96,523 research outputs found
Object-oriented querying of existing relational databases
In this paper, we present algorithms which allow an object-oriented
querying of existing relational databases. Our goal is to provide an improved query
interface for relational systems with better query facilities than SQL. This
seems to be very important since, in real world applications, relational systems
are most commonly used and their dominance will remain in the near future. To
overcome the drawbacks of relational systems, especially the poor query facilities
of SQL, we propose a schema transformation and a query translation algorithm.
The schema transformation algorithm uses additional semantic information to enhance
the relational schema and transform it into a corresponding object-oriented
schema. If the additional semantic information can be deducted from an underlying
entity-relationship design schema, the schema transformation may be done
fully automatically. To query the created object-oriented schema, we use the
Structured Object Query Language (SOQL) which provides declarative query facilities
on objects. SOQL queries using the created object-oriented schema are
much shorter, easier to write and understand and more intuitive than corresponding
S Q L queries leading to an enhanced usability and an improved querying of
the database. The query translation algorithm automatically translates SOQL queries
into equivalent SQL queries for the original relational schema
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model
Assistive systems for persons with cognitive disabilities (e.g. dementia) are
difficult to build due to the wide range of different approaches people can
take to accomplishing the same task, and the significant uncertainties that
arise from both the unpredictability of client's behaviours and from noise in
sensor readings. Partially observable Markov decision process (POMDP) models
have been used successfully as the reasoning engine behind such assistive
systems for small multi-step tasks such as hand washing. POMDP models are a
powerful, yet flexible framework for modelling assistance that can deal with
uncertainty and utility. Unfortunately, POMDPs usually require a very labour
intensive, manual procedure for their definition and construction. Our previous
work has described a knowledge driven method for automatically generating POMDP
activity recognition and context sensitive prompting systems for complex tasks.
We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The
spreadsheet-like result of the analysis does not correspond to the POMDP model
directly and the translation to a formal POMDP representation is required. To
date, this translation had to be performed manually by a trained POMDP expert.
In this paper, we formalise and automate this translation process using a
probabilistic relational model (PRM) encoded in a relational database. We
demonstrate the method by eliciting three assistance tasks from non-experts. We
validate the resulting POMDP models using case-based simulations to show that
they are reasonable for the domains. We also show a complete case study of a
designer specifying one database, including an evaluation in a real-life
experiment with a human actor
On Spatial Conjunction as Second-Order Logic
Spatial conjunction is a powerful construct for reasoning about dynamically
allocated data structures, as well as concurrent, distributed and mobile
computation. While researchers have identified many uses of spatial
conjunction, its precise expressive power compared to traditional logical
constructs was not previously known. In this paper we establish the expressive
power of spatial conjunction. We construct an embedding from first-order logic
with spatial conjunction into second-order logic, and more surprisingly, an
embedding from full second order logic into first-order logic with spatial
conjunction. These embeddings show that the satisfiability of formulas in
first-order logic with spatial conjunction is equivalent to the satisfiability
of formulas in second-order logic. These results explain the great expressive
power of spatial conjunction and can be used to show that adding unrestricted
spatial conjunction to a decidable logic leads to an undecidable logic. As one
example, we show that adding unrestricted spatial conjunction to two-variable
logic leads to undecidability. On the side of decidability, the embedding into
second-order logic immediately implies the decidability of first-order logic
with a form of spatial conjunction over trees. The embedding into spatial
conjunction also has useful consequences: because a restricted form of spatial
conjunction in two-variable logic preserves decidability, we obtain that a
correspondingly restricted form of second-order quantification in two-variable
logic is decidable. The resulting language generalizes the first-order theory
of boolean algebra over sets and is useful in reasoning about the contents of
data structures in object-oriented languages.Comment: 16 page
Model Checking Social Network Models
A social network service is a platform to build social relations among people
sharing similar interests and activities. The underlying structure of a social
networks service is the social graph, where nodes represent users and the arcs
represent the users' social links and other kind of connections. One important
concern in social networks is privacy: what others are (not) allowed to know
about us. The "logic of knowledge" (epistemic logic) is thus a good formalism
to define, and reason about, privacy policies. In this paper we consider the
problem of verifying knowledge properties over social network models (SNMs),
that is social graphs enriched with knowledge bases containing the information
that the users know. More concretely, our contributions are: i) We prove that
the model checking problem for epistemic properties over SNMs is decidable; ii)
We prove that a number of properties of knowledge that are sound w.r.t. Kripke
models are also sound w.r.t. SNMs; iii) We give a satisfaction-preserving
encoding of SNMs into canonical Kripke models, and we also characterise which
Kripke models may be translated into SNMs; iv) We show that, for SNMs, the
model checking problem is cheaper than the one based on standard Kripke models.
Finally, we have developed a proof-of-concept implementation of the
model-checking algorithm for SNMs.Comment: In Proceedings GandALF 2017, arXiv:1709.0176
On relating CTL to Datalog
CTL is the dominant temporal specification language in practice mainly due to
the fact that it admits model checking in linear time. Logic programming and
the database query language Datalog are often used as an implementation
platform for logic languages. In this paper we present the exact relation
between CTL and Datalog and moreover we build on this relation and known
efficient algorithms for CTL to obtain efficient algorithms for fragments of
stratified Datalog. The contributions of this paper are: a) We embed CTL into
STD which is a proper fragment of stratified Datalog. Moreover we show that STD
expresses exactly CTL -- we prove that by embedding STD into CTL. Both
embeddings are linear. b) CTL can also be embedded to fragments of Datalog
without negation. We define a fragment of Datalog with the successor build-in
predicate that we call TDS and we embed CTL into TDS in linear time. We build
on the above relations to answer open problems of stratified Datalog. We prove
that query evaluation is linear and that containment and satisfiability
problems are both decidable. The results presented in this paper are the first
for fragments of stratified Datalog that are more general than those containing
only unary EDBs.Comment: 34 pages, 1 figure (file .eps
Provenance for SPARQL queries
Determining trust of data available in the Semantic Web is fundamental for
applications and users, in particular for linked open data obtained from SPARQL
endpoints. There exist several proposals in the literature to annotate SPARQL
query results with values from abstract models, adapting the seminal works on
provenance for annotated relational databases. We provide an approach capable
of providing provenance information for a large and significant fragment of
SPARQL 1.1, including for the first time the major non-monotonic constructs
under multiset semantics. The approach is based on the translation of SPARQL
into relational queries over annotated relations with values of the most
general m-semiring, and in this way also refuting a claim in the literature
that the OPTIONAL construct of SPARQL cannot be captured appropriately with the
known abstract models.Comment: 22 pages, extended version of the ISWC 2012 paper including proof
Queries with Guarded Negation (full version)
A well-established and fundamental insight in database theory is that
negation (also known as complementation) tends to make queries difficult to
process and difficult to reason about. Many basic problems are decidable and
admit practical algorithms in the case of unions of conjunctive queries, but
become difficult or even undecidable when queries are allowed to contain
negation. Inspired by recent results in finite model theory, we consider a
restricted form of negation, guarded negation. We introduce a fragment of SQL,
called GN-SQL, as well as a fragment of Datalog with stratified negation,
called GN-Datalog, that allow only guarded negation, and we show that these
query languages are computationally well behaved, in terms of testing query
containment, query evaluation, open-world query answering, and boundedness.
GN-SQL and GN-Datalog subsume a number of well known query languages and
constraint languages, such as unions of conjunctive queries, monadic Datalog,
and frontier-guarded tgds. In addition, an analysis of standard benchmark
workloads shows that most usage of negation in SQL in practice is guarded
negation
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