simplicity, XML Schema Definitions (XSDs) are notoriously difficult. In this paper, we introduce the XML specification language BonXai which possesses most features of XSDs, including its expressivity, while retaining the simplicity of DTDs. In brief, the latter is achieved by sacrificing the explicit use of types in favor of simple patterns expressing contexts for elements. The goal of BonXai is by no means to replace XML Schema, but rather to provide a simpler DTD-like alternative to schema designers that do not need the explicit use of types. Therefore, BonXai can be seen as a practical front-end for XML Schema. A particular strong point of BonXai is its solid foundation rooted in a decade of theoretical work around pattern-based schemas. We present in detail the formal model for BonXai and discuss translation algorithms to and from XML Schema.

Inspired by the recent working effort towards a recommendation by the World Wide Web Consortium (W3C) for tabular data and metadata on the Web, we present in this paper a concept for a schema language for tabular web data called SCULPT. The language consists of rules constraining and defining the structure of regions in the table. These regions are defined through the novel formalism of region selection expressions. We present a formal model for SCULPT and obtain a linear time combined complexity evaluation algorithm. In addition, we consider weak and strong streaming evaluation for SCULPT and present a SCULPT fragment for each of these streaming variants. Finally, we discuss several extensions of SCULPT including alternative semantics, types, complex content, and explore region selection expressions as a basis for a transformation language.

Declarative networking is an approach where distributed computations are modeled and programmed using declarative formalisms based on extensions of Datalog. On a logical level, programs (queries) are specified over a global schema and are computed by multiple distributed computing nodes over which the input database is partitioned. These nodes can perform local computations and communicate asynchronously with each other via messages. The model operates under the assumption that messages can never be lost but can be arbitrarily delayed. An inherent source of inefficiency in such systems are the global barriers raised by the need for synchronization in computing the result of queries. This source of inefficiency inspired Hellerstein to formulate the CALM-principle which suggests a link between logical monotonicity on the one hand and distributed consistency without the need for coordination on the other hand (CALM stands for Consistency And Logical Monotonicity). A crucial property of Datalog programs (and monotone programs in general) is that derived facts must never be retracted when new data arrives. The latter implies a simple coordination-free execution strategy: every node sends all relevant data to every other node in the network and outputs new facts from the moment they can be derived. No coordination is needed and the output of all computing nodes is consistent. This observation led to the CALM-conjecture which, in its revised form, states “A query has a coordination-free execution strategy iff the query is monotone.” In this talk, we discuss the CALM-conjecture (what does it even mean to be coordination-free?) and its status: when computing nodes are increasingly more knowledgeable on how tuples are distributed, increasingly more queries can be computed in a coordination-free manner. In fact, these classes can be characterized in terms of increasingly weaker forms of monotonicity thereby adding a new dimension to The original conjecture replaced monotone by Datalog. (c) 2014, Copyright is with the authors. Published in Proc. 17th International Conference on Database Theory (ICDT), March 24-28, 2014, Athens, Greece: ISBN 978-3-89318066-1, on OpenProceedings.org. Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0. the CALM-conjecture. Furthermore, we discuss novel (and more traditional) fragments of Datalog (with negation) capturing the above classes. At the end, we point out future directions and challenges for the community.

The CALM-conjecture, first stated by Hellerstein [23] and proved in its revised form by Ameloot et al. [13] within the framework of relational transducer networks, asserts that a query has a coordination-free execution strategy if and only if the query is monotone. Zinn et al. [32] extended the framework of relational transducer networks to allow for specific data distribution strategies and showed that the nonmonotone win-move query is coordination-free for domain-guided data distributions. In this paper, we complete the story by equating increasingly larger classes of coordination-free computations with increasingly weaker forms of monotonicity and make Datalog variants explicit that capture each of these classes. One such fragment is based on stratified Datalog where rules are required to be connected with the exception of the last stratum. In addition, we characterize coordination-freeness as those computations that do not require knowledge about all other nodes in the network, and therefore, can not globally coordinate. The results in this paper can be interpreted as a more fine-grained answer to the CALM-conjecture.

A great deal of research into the learning of schemas from XML data has been conducted in recent years to enable the automatic discovery of XML schemas from XML documents when no schema or only a low-quality one is available. Unfortunately, and in strong contrast to, for instance, the relational model, the automatic discovery of even the simplest of XML constraints, namely XML keys, has been left largely unexplored in this context. A major obstacle here is the unavailability of a theory on reasoning about XML keys in the presence of XML schemas, which is needed to validate the quality of candidate keys. The present article embarks on a fundamental study of such a theory and classifies the complexity of several crucial properties concerning XML keys in the presence of an XSD, like, for instance, testing for consistency, boundedness, satisfiability, universality, and equivalence. Of independent interest, novel results are obtained related to cardinality estimation of XPath result sets. A mining algorithm is then developed within the framework of levelwise search. The algorithm leverages known discovery algorithms for functional dependencies in the relational model, but incorporates the properties mentioned before to assess and refine the quality of derived keys. An experimental study on an extensive body of real-world XML data evaluating the effectiveness of the proposed algorithm is provided.

Given a graph, a relation on its nodes, and a query language Q of interest, we study the Q-definability problem which amounts to deciding whether there exists a query in Q defining precisely the given relation over the given graph. Previous research has identified the complexity of FO- and CQ-definability. In this paper, we consider the definability problem for regular paths and conjunctive regular path queries (CRPQs) over labelled graphs.

A great deal of research into the learning of schemas from XML data has been conducted in recent years to enable the automatic discovery of XML Schemas from XML documents when no schema, or only a low-quality one is available. Unfortunately, and in strong contrast to, for instance, the relational model, the automatic discovery of even the simplest of XML constraints, namely XML keys, has been left largely unexplored in this context. A major obstacle here is the unavailability of a theory on reasoning about XML keys in the presence of XML schemas, which is needed to validate the quality of candidate keys. The present paper embarks on a fundamental study of such a theory and classifies the complexity of several crucial properties concerning XML keys in the presence of an XSD, like, for instance, testing for consistency, boundedness, satisfiability, universality, and equivalence. Of independent interest, novel results are obtained related to cardinality estimation of XPath result sets. A mining algorithm is then developed within the framework of levelwise search. The algorithm leverages known discovery algorithms for functional dependencies in the relational model, but incorporates the above mentioned properties to assess and refine the quality of derived keys. An experimental study on an extensive body of real world XML data evaluating the effectiveness of the proposed algorithm is provided.

A query class is traditionally considered tractable if there exists a polynomial-time (PTIME) algorithm to answer its queries. When it comes to big data, however, PTIME algorithms often become infeasible in practice. A traditional and effective approach to coping with this is to preprocess data off-line, so that queries in the class can be subsequently evaluated on the data efficiently. This paper aims to provide a formal foundation for this approach in terms of computational complexity. (1) We propose a set of Π-tractable queries, denoted by ΠT Q 0 , to characterize classes of queries that can be answered in parallel poly-logarithmic time (NC) after PTIME preprocessing. (2) We show that several natural query classes are Π-tractable and are feasible on big data. (3) We also study a set ΠT Q of query classes that can be effectively converted to Π-tractable queries by refactorizing its data and queries for preprocessing. We introduce a form of NC reductions to characterize such conversions. (4) We show that a natural query class is complete for ΠT Q . (5) We also show that ΠT Q 0 ⊂ P unless P = NC, i.e., the set ΠT Q 0 of all Π-tractable queries is properly contained in the set P of all PTIME queries. Nonetheless, ΠT Q = P, i.e., all PTIME query classes can be made Π-tractable via proper refactorizations. This work is a step towards understanding the tractability of queries in the context of big data.

Node selecting tree automata (NSTAs) constitute a general formalism defining unary queries over trees. Basically, a node is selected by an NSTA when it is visited in a selecting state during an accepting run. We consider twig patterns as an abstraction of XPath. Since the queries definable by NSTAs form a strict superset of twig-definable queries, we study the complexity of the problem to decide whether the query by a given NSTA is twig-definable. In particular, we obtain that the latter problem is EXPTIME-complete. In addition, we show that it is also EXPTIME-complete to decide whether the query by a given NSTA is definable by a node selecting string automaton.

BonXai is a versatile schema specification language expressively equivalent to XML Schema. It is not intended as a replacement for XML Schema but it can serve as an additional, user-friendly front-end. It offers a simple way and a lightweight syntax to specify the context of elements based on regular expressions rather than on types. In this demo we show the front-end capabilities of BonXai and exemplify its potential to offer a novel way to view existing XML Schema Definitions. In particular, we present several usage scenarios specifically targeted to showcase the ease of specifying, modifying, and understanding XML Schema Definitions through BonXai.

To experimentally validate learning and approximation algorithms for XML Schema Definitions (XSDs), we need algorithms to generate uniformly at random a corpus of XSDs as well as a similarity measure to compare how close the generated XSD resembles the target schema. In this paper, we provide the formal foundation for such a testbed. We adopt similarity measures based on counting the number of common and different trees in the two languages, and we develop the necessary machinery for computing them. We use the formalism of extended DTDs (EDTDs) to represent the unranked regular tree languages. In particular, we obtain an efficient algorithm to count the number of trees up to a certain size in an unambiguous EDTD. The latter class of unambiguous EDTDs encompasses the more familiar classes of single-type, restrained competition and bottom-up deterministic EDTDs. The single-type EDTDs correspond precisely to the core of XML Schema, while the others are strictly more expressive. We also show how constraints on the shape of allowed trees can be incorporated. As we make use of a translation into a well-known formalism for combinatorial specifications, we get for free a sampling procedure to draw members of any unambiguous EDTD. When dropping the restriction to unambiguous EDTDs, i.e. taking the full class of EDTDs into account, we show that the counting problem becomes #P-complete and provide an approximation algorithm. Finally, we discuss uniform generation of single-type EDTDs, i.e., the formal abstraction of XSDs. To this end, we provide an algorithm to generate k-occurrence automata (k-OAs) uniformly at random and show how this leads to uniform generation of single-type EDTDs.

Motivated by a recent conjecture concerning the expressiveness of declarative networking, we propose a formal computation model for "eventually consistent" distributed querying, based on relational transducers. A tight link has been conjectured between coordination-freeness of computations, and monotonicity of the queries expressed by such computations. Indeed, we propose a formal definition of coordination-freeness and confirm that the class of monotone queries is captured by coordination-free transducer networks. Coordination-freeness is a semantic property, but the syntactic class that we define of "oblivious" transducers also captures the same class of monotone queries. Transducer networks that are not coordination-free are much more powerful.

While XML is nowadays adopted as the de facto standard for data exchange, historically, its predecessor SGML was invented for describing electronic documents, i.e., marked up text. Actually, today there are still large volumes of such XML texts. We consider simple transformations which can change the internal structure of documents, that is, the mark-up, and can filter out parts of the text but do not disrupt the ordering of the words. Specifically, we focus on XML transformations where the transformed document is a subsequence of the input document when ignoring mark-up. We call the latter text-preserving XML transformations. We characterize such transformations as copy- and rearrange-free transductions. Furthermore, we study the problem of deciding whether a given XML transducer is text-preserving over a given tree language. We consider top-down transducers as well as the abstraction of XSLT called DTL. We show that deciding whether a transformation is text-preserving over an unranked regular tree language is in PTime for top-down transducers, EXPTime-complete for DTL with XPath, and decidable for DTL with MSO patterns. Finally, we obtain that for every transducer in one of the above mentioned classes, the maximal subset of the input schema can be computed on which the transformation is text-preserving.

XML Schema Definitions (XSDs) can be adequately abstracted by the single-type regular tree languages. It is well-known, that these form a strict subclass of the robust class of regular unranked tree languages. Sadly, in this respect, XSDs are not closed under the basic operations of union and set difference, complicating important tasks in schema integration and evolution. The purpose of this paper is to investigate how the union and difference of two XSDs can be approximated within the framework of single-type regular tree languages. We consider both optimal lower and upper approximations. We also address the more general question of how to approximate an arbitrary regular tree language by an XSD and consider the complexity of associated decision problems.

We consider the problem of inferring a concise Document Type Definition (DTD) for a given set of XML-documents, a problem that basically reduces to learning concise regular expressions from positive examples strings. We identify two classes of concise regular expressions—the single occurrence regular expressions (SOREs) and the chain regular expressions (CHAREs)—that capture the far majority of expressions used in practical DTDs. For the inference of SOREs we present several algorithms that first infer an automaton for a given set of example strings and then translate that automaton to a corresponding SORE, possibly repairing the automaton when no equivalent SORE can be found. In the process, we introduce a novel automaton to regular expression rewrite technique which is of independent interest. When only a very small amount of XML data is available, however (for instance when the data is generated by Web service requests or by answers to queries), these algorithms produce regular expressions that are too specific. Therefore, we introduce a novel learning algorithm crx that directly infers CHAREs (which form a subclass of SOREs) without going through an automaton representation. We show that crx performs very well within its target class on very small datasets.

Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of deterministic regular expressions in which each alphabet symbol occurs at most k times, for some small k . We refer to such expressions as k -occurrence regular expressions ( k -OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k -OREs for increasing values of k, and selects the deterministic one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.

Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that CMM is an NP-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the method SLIDER which uses local search with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that SLIDER outperforms existing motif-driven CMM methods and scales to large protein-protein interaction networks.

Whether beloved or despised, XML Schema is momentarily the only industrially accepted schema language for XML and is unlikely to become obsolete any time soon. Nevertheless, many nontransparent restrictions unnecessarily complicate the design of XSDs. For instance, complex content models in XML Schema are constrained by the infamous unique particle attribution (UPA) constraint. In formal language theoretic terms, this constraint restricts content models to deterministic regular expressions. As the latter constitute a semantic notion and no simple corresponding syntactical characterization is known, it is very difficult for non-expert users to understand exactly when and why content models do or do not violate UPA. In the present paper, we therefore investigate solutions to relieve users from the burden of UPA by automatically transforming nondeterministic expressions into concise deterministic ones defining the same language or constituting good approximations. The presented techniques facilitate XSD construction by reducing the design task at hand more towards the complexity of the modeling task. In addition, our algorithms can serve as a plug-in for any model management tool which supports export to XML Schema format.

Scientific data are available through an increasing number of heterogeneous, independently evolving, sources. Although the sources themselves are independently evolving, the data stored in them are not. There exist inherent and intricate relationships between the distributed data-sets and scientists are routinely required to write distributed queries in this setting. Being non-experts in computer science, the scientists are faced with two major challenges: (i) How to express such distributed queries. This is a non-trivial task, even if we assume that scientists are familiar with query languages like SQL. Such queries can get arbitrarily complex as more sources are considered; (ii) How to efficiently evaluate such distributed queries. An efficient evaluation must account for batches of hundreds (or even thousands) of submitted queries and must optimize all of them as a whole.

The paper investigates fundamental decision problems and composition synthesis for Web services commonly found in practice. We propose a notion of synthesized Web services (ASTs) to specify the behaviors of the services. Upon receiving a sequence of input messages, an AST issues multiple queries to a database and generates actions, in parallel; it produces external messages and database updates by synthesizing the actions parallelly generated. In contrast to previous models for Web services, ASTs advocate parallel processing and (deterministic) synthesis of actions. We classify ASTs based on what queries an AST can issue, how the synthesis of actions is expressed, and whether unbounded input sequences are allowed in a single interaction session. We show that the behaviors of Web services supported by various prior models, data-driven or not, can be specified by different AST classes. For each of these classes we study the non-emptiness, validation and equivalence problems, and establish matching upper and lower bounds on these problems. We also provide complexity bounds on composition synthesis for these AST classes, identifying decidable cases.

We present SchemaScope, a system to derive Document Type Definitions and XML schema from a corpus of sample XML documents. Tools are provided to visualize, clean and refine existing or inferred schemas. A number of use cases illustrate the versatility of the system, as well as various types of applications.

Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of regular expressions in which each alphabet symbol occurs at most k times, for some small k. We refer to such expressions as k-occurrence regular expressions (k-OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k-OREs for increasing values of k, and selects the one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.

The diversity and large volumes of data processed in the Natural Sciences today has led to a proliferation of highly-specialized and autonomous scientific databases with inherent and often intricate relationships. As a user-friendly method for querying this complex, ever-expanding network of sources for correlations, we propose exploratory queries. Exploratory queries are loosely-structured, hence requiring only minimal user knowledge of the source network. Evaluating an exploratory query usually involves the evaluation of many distributed queries. As the number of such distributed queries can quickly become large, we attack the optimization problem for exploratory queries by proposing several multi-query optimization algorithms that compute a global evaluation plan while minimizing the total communication cost, a key bottleneck in distributed settings. The proposed algorithms are necessarily heuristics, as computing an optimal global evaluation plan is shown to be NP-hard. Finally, we present an implementation of our algorithms, along with experiments that illustrate their potential not only for the optimization of exploratory queries, but also for the multiquery optimization of large batches of standard queries.

A number of languages have been developed for specifying XML publishing, that is, transformations of relational data into XML trees. These languages generally describe the behaviors of a middleware controller that builds an output tree iteratively, issuing queries to a relational source and expanding the tree with the query results at each step. To study the complexity and expressive power of XML publishing languages, this article proposes a notion of publishing transducers , which generate XML trees from relational data. We study a variety of publishing transducers based on what relational queries a transducer can issue, what temporary stores a transducer can use during tree generation, and whether or not some tree nodes are allowed to be virtual, that is, excluded from the output tree. We first show how existing XML publishing languages can be characterized by such transducers, and thus provide a synergy between theory and practice. We then study the membership, emptiness, and equivalence problems for various classes of transducers. We establish lower and upper bounds, all matching, ranging from PTIME to undecidable. Finally, we investigate the expressive power of these transducers and existing languages. We show that when treated as relational query languages, different classes of transducers capture either complexity classes (e.g., PSPACE) or fragments of datalog (e.g., linear datalog). For tree generation, we establish connections between publishing transducers and logical transductions, among other things.

A number of languages have been developed for specifying XML publishing, i.e., transformations of relational data into XML trees. These languages generally describe the behaviors of a middleware controller that builds an output tree iteratively, issuing queries to a relational source and expanding the tree with the query results at each step. To study the complexity and expressive power of XML publishing languages, this paper proposes a notion of publishing transducers. Unlike automata for querying XML data, a publishing transducer generates a new XML tree rather than performing a query on an existing tree. We study a variety of publishing transducers based on what relational queries a transducer can issue, what temporary stores a transducer can use during tree generation, and whether or not some tree nodes are allowed to be virtual, i.e., excluded from the output tree. We first show how existing XML publishing languages can be characterized by such transducers. We then study the members ip, emptiness and equivalence problems for various classes of transducers and existing publishing languages. We establish lower and upper bounds, all matching except one, ranging from PTIME to undecidable. Finally, we investigate the expressive power of these transducers and existing languages. We show that when treated as relational query languages, different classes of transducers capture either complexity classes (e.g., PSPACE) or fragments of datalog (e.g., linear datalog). For tree generation, we establish connections between publishing transducers and logical transductions.

The common abstraction of XML Schema by unranked regular tree languages is not entirely accurate. To shed some light on the actual expressive power of XML Schema, intuitive semantical characterizations of the Element Declarations Consistent (EDC) rule are provided. In particular, it is obtained that schemas satisfying EDC can only reason about regular properties of ancestors of nodes. Hence, with respect to expressive power, XML Schema is closer to DTDs than to tree automata. These theoretical results are complemented with an investigation of the XML Schema Definitions (XSDs) occurring in practice, revealing that the extra expressiveness of XSDs over DTDs is only used to a very limited extent. As this might be due to the complexity of the XML Schema specification and the difficulty of understanding the effect of constraints on typing and validation of schemas, a simpler formalism equivalent to XSDs is proposed. It is based on contextual patterns rather than on recursive types and it might serve as a light-weight front end for XML Schema. Next, the effect of EDC on the way XML documents can be typed is discussed. It is argued that a cleaner, more robust, larger but equally feasible class is obtained by replacing EDC with the notion of 1-pass preorder typing (1PPT): schemas that allow one to determine the type of an element of a streaming document when its opening tag is met. This notion can be defined in terms of grammars with restrained competition regular expressions and there is again an equivalent syntactical formalism based on contextual patterns. Finally, algorithms for recognition, simplification, and inclusion of schemas for the various classes are given.

On an abstract level, XML Schema increases the limited expressive power of Document Type Definitions (DTDs) by extending them with a recursive typing mechanism. However, an investigation of the XML Schema Definitions (XSDs) occurring in practice reveals that the vast majority of them are structurally equivalent to DTDs. This might be due to the complexity of the XML Schema specification and the difficulty to understand the effect of constraints on typing and validation of schemas. To shed some light on the actual expressive power of XSDs this paper studies the impact of the Element Declarations Consistent (EDC) and the Unique Particle Attribution (UPA) rule. An equivalent formalism based on contextual patterns rather than on recursive types is proposed which might serve as a light-weight front end for XML Schema. Finally, the effect of EDC and UPA on the way XML documents can be typed is discussed. It is argued that a cleaner, more robust, stronger but equally efficient class is obtained by replacing EDC and UPA with the notion of 1-pass preorder typing: schemas that allow to determine the type of an element of a streaming document when its opening tag is met. This notion can be defined in terms of restrained competition regular expressions and there is again an equivalent syntactical formalism based on contextual patterns.

Typechecking consists of statically verifying whether the output of an XML transformation is always conform to an output type for documents satisfying a given input type. We focus on complete algorithms which always produce the correct answer. We consider top-down XML transformations incorporating XPath expressions and abstract document types by grammars and tree automata. By restricting schema languages and transformations, we identify several practical settings for which typechecking is in polynomial time. Moreover, the resulting framework provides a rather complete picture as we show that most scenarios can not be enlarged without rendering the typechecking problem intractable. So, the present research sheds light on when to use fast complete algorithms and when to reside to sound but incomplete ones.

Among the various proposals answering the shortcomings of Document Type Definitions (DTDs), XML Schema is the most widely used. Although DTDs and XML Schema Defintions (XSDs) differ syntactically, they are still quite related on an abstract level. Indeed, freed from all syntactic sugar, XML Schemas can be seen as an extension of DTDs with a restricted form of specialization. In the present paper, we inspect a number of DTDs and XSDs harvested from the web and try to answer the following questions: (1) which of the extra features/expressiveness of XML Schema not allowed by DTDs are effectively used in practice; and, (2) how sophisticated are the structural properties (i.e. the nature of regular expressions) of the two formalisms. It turns out that at present real-world XSDs only sparingly use the new features introduced by XML Schema: on a structural level the vast majority of them can already be defined by DTDs. Further, we introduce a class of simple regular expressions and obtain that a surprisingly high fraction of the content models belong to this class. The latter result sheds light on the justification of simplifying assumptions that sometimes have to be made in XML research.

XSLT is the prime example of an XML query language based on tree-walking. Indeed, stripped down, XSLT is just a tree-walking tree-transducer equipped with registers and look-ahead. Motivated by this connection, we want to pinpoint the computational power of devices based on tree-walking. We show that in the absence of unique identifiers even very powerful extensions of the tree-walking paradigm are not relationally complete. That is, these extensions do not capture all of first-order logic. In contrast, when unique identifiers are available, we show that various restrictions allow to capture LOGSPACE, PTIME, PSPACE, and EXPTIME. These complexity classes are defined w.r.t. a Turing machine model working directly on (attributed) trees. When no attributes are present, relational storage does not add power; whether look-ahead adds power is related to the open question whether tree-walking captures the regular tree languages.

We investigate the type checking problem for XML queries: statically verifying that every answer to a query conforms to a given output DTD, for inputs satisfying a given input DTD. This problem had been studied by a subset of the authors in a simplified framework that captured the structure of XML documents but ignored data values. We revisit here the type checking problem in the more realistic case when data values are present in documents and tested by queries. In this extended framework, type checking quickly becomes undecidable. However, it remains decidable for large classes of queries and DTDs of practical interest. The main contribution of the present paper is to trace a fairly tight boundary of decidability for type checking with data values. The complexity of type checking in the decidable cases is also considered.

It would be desirable to have a query language for tree-structured data that is (1) as easily usable as SQL, (2) as expressive as monadic second-order logic (MSO), and (3) efficiently evaluable. The paper develops some ideas in this direction. Towards (1) the specification of sets of vertices of a tree by combining conditions on their induced subtree with conditions on their path to the root is proposed. Existing query languages allow regular expressions (hence MSO logic) in path conditions but are limited in expressing subtree conditions. It is shown that such query languages fall short of capturing all MSO queries. On the other hand, allowing a certain guarded fragment of MSO-logic in the specification of subtree conditions results in a language fulfilling (2), (3) and, anguably, (1).

It is common to model structured document databases by context-free and extended context-free grammars.A crucial difference is that the derivation trees of the former are ranked, while those of the latter are not.A main task in document transformation and information retrieval is locating subtrees satisfying some pattern.Therefore, unary queries, i.e., queries that map a tree to a set of its nodes, play an important role in the context of structured document databases.We want to understand how the natural and well-studied computation model of tree automata can be used to express such queries.We define a query automaton (QA) as a deterministic two-way finite automaton over trees that has the ability to select nodes depending on the state and the label at those nodes.We study QAs over ranked as well as over unranked trees.More precisely, we characterize the expressiveness of the different formalisms by linking them to monadic second-order logic, and we establish the complexity of their non-emptiness and equivalence problem.

Article Expressiveness of structured document query languages based on attribute grammars Share on Authors: Frank Neven Limburgs Universitair Centrum Limburgs Universitair CentrumView Profile , Jan Van den Bussche Limburgs Universitair Centrum Limburgs Universitair CentrumView Profile Authors Info & Claims PODS '98: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systemsMay 1998 Pages 11–17https://doi.org/10.1145/275487.275489Online:01 May 1998Publication History 17citation201DownloadsMetricsTotal Citations17Total Downloads201Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This paper introduces and studies the relational meta a1gebra, a statically typed extension of the relational algebra to nllow for m&a programming in databases.In this meta algebra one can manipulate database relations involving not only stored data values (as in classical relational databases) but also stored relational algebra expressions.Topics diicussed include modeling of advanced database applications involving "procedural data"; desirability as well as limitations of a strict typing discipline in this context; equivalence with a first-order calculus; and global expressive power and non-redundancy of the proposed formalism.