1,213 research outputs found
Ubiquitous Nature of Event-Driven Approaches: A Retrospective View
This paper retrospectively analyzes the progress of event-based capability and
their applicability in various domains. Although research on event-based
approaches started in a humble manner with the intention of introducing
triggers in database management systems for monitoring application state and to
automate applications by reducing/eliminating user intervention, currently it
has become a force to reckon with as it finds use in many diverse domains. This
is primarily due to the fact that a large number of real-world applications are
indeed event-driven and hence the paradigm is apposite.
In this paper, we briefly overview the development of the ECA (or
event-condition-action) paradigm. We briefly discuss the evolution of the ECA
paradigm (or active capability) in relational and Object-oriented systems. We
then describe several diverse applications where the ECA paradigm has been used
effectively. The applications range from customized monitoring of web pages to
specification and enforcement of access control policies using RBAC (role-based
access control). The multitude of applications clearly demonstrate the
ubiquitous nature of event-based approaches to problems that were not
envisioned as the ones where the active capability would be applicable.
Finally, we indicate some future trends that can benefit from the ECA paradigm
SciQL, Bridging the Gap between Science and Relational DBMS
Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies. To bridge the gap between the needs of the Data-Intensive Research fields and the current DBMS technologies, we propose SciQL (pronounced as ‘cycle’), the first SQL-based query language for scientific applications with both tables and arrays as first class citizens. It provides a seamless symbiosis of array-, set- and sequence- interpretations. A key innovation is the extension of value-based grouping of SQL:2003 with structural grouping, i.e., fixed-sized and unbounded groups based on explicit relationships between elements positions. This leads to a generalisation of window-based query processing with wide applicability in science domains. This paper describes the main language features of SciQL and illustrates
it using time-series concepts
The SP theory of intelligence: benefits and applications
This article describes existing and expected benefits of the "SP theory of
intelligence", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the "SP machine" -- an expression
of the SP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The SP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is at least $190 billion each year. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the SP machine, available to researchers everywhere.Comment: arXiv admin note: substantial text overlap with arXiv:1212.022
Integration of decision support systems to improve decision support performance
Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines
Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing
(HPC), and machine learning (ML) training and scoring—become
increasingly common in practice. Interestingly, systems of these
areas share many compilation and runtime techniques, and the
used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource
management, data formats and representations, as well as execution
strategies differ substantially. DAPHNE is an open and extensible
system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for
increasing productivity and eliminating unnecessary overheads. In
this paper, we make a case for IDA pipelines, describe the overall
DAPHNE system architecture, its key components, and the design
of a vectorized execution engine for computational storage, HW
accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas,
DuckDB, and TensorFlow show promising results
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