134 research outputs found
On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds
Data exchange heavily relies on the notion of incomplete database instances. Several semantics for such instances have been proposed and include open (OWA), closed (CWA), and open-closed (OCWA) world. For all these semantics important questions are: whether one incomplete instance semantically implies another; when two are semantically equivalent; and whether a smaller or smallest semantically equivalent instance exists. For OWA and CWA these questions are fully answered. For several variants of OCWA, however, they remain open. In this work we adress these questions for Closed Powerset semantics and the OCWA semantics of [Leonid Libkin and Cristina Sirangelo, 2011]. We define a new OCWA semantics, called OCWA*, in terms of homomorphic covers that subsumes both semantics, and characterize semantic implication and equivalence in terms of such covers. This characterization yields a guess-and-check algorithm to decide equivalence, and shows that the problem is NP-complete. For the minimization problem we show that for several common notions of minimality there is in general no unique minimal equivalent instance for Closed Powerset semantics, and consequently not for the more expressive OCWA* either. However, for Closed Powerset semantics we show that one can find, for any incomplete database, a unique finite set of its subinstances which are subinstances (up to renaming of nulls) of all instances semantically equivalent to the original incomplete one. We study properties of this set, and extend the analysis to OCWA*
Answering Queries using Views over Probabilistic XML: Complexity and Tractability
We study the complexity of query answering using views in a probabilistic XML
setting, identifying large classes of XPath queries -- with child and
descendant navigation and predicates -- for which there are efficient (PTime)
algorithms. We consider this problem under the two possible semantics for XML
query results: with persistent node identifiers and in their absence.
Accordingly, we consider rewritings that can exploit a single view, by means of
compensation, and rewritings that can use multiple views, by means of
intersection. Since in a probabilistic setting queries return answers with
probabilities, the problem of rewriting goes beyond the classic one of
retrieving XML answers from views. For both semantics of XML queries, we show
that, even when XML answers can be retrieved from views, their probabilities
may not be computable. For rewritings that use only compensation, we describe a
PTime decision procedure, based on easily verifiable criteria that distinguish
between the feasible cases -- when probabilistic XML results are computable --
and the unfeasible ones. For rewritings that can use multiple views, with
compensation and intersection, we identify the most permissive conditions that
make probabilistic rewriting feasible, and we describe an algorithm that is
sound in general, and becomes complete under fairly permissive restrictions,
running in PTime modulo worst-case exponential time equivalence tests. This is
the best we can hope for since intersection makes query equivalence intractable
already over deterministic data. Our algorithm runs in PTime whenever
deterministic rewritings can be found in PTime.Comment: VLDB201
Increasing environmental compatibility of metal production
Building materials production generates a large amount of harmful substances poisoning the atmosphere. One of the major sources polluting cities environment is metallurgical industry. Concentration is one of the most important processes where empty components are extracted from the rock. During ore concentration, an increasing number of man-made wastes are generated; they pollute the air and huge area around the factories discharging these wastes. This reduces both space for people to live and place for cities to function and develop. It should be noted that metal production enterprises have accumulated billions of tons of industrial wastes (tailings) that include a large amount of iron-containing materials and rocks; these can be used as building materials, for example, when preparing fine-grained concrete as a mineral powder as well as in construction of roads, houses, in paint production, etc
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding
(KGE), which attempts to learn the embeddings of the entities and relations as
numerical vectors and mathematical mappings via machine learning (ML). However,
there has been limited research that applies KGE for industrial problems in
manufacturing. This paper investigates whether and to what extent KGE can be
used for an important problem: quality monitoring for welding in manufacturing
industry, which is an impactful process accounting for production of millions
of cars annually. The work is in line with Bosch research of data-driven
solutions that intends to replace the traditional way of destroying cars, which
is extremely costly and produces waste. The paper tackles two very challenging
questions simultaneously: how large the welding spot diameter is; and to which
car body the welded spot belongs to. The problem setting is difficult for
traditional ML because there exist a high number of car bodies that should be
assigned as class labels. We formulate the problem as link prediction, and
experimented popular KGE methods on real industry data, with consideration of
literals. Our results reveal both limitations and promising aspects of adapted
KGE methods.Comment: Paper accepted at ISWC2023 In-Use trac
- …