108,890 research outputs found
LOGICAL AND PHYSICAL TEMPORAL-DATABASE DESIGN
This paper examines problems and approaches to logical and physical temporal-database design. The logical model is used to determine the functionality required of the physical design. Due to the special nature of temporal data, existing database structures are inadequate. The nature of physical design problems are examined and some solutions proposed. A multi-dimensional file partitioning algorithm is proposed; this algorithm is appropriate for certain temporal-database environments
SODA: Generating SQL for Business Users
The purpose of data warehouses is to enable business analysts to make better
decisions. Over the years the technology has matured and data warehouses have
become extremely successful. As a consequence, more and more data has been
added to the data warehouses and their schemas have become increasingly
complex. These systems still work great in order to generate pre-canned
reports. However, with their current complexity, they tend to be a poor match
for non tech-savvy business analysts who need answers to ad-hoc queries that
were not anticipated. This paper describes the design, implementation, and
experience of the SODA system (Search over DAta Warehouse). SODA bridges the
gap between the business needs of analysts and the technical complexity of
current data warehouses. SODA enables a Google-like search experience for data
warehouses by taking keyword queries of business users and automatically
generating executable SQL. The key idea is to use a graph pattern matching
algorithm that uses the metadata model of the data warehouse. Our results with
real data from a global player in the financial services industry show that
SODA produces queries with high precision and recall, and makes it much easier
for business users to interactively explore highly-complex data warehouses.Comment: VLDB201
Content-based Video Retrieval
no abstract
Exploring sensor data management
The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud
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Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data
Automatic instantiation of abstract tests on specific configurations for large critical control systems
Computer-based control systems have grown in size, complexity, distribution
and criticality. In this paper a methodology is presented to perform an
abstract testing of such large control systems in an efficient way: an abstract
test is specified directly from system functional requirements and has to be
instantiated in more test runs to cover a specific configuration, comprising
any number of control entities (sensors, actuators and logic processes). Such a
process is usually performed by hand for each installation of the control
system, requiring a considerable time effort and being an error prone
verification activity. To automate a safe passage from abstract tests, related
to the so called generic software application, to any specific installation, an
algorithm is provided, starting from a reference architecture and a state-based
behavioural model of the control software. The presented approach has been
applied to a railway interlocking system, demonstrating its feasibility and
effectiveness in several years of testing experience
Some Ontological Principles for Designing Upper Level Lexical Resources
The purpose of this paper is to explore some semantic problems related to the
use of linguistic ontologies in information systems, and to suggest some
organizing principles aimed to solve such problems. The taxonomic structure of
current ontologies is unfortunately quite complicated and hard to understand,
especially for what concerns the upper levels. I will focus here on the problem
of ISA overloading, which I believe is the main responsible of these
difficulties. To this purpose, I will carefully analyze the ontological nature
of the categories used in current upper-level structures, considering the
necessity of splitting them according to more subtle distinctions or the
opportunity of excluding them because of their limited organizational role.Comment: 8 pages - gzipped postscript file - A4 forma
Ontology-based knowledge representation of experiment metadata in biological data mining
According to the PubMed resource from the U.S. National Library of Medicine,
over 750,000 scientific articles have been published in the ~5000 biomedical journals
worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes
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