34 research outputs found

    Automatic physical database design : recommending materialized views

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    This work discusses physical database design while focusing on the problem of selecting materialized views for improving the performance of a database system. We first address the satisfiability and implication problems for mixed arithmetic constraints. The results are used to support the construction of a search space for view selection problems. We proposed an approach for constructing a search space based on identifying maximum commonalities among queries and on rewriting queries using views. These commonalities are used to define candidate views for materialization from which an optimal or near-optimal set can be chosen as a solution to the view selection problem. Using a search space constructed this way, we address a specific instance of the view selection problem that aims at minimizing the view maintenance cost of multiple materialized views using multi-query optimization techniques. Further, we study this same problem in the context of a commercial database management system in the presence of memory and time restrictions. We also suggest a heuristic approach for maintaining the views while guaranteeing that the restrictions are satisfied. Finally, we consider a dynamic version of the view selection problem where the workload is a sequence of query and update statements. In this case, the views can be created (materialized) and dropped during the execution of the workload. We have implemented our approaches to the dynamic view selection problem and performed extensive experimental testing. Our experiments show that our approaches perform in most cases better than previous ones in terms of effectiveness and efficiency

    Scalable Automated Incrementalization for Real-Time Static Analyses

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    This thesis proposes a framework for easy development of static analyses, whose results are incrementalized to provide instantaneous feedback in an integrated development environment (IDE). Today, IDEs feature many tools that have static analyses as their foundation to assess software quality and catch correctness problems. Yet, these tools often fail to provide instantaneous feedback and are thus restricted to nightly build processes. This precludes developers from fixing issues at their inception time, i.e., when the problem and the developed solution are both still fresh in mind. In order to provide instantaneous feedback, incrementalization is a well-known technique that utilizes the fact that developers make only small changes to the code and, hence, analysis results can be re-computed fast based on these changes. Yet, incrementalization requires carefully crafted static analyses. Thus, a manual approach to incrementalization is unattractive. Automated incrementalization can alleviate these problems and allows analyses writers to formulate their analyses as queries with the full data set in mind, without worrying over the semantics of incremental changes. Existing approaches to automated incrementalization utilize standard technologies, such as deductive databases, that provide declarative query languages, yet also require to materialize the full dataset in main-memory, i.e., the memory is permanently blocked by the data required for the analyses. Other standard technologies such as relational databases offer better scalability due to persistence, yet require large transaction times for data. Both technologies are not a perfect match for integrating static analyses into an IDE, since the underlying data, i.e., the code base, is already persisted and managed by the IDE. Hence, transitioning the data into a database is redundant work. In this thesis a novel approach is proposed that provides a declarative query language and automated incrementalization, yet retains in memory only a necessary minimum of data, i.e., only the data that is required for the incrementalization. The approach allows to declare static analyses as incrementally maintained views, where the underlying formalism for incrementalization is the relational algebra with extensions for object-orientation and recursion. The algebra allows to deduce which data is the necessary minimum for incremental maintenance and indeed shows that many views are self-maintainable, i.e., do not require to materialize memory at all. In addition an optimization for the algebra is proposed that allows to widen the range of self-maintainable views, based on domain knowledge of the underlying data. The optimization works similar to declaring primary keys for databases, i.e., the optimization is declared on the schema of the data, and defines which data is incrementally maintained in the same scope. The scope makes all analyses (views) that correlate only data within the boundaries of the scope self-maintainable. The approach is implemented as an embedded domain specific language in a general-purpose programming language. The implementation can be understood as a database-like engine with an SQL-style query language and the execution semantics of the relational algebra. As such the system is a general purpose database-like query engine and can be used to incrementalize other domains than static analyses. To evaluate the approach a large variety of static analyses were sampled from real-world tools and formulated as incrementally maintained views in the implemented engine

    Workshop on Database Programming Languages

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    These are the revised proceedings of the Workshop on Database Programming Languages held at Roscoff, Finistère, France in September of 1987. The last few years have seen an enormous activity in the development of new programming languages and new programming environments for databases. The purpose of the workshop was to bring together researchers from both databases and programming languages to discuss recent developments in the two areas in the hope of overcoming some of the obstacles that appear to prevent the construction of a uniform database programming environment. The workshop, which follows a previous workshop held in Appin, Scotland in 1985, was extremely successful. The organizers were delighted with both the quality and volume of the submissions for this meeting, and it was regrettable that more papers could not be accepted. Both the stimulating discussions and the excellent food and scenery of the Brittany coast made the meeting thoroughly enjoyable. There were three main foci for this workshop: the type systems suitable for databases (especially object-oriented and complex-object databases,) the representation and manipulation of persistent structures, and extensions to deductive databases that allow for more general and flexible programming. Many of the papers describe recent results, or work in progress, and are indicative of the latest research trends in database programming languages. The organizers are extremely grateful for the financial support given by CRAI (Italy), Altaïr (France) and AT&T (USA). We would also like to acknowledge the organizational help provided by Florence Deshors, Hélène Gans and Pauline Turcaud of Altaïr, and by Karen Carter of the University of Pennsylvania

    WISM'07 : 4th international workshop on web information systems modeling

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    WISM'07 : 4th international workshop on web information systems modeling

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    Recovering Structural Information for Better Static Analysis

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    Η στατική ανάλυση στοχεύει στην κατανόηση της συμπεριφοράς του προγράμματος, μέσω αυτοματοποιημένων τεχνικών συμπερασμού που βασίζονται καθαρά στον πηγαίο κώδικα του προγράμματος, αλλά δεν προϋποθέτουν την εκτέλεσή του. Για να πετύχουν αυτές οι τεχνικές μία ευρεία κατανόηση του κώδικα, καταφεύγουν στη δημιουργία ενός αφηρημένου μοντέλου της μνήμης, το οποίο καλύπτει όλες τις πιθανές εκτελέσεις. Αφηρημένα μοντέλα τέτοιου τύπου μπορεί γρήγορα να εκφυλιστούν, αν χάσουν σημαντική δομική πληροφορία των αντικειμένων στη μνήμη που περιγράφουν. Αυτό συνήθως συμβαίνει λόγω χρήσης συγκεκριμένων προγραμματιστικών ιδιωμάτων και χαρακτηριστικών της γλώσσας προγραμματισμού, ή λόγω πρακτικών περιορισμών της ανάλυσης. Σε αρκετές περιπτώσεις, ένα σημαντικό μέρος της χαμένης αυτής δομικής πληροφορίας μπορεί να ανακτηθεί μέσω σύνθετης λογικής, η οποία παρακολουθεί την έμμεση χρήση τύπων, και να χρησιμοποιηθεί προς όφελος της στατικής ανάλυσης του προγράμματος. Στη διατριβή αυτή παρουσιάζουμε διάφορους τρόπους ανάκτησης δομικής πληροφορίας, πρώτα (1) σε προγράμματα C/C++, κι έπειτα, σε προγράμματα γλωσσών υψηλότερου επιπέδου που δεν προσφέρουν άμεση πρόσβαση μνήμης, όπως η Java, όπου αναγνωρίζουμε δύο βασικές πηγές απώλειας δομικής πληροφορίας: (2) χρήση ανάκλασης και (3) ανάλυση μερικών προγραμμάτων. Δείχνουμε πως, σε όλες τις παραπάνω περιπτώσεις, η ανάκτηση τέτοιας δομικής πληροφορίας βελτιώνει άμεσα τη στατική ανάλυση του προγράμματος. Παρουσιάζουμε μία ανάλυση δεικτών για C/C++, η οποία βελτιώνει το επίπεδο της αφαίρεσης, βασιζόμενη σε πληροφορία τύπου που ανακαλύπτει κατά τη διάρκεια της ανάλυσης. Παρέχουμε μία υλοποίηση της ανάλυσης αυτής, στο cclyzer, ένα εργαλείο στατικής ανάλυσης για LLVM bitcode. Έπειτα, παρουσιάζουμε επεκτάσεις σε ανάλυση δεικτών για Java, κτίζοντας πάνω σε σύγχρονες τεχνικές χειρισμού μηχανισμών ανάκλασης. Η βασική αρχή είναι παραπλήσια με την περίπτωση της C/C++: καταγράφουμε τη χρήση των ανακλαστικών αντικειμένων, κατά τη διάρκεια της ανάλυσης δεικτών, ώστε να ανακαλύψουμε βασικά δομικά τους στοιχεία, τα οποία μπορούμε να χρησιμοποιήσουμε έπειτα για να βελτιώσουμε τον χειρισμό των εντολών ανάκλασης στην τρέχουσα ανάλυση, με αμοιβαία αναδρομικό τρόπο. Τέλος, ως προς την ανάλυση μερικών προγραμμάτων Java, ορίζουμε το γενικό πρόβλημα της ((συμπλήρωσης προγράμματος)): δοθέντος ενός μερικού προγράμματος, πως να εφεύρουμε ένα υποκατάστατο του κώδικα που λείπει, έτσι ώστε αυτό να ικανοποιεί τους περιορισμούς των στατικών και δυναμικών τύπων που υπονοούνται από τον υπάρχοντα κώδικα. Ή διαφορετικά, πως να ανακτήσουμε τη δομή των τύπων που λείπουν. Πέραν της ανακάλυψης των μελών (πεδίων και μεθόδων) των κλάσεων που λείπουν, η ικανοποίηση των περιορισμών υποτυπισμού μας οδηγεί στον ορισμό ενός πρωτότυπου αλγοριθμικού προβλήματος: τη συμπλήρωση ιεραρχίας τύπων. Παρέχουμε αλγορίθμους που λύνουν το πρόβλημα αυτό σε διάφορα είδη κληρονομικότητας (μονής, πολλαπλής, μεικτής) και τους υλοποιούμε στο JPhantom, ένα νέο εργαλείο συμπλήρωσης Java bytecode κώδικα.Static analysis aims to achieve an understanding of program behavior, by means of automatic reasoning that requires only the program’s source code and not any actual execution. To reach a truly broad level of program understanding, static analysis techniques need to create an abstraction of memory that covers all possible executions. Such abstract models may quickly degenerate after losing essential structural information about the memory objects they describe, due to the use of specific programming idioms and language features, or because of practical analysis limitations. In many cases, some of the lost memory structure may be retrieved, though it requires complex inference that takes advantage of indirect uses of types. Such recovered structural information may, then, greatly benefit static analysis. This dissertation shows how we can recover structural information, first (i) in the context of C/C++, and next, in the context of higher-level languages without direct memory access, like Java, where we identify two primary causes of losing memory structure: (ii) the use of reflection, and (iii) analysis of partial programs. We show that, in all cases, the recovered structural information greatly benefits static analysis on the program. For C/C++, we introduce a structure-sensitive pointer analysis that refines its abstraction based on type information that it discovers on-they-fly. This analysis is implemented in cclyzer, a static analysis tool for LLVM bitcode. Next, we present techniques that extend a standard Java pointer analysis by building on top of state-of-the-art handling of reflection. The principle is similar to that of our structure-sensitive analysis for C/C++: track the use of reflective objects, during pointer analysis, to gain important insights on their structure, which can be used to “patch” the handling of reflective operations on the running analysis, in a mutually recursive fashion. Finally, to address the challenge of analyzing partial Java programs in full generality, we define the problem of “program complementation”: given a partial program we seek to provide definitions for its missing parts so that the “complement” satisfies all static and dynamic typing requirements induced by the code under analysis. Essentially, complementation aims to recover the structure of phantom types. Apart from discovering missing class members (i.e., fields and methods), satisfying the subtyping constraints leads to the formulation of a novel typing problem in the OO context, regarding type hierarchy complementation. We offer algorithms to solve this problem in various inheritance settings, and implement them in JPhantom, a practical tool for Java bytecode complementation

    Studies related to the process of program development

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    The submitted work consists of a collection of publications arising from research carried out at Rhodes University (1970-1980) and at Heriot-Watt University (1980-1992). The theme of this research is the process of program development, i.e. the process of creating a computer program to solve some particular problem. The papers presented cover a number of different topics which relate to this process, viz. (a) Programming methodology programming. (b) Properties of programming languages. aspects of structured. (c) Formal specification of programming languages. (d) Compiler techniques. (e) Declarative programming languages. (f) Program development aids. (g) Automatic program generation. (h) Databases. (i) Algorithms and applications

    Neuere Entwicklungen der deklarativen KI-Programmierung : proceedings

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    The field of declarative AI programming is briefly characterized. Its recent developments in Germany are reflected by a workshop as part of the scientific congress KI-93 at the Berlin Humboldt University. Three tutorials introduce to the state of the art in deductive databases, the programming language Gödel, and the evolution of knowledge bases. Eleven contributed papers treat knowledge revision/program transformation, types, constraints, and type-constraint combinations
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