1,368 research outputs found
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
An Architecture to infer Business Rules from Event Condition Action Rules implemented in the Persistence Layer
The business rules that govern the behaviour of a business process can be hardcoded in different ways in a
software application. The modernization or improvement of these applications to a process-oriented
perspective implies typically the modification of the business rules. Frequently, legacy systems are not
well-documented, and almost always, the documentation they have is not updated. As a consequence
many times is necessary the analysis of source code and databases structures to be transformed into a
business language more understandable by the business experts involved in the modernization process.
Database triggers are one of the artefacts in which business rules are hardcoded. We focus on this kind of
artefacts, having in mind to avoid the manual analysis of the triggers by a database expert, and bringing it
closer to business experts. To get this aim we need to discover business rules that are hardcoded in
triggers, and translate it into vocabularies that are commonly used by business experts. In this paper we
propose an ADM-based architecture to discover business rules and rewrite then into a language that can
be understood by the business experts.Ministerio de Ciencia y TecnologĂa TIN2009-13714Ministerio de Ciencia y TecnologĂa TIN2010-20057-C03-02Ministerio de Ciencia y TecnologĂa TIN2010-21744-C02-
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
DataHub: Collaborative Data Science & Dataset Version Management at Scale
Relational databases have limited support for data collaboration, where teams
collaboratively curate and analyze large datasets. Inspired by software version
control systems like git, we propose (a) a dataset version control system,
giving users the ability to create, branch, merge, difference and search large,
divergent collections of datasets, and (b) a platform, DataHub, that gives
users the ability to perform collaborative data analysis building on this
version control system. We outline the challenges in providing dataset version
control at scale.Comment: 7 page
PG-Triggers: Triggers for Property Graphs
Graph databases are emerging as the leading data management technology for
storing large knowledge graphs; significant efforts are ongoing to produce new
standards (such as the Graph Query Language, GQL), as well as enrich them with
properties, types, schemas, and keys. In this article, we propose PG-Triggers,
a complete proposal for adding triggers to Property Graphs, along the direction
marked by the SQL3 Standard. We define the syntax and semantics of PG-Triggers
and then illustrate how they can be implemented on top of Neo4j, one of the
most popular graph databases. In particular, we introduce a syntax-directed
translation from PG-Triggers into Neo4j, which makes use of the so-called APOC
triggers; APOC is a community-contributed library for augmenting the Cypher
query language supported by Neo4j. We also illustrate the use of PG-Triggers
through a life science application inspired by the COVID-19 pandemic. The main
result of this article is proposing reactive aspects within graph databases as
first-class citizens, so as to turn them into an ideal infrastructure for
supporting reactive knowledge management.Comment: 12 pages, 4 figures, 3 table
Engineering Enterprise Software Systems with Interactive UML Models and Aspect-Oriented Middleware
Large scale enterprise software systems are inherently complex and hard to maintain. To deal with this complexity, current mainstream software engineering practices aim at raising the level of abstraction to visual models described in OMG’s UML modeling language. Current UML tools, however, produce static design diagrams for documentation which quickly become out-of-sync with the software, and thus obsolete. To address this issue, current model-driven software development approaches aim at software automation using generators that translate models into code. However, these solutions don’t have a good answer for dealing with legacy source code and the evolution of existing enterprise software systems. This research investigates an alternative solution by making the process of modeling more interactive with a simulator and integrating simulation with the live software system. Such an approach supports model-driven development at a higher-level of abstraction with models without sacrificing the need to drop into a lower-level with code. Additionally, simulation also supports better evolution since the impact of a change to a particular area of existing software can be better understood using simulated “what-if” scenarios. This project proposes such a solution by developing a web-based UML simulator for modeling use cases and sequence diagrams and integrating the simulator with existing applications using aspect-oriented middleware technology
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