4,651 research outputs found
Some issues in data model mapping
Numerous data models have been reported in the literature since the early 1970's. They have been used as database interfaces and as conceptual design tools. The mapping between schemas expressed according to the same data model or according to different models is interesting for theoretical and practical purposes. This paper addresses some of the issues involved in such a mapping. Of special interest are the identification of the mapping parameters and some current approaches for handling the various situations that require a mapping
Mapping between Alloy specifications and database implementations
The emergence of lightweight formal methods tools such as Alloy improves the software design process, by encouraging developers to model and verify their systems before engaging in hideous implementation details. However, an abstract Alloy specification is far from an actual implementation, and manually refining the former into the latter is unfortunately a non-trivial task. This paper identifies a subset of the Alloy language that is equivalent to a relational database schema with the most conventional integrity constraints, namely functional and inclusion dependencies. This semantic correspondence enables both the automatic translation of Alloy specifications into relational database schemas and the reengineering of legacy databases into Alloy. The paper also discusses how to derive an object-oriented application layer to serve as interface to the underlying database
Declarative Ajax Web Applications through SQL++ on a Unified Application State
Implementing even a conceptually simple web application requires an
inordinate amount of time. FORWARD addresses three problems that reduce
developer productivity: (a) Impedance mismatch across the multiple languages
used at different tiers of the application architecture. (b) Distributed data
access across the multiple data sources of the application (SQL database, user
input of the browser page, session data in the application server, etc). (c)
Asynchronous, incremental modification of the pages, as performed by Ajax
actions.
FORWARD belongs to a novel family of web application frameworks that attack
impedance mismatch by offering a single unifying language. FORWARD's language
is SQL++, a minimally extended SQL. FORWARD's architecture is based on two
novel cornerstones: (a) A Unified Application State (UAS), which is a virtual
database over the multiple data sources. The UAS is accessed via distributed
SQL++ queries, therefore resolving the distributed data access problem. (b)
Declarative page specifications, which treat the data displayed by pages as
rendered SQL++ page queries. The resulting pages are automatically
incrementally modified by FORWARD. User input on the page becomes part of the
UAS.
We show that SQL++ captures the semi-structured nature of web pages and
subsumes the data models of two important data sources of the UAS: SQL
databases and JavaScript components. We show that simple markup is sufficient
for creating Ajax displays and for modeling user input on the page as UAS data
sources. Finally, we discuss the page specification syntax and semantics that
are needed in order to avoid race conditions and conflicts between the user
input and the automated Ajax page modifications.
FORWARD has been used in the development of eight commercial and academic
applications. An alpha-release web-based IDE (itself built in FORWARD) enables
development in the cloud.Comment: Proceedings of the 14th International Symposium on Database
Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento,
Ital
Model inference for spreadsheets
Many errors in spreadsheet formulas can be avoided if spreadsheets are built automati-
cally from higher-level models that can encode and enforce consistency constraints in the generated
spreadsheets. Employing this strategy for legacy spreadsheets is dificult, because the model has
to be reverse engineered from an existing spreadsheet and existing data must be transferred into
the new model-generated spreadsheet.
We have developed and implemented a technique that automatically infers relational schemas
from spreadsheets. This technique uses particularities from the spreadsheet realm to create better
schemas. We have evaluated this technique in two ways: First, we have demonstrated its appli-
cability by using it on a set of real-world spreadsheets. Second, we have run an empirical study
with users. The study has shown that the results produced by our technique are comparable to
the ones developed by experts starting from the same (legacy) spreadsheet data.
Although relational schemas are very useful to model data, they do not t well spreadsheets as
they do not allow to express layout. Thus, we have also introduced a mapping between relational
schemas and ClassSheets. A ClassSheet controls further changes to the spreadsheet and safeguards
it against a large class of formula errors. The developed tool is a contribution to spreadsheet
(reverse) engineering, because it lls an important gap and allows a promising design method
(ClassSheets) to be applied to a huge collection of legacy spreadsheets with minimal effort.We would like to thank Orlando Belo for his help on running and analyzing the empirical study. We would also like to thank Paulo Azevedo for his help in conducting the statistical analysis of our empirical study. We would also like to thank the anonymous reviewers for their suggestions which helped us to improve the paper. This work is funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-010048. The first author was also supported by FCT grant SFRH/BPD/73358/2010
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
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