The idea of using an ontology to enrich user queries with domain knowledge has attracted considerable attention from the database and KR communities during the last fifteen years or so. The ontology and the user query can be conveniently seen as two components of one composite query, called ontology-mediated query (omq), while an omq language (OL,QL) collects all such omqs where the ontology is expressed using the ontology language OL and the user query comes from the query language QL. The evaluation problem for rule-based omq languages of the form (OL,CQ), where OL is a rule-based ontology language, i.e., it collects ontologies modelled using tuple-generating dependencies (a.k.a. existential rules), and CQ is the language of conjunctive queries, has been extensively studied in the literature. In particular, the notion of first-order rewritability of such languages, i.e., the property of being able to rewrite every omq from the language in question to an equivalent first-order query, has been studied in depth. This research effort led an algorithmic characterization of when a rule-based omq language (OL,CQ) is first-order rewritable. More precisely, there is a uniform algorithm Rewrite such that, for every rule-based ontology language OL, the omq language (OL,CQ) is first-order rewritable iff for every omq O from (OL,CQ), the algorithm Rewrite on input O terminates and constructs a first-order rewriting of O. The question that we are interested in is whether the above algorithmic characterization can be extended to rule-based omq languages of the form (OL,nCQ), where nCQ is the language of conjunctive queries with the useful feature of negation. The goal of this work is to initiate effort towards the settlement of the above highly non-trivial question. To this end, we provide a new algorithm, which is a non-trivial extension of the algorithm Rewrite for positive omqs, and show the following: under the Skolem semantics, a well-established approach for defining the answer to a rule-based omq when the user query can use negation, the proposed algorithm is a first-order rewriter for (OL,nCQ), where OL is the language of linear or acyclic tuple-generating dependencies, two central rule-based ontology languages that ensure first-order rewritability for positive omqs. We strongly believe that the new algorithm can serve as a good starting point towards the full settlement of our main question.

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.

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.

Competitor data constitutes information significantly valuable for many business applications. Meltwater provides users with access to a large Company Information System (CIS), Owler, which contains competitor pairs and other useful information about companies. Meltwater has been seeking a practical solution to discover more competitor pairs in Owler. The first attempt, a fully-manual workflow (called MW_Manual) for finding more competitor pairs in Owler consisted of two manual steps: a filtering step that excludes obvious non-competitor company pairs, and a further inspection process that inspects each left company pair after the filtering step. MW_Manual was cost prohibitive because the results of the filtering step contained too many non-competitor pairs. Inspecting such non-competitor pairs caused an overhead to the overall workload. To reduce the manual workload, especially the required human effort in the manual inspection process, Meltwater has transformed MW_Manual into a semi-automatic workflow (called MW_CPFilter) by replacing the manual filtering with an automatic yet more precise process that adopts a system called CPFilter. This paper presents CPFilter, a system used in the filtering process of MW_CPFilter. CPFilter automatically pre-computes likely competitor pairs from existing competitor pairs in Owler. CPFilter combines (i) the generation of new competitor candidate pairs by inference from existing competitors and other company-specific knowledge, with (ii) the validation of each candidate competitor pair of two companies by checking whether or not empirical evidence that indicates the competitor relationships of these two companies can be found. CPFilter has three key advantages compared with the manual filtering process and previous works: (i) it resulted in a high workload reduction rate of 0.81, (ii) it is domain-independent so that it can be applied to different sectors in Owler, and (iii) its results are explainable so that humans can easily understand its results.

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.

Declarative rules such as Prolog and Datalog rules are common formalisms to express expert knowledge and facts. They play an important role in Knowledge Graph (KG) construction and completion. Such rules not only encode the expert background knowledge and the relational patterns among the data, but also infer new knowledge and insights from them. Formalizing rules is often a laborious manual process, while learning them from data automatically can ease this process. Within the rule hypothesis space, current approaches resort to exhaustive search with a number of heuristics and syntactic restrictions on the rule language, which impacts the efficiency and quality of the outcome rules. In this paper, we extend the rule hypothesis space from usual path rules to general Datalog rule space by proposing a novel Genetic Logic Programming algorithm named Evoda. It is an iterative process to learn high-quality rules over large scale KG for a matter of seconds. We have performed experiments over multiple real-world KGs and various evaluation metrics to show its mining capabilities for higher quality rules and more precise predictions. Additionally, we have applied it on the KG completion tasks to illustrate its competitiveness with several state-of-the-art embedding or neural-based models. The experiments demonstrate the feasibility, effectiveness and efficiency of the Evoda algorithm.

The chase procedure, originally introduced for checking implication of database constraints, and later on used for computing data exchange solutions, has recently become a central algorithmic tool in rule-based ontological reasoning. In this context, a key problem is non-uniform chase termination: does the chase of a database w.r.t. a rule-based ontology terminate? And if this is the case, what is the size of the result of the chase? We focus on guarded tuple-generating dependencies (TGDs), which form a robust rule-based ontology language, and study the above central questions for the semi-oblivious version of the chase. One of our main findings is that non-uniform semi-oblivious chase termination for guarded TGDs is feasible in polynomial time w.r.t. the database, and the size of the result of the chase (whenever is finite) is linear w.r.t. the database. Towards our results concerning non-uniform chase termination, we show that basic techniques such as simplification and linearization, originally introduced in the context of ontological query answering, can be safely applied to the chase termination problem.

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.

Vadalog is a system for performing complex reasoning tasks such as those required in advanced knowledge graphs. The logical core of the underlying Vadalog language is the warded fragment of tuple-generating dependencies (TGDs). This formalism ensures tractable reasoning in data complexity, while a recent analysis focusing on a practical implementation led to the reasoning algorithm around which the Vadalog system is built. A fundamental question that has emerged in the context of Vadalog is whether we can limit the recursion allowed by wardedness in order to obtain a formalism that provides a convenient syntax for expressing useful recursive statements, and at the same time achieves space-efficiency. After analyzing several real-life examples of warded sets of TGDs provided by our industrial partners, as well as recent benchmarks, we observed that recursion is often used in a restricted way: the body of a TGD contains at most one atom whose predicate is mutually recursive with a predicate in the head. We show that this type of recursion, known as piece-wise linear in the Datalog literature, is the answer to our main question. We further show that piece-wise linear recursion alone, without the wardedness condition, is not enough as it leads to undecidability. We also study the relative expressiveness of the query languages based on (piece-wise linear) warded sets of TGDs. Finally, we give preliminary experimental evidence for the practical effect of piece-wise linearity on Vadalog.

The Company Control Problem is of central importance to banks, financial intermediaries, financial intelligence units, regulatory and supervisory authorities such as the Central Banks. It consists in understanding who takes decisions in a large company network, that is, who controls the majority of votes for each single company. This has an impact on a large number of business areas, with examples including evaluation of creditworthiness, economic analysis of the control dispersion, anti-money laundering, prevention of potentially hostile takeovers, evaluation of risks, and shock propagation.This paper is based on our experience with the Central Bank of Italy and presents an approach to the solution of the company control problem in distributed settings, especially relevant, as large and distributed ownership graphs reflect European-size applications where scalability is paramount.In particular, we formalize the problem as query answering on a large distributed database. We study how independent subqueries can be executed in each partition and the partial results assembled at a master site to produce the answer. We study the formal properties of the problem, that is not easily parallelizable, and then present a method that supports parallelism at best.We present a thorough experimental evaluation of our approach with the Italian company graph of the Bank of Italy and the European Register of Financial Intermediaries and Affiliates as well as many artificial graphs to fully assess scalability.

Adopting a mature AI strategy is fundamental for modern knowledge companies to govern the proliferation of smart AI-driven applications and to coordinate them within coherent knowledge workflows. We propose knowledge graphs as the reference technology for the enterprise AI context, i.e., the complex of entities, properties and relationships that shape a business domain and constitute a common backbone for all AI-driven applications. We contribute and discuss principles to design software architectures for AI-driven applications based on knowledge graphs. We focus on the Vadalog system, a successful knowledge graph middleware from the University of Oxford and show knowledge graphs in action in a number of use cases from the financial domain.

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.

Vadalog is a system for performing complex reasoning tasks such as those required in advanced knowledge graphs. The logical core of the underlying Vadalog language is the warded fragment of tuple-generating dependencies (TGDs). This formalism ensures tractable reasoning in data complexity, while a recent analysis focusing on a practical implementation led to the reasoning algorithm around which the Vadalog system is built. A fundamental question that has emerged in the context of Vadalog is the following: can we limit the recursion allowed by wardedness in order to obtain a formalism that provides a convenient syntax for expressing useful recursive statements, and at the same time achieves space-efficiency? After analyzing several real-life examples of warded sets of TGDs provided by our industrial partners, as well as recent benchmarks, we observed that recursion is often used in a restricted way: the body of a TGD contains at most one atom whose predicate is mutually recursive with a predicate in the head. We show that this type of recursion, known as piece-wise linear in the Datalog literature, is the answer to our main question. We further show that piece-wise linear recursion alone, without the wardedness condition, is not enough as it leads to the undecidability of reasoning. We finally study the relative expressiveness of the query languages based on (piece-wise linear) warded sets of TGDs.

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.

Data-driven websites are mostly accessed through search interfaces. Such sites follow a common publishing pattern that, surprisingly, has not been fully exploited for unsupervised data extraction yet: the result of a search is presented as a paginated list of result records. Each result record contains the main attributes about one single object, and links to a page dedicated to the details of that object.

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.

Most modern web scrapers use an embedded browser to render web pages and to simulate user actions. Such scrapers (or wrappers) are therefore expensive to execute, in terms of time and network traffic. In contrast, it is magnitudes more resource-efficient to use a "browserless" wrapper which directly accesses a web server through HTTP requests, and takes the desired data directly from the raw replies. However, creating and maintaining browserless wrappers of high precision requires specialists, and is prohibitively labor-intensive at scale. In this paper, we demonstrate the principal feasibility of automatically translating browser-based wrappers into "browserless" wrappers. We present the first algorithm and system performing such an automated translation on suitably restricted types of web sites. This system works in the vast majority of test cases and produces very fast and extremely resource-efficient wrappers. We discuss research challenges for extending our approach to a general method applicable to a yet larger number of cases.

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.

The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome these limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Unfortunately, there are a number of useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. To the best of our knowledge, no efficient RDF query language that combines the above functionalities is known. It is the aim of this work to fill this gap. To this end, we focus on a core fragment of the OWL 2 QL profile of OWL 2 and show that every SPARQL query enriched with the above features can be naturally translated into a query expressed in a language that is based on an extension of Datalog, which allows for value invention and stratified negation. However, the query evaluation problem for this language is highly intractable, which is not surprising since it is expressive enough to encode some inherently hard queries. We identify a natural fragment of it, and we show it to be tractable and powerful enough to define SPARQL queries enhanced with the desired functionalities.

Data wrangling, the multi-faceted process by which the data required by an application is identified, extracted, cleaned and integrated, is often cumbersome and labor intensive. In this paper, we present an architecture that supports a complete data wrangling lifecycle, orchestrates components dynamically, builds on automation wherever possible, is informed by whatever data is available, refines automatically produced results in the light of feedback, takes into account the user's priorities, and supports data scientists with diverse skill sets. The architecture is demonstrated in practice for wrangling property sales and open government data.

Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield adata set that is suitable for exploration and analysis. Although there are widely used Extract, Transform and Load (ETL) techniques and platforms, they often require manual work from technical and domain experts at different stages of the process. When confronted with the 4 V’s of big data (volume, velocity, variety and veracity),manual intervention may make ETL prohibitively expensive. This paper argues that providing cost-effective, highly-automated approaches to data wrangling involves significant research challenges,requiring fundamental changes to established areas such as data extraction,integration and cleaning, and to the ways in which these areas are brought together. Specifically, the paper discusses the importance of comprehensive support for context awareness within data wrangling, and the need for adaptive, pay-as-you-go solutions that automatically tune the wrangling process to the requirements and resources of the specific application.

We describe the 2016 ICDT Test of Time Award which is awarded to Chandra Chekuri and Anand Rajaraman for their 1997 ICDT paper on "Conjunctive Query Containment Revisited".

A conjunctive query (CQ) is semantically acyclic if it is equivalent to an acyclic one. Semantic acyclicity has been studied in the constraint-free case, and deciding whether a query enjoys this property is NP-complete. However, in case the database is subject to constraints such as tuple-generating dependencies (tgds) that can express, e.g., inclusion dependencies, or equality-generating dependencies (egds) that capture, e.g., functional dependencies, a CQ may turn out to be semantically acyclic under the constraints while not semantically acyclic in general. This opens avenues to new query optimization techniques. In this paper we initiate and develop the theory of semantic acyclicity under constraints. More precisely, we study the following natural problem: Given a CQ and a set of constraints, is the query semantically acyclic under the constraints, or, in other words, is the query equivalent to an acyclic one over all those databases that satisfy the set of constraints?

In the database context, the hypertree decomposition method is used for query optimization, whereby conjunctive queries having a low degree of cyclicity can be recognized and decomposed automatically, and efficiently evaluated. Hypertree decompositions were introduced at ACM PODS 1999. The present paper reviews' in form of questions and answers' the main relevant concepts and algorithms and surveys selected related work including applications and test results.

The chase procedure is considered as one of the most fundamental algorithmic tools in database theory. It has been successfully applied to different database problems such as data exchange, and query answering and containment under constraints, to name a few. One of the central problems regarding the chase procedure is all-instance termination, that is, given a set of tuple-generating dependencies (TGDs) (a.k.a. existential rules), decide whether the chase under that set terminates, for every input database. It is well-known that this problem is undecidable, no matter which version of the chase we consider. The crucial question that comes up is whether existing restricted classes of TGDs, proposed in different contexts such as ontological query answering, make the above problem decidable. In this work, we focus our attention on the oblivious and the semi-oblivious versions of the chase procedure, and we give a positive answer for classes of TGDs that are based on the notion of guardedness. To the best of our knowledge, this is the first work that establishes positive results about the (semi-)oblivious chase termination problem. In particular, we first concentrate on the class of linear TGDs, and we syntactically characterize, via rich- and weak-acyclicity, its fragments that guarantee the termination of the oblivious and the semi-oblivious chase, respectively. Those syntactic characterizations, apart from being interesting in their own right, allow us to pinpoint the complexity of the problem, which is PSPACE-complete in general, and NL-complete if we focus on predicates of bounded arity, for both the oblivious and the semi-oblivious chase. We then proceed with the more general classes of guarded and weakly-guarded TGDs. Although we do not provide syntactic characterizations for its relevant fragments, as for linear TGDs, we show that the problem under consideration remains decidable. In fact, we show that it is 2EXPTIME-complete in general, and EXPTIME-complete if we focus on predicates of bounded arity, for both the oblivious and the semi-oblivious chase. Finally, we investigate the expressive power of the query languages obtained from our analysis, and we show that they are equally expressive with standard database query languages. Nevertheless, we have strong indications that they are more succinct.

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.

The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF, since its standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome these limitations, a new version of SPARQL, called SPARQL 1.1, was recently released, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Unfortunately, there are still some useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks of a general mechanism to express recursive queries.

The so-called existential rules have recently gained attention, mainly due to their adequate expressiveness for ontological query answering. Several decidable fragments of such rules have been introduced, employing restriction such as various forms of guardedness to ensure decidability. Some of the more well-known languages in this arena are (weakly) guarded and (weakly) frontier-guarded fragments of existential rules. In this paper, we explore their relative and absolute expressiveness. In particular, we provide a new proof that queries expressed via frontier-guarded and guarded rules can be translated into plain Datalog queries. Since the converse translations are impossible, we develop generalizations of frontier-guarded and guarded rules to nearly frontier-guarded and nearly guarded rules, respectively, which have exactly the expressive power of Datalog. We further show that weakly frontier-guarded rules can be translated into weakly guarded rules, and thus, weakly frontier-guarded and weakly guarded rules have exactly the same expressive power. Such rules cannot be translated into Datalog since their query answering problem is ExpTime-complete in data complexity. We strengthen this result by showing that on ordered databases and with input negation available, weakly guarded rules capture all queries decidable in exponential time. We then show that weakly guarded rules extended with stratified negation are expressive enough to capture all database queries decidable in exponential time, without any assumptions about input databases. Finally, we note that the translations of this paper are, in general, exponential in size, but lead to worst-case optimal algorithms for query answering with considered languages.

The web is overflowing with implicitly structured data, spread over hundreds of thousands of sites, hidden deep behind search forms, or siloed in marketplaces, only accessible as HTML. Automatic extraction of structured data at the scale of thousands of websites has long proven elusive, despite its central role in the "web of data". Through an extensive evaluation spanning over 10000 web sites from multiple application domains, we show that automatic, yet accurate full-site extraction is no longer a distant dream. diadem is the first automatic full-site extraction system that is able to extract structured data from different domains at very high accuracy. It combines automated exploration of websites, identification of relevant data, and induction of exhaustive wrappers. Automating these components is the first challenge. diadem overcomes this challenge by combining phenomenological and ontological knowledge. Integrating these components is the second challenge. diadem overcomes this challenge through a self-adaptive network of relational transducers that produces effective wrappers for a wide variety of websites. Our extensive and publicly available evaluation shows that, for more than 90% of sites from three domains, diadem obtains an effective wrapper that extracts all relevant data with 97% average precision. diadem also tolerates noisy entity recognisers, and its components individually outperform comparable approaches.

Ontological queries are evaluated against a knowledge base consisting of an extensional database and an ontology (i.e., a set of logical assertions and constraints that derive new intensional knowledge from the extensional database), rather than directly on the extensional database. The evaluation and optimization of such queries is an intriguing new problem for database research. In this article, we discuss two important aspects of this problem: query rewriting and query optimization. Query rewriting consists of the compilation of an ontological query into an equivalent first-order query against the underlying extensional database. We present a novel query rewriting algorithm for rather general types of ontological constraints that is well suited for practical implementations. In particular, we show how a conjunctive query against a knowledge base, expressed using linear and sticky existential rules, that is, members of the recently introduced Datalog± family of ontology languages, can be compiled into a union of conjunctive queries (UCQ) against the underlying database. Ontological query optimization, in this context, attempts to improve this rewriting process soas to produce possibly small and cost-effective UCQ rewritings for an input query.

The monotone duality problem is defined as follows: Given two monotone formulas f and g in irredundant DNF, decide whether f and g are dual. This problem is the same as duality testing for hypergraphs, that is, checking whether a hypergraph H consists of precisely all minimal transversals of a hypergraph G. By exploiting a recent problem-decomposition method by Boros and Makino (ICALP 2009), we show that duality testing for hypergraphs, and thus for monotone DNFs, is feasible in DSPACE(log2 n), i.e., in quadratic logspace. As the monotone duality problem is equivalent to a number of problems in the areas of databases, data mining, and knowledge discovery, the results presented here yield new complexity results for those problems, too. For example, it follows from our results that whenever, for a Boolean-valued relation (whose attributes represent items), a number of maximal frequent itemsets and a number of minimal infrequent itemsets are known, then it can be decided in quadratic logspace whether there exist additional frequent or infrequent itemsets.

The Datalog± family of expressive extensions of Datalog has recently been introduced as a new paradigm for query answering over ontologies, which captures and extends several common description logics. It extends plain Datalog by features such as existentially quantified rule heads and, at the same time, restricts the rule syntax so as to achieve decidability and tractability. In this paper, we continue the research on Datalog±. More precisely, we generalize the well-founded semantics (WFS), as the standard semantics for nonmonotonic normal programs in the database context, to Datalog± programs with negation under the unique name assumption (UNA). We prove that for guarded Datalog± with negation under the standard WFS, answering normal Boolean conjunctive queries is decidable, and we provide precise complexity results for this problem, namely, in particular, completeness for PTIME (resp., 2-EXPTIME) in the data (resp., combined) complexity.

Forms are our gates to the web. They enable us to access the deep content of web sites. Automatic form understanding unlocks this content for applications ranging from crawlers to meta-search engines and is essential for improving usability and accessibility of the web. Form understanding has received surprisingly little attention other than as component in specific applications such as crawlers. No comprehensive approach to form understanding exists and previous works disagree even in the definition of the problem. In this paper, we present OPAL, the first comprehensive approach to form understanding. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems OPAL pushes the state of the art: For form labeling, it combines signals from the text, structure, and visual rendering of a web page, yielding robust characterisations of common design patterns. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern web forms OPAL outperforms previous approaches by a significant margin. For form interpretation, we introduce a template language to describe frequent form patterns. These two parts of OPAL combined yield form understanding with near perfect accuracy (> 98%).

Although deep web analysis has been studied extensively, there is no succinct formalism to describe user interactions with AJAX-enabled web applications.

Ontological queries are evaluated against an enterprise ontology rather than directly on a database. The evaluation and optimization of such queries is an intriguing new problem for database research. In this paper we discuss two important aspects of this problem: query rewriting and query optimization. Query rewriting consists of the compilation of an ontological query into an equivalent query against the underlying relational database. The focus here is on soundness and completeness. We review previous results and present a new rewriting algorithm for rather general types of ontological constraints (description logics). In particular, we show how a conjunctive query (CQ) against an enterprise ontology can be compiled into a union of conjunctive queries (UCQ) against the underlying database. Ontological query optimization, in this context, attempts to improve this process so to produce possibly small and cost-effective output UCQ. We review existing optimization methods, and propose an effective new method that works for Linear Datalog±, a description logic that encompasses well-known description logics of the DL-Lite family.

Consider the situation where a query is to be answered using Web sources that restrict the accesses that can be made on backend relational data by requiring some attributes to be given as input of the service. The accesses provide lookups on the collection of attributes values that match the binding. They can differ in whether or not they require arguments to be generated from prior accesses. Prior work has focused on the question of whether a query can be answered using a set of data sources, and in developing static access plans (e.g., Datalog programs) that implement query answering. We are interested in dynamic aspects of the query answering problem: given partial information about the data, which accesses could provide relevant data for answering a given query? We consider immediate and long-term notions of "relevant accesses", and ascertain the complexity of query relevance, for both conjunctive queries and arbitrary positive queries. In the process, we relate dynamic relevance of an access to query containment under access limitations and characterize the complexity of this problem; we produce several complexity results about containment that are of interest by themselves.

Virtual views are a mechanism that facilitates re-use and makes queries easier to express. However the use of iterative view definitions makes very simple query evaluation and analysis problems more complex. In this paper we study classical containment and equivalence problems for queries built up through simple unions of conjunctive queries and view definitions. More precisely, we determine the complexity of containment and equivalence for non-recursive Datalog. We show that the problem is much harder than its classical counterpart -- complete for co-NEXPTIME. We then show that this remains true even with restrictions on the schema and queries in place. Finally, we isolate subcases that are more tractable, ranging from NP to PSPACE.

Ontology-based data access is a powerful form of extending database technology, where a classical extensional database (EDB) is enhanced by an ontology that generates new intensional knowledge which may contribute to answer a query. The ontological integrity constraints for generating this intensional knowledge can be specified in description logics such as DL-Lite. It was recently shown that these formalisms allow for very efficient query-answering. They are, however, too weak to express simple and useful integrity constraints that involve joins. In this paper we introduce a more expressive formalism that takes joins into account, while still enjoying the same low query-answering complexity. In our framework, ontological constraints are expressed by sets of rules that are so-called tuple-generating dependencies (TGDs) . We propose the language of sticky sets of TGDs, which are sets of TGDs with a restriction on multiple occurrences of variables (including joins) in the rule bodies. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that ontological conjunctive queries can be compiled into first-order and thus SQL queries over the given EDB instance. We also show how sticky sets of TGDs can be combined with functional dependencies. In summary, we obtain a highly expressive and effective ontological modeling language that unifies and generalizes both classical database constraints and important features of the most widespread tractable description logics.

We report on a recently introduced family of expressive extensions of Datalog, called Datalog±, which is a new framework for representing ontological axioms in form of integrity constraints, and for query answering under such constraints. Datalog± is derived from Datalog by allowing existentially quantified variables in rule heads, and by enforcing suitable properties in rule bodies, to ensure decidable and efficient query answering. We first present different languages in the Datalog± family, providing tight complexity bounds for all cases but one (where we have a low complexity AC0 upper bound). We then show that such languages are general enough to capture the most common tractable ontology languages. In particular, we show that the DL-Lite family of description logics and F-Logic Lite are expressible in Datalog±. We finally show how stratified negation can be added to Datalog± while keeping ontology querying tractable in the data complexity. Datalog± is a natural and very general framework that can be successfully employed in different contexts such as data integration and exchange. This survey mainly summarizes two recent papers.

A distributed XML document is an XML document that spans several machines or Web repositories. We assume that a distribution design of the document tree is given, providing an XML tree some of whose leaves are "docking points", to which XML subtrees can be attached. These subtrees may be provided and controlled by peers at remote locations, or may correspond to the result of function calls, e.g., Web services. If a global type τ, e.g. a DTD, is specified for a distributed document T, it would be most desirable to be able to break this type into a collection of local types, called a local typing, such that the document satisfies τ if and only if each peer (or function) satisfies its local type. In this paper we lay out the fundamentals of a theory of local typing and provide formal definitions of three main variants of locality: local typing, maximal local typing, and perfect typing, the latter being the most desirable. We study the following relevant decision problems: (i) given a typing for a design, determine whether it is local, maximal local, or perfect; (ii) given a design, establish whether a (maximal) local, or perfect typing does exist. For some of these problems we provide tight complexity bounds (polynomial space), while for the others we show exponential upper bounds. A main contribution is a polynomial-space algorithm for computing a perfect typing in this context, if it exists.

In this paper, we introduce a family of expressive extensions of Datalog, called Datalog+/-, as a new paradigm for query answering over ontologies. The Datalog+/- family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. We show in particular that Datalog+/- generalizes the DL-Lite family of tractable description logics, which are the most common tractable ontology languages in the context of the Semantic Web and databases. We also show how stratified negation can be added to Datalog+/- while keeping ontology querying tractable. Furthermore, the Datalog+/- family is of interest in its own right and can, moreover, be used in various contexts such as data integration and data exchange.

This paper provides new worst-case bounds for the size and treewith of the result Q(D) of a conjunctive query Q to a database D. We derive bounds for the result size |Q(D)| in terms of structural properties of Q, both in the absence and in the presence of keys and functional dependencies. These bounds are based on a novel "coloring" of the query variables that associates a coloring number C(Q) to each query Q. Using this coloring number, we derive tight bounds for the size of Q(D) in case (i) no functional dependencies or keys are specified, and (ii) simple (one-attribute) keys are given. These results generalize recent size-bounds for join queries obtained by Atserias, Grohe, and Marx (FOCS 2008). An extension of our coloring technique also gives a lower bound for |Q(D)| in the general setting of a query with arbitrary functional dependencies. Our new coloring scheme also allows us to precisely characterize (both in the absence of keys and with simple keys) the treewidth-preserving queries--the queries for which the output treewidth is bounded by a function of the input treewidth. Finally we characterize the queries that preserve the sparsity of the input in the general setting with arbitrary functional dependencies.

Online market intelligence (OMI), in particular competitive intelligence for product pricing, is a very important application area for Web data extraction. However, OMI presents non-trivial challenges to data extraction technology. Sophisticated and highly parameterized navigation and extraction tasks are required. On-the-fly data cleansing is necessary in order two identify identical products from different suppliers. It must be possible to smoothly define data flow scenarios that merge and filter streams of extracted data stemming from several Web sites and store the resulting data into a data warehouse, where the data is subjected to market intelligence analytics. Finally, the system must be highly scalable, in order to be able to extract and process massive amounts of data in a short time. Lixto (www.lixto.com), a company offering data extraction tools and services, has been providing OMI solutions for several customers. In this paper we show how Lixto has tackled each of the above challenges by improving and extending its original data extraction software. Most importantly, we show how high scalability is achieved through cloud computing. This paper also features a case study from the computers and electronics market.

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.

We introduce a theoretical framework for discovering relationships between two database instances over distinct and unknown schemata. This framework is grounded in the context of data exchange. We formalize the problem of understanding the relationship between two instances as that of obtaining a schema mapping so that a minimum repair of this mapping provides a perfect description of the target instance given the source instance. We show that this definition yields "intuitive" results when applied on database instances derived from each other by basic operations. We study the complexity of decision problems related to this optimality notion in the context of different logical languages and show that, even in very restricted cases, the problem is of high complexity.

This paper shows how technologies for Web data extraction, syndication and integration allow for new applications and services in the Business Intelligence and the Semantic Web domain. First, we demonstrate how knowledge about market developments and competitor activities on the market can be extracted dynamically and automatically from semi-structured information sources on the Web. Then, we show how the data can be integrated in Business Intelligence Systems and how data can be classified, re-assigned and transformed with the aid of Semantic Web ontological domain knowledge. Existing Semantic Web and Business Intelligence applications and scenarios using our technology illustrate the whole process. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The generalized hypertree width GHW(H) of a hypergraph H is a measure of its cyclicity. Classes of conjunctive queries or constraint satisfaction problems whose associated hypergraphs have bounded GHW are known to be solvable in polynomial time. However,it has been an open problem for several years if for a fixed constant k and input hypergraph H it can be determined in polynomial time whether GHW(H)< k. Here, this problem is settled by proving that even for k=3 the problem is already NP-hard. On the way to this result, another long standing open problem, originally raised by Goodman and Shmueli in 1984 all in the context of join optimization is solved. It is proven that determining whether a hypergraph H admits a tree projection with respect to a hypergraph G is NP-complete. Our intractability results on generalized hypertree width motivate further research on more restrictive tractable hypergraph decomposition methods that approximate general hypertree decomposition (GHD). We show that each such method is dnominated by a tractable decomposition method definable through a function that associates a set of partial edges to a hypergraph. By using one particular such function, we define the new Component Hypertree Decomposition method, which is tractable and strictly more general than other approximations to GHD published so far.

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.

Data exchange deals with inserting data from one database into another database having a different schema. We study and solve a central computational problem of data exchange, namely, computing the core of a universal solution to a data exchange problem. Fagin, Kolaitis, and Popa [9], have shown that among the universal solutions of a solvable data exchange problem, there exists a most compact one (up to isomorphism), "the core" (of any universal solution), and have convincingly argued that this core should be the solution of choice. They stated as an important open problem whether the core of a universal solution can be computed in polynomial time in the general setting where the source-to-target constraints are arbitrary tuple generating dependencies (TGDs) and the target constraints consist of equation generating dependencies (EGDs) and weakly-acyclic TGDs. In this paper we solve this problem by developing new efficient methods for computing the core of a universal solution. This positive result shows that the core approach of Fagin, Kolaitis, and Popa is feasible and applicable in a very general setting and thus provides a further momentum to the use of cores in data exchange.

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.

Data Exchange is the problem of inserting data structured under a source schema into a target schema of different structure (possibly with integrity constraints), while reflecting the source data as accurately as possible. We study computational issues related to data exchange in the setting of Fagin, Kolaitis, and Popa(PODS'03). We use the technique of hypertree decompositions to derive improved algorithms for computing the core of a relational instance with labeled nulls, a problem we show to be fixed-parameter intractable with respect to the block size of the input instances. We show that computing the core of a data exchange problem is tractable for two large and useful classes of target constraints. The first class includes functional dependencies and weakly acyclic inclusion dependencies. The second class consists of full tuple generating dependencies and arbitrary equation generating dependencies. Finally, we show that computing cores is NP-hard in presence of a system-predicate NULL(x), which is true iff x is a null value.

The task of an information integration system is to combine data residing at different sources, providing the user with a unified view of them, called global schema. Users formulate queries over the global schema, and the system suitably queries the sources, providing an answer to the user, who is not obliged to have any information about the sources. Recent developments in IT such as the expansion of the Internet and the World Wide Web, have made available to users a huge number of information sources, generally autonomous, heterogeneous and widely distributed: as a consequence, information integration has emerged as a crucial issue in many application domains, e.g., distributed databases, cooperative information systems, data warehousing, or on-demand computing. Recent estimates view information integration to be a $10 Billion market by 2006 [14].

DATA

We study the complexity and expressive power of conjunctive queries over unranked labeled trees, where the tree structure are represented using "axis relations" such as "child", "descendant", and "following" (we consider a superset of the XPath axes) as well as unary relations for node labels. (Cyclic) conjunctive queries over trees occur in a wide range of data management scenarios related to XML, the Web, and computational linguistics. We establish a framework for characterizing structures representing trees for which conjunctive queries can be evaluated efficiently. Then we completely chart the tractability frontier of the problem for our axis relations, i.e., we find all subset maximal sets of axes for which query evaluation is in polynomial time. All polynomial-time results are obtained immediately using the proof techniques from our framework. Finally, we study the expressiveness of conjunctive queries over trees and compare it to the expressive power of fragments of XPath. We show that for each conjunctive query, there is an equivalent acyclic positive query (i.e., a set of acyclic conjunctive queries), but that in general this query is not of polynomial size.

We demonstrate the Lixto Suite, a Web data extraction and transformation software kit for retrieving and converting information from various sources to various customer devices. With the Lixto Suite, nontechnical content managers can rapidly develop applications in the areas of m-commerce, e-commerce, content integration and corporate portals.

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.

Research on information extraction from Web pages (wrapping) has seen much activity in recent times (particularly systems implementations), but little work has been done on formally studying the expressiveness of the formalisms proposed or on the theoretical foundations of wrapping.In this paper, we first study monadic datalog as a wrapping language (over ranked or unranked tree structures). Using previous work by Neven and Schwentick, we show that this simple language is equivalent to full monadic second order logic (MSO) in its ability to specify wrappers. We believe that MSO has the right expressiveness required for Web information extraction and thus propose MSO as a yardstick for evaluating and comparing wrappers.Using the above result, we study the kernel fragment Elog- of the Elog wrapping language used in the Lixto system (a visual wrapper generator). The striking fact here is that Elog- exactly captures MSO, yet is easier to use. Indeed, programs in this language can be entirely visually specified. We also formally compare Elog to other wrapping languages proposed in the literature.

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.

In a previous paper [10], the authors introduced the notion of hypertree decomposition and the corresponding concept of hypertree width and showed that the conjunctive queries whose hypergraphs have bounded hypertree-width can be evaluated in polynomial time. Bounded hypertree-width generalizes the notions of acyclicity and bounded treewidth and corresponds to larger classes of tractable queries. In the present paper, we provide natural characterizations of hypergraphs and queries having bounded hypertree-width in terms of game-theory and logic.

Article Hypertree decompositions and tractable queries Share on Authors: Georg Gottlob Inst. für Informationssysteme, Technische Universität Wien, A-1040 Vienna, Austria Inst. für Informationssysteme, Technische Universität Wien, A-1040 Vienna, AustriaView Profile , Nicola Leone Inst. für Informationssysteme, Technische Universität Wien, A-1040 Vienna, Austria Inst. für Informationssysteme, Technische Universität Wien, A-1040 Vienna, AustriaView Profile , Francesco Scarcello ISI-CNR, Via P. Bucci 41/C, I-87030 Rende, Italy ISI-CNR, Via P. Bucci 41/C, I-87030 Rende, ItalyView Profile Authors Info & Claims PODS '99: Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systemsMay 1999 Pages 21–32https://doi.org/10.1145/303976.303979Online:01 May 1999Publication History 52citation1,051DownloadsMetricsTotal Citations52Total Downloads1,051Last 12 Months9Last 6 weeks1 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

Research in nonmonotonic reasoning has focused largely on the idea of representing knowledge about the world via rules that are generally true but can be defeated. Even if relational databases are nowadays the main tool for storing very large sets of data, the approach of using nonmonotonic AI formalisms as relational database query languages has been investigated to a much smaller extent. In this work, we propose a novel application of Reiter's default logic by introducing a default query language (DQL) for finite relational databases, which is based on default rules. The main result of this paper is that DQL is as expressive as SO/sub /spl exist//spl forall// the existential-universal fragment of second-order logic. This result is not only of theoretical importance: We exhibit queries-which are useful in practice-that can be expressed with DQL and cannot with other query languages based on nonmonotonic logics such as DATALOG with negation under the stable model semantics. In particular, we show that DQL is well-suited for diagnostic reasoning.

We consider disjunctive Datalog, a powerful database query language based on disjunctive logic programming. Briefly, disjunctive Datalog is a variant of Datalog where disjunctions may appear in the rule heads; advanced versions also allow for negation in the bodies which can be handled according to a semantics for negation in disjunctive logic programming. In particular, we investigate three different semantics for disjunctive Datalog: the minimal model semantics the perfect model semantics, and the stable model semantics. For each of these semantics, the expressive power and complexity are studied. We show that the possibility variants of these semantics express the same set of queries. In fact, they precisely capture the complexity class Σ P 2 . Thus, unless the Polynomial Hierarchy collapses, disjunctive Datalog is more expressive that normal logic programming with negation. These results are not only of theoretical interest; we demonstrate that problems relevant in practice such as computing the optimal tour value in the Traveling Salesman Problem and eigenvector computations can be handled in disjunctive Datalog, but not Datalog with negation (unless the Polynomial Hierarchy collapses). In addition, we study modularity properties of disjunctive Datalog and investigate syntactic restrictions of the formalisms.

In many class-based object-oriented systems the association between as instance and a class is exclusive and permanent. Therefore these systems have serious difficulties in representing objects taking on different roles over time. Such objects must be reclassified any time they evolve (e.g., if a person becomes a student and later an employee). Class hierarchies must be planned carefully and may grow exponentially if entities may take on serveral independent roles. The problem is even more servere for object-oriented databases than for common object-oriented programming. Databases store objects over longer periods, during which the represented entities evolve. This article shows how class-based object-oriented systems can be extended to handle evolving objects well. Class hierarchies are complemented by role hierarchies, whose nodes represent role types an object classified in the root may take on. At any point in time, an entity is represented by an instance of the root and an instance of every role type whose role it currently plays. In a natural way, the approach extends traditional object-oriented concepts, such as classification, object identity, specialization, inheritance, and polymorphism in a natural way. The practicability of the approach is demonstrated by an implementation in Smalltalk. Smalltalk was chosen because it is widely known, which is not true for any particular class-based object-oriented database programming language. Roles can be provided in Smalltalk by adding a few classes. There is no need to modify the semantics of Smalltalk itself. Role hierarchies are mapped transparently onto ordinary classes. The presented implementation can easily be ported to object-oriented database programming languages based on Smalltalk, such as Gemstone's OPAL hierarchies are complemented by role hierarchies, whose nodes represent role types an object classified in the root may take on. At any point in time, an entity is represented by an instance of the root and an instance of every role type whose role in currently plays.

We study the expressive power and complexity of disjunctive datalog, i.e., datalog with disjunctive rule heads, under three different semantics: the minimal model semantics, the perfect models semantics, and the stable model semantics. We show that the brave variants of these semantics express the same set of queries. In fact, they precisely capture the complexity of class ΣP/2. The combined complexity of disjunctive datalog is shown to be NEXPTIMENP-complete.

This paper addresses complexity issues for important problems arising with disjunctive databases. In particular, the complexity of inference of a literal and a formula from a propositional disjunctive database under a variety of well-known disjunctive database semantics is investigated, as well deciding whether a disjunctive database has a model under a particular semantics. The problems are located in appropriate slots of the polynomial hierarchy.

We study the complexity of several recently proposed methods for updating or revising propositional knowledge bases. In particular, we derive complexity results for the following problem: given a knowledge base T, an update p, and a formula q, decide whether q is derivable from Top, the updated (or revised) knowledge base. This problem amounts to evaluating the counterfactual p > q over T. Besides the general case, also subcases are considered, in particular where T is a conjunction of Horn clauses, or where the size of p is bounded by a constant.

We study the properties of the magic semi-join, a new algebraic operator. In essence, a magic semi-join is the composition of a semi-join and a transitive closure. We present a theory for magic semi-joins that mirrors the theory for semi-joins; in particular, we define equivalence transformations of algebraic formulas using magic semi-joins, and we introduce the notion of full reducer program in this framework. The application of magic semi-joins is in the efficient evaluation of recursive DATALOG queries in centralized and in distributed databases.

Datalog, a database query language based on the logic programming paradigm, is described. The syntax and semantics of Datalog and its use for querying a relational database are presented. Optimization methods for achieving efficient evaluations of Datalog queries are classified, and the most relevant methods are presented. Various improvements of Datalog currently under study are discussed, and what is still needed in order to extend Datalog's applicability to the solution of real-life problems is indicated.>

The problem of translating view updates to database updates is considered. Both databases and views are modeled as data abstractions. A data abstraction consists of a set of states and of a set of primitive update operators representing state transition functions. It is shown how complex update programs can be built from primitive update operators and how view update programs are translated into database update programs. Special attention is paid to a class of views that we call “consistent.” Loosely speaking, a consistent view is a view with the following property: If the effect of a view update program on a view state is determined, then the effect of the corresponding database update is unambiguously determined. Thus, in order to know how to translate a given view update into a database update, it is sufficient to be aware of a functional specification of such a program. We show that consistent views have a number of interesting properties with respect to the concurrency of (high-level) update transactions. Moreover we show that the class of consistent views includes as a subset the class of views that translate updates under maintenance of a constant complement. However, we show that there exist consistent views that do not translate under constant complement. The results of Bancilhon and Spyratos [6] are generalized in order to capture the update semantics of the entire class of consistent views. In particular we show that the class of consistent views is obtained if we relax the requirement of a constant complement by allowing the complement to decrease according to a suitable partial order.

This paper deals with the problem of computing covers for the functional dependencies embedded in a subset of a given relation schema. We show how this problem can be simplified and present a new and efficient algorithm “Reduction. By Resolution” (RBR) for its solution. Though the problem of computing covers for embedded dependencies is inherently exponential, our algorithm behaves polynomially for several classes of inputs. RBR can be used for the solution of some related problems in the theory of database design, such as deciding whether a given database scheme is in Boyce-Codd Normal Form or decomposing a scheme into Boyce-Codd Normal Form.