The close relationship between Conjunctive Queries (CQs) and Constraint Satisfaction Problems (CSPs) has long been known. Nevertheless, apart from decomposition methods, research on efficient query evaluation or constraint solving algorithms has developed rather independently. In this article, we illustrate how search algorithms originating from the CSP community can be fruitfully applied to query evaluation - either by further developing the original search algorithms or by combining them with query decomposition methods. It turns out that the resulting approaches may indeed lead to lower time and/or space complexity than previous query evaluation methods.

Aggregate queries often require computing large intermediate joins despite producing only small outputs. We identify broad classes of acyclic aggregate queries that can be evaluated without materialising any join results, using a bottom-up, semi-join–based propagation of cardinalities and partial aggregates. An implementation in Spark SQL shows that this approach is widely applicable and yields substantial performance gains on standard benchmarks.

Fuzzy relation equations (FRE) are a useful formalism with a broad number of applications in different computer science areas. Testing if a solution exists and, if so, computing the unique greatest solution is straightforward. In contrast, the computation of minimal solutions is more complex. In particular, even in FRE with a very simple structure, the number of minimal solutions can increase exponentially. However, minimal solutions are immensely useful since, under mild conditions, they (together with the greatest solution) allow one to describe the entire space of solutions to an FRE. The main result of this work is a new method for enumerating the set of minimal solutions. It works by establishing a relationship between coverings of FRE and maximal independent elements of (hyper-)boxes. We can thus make efficient enumeration methods for maximal independent elements of (hyper-)boxes applicable also to our setting of FRE, where the operator considered in the composition of fuzzy relations only needs to preserve suprema of arbitrary subsets and infima of non-empty subsets. More specifically, we thus show that the enumeration of the minimal solutions of an FRE can be done with incremental quasi-polynomial delay.

When query evaluation produces too many tuples, a new approach in query answering is to retrieve a diverse subset of them. The standard approach for measuring the diversity of a set of tuples is to use a distance function between tuples, which measures the dissimilarity between them, to then aggregate the pairwise distances of the set into a score (e.g., by using sum or min aggregation). However, as we will point out in this work, the resulting diversity measures may display some unintuitive behavior. Moreover, even in very simple settings, finding a maximally diverse subset of the answers of fixed size is, in general, intractable and little is known about approximations apart from some hand-picked distance-aggregator pairs. In this work, we introduce a novel approach for computing the diversity of tuples based on volume instead of distance. We present a framework for defining volume-based diversity functions and provide several examples of these measures applied to relational data. Although query answering of conjunctive queries (CQ) under this setting is intractable in general, we show that one can always compute a (1-1/e)-approximation for any volume-based diversity function. Furthermore, in terms of combined complexity, we connect the evaluation of CQs under volume-based diversity functions with the ranked enumeration of solutions, finding general conditions under which a (1-1/e)-approximation can be computed in polynomial time.

While extensive research on query evaluation has achieved consistent improvements in the time complexity of algorithms, the space complexity of query evaluation has been largely ignored. This is a particular challenge in settings with strict pre-defined space constraints. In this paper, we examine the combined space-time complexity of conjunctive queries (CQs) and, more generally, of sum-product queries (SPQs). We propose several classes of space-efficient algorithms for evaluating SPQs, and we show that the optimal time complexity is almost always achievable with asymptotically lower space complexity than traditional approaches.

Hypertree decompositions provide a way to evaluate Conjunctive Queries (CQs) in polynomial time, where the exponent of this polynomial is determined by the width of the decomposition. In theory, the goal of efficient CQ evaluation therefore has to be a minimisation of the width. However, in practical settings, it turns out that there are also other properties of a decomposition that influence the performance of query evaluation. It is therefore of interest to restrict the computation of decompositions by constraints and to guide this computation by preferences. To this end, we propose a novel framework based on candidate tree decompositions, which allows us to introduce soft hypertree width (shw). This width measure is a relaxation of hypertree width (hw); it is never greater than hw and, in some cases, shw may actually be lower than hw. Most importantly, shw preserves the tractability of deciding if a given CQ is below some fixed bound, while offering more algorithmic flexibility. In particular, it provides a natural way to incorporate preferences and constraints into the computation of decompositions. A prototype implementation and preliminary experiments confirm that this novel framework can indeed have a practical impact on query evaluation.

Optimising queries with many joins is known to be a hard problem. The explosion of intermediate results as opposed to a much smaller final result poses a serious challenge to modern database management systems (DBMSs). This is particularly glaring in case of analytical queries that join many tables but ultimately only output comparatively small aggregate information. Analogous problems are faced by graph database systems when processing analytical queries with aggregates on top of complex path queries. In this work, we propose novel optimisation techniques, both on the logical, and physical level, that allow us to avoid the materialisation of join results for certain types of aggregate queries. The key to these optimisations is the notion of guardedness , by which we impose restrictions on the occurrence of attributes in GROUP BY clauses and in aggregate expressions. The efficacy of our optimisations is validated through their implementation in Spark SQL and extensive empirical evaluation on various standard benchmarks.

The set of answers to a query may be very large, potentially overwhelming users when presented with the entire set. In such cases, presenting only a small subset of the answers to the user may be preferable. A natural requirement for this subset is that it should be as diverse as possible to reflect the variety of the entire population. To achieve this, the diversity of a subset is measured using a metric that determines how different two solutions are and a diversity function that extends this metric from pairs to sets. In the past, several studies have shown that finding a diverse subset from an explicitly given set is intractable even for simple metrics (like Hamming distance) and simple diversity functions (like summing all pairwise distances). This complexity barrier becomes even more challenging when trying to output a diverse subset from a set that is only implicitly given (such as the query answers for a given query and a database). Until now, tractable cases have been found only for restricted problems and particular diversity functions. To overcome these limitations, we focus in this work on the notion of ultrametrics, which have been widely studied and used in many applications. Starting from any ultrametric d and a diversity function δ extending d , we provide sufficient conditions over δ for having polynomial-time algorithms to construct diverse answers. To the best of our knowledge, these conditions are satisfied by all the diversity functions considered in the literature. Moreover, we complement these results with lower bounds that show specific cases when these conditions are not satisfied and finding diverse subsets becomes intractable. We conclude by applying these results to the evaluation of conjunctive queries, demonstrating efficient algorithms for finding a diverse subset of solutions for acyclic conjunctive queries when the attribute order is used to measure diversity.

Various classic reasoning problems with natural hypergraph representations are known to be tractable if a hypertree decomposition (HD) of low width exists. The resulting algorithms are attractive for practical use in fields like databases and constraint satisfaction. However, algorithmic use of HDs relies on the difficult task of first computing a decomposition of the hypergraph underlying a given problem instance, which is then used to guide the algorithm for this particular instance. The performance of purely sequential methods for computing HDs is inherently limited, yet the problem is, theoretically, amenable to parallelisation. In this article, we propose the first algorithm for computing hypertree decompositions that is well suited for parallelisation. The newly proposed algorithm log- k -decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to so-called balanced separators. We provide a detailed experimental evaluation over the HyperBench benchmark and demonstrate that log- k -decomp outperforms the current state of the art significantly.

Enumeration problems aim at outputting, without repetition, the set of solutions to a given problem instance. However, outputting the entire solution set may be prohibitively expensive if it is too big. In this case, outputting a small, sufficiently diverse subset of the solutions would be preferable. This leads to the Diverse-version of the original enumeration problem, where the goal is to achieve a certain level d of diversity by selecting k solutions. In this paper, we look at the Diverse-version of the query answering problem for Conjunctive Queries and extensions thereof. That is, we study the problem if it is possible to achieve a certain level d of diversity by selecting k answers to the given query and, in the positive case, to actually compute such k answers.

Over the past decade, Knowledge Graphs have received enormous interest both from industry and from academia. Research in this area has been driven, above all, by the Database (DB) community and the Semantic Web (SW) community. However, there still remains a certain divide between approaches coming from these two communities. For instance, while languages such as SQL or Datalog are widely used in the DB area, a different set of languages such as SPARQL and OWL is used in the SW area. Interoperability between such technologies is still a challenge. The goal of this work is to present a uniform and consistent framework meeting important requirements from both, the SW and DB field.

Various classic reasoning problems with natural hypergraph representations are known to be tractable when a hypertree decomposition (HD) of low width exists. The resulting algorithms are attractive for practical use in fields like databases and constraint satisfaction. However, algorithmic use of HDs relies on the difficult task of first computing a decomposition of the hypergraph underlying a given problem instance, which is then used to guide the algorithm for this particular instance. The performance of purely sequential methods for computing HDs is inherently limited, yet the problem is, theoretically, amenable to parallelisation.

Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this paper we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naive evaluation algorithm on any datalog program.

Most work on query optimization has concentrated on loop-free queries. However, data science and machine learning workloads today typically involve recursive or iterative computation. In this work, we propose a novel framework for optimizing recursive queries using methods from program synthesis. In particular, we introduce a simple yet powerful optimization rule called the "FGH-rule" which aims to find a faster way to evaluate a recursive program. The solution is found by making use of powerful tools, such as a program synthesizer, an SMT-solver, and an equality saturation system. We demonstrate the strength of the optimization by showing that the FGH-rule can lead to speedups up to 4 orders of magnitude on three, already optimized Datalog systems.

During the past 15 years, schema mappings have been extensively used in formalizing and studying such critical data interoperability tasks as data exchange and data integration. Much of the work has focused on GLAV mappings, i.e., schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), and on schema mappings specified by second-order tgds (SO tgds), which constitute the closure of GLAV mappings under composition. In addition, nested GLAV mappings have also been considered, i.e., schema mappings specified by nested tgds, which have expressive power intermediate between s-t tgds and SO tgds. Even though nested GLAV mappings have been used in data exchange systems, such as IBM’s Clio, no systematic investigation of this class of schema mappings has been carried out so far. In this article, we embark on such an investigation by focusing on the basic reasoning tasks, algorithmic problems, and structural properties of nested GLAV mappings. One of our main results is the decidability of the implication problem for nested tgds. We also analyze the structure of the core of universal solutions with respect to nested GLAV mappings and develop useful tools for telling apart SO tgds from nested tgds. By discovering deeper structural properties of nested GLAV mappings, we show that also the following problem is decidable: Given a nested GLAV mapping, is it logically equivalent to a GLAV mapping?

Duplicates in data management are common and problematic. In this work, we present a translation of Datalog under bag semantics into a well-behaved extension of Datalog, the so-called warded Datalog^+/-, under set semantics. From a theoretical point of view, this allows us to reason on bag semantics by making use of the well-established theoretical foundations of set semantics. From a practical point of view, this allows us to handle the bag semantics of Datalog by powerful, existing query engines for the required extension of Datalog. This use of Datalog^+/- is extended to give a set semantics to duplicates in Datalog^+/- itself. We investigate the properties of the resulting Datalog^+/- programs, the problem of deciding multiplicities, and expressibility of some bag operations. Moreover, the proposed translation has the potential for interesting applications such as to Multiset Relational Algebra and the semantic web query language SPARQL with bag semantics.

To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed - giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw). Given the increasing interest in using such decomposition methods in practice, a publicly accessible repository of decomposition software, as well as a large set of benchmarks, and a web-accessible workbench for inserting, analysing, and retrieving hypergraphs are called for. We address this need by providing (i) concrete implementations of hypergraph decompositions (including new practical algorithms), (ii) a new, comprehensive benchmark of hypergraphs stemming from disparate CQ and CSP collections, and (iii) HyperBench, our new web-interface for accessing the benchmark and the results of our analyses. In addition, we describe a number of actual experiments we carried out with this new infrastructure.

Hypertree decompositions, as well as the more powerful generalized hypertree decompositions (GHDs), and the yet more general fractional hypertree decompositions (FHD) are hypergraph decomposition methods successfully used for answering conjunctive queries and for the solution of constraint satisfaction problems. Every hypergraph H has a width relative to each of these methods: its hypertree width hw(H), its generalized hypertree width ghw(H), and its fractional hypertree width fhw(H), respectively. It is known that hw(H) ≤ k can be checked in polynomial time for fixed k, while checking ghw(H) ≤ k is NP-complete for k >= 3. The complexity of checking fhw(H) ≤ k for a fixed k has been open for over a decade. We settle this open problem by showing that checking fhw(H) ≤ k is NP-complete, even for k=2. The same construction allows us to prove also the NP-completeness of checking ghw(H) ≤ k for k=2. After proving these results, we identify meaningful restrictions, for which checking for bounded ghw or fhw becomes tractable.

Conjunctive queries (CQs) fail to provide an answer when the pattern described by the query does not exactly match the data. CQs might thus be too restrictive as a querying mechanism when data is semistructured or incomplete. The semantic web therefore provides a formalism—known as (projected) well-designed pattern trees (pWDPTs)—that tackles this problem: pWDPTs allow us to formulate queries that match parts of the query over the data if available, but do not ignore answers of the remaining query otherwise. Here we abstract away the specifics of semantic web applications and study pWDPTs over arbitrary relational schemas. Since the language of pWDPTs subsumes CQs, their evaluation problem is intractable. We identify structural properties of pWDPTs that lead to (fixed-parameter) tractability of various variants of the evaluation problem. We also show that checking if a pWDPT is equivalent to one in our tractable class is in 2EXPTIME. As a corollary, we obtain fixed-parameter tractability of evaluation for pWDPTs with such good behavior. Our techniques also allow us to develop a theory of approximations for pWDPTs.

Inconsistent databases (i.e., databases violating some given set of integrity constraints) may arise in many applications such as, for instance, data integration. Hence, the handling of inconsistent data has evolved as an active field of research. In this paper, we consider two fundamental problems in this context: Repair Checking (RC) and Consistent Query Answering (CQA). So far, these problems have been mainly studied from the point of view of data complexity (where all parts of the input except for the database are considered as fixed). While for some kinds of integrity constraints, also combined complexity (where all parts of the input are allowed to vary) has been considered, for several other kinds of integrity constraints, combined complexity has been left unexplored. Moreover, a more detailed analysis (keeping other parts of the input fixed - e.g., the constraints only) is completely missing. The goal of our work is a thorough analysis of the complexity of the RC and CQA problems. Our contribution is a complete picture of the complexity of these problems for a wide range of integrity constraints. Our analysis thus allows us to get a better understanding of the true sources of complexity.

Schema mappings have been extensively studied in the context of data exchange and data integration, where they have turned out to be the right level of abstraction for formalizing data inter-operability tasks. Up to now and for the most part, schema mappings have been studied as static objects, in the sense that each time the focus has been on a single schema mapping of interest or, in the case of composition, on a pair of schema mappings of interest. In this paper, we adopt a dynamic viewpoint and embark on a study of sequences of schema mappings and of the limiting behavior of such sequences. To this effect, we first introduce a natural notion of distance on sets of finite target instances that expresses how "close" two sets of target instances are as regards the certain answers of conjunctive queries on these sets. Using this notion of distance, we investigate pointwise limits and uniform limits of sequences of schema mappings, as well as the companion notions of pointwise Cauchy and uniformly Cauchy sequences of schema mappings. We obtain a number of results about the limits of sequences of GAV schema mappings and the limits of sequences of LAV schema mappings that reveal striking differences between these two classes of schema mappings. We also consider the completion of the metric space of sets of target instances and obtain concrete representations of limits of sequences of schema mappings in terms of generalized schema mappings, i.e., schema mappings with infinite target instances as solutions to (finite) source instances.

Well-designed pattern trees (wdPTs) have been introduced as an extension of conjunctive queries to allow for partial matching - analogously to the OPTIONAL operator of the semantic web query language SPARQL. Several computational problems of wdPTs have been studied in recent years, such as the evaluation problem in various settings, the counting problem, as well as static analysis tasks including the containment and equivalence problems. Also restrictions needed to achieve tractability of these tasks have been proposed. In contrast, the problem of enumerating the answers to a wdPT has been largely ignored so far. In this work, we embark on a systematic study of the complexity of the enumeration problem of wdPTs. As our main result, we identify several tractable and intractable cases of this problem both from a classical complexity point of view and from a parameterized complexity point of view.

Conjunctive queries (CQs) fail to provide an answer when the pattern described by the query does not exactly match the data. CQs might thus be too restrictive as a querying mechanism when data is semistructured or incomplete. The semantic web therefore provides a formalism - known as well-designed pattern trees (WDPTs) - that tackles this problem: WDPTs allow us to match patterns over the data, if available, but do not fail to give an answer otherwise. Here we abstract away the specifics of semantic web applications and study WDPTs over arbitrary relational schemas. Our language properly subsumes the class of CQs. Hence, WDPT evaluation is intractable. We identify structural proper ties of WDPTs that lead to tractability of various variants of the evaluation problem. For checking if a WDPT is equivalent to one in our tractable class, we prove 2EXPTIME-membership. As a corollary, we obtain fixed-parameter tractability of (variants of) the evaluation problem. Our techniques also allow us to develop a theory of approximations for WDPTs.

Tuple-generating dependencies -- for short tgds -- have been a staple of database research throughout most of its history. Yet one of the central aspects of tgds, namely the role of existential quantifiers, has not seen much investigation so far. When studying dependencies, existential quantifiers and -- in their Skolemized form -- function symbols are often viewed as two ways to express the same concept. But in fact, tgds are quite restrictive in the way that functional terms can occur.

SPARQL entailment regimes are strongly influenced by the big body of works on ontology-based query answering, notably in the area of Description Logics (DLs). However, the semantics of query answering under SPARQL entailment regimes is defined in a more naive and much less expressive way than the certain answer semantics usually adopted in DLs. The goal of this work is to introduce an intuitive certain answer semantics also for SPARQL and to show the feasibility of this approach. For OWL 2 QL entailment, we present algorithms for the evaluation of an interesting fragment of SPARQL (the so-called well-designed SPARQL). Moreover, we show that the complexity of the most fundamental query analysis tasks (such as query containment and equivalence testing) is not negatively affected by the presence of OWL 2 QL entailment under the proposed semantics.

During the past decade, schema mappings have been extensively used in formalizing and studying such critical data interoperability tasks as data exchange and data integration. Much of the work has focused on GLAV mappings, i.e., schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), and on schema mappings specified by second-order tgds (SO tgds), which constitute the closure of GLAV mappings under composition. In addition, nested GLAV mappings have also been considered, i.e., schema mappings specified by nested tgds, which have expressive power intermediate between s-t tgds and SO tgds. Even though nested GLAV mappings have been used in data exchange systems, such as IBM's Clio, no systematic investigation of this class of schema mappings has been carried out so far. In this paper, we embark on such an investigation by focusing on the basic reasoning tasks, algorithmic problems, and structural properties of nested GLAV mappings. One of our main results is the decidability of the implication problem for nested tgds. We also analyze the structure of the core of universal solutions with respect to nested GLAV mappings and develop useful tools for telling apart SO tgds from nested tgds. By discovering deeper structural properties of nested GLAV mappings, we show that also the following problem is decidable: given a nested GLAV mapping, is it logically equivalent to a GLAV mapping?

Query containment and query equivalence constitute important computational problems in the context of static query analysis and optimization. While these problems have been intensively studied for fragments of relational calculus, almost no works exist for the semantic web query language SPARQL. In this paper, we carry out a comprehensive complexity analysis of containment and equivalence for several fragments of SPARQL: we start with the fundamental fragment of well-designed SPARQL restricted to the AND and OPTIONAL operator. We then study basic extensions in the form of the UNION operator and/or projection. The results obtained range from NP-completeness to undecidability.

Static analysis is a fundamental task in query optimization. In this article we study static analysis and optimization techniques for SPARQL, which is the standard language for querying Semantic Web data. Of particular interest for us is the optionality feature in SPARQL. It is crucial in Semantic Web data management, where data sources are inherently incomplete and the user is usually interested in partial answers to queries. This feature is one of the most complicated constructors in SPARQL and also the one that makes this language depart from classical query languages such as relational conjunctive queries. We focus on the class of well-designed SPARQL queries, which has been proposed in the literature as a fragment of the language with good properties regarding query evaluation. We first propose a tree representation for SPARQL queries, called pattern trees, which captures the class of well-designed SPARQL graph patterns. Among other results, we propose several rules that can be used to transform pattern trees into a simple normal form, and study equivalence and containment. We also study the evaluation and enumeration problems for this class of queries.

Static analysis is a fundamental task in query optimization. In this paper we study static analysis and optimization techniques for SPARQL, which is the standard language for querying Semantic Web data. Of particular interest for us is the optionality feature in SPARQL. It is crucial in Semantic Web data management, where data sources are inherently incomplete and the user is usually interested in partial answers to queries. This feature is one of the most complicated constructors in SPARQL and also the one that makes this language depart from classical query languages such as relational conjunctive queries. We focus on the class of well-designed SPARQL queries, which has been proposed in the literature as a fragment of the language with good properties regarding query evaluation. We first propose a tree representation for SPARQL queries, called pattern trees, which captures the class of well-designed SPARQL graph patterns and which can be considered as a query execution plan. Among other results, we propose several transformation rules for pattern trees, a simple normal form, and study equivalence and containment. We also study the enumeration and counting problems for this class of queries.

SQL developers are used to having elaborate tools which help them in writing queries. In contrast, the creation of tools to assist users in the development of SPARQL queries is still in its infancy. In this system demo, we present the SPARQL Analysis and Manipulation (SPAM) tool, which provides help for the development of SPARQL queries. The main features of the SPAM tool comprise an editor with both text and graphical interface, as well as various functions for the static and dynamic analysis of SPARQL queries.

Dependencies have played an important role in database design for many years. More recently, they have also turned out to be central to data integration and data exchange. In this work we concentrate on tuple generating dependencies (tgds) which enforce the presence of certain tuples in a database instance if certain other tuples are already present. Previous complexity results in data integration and data exchange mainly referred to the data complexity. In this work, we study the query complexity and combined complexity of a fundamental problem related to tgds, namely checking if a given tgd is satisfied by a database instance. We also address an important variant of this problem which deals with updates (by inserts or deletes) of a database: Here we have to check if all previously satisfied tgds are still satisfied after an update. We show that the query complexity and combined complexity of these problems are much higher than the data complexity. However, we also prove sufficient conditions on the tgds to reduce this high complexity.

Recently, two relaxed notions of equivalence of schema mappings have been introduced, which provide more potential of optimizing schema mappings than logical equivalence: data exchange (DE) equivalence and conjunctive query (CQ) equivalence. In this work, we systematically investigate these notions of equivalence for mappings consisting of s-t tgds and target egds and/or target tgds. We prove that both CQ- and DE-equivalence are undecidable and so are some important optimization tasks (like detecting if some dependency is redundant). However, we also identify an important difference between the two notions of equivalence: CQ-equivalence remains undecidable even if the schema mappings consist of s-t tgds and target dependencies in the form of key dependencies only. In contrast, DE-equivalence is decidable for schema mappings with s-t tgds and target dependencies in the form of functional and inclusion dependencies with terminating chase property.

Schema mappings are high-level specifications that describe the relationship between two database schemas. They are an important tool in several areas of database research, notably in data integration and data exchange. However, a concrete theory of schema mapping optimization including the formulation of optimality criteria and the construction of algorithms for computing optimal schema mappings is completely lacking to date. The goal of this work is to fill this gap. We start by presenting a system of rewrite rules to minimize sets of source-to-target tuple-generating dependencies (st-tgds, for short). Moreover, we show that the result of this minimization is unique up to variable renaming. Hence, our optimization also yields a schema mapping normalization. By appropriately extending our rewrite rule system, we also provide a normalization of schema mappings containing equality-generating target-dependencies (egds). An important application of such a normalization is in the area of defining the semantics of query answering in data exchange, since several definitions in this area depend on the concrete syntactic representation of the st-tgds. This is, in particular, the case for queries with negated atoms and for aggregate queries. The normalization of schema mappings allows us to eliminate the effect of the concrete syntactic representation of the st-tgds from the semantics of query answering. We discuss in detail how our results can be fruitfully applied to aggregate queries.

Minimality is an important optimization criterion for solutions of data exchange problems, well captured by the notion of the core. Though tractability of core computation has been proved, it has not yet become a part of any industrial-strength system, still being highly computationally expensive. In this demonstration, we show how core computation can be used in a data exchange modeling tool, allowing data engineers to design more robust data transfer scenarios and better understand the sources of redundancy in the target database.

Bounded treewidth and Monadic Second Order (MSO) logic have proved to be key concepts in establishing fixed-para-meter tractability results. Indeed, by Courcelle's Theorem we know: Any property of finite structures, which is expressible by an MSO sentence, can be decided in linear time (data complexity) if the structures have bounded treewidth.

Given that most elementary problems in database design are NP-hard, the currently used database design algorithms produce suboptimal results. For example, the current 3NF decomposition algorithms may continue further decomposing a relation even though it is already in 3NF. In this paper we study database design problems whose sets of functional dependencies have bounded treewidth. For such sets, which frequently occur in practice, we develop polynomial-time and highly parallelizable algorithms for a number of central database design problems such as: • primality of an attribute • 3NF-test for a relational schema or subschema • BCNF-test for a subschema.For establishing these results, we propose a new characterization for keys and for the primality of a single attribute.In order to define the treewidth of a relational schema, we shall associate a hypergraph with it. Note that there are two main possibilities of defining the treewidth of a hypergraph H: One is via the primal graph of H and one is via the incidence graph of H. Our algorithms apply to the case where the primal graph is considered. However, we also show that the tractability results still hold when the incidence graph is considered instead.

Contemporary XPath query engines evaluate queries in time exponential in the sizes of input queries, a fact that has gone unnoticed for a long time. Recently, the first main-memory evaluation algorithm for XPath 1.0 with polynomial time combined complexity, i.e., which runs in polynomial time both with respect to the size of the data and the queries, has been published (cf. [G. Gottlob, et al., (2002)]. We present several important improvements and extensions of that work, including new XPath processing algorithms with improved time and space efficiency. Moreover, we define a very large and practically relevant fragment of XPath for which a further optimized form of query evaluation is possible. Apart from its immediate relevance for XPath query processing, our work also sheds new light at those features of XPath 1.0 which are most costly relative to their practical usefulness.

In this paper, we study the precise complexity of XPath 1.0 query processing. Even though heavily used by its incorporation into a variety of XML-related standards, the precise cost of evaluating an XPath query is not yet wellunderstood. The first polynomial-time algorithm for XPath processing (with respect to combined complexity) was proposed only recently, and even to this day all major XPath engines take time exponential in the size of the input queries. From the standpoint of theory, the precise complexity of XPath query evaluation is open, and it is thus unknown whether the query evaluation problem can be parallelized.In this work, we show that both the data complexity and the query complexity of XPath 1.0 fall into lower (highly parallelizable) complexity classes, but that the combined complexity is PTIME-hard. Subsequently, we study the sources of this hardness and identify a large and practically important fragment of XPath 1.0 for which the combined complexity is LOGCFL-complete and, therefore, in the highly parallelizable complexity class NC2.

Our experimental analysis of several popular XPath processors reveals a striking fact: Query evaluation in each of the systems requires time exponential in the size of queries in the worst case. We show that XPath can be processed much more efficiently, and propose main-memory algorithms for this problem with polynomial-time combined query evaluation complexity. Moreover, we show how the main ideas of our algorithm can be profitably integrated into existing XPath processors. Finally, we present two fragments of XPath for which linear-time query processing algorithms exist and another fragment with linear-space/quadratic-time query processing.