9,175 research outputs found
Query translation and optimisation for complex value databases
This thesis considers the theory of database queries on the complex value data model
extended with external functions. In modern intelligent database systems, we expect
that query systems be able to handle a wide range of calculus formulas correctly and
efficiently. Accordingly, they will require general query translators and efficient optimisers.
Motivated by these concerns, this thesis undertakes a· comprehensive study of
query evaluation in the complex value model and investigates the following issues:
• identifying recursive sets of complex value formulas which define domain independent
queries;
• implementing complex value calculus queries with the incorporation of functions;
• solving the problem of how to process join operation in complex value databases;
and
• investigating some algebraic properties concerning nested relational operators.
The first part of this thesis extends some classical properties of the relational theory -
particularly those related to query safety - to the context of complex value databases
with fixed external functions and investigates the problem of how to implement calculus
queries. Two notions of syntactic criteria for queries which guarantee domain
independence, namely, embedded evaluable and embedded allowed, are generalised for
this data model. This thesis shows that all embedded-allowed calculus (or fix-point)
queries are external-function domain independent and continuous.
This thesis discusses the topic of "embedded allowed database programs" and proves
that embedded allowed stratified programs satisfying certain constraints are embedded
domain independent. It also develops an algorithm for translating embedded allowed
queries into equivalent algebraic expressions as a basis for evaluating safe queries in all
calculus-based query classes. The second part of this thesis considers the issue of query optimisation for nested
relational databases. Within a restricted set of nested schema trees, a join operator,
called P-join, is proposed. The P-join operator does not require as many restructuring
operators and combines the advantages of the extended natural join and recursive join
for efficient data access. A P-join algorithm which takes advantage of a decomposed
storage model and various join techniques available in the standard relational model
to reduce the cost of join operation in nested relational databases is also proposed.
Finally, this thesis investigates some algebraic properties of nested relational operators
which are useful for query optimisation in the nested relational model and outlines
a heuristic optimisation algorithm for nested relational expressions by adopting algebraic
transformation rules developed in this thesis and previous related work
Query Answering in Probabilistic Data and Knowledge Bases
Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory
Efficient Evaluation of Arbitrary Relational Calculus Queries
The relational calculus (RC) is a concise, declarative query language.
However, existing RC query evaluation approaches are inefficient and often
deviate from established algorithms based on finite tables used in database
management systems. We devise a new translation of an arbitrary RC query into
two safe-range queries, for which the finiteness of the query's evaluation
result is guaranteed. Assuming an infinite domain, the two queries have the
following meaning: The first is closed and characterizes the original query's
relative safety, i.e., whether given a fixed database, the original query
evaluates to a finite relation. The second safe-range query is equivalent to
the original query, if the latter is relatively safe. We compose our
translation with other, more standard ones to ultimately obtain two SQL
queries. This allows us to use standard database management systems to evaluate
arbitrary RC queries. We show that our translation improves the time complexity
over existing approaches, which we also empirically confirm in both realistic
and synthetic experiments.Comment: minor revisio
Depth-bounded bottom-up evaluation of logic programs
AbstractWe present here a depth-bounded bottom-up evaluation algorithm for logic programs. We show that it is sound, complete, and terminating for finite-answer queries if the programs are syntactically restricted to DatalognS, a class of logic programs with limited function symbols. DatalognS is an extension of Datalog capable of representing infinite phenomena. Predicates in DatalognS can have arbitrary unary and limited n-ary function symbols in one distinguished argument. We precisely characterize the computational complexity of depth-bounded evaluation for DatalognS and compare depth-bounded evaluation with other evaluation methods, top-down and Magic Sets among others. We also show that universal safety (finiteness of query answers for any database) is decidable for DatalognS
State-of-the-art on evolution and reactivity
This report starts by, in Chapter 1, outlining aspects of querying and updating resources on
the Web and on the Semantic Web, including the development of query and update languages
to be carried out within the Rewerse project.
From this outline, it becomes clear that several existing research areas and topics are of
interest for this work in Rewerse. In the remainder of this report we further present state of
the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give
an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs;
in Chapter 4 event-condition-action rules, both in the context of active database systems and
in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks
Principles of Security and Trust: 7th International Conference, POST 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceedings
authentication; computer science; computer software selection and evaluation; cryptography; data privacy; formal logic; formal methods; formal specification; internet; privacy; program compilers; programming languages; security analysis; security systems; semantics; separation logic; software engineering; specifications; verification; world wide we
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