Classical algorithms for query optimization presuppose the absence of inconsistencies or uncertainties in the database and exploit only valid semantic knowledge provided, e.g., by integrity constraints. Data inconsistency or uncertainty, however, is a widespread critical issue in ordinary databases: total integrity is often, in fact, an unrealistic assumption and violations to integrity constraints may be introduced in several ways. In this report we present an approach for semantic query optimization that, differently from the traditional ones, relies on not necessarily valid semantic knowledge, e.g., provided by violated or soft integrity constraints, or induced by applying data mining techniques. Query optimization that leverages invalid semantic knowledge cannot guarantee the semantic equivalence between the original user's query and its rewriting: thus a query optimized by our approach yields approximate answers that can be provided to the users whenever fast but possibly partial responses are required. Also, we evaluate the impact of use of invalid semantic knowledge in the rewriting of a query by computing a measure of the quality of the answer returned to the user, and we rely on the recent theory of Belief Logic Programming to deal with the presence of possible correlation in the semantic knowledge used in the rewriting.
Another classical application field of integrity constraints, besides SQO, is cooperative query answering @cite_10 , where however the constraints are used only as long as they are valid.
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Database integrity has two complementary components: validity , which guarantees that all false information is excluded from the database, and completeness , which guarantees that all true information is included in the database. This article describes a uniform model of integrity for relational databases, that considers both validity and completeness. To a large degree, this model subsumes the prevailing model of integrity (i.e., integrity constraints). One of the features of the new model is the determination of the integrity of answers issued by the database system in response to user queries. To users, answers that are accompanied with such detailed certifications of their integrity are more meaningful. First, the model is defined and discussed. Then, a specific mechanism is described that implements this model. With this mechanism, the determination of the integrity of an answer is a process analogous to the determination of the answer itself. New applications of information systems need to integrate a large number of heterogeneous databases over computer networks. Answering a query in these applications usually involves selecting relevant information sources and generating a query plan to combine the data automatically. As significant progress has been made in source selection and plan generation, the critical issue has been shifting to query optimization. This paper presents a semantic query optimization (SQO) approach to optimizing query plans of heterogeneous multidatabase systems. This approach provides global optimization for query plans as well as local optimization for subqueries that retrieve data from individual database sources. An important feature of our local optimization algorithm is that we prove necessary and sufficient conditions to eliminate an unnecessary join in a conjunctive query of arbitrary join topology. This feature allows our optimizer to utilize more expressive relational rules to provide a wider range of possible optimizations than previous work in SQO. The local optimization algorithm also features a new data structure called AND-OR implication graphs to facilitate the search for optimal queries. These features allow the global optimization to effectively use semantic knowledge to reduce the data transmission cost. We have implemented this approach in the PESTO (Plan Enhancement by SemanTic Optimization) query plan optimizer as a part of the SIMS information mediator. Experimental results demonstrate that PESTO can provide significant savings in query execution cost over query plan execution without optimization. The discovery of semantic relations from text becomes increasingly important for applications such as Question Answering, Information Extraction, Text Summarization, Text Understanding, and others. The semantic relations are detected by checking selectional constraints. This paper presents a method and its results for learning semantic constraints to detect part-whole relations. Twenty constraints were found. Their validity was tested on a 10,000 sentence corpus, and the targeted part-whole relations were detected with an accuracy of 83 . The purpose of semantic query optimization is to use semantic knowledge (e.g., integrity constraints) for transforming a query into a form that may be answered more efficiently than the original version. In several previous papers we described and proved the correctness of a method for semantic query optimization in deductive databases couched in first-order logic. This paper consolidates the major results of these papers emphasizing the techniques and their applicability for optimizing relational queries. Additionally, we show how this method subsumes and generalizes earlier work on semantic query optimization. We also indicate how semantic query optimization techniques can be extended to databases that support recursion and integrity constraints that contain disjunction, negation, and recursion. Databases and information systems are often hard to use because they do not explicitly attempt to cooperate with their users. Direct answers to database and knowledge base queries may not always be the best answers. Instead, an answer with extra or alternative information may be more useful and less misleading to a user. This paper surveys foundational work that has been done toward endowing intelligent information systems with the ability to exhibit cooperative behavior. Grice's maxims of cooperative conversation, which provided a starting point for the field of cooperative answering, are presented along with relevant work in natural language dialogue systems, database query answering systems, and logic programming and deductive databases. The paper gives a detailed account of cooperative techniques that have been developed for considering users' beliefs and expectations, presuppositions, and misconceptions. Also, work in intensional answering and generalizing queries and answers is covered. Finally, the Cooperative Answering System at Maryland, which is intended to be a general, portable platform for supporting a wide spectrum of cooperative answering techniques, is described. In this paper we consider the problem of the logical characterization of the notion of consistent answer in a relational database that may violate given integrity constraints. This notion is captured in terms of the possible repaired versions of the database. A method for computing consistent answers is given and its soundness and completeness (for some classes of constraints and queries) proved. The method is based on an iterative procedure whose termination for several classes of constraints is proved as well. The authors present an approach to acquiring knowledge from previously processed queries. By using newly acquired knowledge together with given semantic knowledge, it is possible to make the query processor and or optimizer more intelligent so that future queries can b processed more efficiently. The acquired knowledge is in the form of constraints. While some constraints are to be enforced for all database states, others are known to be valid for the current state of the database. The former constraints are statistic integrity constraints, while the latter are called dynamic integrity constraints. Some situations in which certain dynamic semantic constraints can be automatically extracted are identified. This automatic tool for knowledge acquisition can also be used as an interactive tool for identifying potential static integrity constraints. The concept of minimal knowledge base is introduced, and a method to maintain the knowledge base is presented. An algorithm to compute the restriction (selection) closure, i.e. all deductible restrictions, from a given set of restrictions, join predicates (as given in a query), and constraints is given. > Recently there has been an increasing interest in integrity constraints associated with relational databases and in inconsistent databases, i.e. databases which do not satisfy integrity constraints. In the presence of inconsistencies two main techniques have been proposed: compute repairs, i.e. minimal set of insertion and deletion operations, called database repairs, and compute consistent answers, i.e. identify the sets of atoms which we can assume true, false and undefined without modifying the database. In this paper feasibility conditions and preference criteria are introduced which, associated with integrity constraints, allow to restrict the number of repairs and to increase the power of queries over inconsistent databases. Moreover, it is studied the complexity of computing repairs and the expressive power of relational queries over databases with integrity constraints, feasibility conditions and preference criteria. This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of prob- lem - one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient - outperforming previous algorithms by orders of magnitude - while offering similar flexibility to that of forward-search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the a* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning. We propose and investigate a semantics for peer data exchange systems (or peer data management systems) where different peers are pairwise related to each other by means of data exchange constraints and trust relationships. These two elements plus the data at the peers' sites and the local integrity constraints for a peer are made compatible via the proposed semantics by determining a set of solution instances, which are the intended virtual instances for the peer. The semantically correct answers from a peer to a query, called its peer consistent answers, are defined as those answers that are invariant under all its different solution instances. We show that solution instances can be specified as the models of logic programs with a stable model semantics. Research in consistent query answering studies the definition and computation of "meaningful" answers to queries posed to inconsistent databases, i.e., databases whose data do not satisfy the integrity constraints (ICs) declared on their schema. Computing consistent answers to conjunctive queries is generally coNP-hard in data complexity, even in the presence of very restricted forms of ICs (single, unary keys). Recent studies on consistent query answering for database schemas containing only key dependencies have analyzed the possibility of identifying classes of queries whose consistent answers can be obtained by a first-order rewriting of the query, which in turn can be easily formulated in SQL and directly evaluated through any relational DBMS. In this paper we study consistent query answering in the presence of key dependencies and exclusion dependencies. We first prove that even in the presence of only exclusion dependencies the problem is coNP-hard in data complexity, and define a general method for consistent answering of conjunctive queries under key and exclusion dependencies, based on the rewriting of the query in Datalog with negation. Then, we identify a subclass of conjunctive queries that can be first-order rewritten in the presence of key and exclusion dependencies, and define an algorithm for computing the first-order rewriting of a query belonging to such a class of queries. Finally, we compare the relative efficiency of the two methods for processing queries in the subclass above mentioned. Experimental results, conducted on a real and large database of the computer science engineering degrees of the University of Rome "La Sapienza", clearly show the computational advantage of the first-order based technique. The problem of answering queries posed to a peer who is a member of a peer-to-peer data exchange system is studied. The answers have to be consistent wrt to both the local semantic constraints and the data exchange constraints with other peers; and must also respect certain trust relationships between peers. A semantics for peer consistent answers under exchange constraints and trust relationships is introduced and some techniques for obtaining those answers are presented. How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is written by three of the most respected experts in the field. This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Data integration is the problem of answering queries that span multiple data sources (e.g., databases, web pages). Data integration problems surface in multiple contexts, including enterprise information integration, query processing on the Web, coordination between government agencies and collaboration between scientists. In some cases, data integration is the key bottleneck to making progress in a field. The authors provide a working knowledge of data integration concepts and techniques, giving you the tools you need to develop a complete and concise package of algorithms and applications. *Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. *Enables you to build your own algorithms and implement your own data integration applications *Companion website with numerous project-based exercises and solutions and slides. Links to commercially available software allowing readers to build their own algorithms and implement their own data integration applications. Facebook page for reader input during and after publication. Abductive logic programming offers a formalism to declaratively express and solve problems in areas such as diagnosis, planning, belief revision and hypothetical reasoning. Tabled logic programming offers a computational mechanism that provides a level of declarativity superior to that of Prolog, and which has supported successful applications in fields such as parsing, program analysis, and model checking. In this paper we show how to use tabled logic programming to evaluate queries to abductive frameworks with integrity constraints when these frameworks contain both default and explicit negation. The result is the ability to compute abduction over well-founded semantics with explicit negation and answer sets. Our approach consists of a transformation and an evaluation method. The transformation adjoins to each objective literal @math in a program, an objective literal @math along with rules that ensure that @math will be true if and only if @math is false. We call the resulting program a dual program. The evaluation method, ABDUAL, then operates on the dual program. ABDUAL is sound and complete for evaluating queries to abductive frameworks whose entailment method is based on either the well-founded semantics with explicit negation, or on answer sets. Further, ABDUAL is asymptotically as efficient as any known method for either class of problems. In addition, when abduction is not desired, ABDUAL operating on a dual program provides a novel tabling method for evaluating queries to ground extended programs whose complexity and termination properties are similar to those of the best tabling methods for the well-founded semantics. A publicly available meta-interpreter has been developed for ABDUAL using the XSB system. Sequential pattern mining is advantageous for several applications. For example, it finds out the sequential purchasing behavior of majority customers from a large number of customer transactions. However, the existing researches in the field of discovering sequential patterns are based on the concept of frequency and presume that the customer purchasing behavior sequences do not fluctuate with change in time, purchasing cost and other parameters. To acclimate the sequential patterns to these changes, constraint are integrated with the traditional sequential pattern mining approach. It is possible to discover more user)centered patterns by integrating certain constraints with the sequential mining process. Thus in this paper, monetary and compactness constraints in addition to frequency and length are included in the sequential mining process for discovering pertinent sequential patterns from sequential databases. Also, a CFML)PrefixSpan algorithm is proposed by integrating these constraints with the original PrefixSpan algorithm, which allows discovering all CFML sequential patterns from the sequential database. The proposed CFML)PrefixSpan algorithm has been validated on synthetic sequential databases. The experimental results ensure that the efficacy of the sequential pattern mining process is further enhanced in view of the fact that the purchasing cost, time duration and length are integrated with the sequential pattern mining process. Personalization of e-services poses new challenges to database technology, demanding a powerful and flexible modeling technique for complex preferences. Preference queries have to be answered cooperatively by treating preferences as soft constraints, attempting a best possible match-making. We propose a strict partial order semantics for preferences, which closely matches people's intuition. A variety of natural and of sophisticated preferences are covered by this model. We show how to inductively construct complex preferences by means of various preference constructors. This model is the key to a new discipline called preference engineering and to a preference algebra. Given the Best-Matches-Only (BMO) query model we investigate how complex preference queries can be decomposed into simpler ones, preparing the ground for divide & conquer algorithms. Standard SQL and XPATH can be extended seamlessly by such preferences (presented in detail in the companion paper [15]). We believe that this model is appropriate to extend database technology towards effective support of personalization. Supporting database schema evolution represents a long-standing challenge of practical and theoretical importance for modern information systems. In this paper, we describe techniques and systems for automating the critical tasks of migrating the database and rewriting the legacy applications. In addition to labor saving, the benefits delivered by these advances are many and include reliable prediction of outcome, minimization of downtime, system-produced documentation, and support for archiving, historical queries, and provenance. The PRISM PRISM++ system delivers these benefits, by solving the difficult problem of automating the migration of databases and the rewriting of queries and updates. In this paper, we present the PRISM PRISM++ system and the novel technology that made it possible. In particular, we focus on the difficult and previously unsolved problem of supporting legacy queries and updates under schema and integrity constraints evolution. The PRISM PRISM++ approach consists in providing the users with a set of SQL-based Schema Modification Operators (SMOs), which describe how the tables in the old schema are modified into those in the new schema. In order to support updates, SMOs are extended with integrity constraints modification operators. By using recent results on schema mapping, the paper (i) characterizes the impact on integrity constraints of structural schema changes, (ii) devises representations that enable the rewriting of updates, and (iii) develop a unified approach for query and update rewriting under constraints. We complement the system with two novel tools: the first automatically collects and provides statistics on schema evolution histories, whereas the second derives equivalent sequences of SMOs from the migration scripts that were used for schema upgrades. These tools were used to produce an extensive testbed containing 15 evolution histories of scientific databases and web information systems, providing over 100 years of aggregate evolution histories and almost 2,000 schema evolution steps. With the fast development of Semantic Web, more and more RDF and OWL ontologies are created and shared. The effective management, such as storage, inference and query, of these ontologies on databases gains increasing attention. This paper addresses ontology query answering on databases by means of Datalog programs. Via epistemic operators, integrity constraints are introduced, and used for conveying semantic aspects of OWL that are not covered by Datalog-style rule languages. We believe such a processing suitable to capture ontologies in the database flavor, while keeping reasoning tractable. Here, we present a logically equivalent knowledge base whose (sound and complete) inference system appears as a Datalog program. As such, SPARQL query answering on OWL ontologies could be solved in databases. Bi-directional strategies, taking advantage of both forward and backward chaining, are then studied to support this kind of customized Datalog programs, returning exactly answers to the query within our logical framework. This paper reports on the CSIRO group's participation in INEX. We indexed documents and document fragments using PADRE, the core of CSIRO's Panoptic Enterprise Search Engine. A query translator converts the INEX topics into queries containing selection and projection constraints for the results. Answers are extracted from ranked documents and document fragments based on the projection constraints in the query. Current search engines can hardly cope adequately with fuzzy predicates defined by complex preferences. The biggest problem of search engines implemented with standard SQL is that SQL does not directly understand the notion of preferences. Preference SQL extends SQL by a preference model based on strict partial orders (presented in more detail in the companion paper [Kie02]), where preference queries behave like soft selection constraints. Several built-in base preference types and the powerful Pareto operator, combined with the adherence to declarative SQL programming style, guarantees great programming productivity. The Preference SQL optimizer does an efficient re-writing into standard SQL, including a high-level implementation of the skyline perator for Pareto-optimal sets. This pre-processor approach enables a seamless application integration, making Preference SQL available on all major SQL platforms. Several commercial B2C portals are powered by Preference SQL. Its benefits comprise cooperative query answering and smart customer advice, leading to higher e-customer satisfaction and shorter development times of personalized search engines. We report practical experiences ranging from m-commerce and comparison shopping to a large-scale performance test for a job portal.